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| Publications of Leslie M. Collins :chronological alphabetical combined listing:%% Papers Published @booklet{Stohl09, Author = {J. S. Stohl and C. S. Throckmorton and L. M. Collins}, Title = {Investigating the effects of stimulus duration and context on pitch perception by cochlear implant users}, Journal = {Journal Of The Acoustical Society Of America}, Volume = {126}, Number = {1}, Pages = {318 -- 326}, Year = {2009}, Month = {July}, ISSN = {0001-4966}, Abstract = {Cochlear implant sound processing strategies that use time-varying pulse rates to transmit fine structure information are one proposed method for improving the spectral representation of a sound with the eventual goal of improving speech recognition in noisy conditions, speech recognition in tonal languages, and music identification and appreciation. However, many of the perceptual phenomena associated with time-varying rates are not well understood. In this study, the effects of stimulus duration on both the place and rate-pitch percepts were investigated via psychophysical experiments. Four Nucleus CI24 cochlear implant users participated in these experiments, which included a short-duration pitch ranking task and three adaptive pulse rate discrimination tasks. When duration was fixed from trial-to-trial and rate was varied adaptively, results suggested that both the place-pitch and rate-pitch percepts may be independent of duration for durations above 10 and 20 ms, respectively. When duration was varied and pulse rates were fixed, performance was highly variable within and across subjects. Implications for multi-rate sound processing strategies are discussed.}, Key = {Stohl09} } @booklet{Tantum09, Author = {S. L. Tantum and Q. Zhu and P. A. Torrione and L. M. Collins}, Title = {Modeling position error probability density functions for statistical inversions using a Goff-Jordan rough surface model}, Journal = {Stochastic Environmental Research And Risk Assessment}, Volume = {23}, Number = {2}, Pages = {155 -- 167}, Year = {2009}, Month = {February}, ISSN = {1436-3240}, Abstract = {Buried unexploded ordnance (UXO) continues to be a difficult remediation problem from both a sensing and a discrimination point of view. Modern approaches to both the sensing and discrimination problems utilize high bandwidth electromagnetic induction (EMI) sensors to collect geo-referenced data which is then inverted, or fit, using a forward model in order to obtain features that can be directly interpreted using the physics associated with electromagnetic induction-based sensing. These features are then used in a variety of classification architectures. One aspect of this process that has captured recent interest is that uncertainty in the positions at which data was collected can degrade the inversion performance and thus the subsequent classification. Several mechanisms to address this issue have been explored that range from filtering and prediction of actual positions to exploiting Bayesian approaches for uncertainty mitigation. In the Bayesian approach, a statistical model of the position errors is used as a prior for integrating over the uncertainty in the inversion process. In this study, we demonstrate that errors in the statistical priors used in this process can negatively impact subsequent classification performance, thus highlighting the need for an accurate statistical model for the position errors. Next, we propose a mechanism by which to obtain such models. Specifically, we utilize a Goff-Jordan rough surface model and simulate the sensor data collection system motion over the simulated ground or ocean surfaces to calculate errors and generate statistical models. Our results suggest that this approach can be used to develop the statistical models necessary for mitigating uncertain position information.}, Key = {Tantum09} } @booklet{Morton08, Author = {K. D. Morton and P. A. Torrione and C. S. Throckmorton and L. M. Collins}, Title = {Mandarin Chinese tone identification in cochlear implants: Predictions from acoustic models}, Journal = {Hearing Research}, Volume = {244}, Number = {1-2}, Pages = {66 -- 76}, Year = {2008}, Month = {October}, ISSN = {0378-5955}, Abstract = {It has been established that current cochlear implants do not supply adequate spectral information for perception of tonal languages. Comprehension of a tonal language, such as Mandarin Chinese, requires recognition of lexical tones. New strategies of cochlear stimulation such as variable stimulation rate and current steering may provide the means of delivering more spectral information and thus may provide the auditory fine-structure required for tone recognition. Several cochlear implant signal processing strategies are examined in this study, the continuous interleaved sampling (CIS) algorithm, the frequency amplitude modulation encoding (FAME) algorithm, and the multiple carrier frequency algorithm (MCFA). These strategies provide different types and amounts of spectral information. Pattern recognition techniques can be applied to data from Mandarin Chinese tone recognition tasks using acoustic models as a means of testing the abilities of these algorithms to transmit the changes in fundamental frequency indicative of the four lexical tones. The ability of processed Mandarin Chinese tones to be correctly classified may predict trends in the effectiveness of different signal processing algorithms in cochlear implants. The proposed techniques can predict trends in performance of the signal processing techniques in quiet conditions but fail to do so in noise. (C) 2008 Elsevier B.V. All rights reserved.}, Key = {Morton08} } @booklet{Stohl08, Author = {J. S. Stohl and C. S. Throckmorton and L. M. Collins}, Title = {Assessing the pitch structure associated with multiple rates and places for cochlear implant users}, Journal = {Journal Of The Acoustical Society Of America}, Volume = {123}, Number = {2}, Pages = {1043 -- 1053}, Year = {2008}, Month = {February}, ISSN = {0001-4966}, Abstract = {Cochlear implant subjects continue to experience difficulty understanding speech in noise and performing pitch-based musical tasks. Acoustic model studies have suggested that transmitting additional fine structure via multiple stimulation rates is a potential mechanism for addressing these issues [Nie et al., IEEE Trans. Biomed. Eng. 52, 64-73 (2005); Throckmorton et al., Hear. Res. 218, 30-42 (2006)]; however, results from preliminary cochlear implant studies have been less compelling. Multirate speech processing algorithms previously assumed a place-dependent pitch structure in that a basal electrode would always elicit a higher pitch percept than an apical electrode, independent of stimulation rate. Some subjective evidence contradicts this assumption [H. J. McDermott and C. M. McKay, J. Acoust. Soc. Am. 101, 1622-1630 (1997); R. V. Shannon, Hear. Res. 11 157-189 (1983)]. The purpose of this study is to test the hypothesis that the introduction of multiple rates may invalidate the tonotopic pitch structure resulting from place-pitch alone. The SPEAR3 developmental speech processor was used to collect psychophysical data from five cochlear implant users to assess the tonotopic structure for stimuli presented at two rates on all active electrodes. Pitch ranking data indicated many cases where pitch percepts overlapped across electrodes and rates. Thus, the results from this study suggest that pitch-based tuning across rate and electrode may be necessary to optimize performance of a multirate sound processing strategy in cochlear implant subjects. (c) 2008.}, Key = {Stohl08} } @booklet{Remus08, Author = {J. J. Remus and L. M. Collins}, Title = {Comparison of adaptive psychometric procedures motivated by the Theory of Optimal Experiments: Simulated and experimental results}, Journal = {Journal Of The Acoustical Society Of America}, Volume = {123}, Number = {1}, Pages = {315 -- 326}, Year = {2008}, Month = {January}, ISSN = {0001-4966}, Abstract = {The wide use of psychometric assessments and the time necessary to conduct comprehensive psychometric tests has motivated significant research into the development of psychometric testing procedures that will provide accurate and efficient estimates of the parameters of interest. One potential framework for developing adaptive psychometric procedures is the Theory of Optimal Experiments. The Theory of Optimal Experiments provides several metrics for determining informative stimulus values based on a model of the Psychometric function to be provided by the investigator. In this study, two methods based on a previous implementation of the Theory of Optimal Experiments are presented for comparison to two fixed step size staircase methods and also an existing adaptive method that utilizes a Bayesian framework. The psychometric procedures were used to measure detection thresholds and discrimination limens on two separate psychoacoustic tasks with normal-hearing subjects. Computer simulations were performed based on the outcomes of the experimental psychoacoustic detection task to analyze performance over a large sample size in the case of known truth. Results suggest that the proposed stimulus selection rules motivated by the Theory of Optimal Experiments perform better than previous techniques and also extend estimation to. multiple parameters. (c) 2008 Acoustical Society of America.}, Key = {Remus08} } @booklet{Tantum08, Author = {S. L. Tantum and Y. L. Yu and L. M. Collins}, Title = {Bayesian mitigation of sensor position errors to improve unexploded ordnance detection}, Journal = {Ieee Geoscience And Remote Sensing Letters}, Volume = {5}, Number = {1}, Pages = {103 -- 107}, Year = {2008}, Month = {January}, ISSN = {1545-598X}, Abstract = {Phenomenological modeling coupled with statistical signal processing has been shown to significantly improve capabilities for discriminating unexploded ordnance (UXO) from benign clutter using electromagnetic induction (EMI) sensor data. The general premise underlying the majority of these coupled approaches is that a phenomenological model is fit to the measured data, and the parameters estimated from this model inversion, which characterize the interrogated target, are utilized in subsequent statistical signal processing algorithms to classify the target as either UXO or clutter. A potential limitation of this coupled approach is that the inversion has been shown to be sensitive to uncertainty associated with the sensor positions. When the measurement positions are uncertain, the inversion results are more variable, and consequently, discrimination performance degrades. In this letter, a Bayesian methodology is applied to estimate the desired features from the measured data. This method explicitly acknowledges that uncertainty in the sensor positions exists and incorporates this knowledge to find the maximum-likelihood feature estimates by integrating over the uncertain measurement positions. Due to the high dimensionality of the integration, Monte Carlo integration, a statistical technique to estimate the value of an integral, is employed. Simulation results show that this Bayesian approach in mitigating sensor position uncertainty produces features with lower variability and, therefore, provides improved discrimination performance.}, Key = {Tantum08} } @booklet{Remus07, Author = {J. J. Remus and C. S. Throckmorton and L. M. Collins}, Title = {Expediting the identification of impaired channels in cochlear implants via analysis of speech-based confusion matrices}, Journal = {Ieee Transactions On Biomedical Engineering}, Volume = {54}, Number = {12}, Pages = {2193 -- 2204}, Year = {2007}, Month = {December}, ISSN = {0018-9294}, Abstract = {There is significant variability in the benefit provided by cochlear implants to severely deafened individuals. The reasons why some subjects exhibit low speech recognition scores are unknown; however, underlying physiological or psychophysical factors may be involved. Certain phenomena, such as indiscriminable electrodes and nonmonotonic pitch rankings, might hint at limitations in the ability of individual channels in the cochlear implant and/or sensorineural pathway to convey speech information. In this paper, four approaches for analyzing the results of a simple listening test using speech stimuli are investigated for the purpose of targeting channels of concern in order for follow-on psychophysical experiments to correctly identify channels performing in an "impaired" or anomalous manner. Listening tests were first conducted with normal-hearing subjects and acoustic models simulating channel-specific anomalies. Results indicate that these proposed analyses perform significantly better than chance in providing information about the location of anomalous channels. Vowel and consonant confusion matrices from six cochlear implant subjects were also analyzed to test the robustness of the proposed analyses to variability intrinsic to cochlear implant data. The current study suggests that confusion matrix analyses have the potential to expedite the identification of impaired channels by providing preliminary information prior to exhaustive psychophysical testing.}, Key = {Remus07} } @booklet{Xu07, Author = {Y. F. Xu and L. M. Collins}, Title = {Predictions of psychophysical measurements for sinusoidal amplitude modulated (SAM) pulse-train stimuli from a Stochastic model}, Journal = {Ieee Transactions On Biomedical Engineering}, Volume = {54}, Number = {8}, Pages = {1389 -- 1398}, Year = {2007}, Month = {August}, ISSN = {0018-9294}, Abstract = {Two approaches have been proposed to reduce the synchrony of the neural response to electrical stimuli in cochlear implants. One approach involves adding noise to the pulse-train stimulus, and the other is based on using a high-rate pulse-train carrier. Hypotheses regarding the efficacy of the two approaches can be tested using computational models of neural responsiveness prior to time-intensive psychophysical studies. In our previous work, we have used such models to examine the effects of noise on several psychophysical measures important to speech recognition. However, to date there has been no parallel analytic solution investigating the neural response to the high-rate pulse-train stimuli and their effect on psychophysical measures. This work investigates the properties of the neural response to high-rate pulse-train stimuli with amplitude modulated envelopes using a stochastic auditory nerve model. The statistics governing the neural response to each pulse are derived using a recursive method. The agreement between the theoretical predictions and model simulations is demonstrated for sinusoidal amplitude modulated (SAM) high rate pulse-train stimuli. With our approach, predicting the neural response in modern implant devices becomes tractable. Psychophysical measurements are also predicted using the stochastic auditory nerve model for SAM high-rate pulse-train stimuli. Changes in dynamic range (DR) and intensity discrimination are compared with that observed for noise-modulated pulse-train stimuli. Modulation frequency discrimination is also studied as a function of stimulus level and pulse rate. Results suggest that high. rate carriers may positively impact such psychophysical measures.}, Key = {Xu07} } @booklet{Torrione07, Author = {P. Torrione and L. M. Collins}, Title = {Texture features for antitank landmine detection using ground penetrating radar}, Journal = {Ieee Transactions On Geoscience And Remote Sensing}, Volume = {45}, Number = {7}, Pages = {2374 -- 2382}, Year = {2007}, Month = {July}, ISSN = {0196-2892}, Abstract = {In this paper, we consider, the application of texture features for antitank landmine detection in ground-penetrating-radar data in the difficult scenario of very high clutter environments. In particular, we develop a technique for 3-D texture feature extraction, and we compare the results for landmine/clutter discrimination using classifiers that are built on 3-D as well as on 2-D texture feature sets. Our results indicate performance improvements across several different challenging testing scenarios when using the relevance-vector-machine classifiers that are trained on our 3-D feature sets as compared to the performance using the 2-D texture feature sets.}, Key = {Torrione07} } @booklet{Throckmorton07, Author = {C. S. Throckmorton and M. S. Kucukoglu and J. J. Remus and L. M. Collins}, Title = {The effect of frequency estimation on speech recognition using an acoustic model of a cochlear implant}, Journal = {Hearing Research}, Volume = {228}, Number = {1-2}, Pages = {230 -- 231}, Year = {2007}, Month = {June}, ISSN = {0378-5955}, Key = {Throckmorton07} } @booklet{Kolba07, Author = {M. P. Kolba and L. M. Collins}, Title = {Information-based sensor management in the presence of uncertainty}, Journal = {Ieee Transactions On Signal Processing}, Volume = {55}, Number = {6}, Pages = {2731 -- 2735}, Year = {2007}, Month = {June}, ISSN = {1053-587X}, Abstract = {Recognizing the uncertainty present in all real-world problems, this correspondence extends the mathematical framework of a previously presented information-based sensor manager to permit operation with uncertain sensor probabilities of detection and false alarm. Simulation results demonstrate the importance of proper uncertainty modeling for the attainment of robust sensor manager performance.}, Key = {Kolba07} } @booklet{Remus07a, Author = {J. J. Remus and L. M. Collins}, Title = {A comparison of adaptive psychometric procedures based on the theory of optimal experiments and Bayesian techniques: Implications for cochlear implant testing}, Journal = {Perception \& Psychophysics}, Volume = {69}, Number = {3}, Pages = {311 -- 323}, Year = {2007}, Month = {April}, ISSN = {0031-5117}, Abstract = {Numerous previous studies have focused on the development of quick and efficient adaptive psychometric procedures. In psychophysics, there is often a model of the psychometric function supported by previous studies for the task of interest. The theory of optimal experiments provides a framework for utilizing a model of the process to develop quick and efficient sequential-testing strategies for estimating model parameters, making it appropriate for developing adaptive psychophysical-testing methods. In this study, we investigated the application of sequential parameter search strategies based on the theory of optimal experiments and Bayesian adaptive procedures for measuring psychophysical variables. The results presented in this article suggest that more sophisticated psychometric procedures can expedite the measurement of psychophysical variables. Such techniques for quickly collecting psychophysical data may be particularly useful in cochlear implant research, where a large set of psychophysical variables are useful for characterizing the performance of an implanted device. It is to be hoped that further development of these techniques will make psychophysical measurements available to clinicians for tuning and optimizing the speech processors of individual cochlear implant patients.}, Key = {Remus07a} } @booklet{Throckmorton07a, Author = {C. S. Throckmorton and S. L. Tantum and Y. Y. Tan and L. M. Collins}, Title = {Independent component analysis for UXO detection in highly cluttered environments}, Journal = {Journal Of Applied Geophysics}, Volume = {61}, Number = {3-4}, Pages = {304 -- 317}, Year = {2007}, Month = {March}, ISSN = {0926-9851}, Abstract = {Statistical signal processing techniques have shown progress in discriminating UXO from clutter when the objects occur in isolation. Under this condition, only a single object contributes to the measured sensor data. For multiple closely spaced subsurface objects, however, the unprocessed sensor measurement is a mixture of the responses from several objects. Consequently, the unprocessed measurements cannot be used directly to discriminate UXO from clutter. In this paper, we implement independent component analysis (ICA), a well-established blind source separation (BSS) technique, to recover the unobserved object signatures from the mixed measurement data obtained by simulating electromagnetic induction (EMI) sensor data, and then use the recovered signatures for UXO/clutter discrimination. Discrimination performance depends on multiple factors, including the number of clutter objects in proximity to the UXO, the separation distance between the UXO and clutter, and the number of mixed measurements available. Simulation results are presented illustrating the impact of these factors on discrimination performance. (c) 2006 Elsevier B.V. All rights reserved.}, Key = {Throckmorton07a} } @article{070910453858, Author = {Throckmorton, Chandra S. and Tantum, Stacy L. and Tan, Yingyi and Collins, Leslie M.}, Title = {Independent component analysis for UXO detection in highly cluttered environments}, Journal = {Journal of Applied Geophysics}, Volume = {61}, Number = {3-4}, Pages = {304 - 317}, Year = {2007}, url = {http://dx.doi.org/10.1016/j.jappgeo.2006.05.007}, Keywords = {Blind source separation;Computer simulation;Electromagnetic induction;Independent component analysis;Radar clutter;Sensor data fusion;Signal processing;}, Abstract = {Statistical signal processing techniques have shown progress in discriminating UXO from clutter when the objects occur in isolation. Under this condition, only a single object contributes to the measured sensor data. For multiple closely spaced subsurface objects, however, the unprocessed sensor measurement is a mixture of the responses from several objects. Consequently, the unprocessed measurements cannot be used directly to discriminate UXO from clutter. In this paper, we implement independent component analysis (ICA), a well-established blind source separation (BSS) technique, to recover the unobserved object signatures from the mixed measurement data obtained by simulating electromagnetic induction (EMI) sensor data, and then use the recovered signatures for UXO/clutter discrimination. Discrimination performance depends on multiple factors, including the number of clutter objects in proximity to the UXO, the separation distance between the UXO and clutter, and the number of mixed measurements available. Simulation results are presented illustrating the impact of these factors on discrimination performance. © 2006 Elsevier B.V. All rights reserved.}, Key = {070910453858} } @article{070610408930, Author = {Liao, Yuwei and Nolte, Loren W. and Collins, Leslie M.}, Title = {Decision fusion of ground-penetrating radar and metal detector algorithms - A robust approach}, Journal = {IEEE Transactions on Geoscience and Remote Sensing}, Volume = {45}, Number = {2}, Pages = {398 - 409}, Year = {2007}, url = {http://dx.doi.org/10.1109/TGRS.2006.888096}, Keywords = {Algorithms;Ground penetrating radar systems;Probability density function;Sensors;Signal detection;}, Abstract = {Numerous detection algorithms, using various sensor modalities, have been developed for the detection of mines in cluttered and noisy backgrounds. The performance for each detection algorithm is typically reported in terms of the receiver operating characteristic (ROC), which is a plot of the probability of detection versus false alarm as a function of the threshold setting on the output decision variable of each algorithm. In this paper, we present multisensor decision-fusion algorithms that combine the local decisions of existing detection algorithms for different sensors. This offers an expedient, attractive, and much simpler alternative to the design of an algorithm that fuses multiple sensors at the data level, especially in cases of limited training data where it is difficult to make accurate estimates of multidimensional probability density functions. The goal of our multisensor decision-fusion approach is to exploit the complimentary strengths of existing multisensor algorithms so as to achieve performance (ROC) that exceeds the performance of any sensor algorithm operating in isolation. Our approach to multisensor decision fusion is based on the optimal signal detection theory using the likelihood ratio. We consider the optimal fusion of local decisions for two sensors: a ground-penetrating radar and a metal detector. A new robust algorithm for decision fusion that addresses the problem in which the statistics of the training data are not likely to exactly match the statistics of the test data is presented. ROCs are presented and compared for field data. © 2007 IEEE.}, Key = {070610408930} } @article{9096129, Author = {Kolba, M.P. and Collins, L.M.}, Title = {Sensor management with uncertain performance characteristics}, Journal = {2006 IEEE Sensor Array and Multichannel Signal Processing Workshop (IEEE Cat. No. 06EX1345C)}, Pages = {456 - 600}, Address = {Waltham, MA, USA}, Year = {2006}, Keywords = {array signal processing;management;sensors;}, Abstract = {Previous work has presented an information-theoretic sensor management framework for the detection of static targets. This framework is based on the expected discrimination gain maximization technique of Kastella. The sensor manager searches for N targets within a grid of cells using M sensors, which may be thought of as a reconfigurable sensor array. Sensor probabilities of detection and false alarm are used in the mathematical structure of the sensor manager, and these probabilities have previously been assumed to be certain. Realistic problems, however, will inevitably involve uncertainty. This paper introduces uncertain sensor P<sub>d</sub> and P<sub>f</sub> values into the mathematical framework and allows for their incorporation into the sequential structure of the sensor manager. The performance of the presented sensor management technique is then compared to direct search, where the sensors sweep through the grid in a predefined sampling pattern. The sensor manager is found to be superior to direct search when the uncertainty present in the problem is properly modeled. When uncertainty is present but not modeled, the performance of the sensor manager is severely degraded. This result indicates that uncertainty modeling will be important and necessary for the successful application of the presented sensor manager to real-world problems. Additional simulations examine the robustness of the sensor manager to errors in the assumed densities for P<sub>d</sub> and P<sub>f</sub>, and the performance of the sensor manager is found to remain strong even when the assumed P<sub>d</sub> and P<sub>f</sub> densities differ from the true densities}, Key = {9096129} } @article{063410078441, Author = {Torrione, Peter A. and Collins, Leslie and Clodfelter, Fred and Lulich, Dan and Patrikar, Ajay and Howard, Peter and Weaver, Richard and Rosen, Erik}, Title = {Constrained filter optimization for subsurface landmine detection}, Journal = {Proceedings of SPIE - The International Society for Optical Engineering}, Volume = {6217 II}, Pages = {62172 -}, Address = {Kissimmee, FL, United States}, Year = {2006}, url = {http://dx.doi.org/10.1117/12.665780}, Keywords = {Ground penetrating radar systems;Adaptive systems;Optimization;Genetic algorithms;Regression analysis;Mines;}, Abstract = {Previous large-scale blind tests of anti-tank landmine detection utilizing the NIITEK ground penetrating radar indicated the potential for very high anti-tank landmine detection probabilities at very low false alarm rates for algorithms based on adaptive background cancellation schemes. Recent data collections under more heterogeneous multi-layered road-scenarios seem to indicate that although adaptive solutions to background cancellation are effective, the adaptive solutions to background cancellation under different road conditions can differ significantly, and misapplication of these adaptive solutions can reduce landmine detection performance in terms of PD/FAR. In this work we present a framework for the constrained optimization of background-estimation filters that specifically seeks to optimize PD/FAR performance as measured by the area under the ROC curve between two FARs. We also consider the application of genetic algorithms to the problem of filter optimization for landmine detection. Results indicate robust results for both static and adaptive background cancellation schemes, and possible real-world advantages and disadvantages of static and adaptive approaches arc discussed.}, Key = {063410078441} } @article{063410078436, Author = {Torrione, Peter and Remus, Jeremiah and Collins, Leslie}, Title = {Comparison of pattern recognition approaches for multi-sensor detection and discrimination of anti-personnel and anti-tank landmines}, Journal = {Proceedings of SPIE - The International Society for Optical Engineering}, Volume = {6217 II}, Pages = {62172 -}, Address = {Kissimmee, FL, United States}, Year = {2006}, url = {http://dx.doi.org/10.1117/12.665660}, Keywords = {Pattern recognition;Ground penetrating radar systems;Electromagnetism;Feature extraction;Land fill;Sensors;Computational complexity;Radial basis function networks;}, Abstract = {In this work we explore and compare several statistical pattern recognition techniques for classification and identification of buried landmines using both electromagnetic induction and ground penetrating radar data. In particular we explore application of different feature extraction approaches to the problem of landmine/clutter classification in blind- and known- ground truth scenarios using data from the NIITEK ground penetrating radar and the Vallon EMI sensor as well as the CyTerra GPR and Minelab EMI sensors. We also compare and contrast the generalization capabilities of different kernels including radial basis function, linear, and direct kernels within the relevance vector machine framework. Results are presented for blind-test scenarios that illustrate robust classification for features that can be extracted with low computational complexity.}, Key = {063410078436} } @article{06289994871, Author = {Torrione, Peter A. and Throckmorton, Chandra S. and Collins, Leslie M.}, Title = {Performance of an adaptive feature-based processor for a wideband ground penetrating radar system}, Journal = {IEEE Transactions on Aerospace and Electronic Systems}, Volume = {42}, Number = {2}, Pages = {644 - 658}, Year = {2006}, url = {http://dx.doi.org/10.1109/TAES.2006.1642579}, Keywords = {Radar clutter;Tracking radar;Microprocessor chips;Algorithms;}, Abstract = {A two-stage algorithm for landmine detection with a ground penetrating radar (GPR) system is described. First, 3-D data sets are processed using a computationally inexpensive pre-screening algorithm which flags potential locations of interest. These flagged locations are then passed to a feature-based processer which further discriminates target-like anomalies from naturally occurring clutter. Current field trial (over 6500 square meters) and blind test results (over 39000 square meters) are presented and these show at least an order of magnitude improvement over other radar system-based detection algorithms on the same test lanes. © 2006 IEEE.}, Key = {06289994871} } @article{063410078438, Author = {Kolba, Mark P. and Collins, Leslie M.}, Title = {The effects of uncertainty and uncertainty modeling on information-based sensor manager performance}, Journal = {Proceedings of SPIE - The International Society for Optical Engineering}, Volume = {6217 II}, Pages = {62172 -}, Address = {Kissimmee, FL, United States}, Year = {2006}, url = {http://dx.doi.org/10.1117/12.665595}, Keywords = {Explosives;Probability;Vegetation;Computer simulation;}, Abstract = {A proliferation of the number and variety of sensors for the landmine detection problem has created the need for a sensor manager that is able to intelligently task and coordinate the operation of a suite of landmine sensors. Previous work has developed a framework for sensor management that takes into account the context of the landmine detection problem. The sensor manager searches for N targets in a grid using M multimodal sensors by seeking to maximize the expected information gain. The probabilities of detection and false alarm of the sensors are assumed to be known and are used in the sensor manager calculations. However, in a real-world landmine detection setting, the performance characteristics of the sensors will in fact be unknown. Uneven and irregular ground, vegetation, unanticipated clutter objects, even bad weather - all these can affect the performance of a landmine sensor. This paper examines the effects of uncertainty in the probabilities of detection and false alarm on the performance of the previously presented sensor manager and further examines the performance effects of properly and improperly modeling this uncertainty. Performance is, naturally, found to be adversely affected by uncertainty. However, it is demonstrated that properly modeling the uncertainty present in the problem helps to recover some of the performance that is lost through the introduction of uncertainty.}, Key = {063410078438} } @article{06239927448, Author = {Song, Jiayu and Liu, Qing Huo and Torrione, Pete and Collins, Leslie}, Title = {Two-dimensional and three-dimensional NUFFT migration method for landmine detection using ground-penetrating radar}, Journal = {IEEE Transactions on Geoscience and Remote Sensing}, Volume = {44}, Number = {6}, Pages = {1462 - 1469}, Year = {2006}, url = {http://dx.doi.org/10.1109/TGRS.2006.870412}, Keywords = {Signal to noise ratio;Image analysis;Data processing;Phase shift;Interpolation;Fourier transforms;Algorithms;Least squares approximations;}, Abstract = {Ground-penetrating radar (GPR) has been widely used for landmine detection due to its high signal-to-noise ratio (SNR) and superior ability to image nonmetallic landmines. Processing GPR data to obtain better target images and to assist further object detection has been an active research area. Phase-shift migration is a widely used method; however, its wavenumber space is nonuniformly sampled because of the nonlinear relationship between the uniform frequency samples and the wavenumbers. Conventional methods use linear interpolation to obtain uniform wavenumber samples and compute the fast Fourier transform (TFT). This paper develops two- and three-dimensional migration methods that process GPR data to obtain images close to the actual target geometries using a nonuniform fast Fourier transform (NUFFT) algorithm. The proposed method is first compared to the conventional migration approaches on simulated data and then applied to landmine field data sets. Results suggest that the NUFFT migration method is useful in focusing images, estimating landmine structure, and retaining relatively high signal-to-noise ratio in the migrated data. The processed data sets are then fed to the normalized energy and least-mean-square-based anomaly detectors. Receiver operating characteristic curves of data sets processed by different migration methods are compared. The NUFFT migration shows potential improvements on both classifiers with a reduced false alarm rate at most probabilities of detection. © 2006 IEEE.}, Key = {06239927448} } @article{9135756, Author = {Collins, L.M. and Throckmorton, C.S. and Kucukoglu, M.S. and Remus, J.J.}, Title = {Acoustic model investigation of a multiple carrier frequency algorithm for encoding fine frequency structure: Implications for cochlear implants}, Journal = {Hear. Res. (Netherlands)}, Volume = {218}, Number = {1-2}, Pages = {30 - 42}, Year = {2006}, url = {http://dx.doi.org/10.1016/j.heares.2006.03.020}, Keywords = {acoustic signal processing;biomedical electrodes;ear;frequency modulation;hearing;medical signal processing;prosthetics;speech coding;speech recognition;}, Abstract = {Current cochlear implants provide frequency resolution through the number of channels. Improving resolution by increasing channels is limited by factors such as the physiological feasibility of increasing the number of electrodes, the inability to increase the number of channels for those already implanted, and the increased possibility of channel interactions reducing channel efficacy. Recent studies have suggested an alternative method: providing a continuum of pitch percepts for each channel based on the frequency content of that channel. This study seeks to determine the frequency resolution necessary for the highest performance gain, which may give some indication of the feasibility for implementation in implants. A discrete set of carrier frequencies, instead of a continuum, are evaluated using an acoustic model to measure speech recognition. Performance increased as the number of available frequencies increased, and substantive improvement was seen with as few as two frequencies per channel. The effect of variable frequency discrimination was also assessed, and the results suggest that frequency modulation can still provide benefits with poor frequency discrimination on some channels. These results suggest that if two or more discriminable frequencies per channel can be generated for cochlear implant subjects then an improvement in speech recognition may be possible. [All rights reserved Elsevier]}, Key = {9135756} } @article{05439428677, Author = {Torrione, Peter A. and Throckmorton, Chandra S. and Collins, Leslie and Clodfelter, Fred and Frasier, Shane and Starnes, Ian and Bishop, Steven and Gugino, Peter and Howard, Peter and Weaver, Richard and Rosen, Erik}, Title = {Application of matched subspace detectors to target detection and identification in ground penetrating radar data}, Journal = {Proceedings of SPIE - The International Society for Optical Engineering}, Volume = {5794}, Number = {PART II}, Pages = {1160 - 1171}, Address = {Orlando, FL, United States}, Year = {2005}, url = {http://dx.doi.org/10.1117/12.602754}, Keywords = {Ground penetrating radar systems;Identification (control systems);Automatic target recognition;Problem solving;Feature extraction;Mathematical models;}, Abstract = {In this work we present an application of matched subspace detectors to the problem of target detection and identification using ground penetrating radar data. In particular we apply sets of matched subspace detector filter banks to data containing both anti-personnel and anti-tank targets as well as metallic and non-metallic clutter objects. Current results indicate the potential for robust target detection and identification but further improvements via subspace modeling and signal extraction/enhancement may also improve performance.}, Key = {05439428677} } @article{05319271593, Author = {Huettel, Lisa G. and Collins, Leslie M.}, Title = {A vertically-integrated application-driven signal processing laboratory}, Journal = {ASEE Annual Conference and Exposition, Conference Proceedings}, Pages = {15613 - 15623}, Address = {Portland, OR, United States}, Year = {2005}, Keywords = {Computer programming;Algorithms;Mathematical models;Computer hardware;Curricula;Personnel training;}, Abstract = {Hardware-based laboratories have been successfully integrated into individual Digital Signal Processing (DSP) courses at many universities. Typically, most hardware-based DSP laboratory experiences are offered to upper-level students and focus on programming the signal processor. Although fundamental concepts are explored in laboratory exercises, the emphasis often remains on the mechanics of hardware implementation. Thus, topics are not presented in the context of realistic applications. While such an approach may be ideal for preparing motivated upper-level students for future careers in signal processing, it is not suitable for students with no prior experience in the field. The signal processing laboratory being developed at Duke University is modeled, in part, after existing successful signal processing laboratories, but introduces two innovative features. First, the new laboratory will be integrated into multiple courses from the sophomore to senior level, rather than a single course. Second, the laboratory exercises will be application-driven and will emphasize the development of signal processing algorithms to be implemented on the hardware. As the students advance through the signal processing curriculum, they will transition from high-level algorithm generation to hardware-level design and implementation. This hierarchical training will provide a thorough, extended, and increasingly focused exposure to signal processing.}, Key = {05319271593} } @article{05439428670, Author = {Kolba, Mark P. and Torrione, Peter A. and Collins, Leslie M.}, Title = {Information-based sensor management for landmine detection using multimodal sensors}, Journal = {Proceedings of SPIE - The International Society for Optical Engineering}, Volume = {5794}, Number = {PART II}, Pages = {1098 - 1107}, Address = {Orlando, FL, United States}, Year = {2005}, url = {http://dx.doi.org/10.1117/12.603582}, Keywords = {Information management;Mines;Information analysis;Algorithms;Data reduction;Probability;Computer simulation;}, Abstract = {We consider an information-theoretic approach for sensor management that chooses sensors and sensor parameters in order to maximize the expected discrimination gain associated with each new sensor measurement. We analyze the problem of searching for N targets with M multimodal sensors, where each sensor has its own probability of detection, probability of false alarm, and cost of use. Other information, such as the prior distribution of the targets in space and the degree of constraint of the sensor motion, is also utilized in our formulation. Performance of the sensor management algorithm is then compared to the performance of a direct-search procedure in which the sensors blindly search through all cells in a predetermined path. The information-based sensor manager is found to have significant performance gains over the direct-search approach. Algorithm performance is also analyzed using real landmine data taken with three different sensing modalities. Detection performance using the sensor management algorithm is again found to be superior to detection performance using a blind search procedure. The simulation and real-data results also both illuminate the increased performance available through multimodal sensing.}, Key = {05439428670} } @article{05439428681, Author = {Wang, Chunmei and Collins, Leslie}, Title = {Feature selection for physics model based object discrimination}, Journal = {Proceedings of SPIE - The International Society for Optical Engineering}, Volume = {5794}, Number = {PART II}, Pages = {1200 - 1208}, Address = {Orlando, FL, United States}, Year = {2005}, url = {http://dx.doi.org/10.1117/12.603451}, Keywords = {Object recognition;Algorithms;Classification (of information);Optimization;}, Abstract = {We investigated the application of two state-of-the-art feature selection algorithms for subsurface target discrimination. One is called joint classification and feature optimization (JCFO), which imposes a sparse prior on the features, and optimizes the classifier and its predictors simultaneously via an expectation maximization (EM) algorithm. The other selects features by directly maximizing the hypothesis margin between targets and clutter. The results of feature selection and target discrimination are demonstrated using wideband electromagnetic induction data measured at data collected at the Aberdeen Proving Ground Standardized Test Site for UXO discrimination. It is shown that the classification performance is significantly improved by only including a compact set of relevant features.}, Key = {05439428681} } @article{9026093, Author = {Collins, L.M. and Mane, K.K. and Martinez, M.L.B. and Hussell, J.A.T. and Luce, R.E.}, Title = {ScienceSifter: facilitating activity awareness in collaborative research groups through focused information feeds}, Journal = {Proceedings. First International Conference on e-Science and Grid Computing}, Pages = {8 pp. -}, Address = {Melbourne, Vic., Australia}, Year = {2005}, Keywords = {data visualisation;grid computing;groupware;information filters;natural sciences computing;}, Abstract = {As the amount of scientific information available to researchers increases, the challenge of sifting through the information to find what is truly important to their work increases, as well. In this paper we describe ScienceSifter, a tool that addresses this challenge by enabling groups of researchers and channel editors to create and customize information feeds. Using ScienceSifter, users can combine several information feeds, then filter them by keywords to create a focused information feed. They can view the feed in a shared information space in the form of a list, a list with descriptions, or a hyberbolic tree visualization, and they can save items to a shared list. Thus ScienceSifter can reduce the amount of time researchers spend finding and sharing information. It can facilitate shared intellectual activity and activity awareness among the members of the group}, Key = {9026093} } @article{05259172985, Author = {Xu, Yifang and Collins, Leslie M.}, Title = {Predicting dynamic range and intensity discrimination for electrical pulse-train stimuli using a stochastic auditory nerve model: The effects of stimulus noise}, Journal = {IEEE Transactions on Biomedical Engineering}, Volume = {52}, Number = {6}, Pages = {1040 - 1049}, Year = {2005}, url = {http://dx.doi.org/10.1109/TBME.2005.846718}, Keywords = {Random processes;Neurology;Mathematical models;Signal theory;Probability;Signal encoding;Biological membranes;}, Abstract = {This work investigates dynamic range and intensity discrimination for electrical pulse-train stimuli that are modulated by noise using a stochastic auditory nerve model. Based on a hypothesized monotonic relationship between loudness and the number of spikes elicited by a stimulus, theoretical prediction of the uncomfortable level has previously been determined by comparing spike counts to a fixed threshold, N<sub>ucl</sub>. However, no specific rule for determining N<sub>ucl</sub> has been suggested. Our work determines the uncomfortable level based on the excitation pattern of the neural response in a normal ear. The number of fibers corresponding to the portion of the basilar membrane driven by a stimulus at an uncomfortable level in a normal ear is related to N<sub>ucl</sub> at an uncomfortable level of the electrical stimulus. Intensity discrimination limens are predicted using signal detection theory via the probability mass function of the neural response and via experimental simulations. The results show that the uncomfortable level for pulse-train stimuli increases slightly as noise level increases. Combining this with our previous threshold predictions, we hypothesize that the dynamic range for noise-modulated pulse-train stimuli should increase with additive noise. However, since our predictions indicate that intensity discrimination under noise degrades, overall intensity coding performance may not improve significantly. © 2005 IEEE.}, Key = {05259172985} } @article{06239919102, Author = {Remus, Jeremiah J. and Collins, Leslie M.}, Title = {Expediting the identification of impaired cochlear implant acoustic model channels through confusion matrix analysis}, Journal = {2nd International IEEE EMBS Conference on Neural Engineering}, Volume = {2005}, Pages = {418 - 421}, Address = {Arlington, VA, United States}, Year = {2005}, url = {http://dx.doi.org/10.1109/CNE.2005.1419648}, Keywords = {Implants (surgical);Speech recognition;Acoustic signal processing;Psychophysiology;Large scale systems;Electrodes;}, Abstract = {Psychophysical studies with cochlear implant patients have demonstrated performance variability across the electrode array, in some cases with electrodes displaying quite anomalous behavior. Aberrant cochlear implant channels have been linked to reduced speech recognition ability and poor transmission of auditory information. However, psychophysical tests to completely assess all psychophysical metrics and detect impaired electrodes are time consuming. This study proposes using the confusion matrices from closed response set listening tests to identify impaired electrodes. A listening experiment was performed in which normal-hearing subjects identified vowel and consonant tokens processed with an impaired cochlear implant acoustic model. Two methods of analyzing the vowel and consonant confusion matrices to identify the impaired acoustic model channels are considered. The performance of the two analysis methods motivates further study of the uses of relatively simple listening tests to provide information that can expedite larger scale testing of psychophysical metrics and impaired electrodes. © 2005 IEEE.}, Key = {06239919102} } @article{064810268014, Author = {Remus, Jeremiah J. and Collins, Leslie M.}, Title = {The effects of noise on speech recognition in cochlear implant subjects: Predictions and analysis using acoustic models}, Journal = {Eurasip Journal on Applied Signal Processing}, Volume = {2005}, Number = {18}, Pages = {2979 - 2990}, Year = {2005}, url = {http://dx.doi.org/10.1155/ASP.2005.2979}, Keywords = {Acoustic noise;Implants (surgical);Optical resolving power;Acoustic signal processing;Mathematical models;}, Abstract = {Cochlear implants can provide partial restoration of hearing, even with limited spectral resolution and loss of fine temporal structure, to severely deafened individuals. Studies have indicated that background noise has significant deleterious effects on the speech recognition performance of cochlear implant patients. This study investigates the effects of noise on speech recognition using acoustic models of two cochlear implant speech processors and several predictive signal-processing-based analyses. The results of a listening test for vowel and consonant recognition in noise are presented and analyzed using the rate of phonemic feature transmission for each acoustic model. Three methods for predicting patterns of consonant and vowel confusion that are based on signal processing techniques calculating a quantitative difference between speech tokens are developed and tested using the listening test results. Results of the listening test and confusion predictions are discussed in terms of comparisons between acoustic models and confusion prediction performance. © 2005 Hindawi Publishing Corporation.}, Key = {064810268014} } @article{05319271476, Author = {Collins, Leslie M. and Huettel, Lisa G. and Brown, April S. and Ybarra, Gary A. and Holmes, Joseph S. and Board, John A. and Cummer, Steven A. and Gustafson, Michael R. and Kim, Jungsang and Massoud, Hisham Z.}, Title = {Theme-based redesign of the duke university ECE curriculum: The first steps}, Journal = {ASEE Annual Conference and Exposition, Conference Proceedings}, Pages = {14313 - 14326}, Address = {Portland, OR, United States}, Year = {2005}, Keywords = {Students;Curricula;Planning;Data processing;Electronic equipment;Benchmarking;}, Abstract = {Undergraduates in Electrical and Computer Engineering (ECE) at Duke University have benefited from the combination of curricular flexibility and rigorous coursework. The current curriculum is further limited in that the core courses do not offer a vertically integrated thematic introduction to ECE as a discipline nor are they reflective of the broader scope of ECE field of study. The course has streamlined structure, which is consistent with an educational theme. Results from Educational BEnchmark Inc. (EBI) survey of students confirmed that they too perceive the oppurtinities for improvement in curriculum.}, Key = {05319271476} } @article{05068828494, Author = {Zhu, Quan and Collins, Leslie M.}, Title = {Application of feature extraction methods for landmine detection using the wichmann/niitek ground-penetrating radar}, Journal = {IEEE Transactions on Geoscience and Remote Sensing}, Volume = {43}, Number = {1}, Pages = {81 - 85}, Year = {2005}, url = {http://dx.doi.org/10.1109/TGRS.2004.839431}, Keywords = {Ground penetrating radar systems;Radar clutter;Sensors;Algorithms;Markov processes;Polynomials;Mathematical models;}, Abstract = {Ground-penetrating radar (GPR) has been proposed as an alternative to classical electromagnetic induction techniques for the landmine detection problem. The Wichmann/Niitek system provides a good platform for novel GPR-based antitank mine detection and classification algorithm development due to its extremely high SNR. When the GPR sensor is mounted on a moving vehicle, the target signatures are hyperbolas in a time-domain data record. The goal of this work is to extract useful features that exploit this knowledge in order to improve target detection. The algorithms can be divided into two steps: feature extraction and classification. Preprocessing is also considered to remove both stationary effects and nonstationary drift of the data and to improve the contrast of the desired hyperbolas. The algorithm is evaluated using real data over primarily plastic antitank mines ^collected with a fielded GPR sensor at a government test site.}, Key = {05068828494} } @article{05359325948, Author = {Tan, Yingyi and Tantum, Stacy L. and Collins, Leslie M.}, Title = {Kalman filtering for enhanced landmine detection using quadrupole resonance}, Journal = {IEEE Transactions on Geoscience and Remote Sensing}, Volume = {43}, Number = {7}, Pages = {1507 - 1516}, Year = {2005}, url = {http://dx.doi.org/10.1109/TGRS.2005.846863}, Keywords = {Explosives;Metal detectors;Resonance;Radio systems;Sensors;Gaussian noise (electronic);Signal processing;Computer simulation;Nuclear magnetic resonance;Algorithms;Difference equations;}, Abstract = {Quadrupole resonance (QR) is a novel technology recently applied to landmine detection. The detection process is specific to the chemistry of the explosive, and therefore is less susceptible to the types of false alarms experienced by metal detectors and ground-penetrating radars. Although QR is vulnerable to radio-frequency interference (RFI) when the sensor is deployed in the field, adaptive RFI mitigation can remove most of the RFI. In this paper, advanced signal processing algorithms applied to the postmitigation signal are studied to enhance explosive detection. A new Kalman filtering strategy is proposed to estimate and detect the QR signal in the postmitigation signal. The results using both simulated data and experimental data show that the proposed algorithm can provide robust landmine detection performance. © 2005 IEEE.}, Key = {05359325948} } @article{05439428671, Author = {Yu, Yongli and Collins, Leslie M.}, Title = {Multi-modal Iterative Adaptive Processing (MIAP) performance in the discrimination mode for landmine detection}, Journal = {Proceedings of SPIE - The International Society for Optical Engineering}, Volume = {5794}, Number = {PART II}, Pages = {1108 - 1117}, Address = {Orlando, FL, United States}, Year = {2005}, url = {http://dx.doi.org/10.1117/12.603848}, Keywords = {Mines;Iterative methods;Data processing;Probability;Sensor data fusion;Algorithms;Sensors;Ground penetrating radar systems;Electromagnetism;}, Abstract = {Due to the nature of landmine detection, a high detection probability (P<sub>d</sub>) is required to avoid casualties and injuries. However, high P<sub>d</sub> is often obtained at the price of extremely high false alarm rates. It is widely accepted that no single sensor technology has the ability to achieve the required detection rate while keeping acceptably low false alarm rates for all types of mines in all types of soil and with all types of false targets. Remarkable advances in sensor technology for landmine detection have made multi-sensor fusion an attractive alternative to single sensor detection techniques. Hence, multi-sensor fusion mine detection systems, which use complementary sensor technologies, are proposed. Previously we proposed a new multi-sensor fusion algorithm called Multi-modal Iterative Adaptive Processing (MIAP), which incorporates information from multiple sensors in an adaptive Bayesian decision framework and the identification capabilities of multiple sensors are utilized to modify the statistical models utilized by the mine detector. Simulation results demonstrate the improvement in performance obtained using the MIAP algorithm. In this paper, we assume a hand-held mine detection system utilizing both an electromagnetic induction sensor (EMI) and a ground-penetrating radar (GPR). The hand-held mine detection sensors are designed to have two modes of operations: search mode and discrimination mode. Search mode generates an initial causal detection on the suspected location; and discrimination mode confirms whether there is a mine. The MIAP algorithm is applied in the discrimination mode for hand-held mine detection. The performance of the detector is evaluated on a data set collected by the government, and the performance is compared with the other traditional fusion results.}, Key = {05439428671} } @article{05439428662, Author = {Zachery, Karen Norris and Schultz, Gregory M. and Collins, Leslie M.}, Title = {Force Protection Demining System (FPDS) detection subsystem}, Journal = {Proceedings of SPIE - The International Society for Optical Engineering}, Volume = {5794}, Number = {PART II}, Pages = {1018 - 1029}, Address = {Orlando, FL, United States}, Year = {2005}, url = {http://dx.doi.org/10.1117/12.603021}, Keywords = {Mining;Sensor data fusion;Signal interference;Ground penetrating radar systems;Algorithms;Computer software;Automatic target recognition;Image analysis;Data acquisition;}, Abstract = {This study describes the U.S. Army Force Protection Demining System (FPDS); a remotely-operated, multisensor platform developed for reliable detection and neutralization of both anti-tank and anti-personnel landmines. The ongoing development of the prototype multisensor detection subsystem is presented, which integrates an advanced electromagnetic pulsed-induction array and ground penetrating synthetic aperture radar array on a single standoff platform. The FPDS detection subsystem is mounted on a robotic rubber-tracked vehicle and incorporates an accurate and precise navigation/positioning module making it well suited for operation in varied and irregular terrains. Detection sensors are optimally configured to minimize interference without loss in sensitivity or performance. Mine lane test data acquired from the prototype sensors are processed to extract signal- and image-based features for automatic target recognition. Preliminary results using optimal feature and classifier selection indicate the potential of the system to achieve high probabilities of detection while minimizing false alarms. The FPDS detection software system also exploits modern multi-sensor data fusion algorithms to provide real-time detection and discrimination information to the user.}, Key = {05439428662} } @article{04528738231, Author = {Throckmorton, Chandra S. and Torrione, Peter A. and Collins, Leslie M. and Gader, Paul and Lee, Wen-Hsuing and Wilson, Joseph N.}, Title = {The efficacy of human observation for discrimination and feature identification of targets measured by the NIITEK ground penetrating radar}, Journal = {Proceedings of SPIE - The International Society for Optical Engineering}, Volume = {5415}, Number = {PART 2}, Pages = {963 - 972}, Address = {Orlando, FL, United States}, Year = {2004}, url = {http://dx.doi.org/10.1117/12.541168}, Keywords = {Sensors;Data processing;Alarm systems;Sustainable development;Bandwidth;Algorithms;}, Abstract = {Recently, blind tests of several automated detection algorithms operating on the NIITEK ground penetrating radar data (GPR) have resulted in quite promising performance results. Anecdotally, human observers have also shown notable skill in detecting landmines and rejecting false alarms in this same data; however, the basis of human performance has not been studied in depth. In this study, human observers are recruited from the undergraduate and graduate student population at Duke University and are trained to visually detect landmines in the NIITEK GPR data. Subjects are then presented with GPR responses associated with blanks, clutter items (including emplaced clutter), and landmines in a blind test scenario. Subjects are asked to make the decision as to whether they are viewing a landmine response or a false alarm, and their performance is scored. A variety of landmines, measured at several test sites, are presented to determine the relative difficulty in detecting each mine type. Subject performance is compared to the performance of two automated algorithms already under development for the NIITEK radar system: LMS and FROSAW. In addition, subjects are given a subset of features for each alarm from which they may indicate the reason behind their decision. These last data may provide a basis for the design of an automated algorithm that takes advantage of the most useful of the observed features.}, Key = {04528738231} } @article{04528738216, Author = {Tan, Yingyi and Tantum, Stacy L. and Collins, Leslie M.}, Title = {Signal processing for improved explosives detection using quadrupole resonance}, Journal = {Proceedings of SPIE - The International Society for Optical Engineering}, Volume = {5415}, Number = {PART 2}, Pages = {822 - 833}, Address = {Orlando, FL, United States}, Year = {2004}, url = {http://dx.doi.org/10.1117/12.542553}, Keywords = {Demodulation;Frequencies;Radio interference;Sensor data fusion;Statistical methods;Computer simulation;}, Abstract = {Quadrupole resonance (QR) technology for explosives detection is of crucial importance in an increasing number of applications. For landmine detection, where the detection system cannot be adequately shielded, QR has proven to be highly effective if the QR sensor is not exposed to radio frequency interference (RFI). However, strong non-Gaussian RFI in the field is unavoidable, making RFI mitigation a critical part of the signal processing. In this paper, a statistical model of the non-Gaussian RFI is presented. The QR model is used within the context of an adaptive filtering methodology to mitigate RFI, and this approach is compared to other RFI mitigation techniques. Results obtained using both simulated and measured QR data are presented.}, Key = {04528738216} } @article{04528738233, Author = {Torrione, Peter A. and Throckmorton, Chandra S. and Collins, Leslie and Clodfelter, Fred and Frasier, Shane and Starnes, Ian and Bishop, Steven and Gugino, Peter and Howard, Peter and Weaver, Richard and Rosen, Erik}, Title = {Feature-based processing of pre-screener generated alarms for performance improvements in target identification using the niitek ground-penetrating radar system}, Journal = {Proceedings of SPIE - The International Society for Optical Engineering}, Volume = {5415}, Number = {PART 2}, Pages = {984 - 995}, Address = {Orlando, FL, United States}, Year = {2004}, url = {http://dx.doi.org/10.1117/12.540926}, Keywords = {Alarm systems;Sensors;Signal processing;Algorithms;Coupled circuits;Least squares approximations;}, Abstract = {In this paper we present a multi-stage algorithm for target/clutter discrimination and target identification using the Niitek/Wichmann ground penetrating radar (GPR). To identify small subsets of GPR data for feature-processing, a pre-screening algorithm based on the 2-D lattice least mean squares (LMS) algorithm is used to flag locations of interest. Features of the measured GPR data at these flagged locations are then generated and pattern recognition techniques are used to identify targets using these feature sets. It has been observed that trained human subjects are often quite successful at discriminating targets from clutter. Some features are designed to take advantage of the visual aberrations that a human observer might use. Other features based on a variety of image and signal processing techniques are also considered. Results presented indicate improvements for feature-based processors over pre-screener algorithms.}, Key = {04528738233} } @article{04228188117, Author = {Huettel, Lisa G. and Collins, Leslie M.}, Title = {A theoretical analysis of normal- And impaired-hearing intensity discrimination}, Journal = {IEEE Transactions on Speech and Audio Processing}, Volume = {12}, Number = {3}, Pages = {323 - 333}, Year = {2004}, url = {http://dx.doi.org/10.1109/TSA.2004.825672}, Keywords = {Acoustic intensity;Speech recognition;Signal detection;Physiology;Computational methods;Computer simulation;}, Abstract = {Interpretation of psychophysical data from impaired-hearing individuals on intensity discrimination tasks has been confounded by the fact that some impaired individuals' performance is near-normal in quiet, whereas for others, the difference limen is elevated. It has been observed that a subject's discrimination abilities may be related to the underlying audiogram configuration, which is often dependent on the type of physiological damage that has occurred. This suggests that data be grouped and analyzed according to the type of hearing loss. An experimental study by Schroder et al. (1994) tested the hypothesis that the near-normal performance of some impaired subjects was the result of a normal spread of excitation, rather than greater intensity resolution due to loudness recruitment. In this paper, we replicate the trends observed in Schroder et al.'s experimental data using a combination of signal detection theory and a computational auditory model. By linking simulated psychophysical predictions with the corresponding simulated physiological responses, this theoretical analysis provides further qualitative support for the hypothesis that the observed performance is due to the spread of excitation.}, Key = {04228188117} } @article{04408381857, Author = {Xu, Yifang and Collins, Leslie M.}, Title = {Theoretical prediction of dynamic range and intensity discrimination for electrical noise-modulated pulse-train stimuli}, Journal = {ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings}, Volume = {4}, Pages = {-21--24 -}, Address = {Montreal, Que, Canada}, Year = {2004}, url = {http://dx.doi.org/10.1109/ICASSP.2004.1326753}, Keywords = {Noise abatement;Audition;Hearing aids;Implants (surgical);Data acquisition;Probability;Random processes;}, Abstract = {This work investigates dynamic range and intensity discrimination for electrical noise-modulated pulse-train stimuli using a stochastic auditory nerve model, Based on a hypothesized monotonic relationship between loudness and the number of spikes, theoretical prediction of the most uncomfortable level was determined by comparing spike counts to a fixed threshold. However, no specific rule for determining this fixed number has previously been suggested. Our work determines the most uncomfortable level based on the excitation pattern of the basilar membrane in a normal ear. The number of fibers corresponding to the portion of the basilar membrane driven at an uncomfortable stimulus level in a normal ear is related to the most uncomfortable spiking number. The intensity discrimination limens are predicted using signal detection theory via the probability mass function (PMF) of the neural response and via experimental simulations. The results show that the uncomfortable level for a pulse-train stimulus increases slightly as noise level increases. Combining this with our previous threshold predictions, we hypothesize that the dynamic range for noise-modulated pulse-train stimuli increases with additive noise. However, since our predictions indicate that intensity discrimination under noise degrades, the overall intensity coding performance does not improve significantly.}, Key = {04408381857} } @article{04098041790, Author = {Ho, K.C. and Collins, Leslie M. and Huettel, Lisa G. and Gader, Paul D.}, Title = {Discrimination mode processing for EMI and GPR sensors for hand-held land mine detection}, Journal = {IEEE Transactions on Geoscience and Remote Sensing}, Volume = {42}, Number = {1}, Pages = {249 - 263}, Year = {2004}, url = {http://dx.doi.org/10.1109/TGRS.2003.817804}, Keywords = {Ground penetrating radar systems;Remote sensing;Electromagnetic field effects;Image sensors;Signal processing;Algorithms;Metal detectors;Database systems;Sensor data fusion;}, Abstract = {Signal processing algorithms for hand-held mine detection sensors are described. The goals of the algorithms are to provide alarms to a human operator indicating the likelihood of the presence of a buried mine. Two modes of operations are considered: search mode and discrimination mode. Search mode generates an initial detection at a suspected location and discrimination mode confirms that the suspected location contains a land mine. Search mode requires that the signal processing algorithm generate a detection confidence value immediately at the current sample location and no delay in producing an alarm confidence is tolerable. Search mode detection has a high false-alarm rate. Discrimination mode allows the operator to interrogate the entire suspected location to eliminate false alarms. It does not require that the signal processing algorithm produce an alarm confidence immediately for the current sample location, but rather allows the system to process all the data acquired over the region before producing an alarm. This paper proposes discrimination mode processing algorithms for metal detectors (MDs), or electromagnetic induction sensors (EMIs), ground-penetrating radars (GPRs), and their fusion. The MD discrimination mode algorithm employs a model-based approach and uses the target model parameters to discriminate between mines and clutter objects. The GPR discrimination mode algorithm uses the consistency of detection as well as the shape of the detection peaks over several sweeps to improve the discrimination accuracy. The performances of the proposed algorithms were examined on a dataset collected at a government test site, and performance was compared with baseline techniques. Experimental results showed that the proposed method can reduce the probability of false alarm by as much as 70% at a 100% correct detection rate and performed comparable to the best human operator on a blind test with data collected at approximately 1000 locations.}, Key = {04098041790} } @article{05159037038, Author = {Torrione, Peter and Collins, Leslie}, Title = {Application of texture feature classification methods to landmine / clutter discrimination in off-lane GPR data}, Journal = {International Geoscience and Remote Sensing Symposium (IGARSS)}, Volume = {3}, Pages = {1621 - 1624}, Address = {Anchorage, AK, United States}, Year = {2004}, Keywords = {Ground penetrating radar systems;Textures;Algorithms;Explosives;Sensors;Signal processing;Screening;Antennas;Vectors;}, Abstract = {Recent advances in ground penetrating radar (GPR) fabrication and algorithm development have yielded significant performance improvements for anti-tank landmine detection in government sponsored blind tests. However these blind tests are typically conducted over well maintained homogeneous testing lanes specifically designed to test landmine detection performance in low-clutter population situations. New GPR data collections over targets emplaced in un-maintained off-lane soils have much higher GPR anomaly populations and provide more stringent tests of landmine detection algorithms. In this work we focus on the application of feature-based class separation techniques to lower false alarm rates in heterogeneous off-road soils. In particular we explore the application of texture feature coding methods (TFCM) which have previously shown promise in fields like tumor detection.}, Key = {05159037038} } @article{04518728512, Author = {Hu, Wei and Tantum, Stacy L. and Collins, Leslie M.}, Title = {EMI-based classification of multiple closely spaced subsurface objects via independent component analysis}, Journal = {IEEE Transactions on Geoscience and Remote Sensing}, Volume = {42}, Number = {11}, Pages = {2544 - 2554}, Year = {2004}, url = {http://dx.doi.org/10.1109/TGRS.2004.835223}, Keywords = {Independent component analysis;Optical communication;Signal processing;Image processing;Database systems;Algorithms;Computer simulation;}, Abstract = {Previous work in subsurface object discrimination using electromagnetic induction data has shown that discrimination algorithms based on statistical signal processing techniques are effective for classifying data from objects that occur in isolation. However, for multiple closely spaced subsurface objects, the raw (unprocessed) measurement is a mixture of the responses from several objects and as such cannot be used directly to determine the identity of each of the individual objects. Thus, we propose to separate individual signatures from the mixture by posing the problem as a blind source separation (BSS) problem and effecting signature separation using independent component analysis. We propose to apply BSS to separate the mixed signatures and then follow the separation process with a Bayesian classifier. This approach is evaluated using both simulated data and data from unexploded ordnance items. The results show that this approach can be used to effectively classify multiple closely spaced objects.}, Key = {04518728512} } @article{04528738213, Author = {Yu, Yongli and Torrione, Peter and Collins, Leslie M.}, Title = {Landmine discrimination via Bayesian adaptive multi-modal processing: Results for handheld and vehicular sensors}, Journal = {Proceedings of SPIE - The International Society for Optical Engineering}, Volume = {5415}, Number = {PART 2}, Pages = {791 - 798}, Address = {Orlando, FL, United States}, Year = {2004}, url = {http://dx.doi.org/10.1117/12.542276}, Keywords = {Magnetoelectric effects;Sensor data fusion;Data processing;Demodulation;Statistical methods;Computer simulation;}, Abstract = {The recent development of high quality sensors paired with development of advanced statistical signal processing algorithms has shown that there are sensors that can not only discriminate targets from clutter, but can also identify subsurface or obscured targets. In a previous theoretical and simulation study, we utilized this identification capability in addition to contextual information in a multi-modal adaptive algorithm where the identification capabilities of multiple sensors are utilized to modify the prior probability density functions associated with statistical models being utilized by other sensors. We assumed that the statistics describing the features associated with each sensor modality follow a Gaussian mixture density, where in many cases the individual Gaussian distributions that make up the mixture result from different target types or target classes. We utilized identification information from one sensor to modify the weights associated with the probability density functions being utilized by algorithms associated with other sensor modalities. In our simulations, this approach is shown to be improve sensor performance by reducing the overall false alarm rate. In this talk, we transition the approach from a simulation study to consider real field data collected by both handheld and vehicular based systems. We show that by appropriate modification of our statistical models to accurately match field data, improved performance can be obtained over traditional sensor fusion algorithms.}, Key = {04528738213} } @article{04148098685, Author = {Xu, Wang and Collins, Leslie M.}, Title = {Predicting the Threshold of Pulse-Train Electrical Stimuli Using a Stochastic Auditory Nerve Model: The Effects of Stimulus Noise}, Journal = {IEEE Transactions on Biomedical Engineering}, Volume = {51}, Number = {4}, Pages = {590 - 603}, Year = {2004}, url = {http://dx.doi.org/10.1109/TBME.2004.824143}, Keywords = {Functional neural stimulation;Spurious signal noise;Audition;Threshold logic;Neurology;Psychophysiology;Biomedical engineering;Stochastic control systems;Probability;Mathematical models;}, Abstract = {The incorporation of low levels of noise into an electrical stimulus has been shown to improve auditory thresholds in some human subjects (Zeng et al., 2000). In this paper, thresholds for noise-modulated pulse-train stimuli are predicted utilizing a stochastic neural-behavioral model of ensemble fiber responses to bi-phasic stimuli. The neural refractory effect is described using a Markov model for a noise-free pulse-train stimulus and a closed-form solution for the steady-state neural response is provided. For noise-modulated pulse-train stimuli, a recursive method using the conditional probability is utilized to track the neural responses to each successive pulse. A neural spike count rule has been presented for both threshold and intensity discrimination under the assumption that auditory perception occurs via integration over a relatively long time period (Bruce et al., 1999). An alternative approach originates from the hypothesis of the multilook model (Viemelster and Wakefield, 1991), which argues that auditory perception is based on several shorter time integrations and may suggest an NofM model for prediction of pulse-train threshold. This motivates analyzing the neural response to each individual pulse within a pulse train, which is considered to be the brief look. A logarithmic rule is hypothesized for pulse-train threshold. Predictions from the multilook model are shown to match trends in psychophysical data for noise-free stimuli that are not always matched by the long-time integration rule. Theoretical predictions indicate that threshold decreases as noise variance increases. Theoretical models of the neural response to pulse-train stimuli not only reduce calculational overhead but also facilitate utilization of signal detection theory and are easily extended to multichannel psychophysical tasks.}, Key = {04148098685} } @article{04528738325, Author = {Borgonovi, Giancarlo M. and Holslin, Daniel T. and Collins, Leslie M. and Tantum, Stacy L.}, Title = {Data analysis for classification of UXO filler using pulsed neutron techniques}, Journal = {Proceedings of SPIE - The International Society for Optical Engineering}, Volume = {5415}, Number = {PART 1}, Pages = {502 - 509}, Address = {Orlando, FL, United States}, Year = {2004}, url = {http://dx.doi.org/10.1117/12.542288}, Keywords = {Gamma rays;Data reduction;Thermoanalysis;Neutron irradiation;Fluxes;Functions;Principal component analysis;}, Abstract = {Irradiating substances with pulsed neutrons results in several types of interactions which cause the emission of gamma rays. The energy of these gamma rays is characteristic of the nuclei with which the reaction occurred, and can therefore be used as an indicator of the presence of an atomic species. The PELAN system uses a pulsed neutron generator, which makes it possible to separate the gamma spectra into inelastic and capture components that are easier to interpret. Historically, the analysis of PELAN data has been based on a least squares method to extract the contribution of different elemental species present in the sample. The approach uses measured response functions for each element of interest, followed by decision rules for the identification of the materials. We have investigated an alternative approach that does not require a model and response functions. Instead, the approach determines features directly from a number of spectra of substances of interest, e.g. explosives and hazardous chemicals. The PCA method has been used to obtain indicators from the spectra. These indicators are then used for detection and identification of substances using the GLRT algorithm. The performance of the data analysis is assessed through ROC curves. A comparison of the two approaches indicates that PCA followed by GLRT technique has better performance and is more robust than the previous approach.}, Key = {04528738325} } @article{05299213804, Author = {Huettel, Lisa and Brown, April and Gustafson, Michael and Massoud, Hisham and Ybarra, Gary and Collins, Leslie}, Title = {Work in progress: Theme-based redesign of an electrical and computer engineering curriculum}, Journal = {Proceedings - Frontiers in Education Conference, FIE}, Volume = {3}, Pages = {2-1-2-2 -}, Address = {Savannah, GA, United States}, Year = {2004}, Keywords = {Engineering education;Software engineering;Electrical engineering;Data processing;Students;Problem solving;Systems analysis;Professional aspects;}, Abstract = {The goal of this work-in-progress is to develop an innovative ECE curriculum that focuses on ECE fundamentals within the construct of real-world integrated system design, analysis, and problem solving. The curriculum will be formulated around the theme of Integrated Sensing and Information Processing (ISIP). The foundation of this new curriculum will be a hands-on theme-based introductory course. This course, taken in the freshman year, will introduce students to the major subdisciplines of ECE in the context of real-world applications. In the laboratory, which will be tightly coupled to the lecture, students will apply basic concepts of sensing, information transmission, information analysis, storage and networking to design and implement an ISIP system, such as a health or weather monitoring station. In this way, students will immediately begin to understand the relationships between the major topic areas of ECE as well as be motivated to explore these topics in further depth. Other components of the redesign include the integration of core and upper-level courses into the ISIP theme, the introduction of new ISIP-related design courses, and the integration of MATLAB throughout the curriculum. © 2004 IEEE.}, Key = {05299213804} } @article{04228185829, Author = {Tan, Yingyi and Tantum, Stacy L. and Collins, Leslie M.}, Title = {Cramer-Rao lower bound for estimating quadrupole resonance signals in non-Gaussian noise}, Journal = {IEEE Signal Processing Letters}, Volume = {11}, Number = {5}, Pages = {490 - 493}, Year = {2004}, url = {http://dx.doi.org/10.1109/LSP.2004.826657}, Keywords = {Spurious signal noise;Signal interference;Frequencies;Estimation;Calculations;}, Abstract = {Quadrupole resonance (QR) technology for the detection of explosives is of crucial importance in an increasing number of applications. For landmine detection, where the detection system cannot be shielded, QR has proven to be highly effective if the QR sensor is not exposed to radio-frequency interference (RFI). However, strong non-Gaussian RFI in the field is unavoidable. A statistical model of such non-Gaussian RFI noise is given in this letter. In addition, the asymptotic Cramer-Rao lower bound for estimating a deterministic QR signal in this non-Gaussian noise is presented. The performance of several convenient estimators is compared to this bound.}, Key = {04228185829} } @article{04408381855, Author = {Remus, Jeremiah J. and Collins, Leslie M.}, Title = {Vowel and consonant confusion in noise by cochlear implant subjects: Predicting performance using signal processing techniques}, Journal = {ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings}, Volume = {4}, Pages = {-13--16 -}, Address = {Montreal, Que, Canada}, Year = {2004}, url = {http://dx.doi.org/10.1109/ICASSP.2004.1326751}, Keywords = {Implants (surgical);Hearing aids;Signal processing;Speech processing;Patient monitoring;Speech recognition;Markov processes;Sampling;}, Abstract = {Cochlear implants are able to restore some degree of hearing to deafened individuals; however implant users are particularly susceptible to background noise. The effect of noise can be assessed using vowel and consonant confusions measured in listening experiments. This paper presents three signal processing methods developed to predict patterns in vowel and consonant confusion in noise for cochlear implant users. Prediction performance is tested using the results of a listening experiment conducted with acoustic models of two cochlear implant speech processors and normal hearing subjects. Confusion prediction is based on prediction metrics calculated using each method's unique representation of the speech tokens.}, Key = {04408381855} } @article{04448431427, Author = {Hu, Wei and Collins, Leslie M.}, Title = {Classification of closely spaced subsurface objects using electromagnetic induction data and blind source separation algorithms}, Journal = {Radio Science}, Volume = {39}, Number = {4}, Pages = {4 - 07}, Year = {2004}, url = {http://dx.doi.org/10.1029/2003RS002968}, Keywords = {Data reduction;Algorithms;Remote sensing;Sensors;Problem solving;Signal processing;Mathematical models;}, Abstract = {Most research in the subsurface object identification area assumes that objects are well isolated from each other and thus that a single signature is measured by the sensing system. In the scenario where multiple closely spaced subsurface objects are present within the field of view of the sensor, the signals measured using electromagnetic induction sensors are mixed, and the mixed measurements cannot be used to determine the identity of each of the individual objects using conventional techniques. Since only the mixed observations are available, and these are usually available at multiple target/sensor orientations, separating individual signals from the set of mixtures can be posed as a blind source separation (BSS) problem. In this paper we consider two approaches to source separation, one based on the second-order statistics and the other based on the fourth-order statistics. Following the source separation, object classification performance is obtained using the separated sources and a Bayesian classifier. We analyze the strengths and weaknesses of each BSS approach and compare their performance.}, Key = {04448431427} } @article{04528738220, Author = {Huettel, Lisa G. and Baier, Jamie M. and Collins, Leslie M.}, Title = {Man versus machine: Robust regional processing of EMI data}, Journal = {Proceedings of SPIE - The International Society for Optical Engineering}, Volume = {5415}, Number = {PART 2}, Pages = {866 - 873}, Address = {Orlando, FL, United States}, Year = {2004}, url = {http://dx.doi.org/10.1117/12.541601}, Keywords = {Metal detectors;Magnetoelectric effects;Robustness (control systems);Robots;Network protocols;Sensors;Algorithms;}, Abstract = {The handheld F3 metal detector, developed by the MineLab Corporation, measures the responses of buried objects to electromagnetic pulses. These responses can be processed to determine whether a landmine is present. The simplest processor calculates the total energy in the response, thereby reducing the entire spatial and temporal response to a single value. This value, proportional to the amount of metal in the object, can then be compared to a predetermined threshold. The drawback of this common approach is that, although the threshold may be set so that few, if any, mines are missed, doing so may result in a high false alarm rate. Previous work has demonstrated that incorporating physics-based features into a Bayesian detection framework and performing simple, one-dimensional regional processing can significantly reduce the false alarm rate while maintaining the desired level of detection. <sup>1</sup> Based on these promising results, this approach has been extended to incorporate two-dimensional regional processing. At the test site, data was collected both manually and robotically using nearly identical protocols. Thus, in theory, measured responses should be similar and algorithm performance equivalent whether the detector was operated by a robot or a human. The robustness of various algorithms was evaluated by comparing performance across manual and robotic data sets. Certain physics-based feature detectors were relatively unaffected by the response variability introduced unintentionally by the human operator. However, other algorithms that incorporate more sensitive, often regional, features were able to provide greater gains for the robotic data set than for the manual data set. These results imply that there may be a tradeoff between performance and practical issues that need to be addressed when selecting an algorithm for implementation in a field setting.}, Key = {04528738220} } @article{04448431426, Author = {Riggs, Lloyd and Chilaka, Sailaja and Collins, Leslie and Lowe, Larry and Weaver, Richard}, Title = {Discrimination experiments with the U.S. Army's standard metal detector}, Journal = {Radio Science}, Volume = {39}, Number = {4}, Pages = {4 - 06}, Year = {2004}, url = {http://dx.doi.org/10.1029/2003RS002955}, Keywords = {Mines;Probability;Soils;Electromagnetic wave polarization;Seismology;X ray scattering;Sensors;Electromagnetism;}, Abstract = {Discrimination experiments with the U.S. Army's standard hand-held metal detector (AN/PSS-12) are described. An appendix describes the functioning of the device as a metal detector, and the body of the paper discusses modifications to the device necessary to carry out the discrimination experiments. Half of the mines in a large blind test grid were correctly identified with nearly zero false alarms, but the false alarm rate increased substantially for detection probabilities greater than one half. Degradation in performance is attributed to low signal-to-noise ratio from low metallic content mines buried deep in the soil. One measurement was taken with the object centered with respect to the search coils and four more with the object between the concentric search coils in the north, south, east, and west directions. Discrimination performance using all spatial measurements was shown to be superior to that obtained when using only the centered measurement, indicating that spatial measurement diversity is needed to adequately define all the unique modes of a target's polarizability tensor.}, Key = {04448431426} } @article{04528738226, Author = {Lang, David A. and Duston, Brian M. and Torrione, Peter and Collins, Leslie}, Title = {Three dimensional features to improve detection using ground penetrating radar}, Journal = {Proceedings of SPIE - The International Society for Optical Engineering}, Volume = {5415}, Number = {PART 2}, Pages = {913 - 922}, Address = {Orlando, FL, United States}, Year = {2004}, url = {http://dx.doi.org/10.1117/12.542946}, Keywords = {Mines;Detectors;Alarm systems;Algorithms;Bandwidth;Data processing;}, Abstract = {Two new features are presented to improve the detection of Anti-Tank (AT) landmines using Ground Penetrating Radar (GPR). A simplified three dimensial physics based model is used as the basis for the features. We combine these features with the results of an algorithm known as LMS. We present promising feature detection algorithms known as Rings N' Things (RNT) and Cross Diagonal Enhancement Processing (CDEP) and our approach to combining the new features with the LMS features using logistic regression techniques. Test results from data gathered at multiple sites covering hundreds of mines and thousands of square meters is analyzed and presented.}, Key = {04528738226} } @article{04068010908, Author = {Huettel, Lisa G. and Collins, Leslie M.}, Title = {Predicting Auditory Tone-in-Noise Detection Performance: The Effects of Neural Variability}, Journal = {IEEE Transactions on Biomedical Engineering}, Volume = {51}, Number = {2}, Pages = {282 - 293}, Year = {2004}, url = {http://dx.doi.org/10.1109/TBME.2003.820395}, Keywords = {Acoustic signal processing;Signal detection;Bioelectric potentials;Physiology;Data acquisition;Cost effectiveness;Forecasting;}, Abstract = {Collecting and analyzing psychophysical data is a fundamental mechanism for the study of auditory processing. However, because this approach relies on human listening experiments, it can be costly in terms of time and money spent gathering the data. The development of a theoretical, model-based procedure capable of accurately predicting psychophysical behavior could alleviate these issues by enabling researchers to rapidly evaluate hypotheses prior to conducting experiments. This approach may also provide additional insight into auditory processing by establishing a link between psychophysical behavior and physiology. Signal detection theory has previously been combined with an auditory model to generate theoretical predictions of psychophysical behavior. Commonly, the ideal processor outperforms human subjects. In order for this model-based technique to enhance the study of auditory processing, discrepancies must be eliminated or explained. In this paper, we investigate the possibility that neural variability, which results from the randomness inherent in auditory nerve fiber responses, may explain some of the previously observed discrepancies. In addition, we study the impact of combining information across nerve fibers and investigate several models of multiple-fiber signal processing. Our findings suggest that neural variability can account for much, but not all, of the discrepancy between theoretical and experimental data.}, Key = {04068010908} } @article{04528738218, Author = {Marr, Bo and Torrione, Peter and Miller, Jonathan and Collins, Leslie}, Title = {Parameterized likelihood ratio method for EMI unexploded ordnance detection}, Journal = {Proceedings of SPIE - The International Society for Optical Engineering}, Volume = {5415}, Number = {PART 2}, Pages = {843 - 854}, Address = {Orlando, FL, United States}, Year = {2004}, url = {http://dx.doi.org/10.1117/12.542297}, Keywords = {Demodulation;Gaussian noise (electronic);Decision making;Parameter estimation;Optimization;Probability;}, Abstract = {With current signal processing techniques, successful discrimination between UXO (Unexploded Ordnance) and clutter depends on characteristics that are consistent across all examples of an ordnance type. Real UXO, however, exhibit many differences from instance to instance, such as varying degrees of damage sustained, degradation over time, orientation in the ground, and even differences in design. Thus, a given ordnance type, such as 60mm shells, will exhibit a wide range of signal responses, making it difficult to distinguish these items from clutter unless these fundamental differences are taken into account using appropriate mathematics. This paper will examine optimal methods of using frequency-domain analysis of wideband electromagnetic induction (EMI) for detection.}, Key = {04528738218} } @article{8410461, Author = {Wei Hu and Collins, L.M.}, Title = {Classification of closely spaced subsurface objects using electromagnetic induction data and blind source separation algorithm}, Journal = {Radio Sci. (USA)}, Volume = {39}, Number = {4}, Pages = {13 pp. -}, Year = {2004}, url = {http://dx.doi.org/10.1029/2003RS002968}, Keywords = {blind source separation;electric sensing devices;electromagnetic induction;landmine detection;pattern classification;statistics;}, Abstract = {Most research in the subsurface object identification area assumes that objects are well isolated from each other and thus that a single signature is measured by the sensing system. In the scenario where multiple closely spaced subsurface objects are present within the field of view of the sensor, the signals measured using electromagnetic induction sensors are mixed, and the mixed measurements cannot be used to determine the identity of each of the individual objects using conventional techniques. Since only the mixed observations are available, and these are usually available at multiple target/sensor orientations, separating individual signals from the set of mixtures can be posed as a blind source separation (BSS) problem. In this paper we consider two approaches to source separation, one based on the second-order statistics and the other based on the fourth-order statistics. Following the source separation, object classification performance is obtained using the separated sources and a Bayesian classifier. We analyze the strengths and weaknesses of each BSS approach and compare their performance}, Key = {8410461} } @article{03487759874, Author = {Torrione, Peter and Collins, Leslie and Clodfelter, Fred and Frasier, Shane and Starnes, Ian}, Title = {Application of the LMS Algorithm to Anomaly Detection Using the Wichmann/Niitek Ground Penetrating Radar}, Journal = {Proceedings of SPIE - The International Society for Optical Engineering}, Volume = {5089}, Number = {2}, Pages = {1127 - 1136}, Address = {Orlando, FL, United States}, Year = {2003}, url = {http://dx.doi.org/10.1117/12.487241}, Keywords = {Alarm systems;Sensor data fusion;Electromagnetic field effects;Clutter (information theory);Radar cross section;Ordnance;Data acquisition;Calibration;Adaptive algorithms;}, Abstract = {This paper describes the application of a 2-dimensional (2-D) lattice LMS algorithm for anomaly detection using the Wichmann/Niitek ground penetrating radar (GPR) system. Sets of 3-dimensional (3-D) data are collected from the GPR system and these are processed in separate 2-D slices. Those 2-D slices that are spatially correlated in depth are combined into separate "depth segments" and these are processed independently. When target/no target declarations need to be made, the individual depth segments are combined to yield a 2-D confidence map. The 2-D confidence map is then thresholded and alarms are placed at the centroids of the remaining 8-connected data points. Calibration lane results are presented for data collected over several soil types under several weather conditions. Results show show a false alarm rate improvement of at least an order of magnitude over other GPR systems, as well as significant improvement over other adaptive algorithms operating on the same data.}, Key = {03487759874} } @article{03387642111, Author = {Xu, Yifang and Collins, Leslie}, Title = {Stochastic resonance in the electrically stimulated auditory nerve: Predictions using a stochastic model of neural responsiveness}, Journal = {Proceedings of SPIE - The International Society for Optical Engineering}, Volume = {5110}, Pages = {77 - 91}, Address = {Santa Fe, NM, United States}, Year = {2003}, url = {http://dx.doi.org/10.1117/12.497265}, Keywords = {Neurology;Bioelectric potentials;Audition;Acoustic wave propagation;Acoustic variables measurement;Markov processes;Resonance;Modulation;Implants (surgical);}, Abstract = {The incorporation of low levels of noise into an electrical stimulus has been shown to improve auditory thresholds in human subjects. In this paper, thresholds for noise-modulated pulse-train stimuli are predicted by utilizing a stochastic neural-behavioral model of ensemble fiber responses to bi-phasic stimuli. A neural spike count comparison rule has been presented for both threshold and intensity discrimination under the assumption that loudness is a monotonic function of the number of neuron spikes. An alternative approach which we have pursued involves analyzing the neural response to each individual pulse within a pulse train to investigate the threshold behavior. The refractory effect is described using a Markov model for a noise-free pulse-train stimulus. A recursive method using the conditional probability is utilized to track the neural responses to each successive pulse for a noise-modulated pulse-train stimulus. After determining the stochastic properties of the auditory nerve response to each pulse within the pulse train, a logarithmic rule is hypothesized for pulse-train threshold and the predictions are shown to match psychophysical data not only for noise-free stimuli but also for noise-modulated stimuli. Results indicate that threshold decreases as noise variance increases.}, Key = {03387642111} } @article{03467720427, Author = {Tantum, Stacy L. and Collins, Leslie M.}, Title = {Performance bounds and a parameter transformation for decay rate estimation}, Journal = {IEEE Transactions on Geoscience and Remote Sensing}, Volume = {41}, Number = {10 PART I}, Pages = {2224 - 2231}, Year = {2003}, url = {http://dx.doi.org/10.1109/TGRS.2003.814660}, Keywords = {Parameter estimation;Time domain analysis;Signal detection;Computer simulation;Signal to noise ratio;Least squares approximations;Matrix algebra;}, Abstract = {Decay rate estimation has been proposed as an effective method for signal characterization in many application areas, including subsurface sensing using time-domain electromagnetic induction (EMI) sensors. The physical basis for this strategy is that every signal of interest, which corresponds to a target or phenomenon of interest, possesses a unique set of decay rates. In theory, the characteristic decay rates can be estimated from the measured signal, and then utilized for signal detection and subsequent identification. Using this approach, signal discrimination performance is dependent upon decay rate estimation performance. The Cramer-Rao lower bound (CRLB) for decay rate and amplitude coefficient estimates is utilized to investigate the fundamental limits of decay rate estimation accuracy. Previous derivations of the CRLB for decay rate estimates have focused on signals which are linearly sampled beginning at time t = 0. Here, the CRLB is generalized to accommodate any arbitrary sampling method and any initial starting time. A parameter transformation which improves decay rate estimation performance is also presented. Simulation results across a wide range of decay rates and SNRs show that nonlinear least squares estimation of the decay rates via the proposed transformation provides estimates with smaller RMS and bias than can be obtained without the parameter transformation. The parameter transformation also provides decay rate estimates that approach the CRLB. Improvement in estimation performance for this class of signals has important ramifications in signal detection, classification, and identification performance in several geophysical application areas.}, Key = {03467720427} } @article{04068008460, Author = {Zhang, Yan and Collins, Leslie M. and Carin, Lawrence}, Title = {Unexploded ordnance detection using Bayesian physics-based data fusion}, Journal = {Integrated Computer-Aided Engineering}, Volume = {10}, Number = {3}, Pages = {231 - 247}, Year = {2003}, Keywords = {Remote sensing;Object recognition;Bombs (ordnance);Radar clutter;Magnetometers;Sensors;Mathematical models;Time domain analysis;Frequency domain analysis;Feature extraction;Computer simulation;Algorithms;}, Abstract = {Detection of unexploded ordnance (UXO) represents a major challenge on closed, closing, and transferred military ranges as well as on active installations. On sites contaminated with UXO, extensive surface and sub-surface clutter is also present. Traditional methods used for UXO remediation have severe difficulty distinguishing buried UXO from these anthropic clutter items as well as from naturally occurring magnetic geologic noise, and thus incur prohibitively high false alarm rates. In this paper, sensor fusion techniques are employed using field data from magnetometer and electromagnetic induction (EMI) sensors in order to mitigate false alarms. Rigorous sensor response models are developed based on the sensor physics for both a traditional time-domain EMI sensor and a recently developed wideband frequency-domain sensor. Features of the target signatures are extracted by inverting the measured sensor data associated with an anomaly using the physical model. The statistical uncertainty in the feature space is explicitly treated using a Bayesian processor to discriminate targets from clutter. Discrimination performance on a seeded field trial conducted previously is reviewed. Performance on a recent field trial where data was collected in a more realistic survey mode is then presented, illustrating the robustness of the approach. Substantial reduction of the false alarm rate is achieved.}, Key = {04068008460} } @article{03487759867, Author = {Nelson, Blaine and Schofield, Deborah and Collins, Leslie}, Title = {A comparison of neural networks and sub-space detectors for the discrimination of low-metal content landmines}, Journal = {Proceedings of SPIE - The International Society for Optical Engineering}, Volume = {5089}, Number = {2}, Pages = {1046 - 1053}, Address = {Orlando, FL, United States}, Year = {2003}, url = {http://dx.doi.org/10.1117/12.487220}, Keywords = {Electromagnetic wave scattering;Electromagnetic fields;Sensors;Decision making;Neural networks;Wave filters;Algorithms;Fuzzy sets;}, Abstract = {Low-metal content landmines can be particularly difficult to detect and classify with electromagnetic induction (EMI) systems. Their responses are often less than that of indigenous clutter and the small amounts of asymmetrically distributed metal results in significant changes in the signature of the mine as the sensor to target orientation varies. A number of algorithms have been previously developed in order to aid in target classification and reduce the false-alarm rate. In our work, multiple data sets were collected for each of five targets, of varying metal content, at several sensor to target heights and horizontal displacements using a prototype frequency-domain EMI sensor, the Geophex GEM-3. The data was then evaluated using one of three classification algorithms including a neural network, a matched filter, and a normalized matched filter. Here, a One Class One Network (OCON) architecture in which only one neural network makes a decision was selected for use. We will discuss the training and testing process for this algorithm. We will also show that the neural network performed much better than the matched filter but slightly worse than the normalized matched filter. In addition, the results demonstrate the necessity of training the algorithms with spatially collected data when precise sensor positioning is not possible.}, Key = {03487759867} } @article{03487759717, Author = {Huettel, Lisa G. and Riggs, Lloyd S. and Collins, Leslie M.}, Title = {Region processing of EMI data for landmine detection}, Journal = {Proceedings of SPIE - The International Society for Optical Engineering}, Volume = {5089}, Number = {1}, Pages = {680 - 688}, Address = {Orlando, FL, United States}, Year = {2003}, url = {http://dx.doi.org/10.1117/12.487308}, Keywords = {Clutter (information theory);Signal processing;Electromagnetic waves;Algorithms;}, Abstract = {A hand-held mine detector has two modes of operation: search and localization. In search mode, the goal is to identify areas where a buried mine might be located. Since minimizing the number of misses is a top priority, many regions identified in this mode may contain clutter. To separate the clutter from the mines, the detector can be switched into the localization mode during which a more thorough interrogation of the region is performed. Because causality is not required in localization mode, the analyzed signal is not limited to a single data point, but instead can consist of the response across an entire spatial "region". Previous work has demonstrated that so called "region processing" can potentially improve the localization performance of the detector.<sup>1,2</sup> We have used the Minelab F1A4 metal detector, an EMI-based system, to collect regional data for a variety of objects including buried mines, metallic and non-metallic clutter, and short-circuited copper loops in free space. Several physics-based processing algorithms were developed and used to predict discrimination performance. Analysis of the loops, whose physical properties were known, indicated that discrimination between objects might be possible using a feature extracted from the detector output. Subsequently, this feature was used as the basis of an algorithm which, when used to process the mine/clutter data, significantly decreased the false alarm rate. This algorithm and its performance were further enhanced by incorporating information about the entire regional response of each object.}, Key = {03487759717} } @article{02517277827, Author = {Huettel, Lisa G. and Collins, Leslie M.}, Title = {A theoretical comparison of information transmission in the peripheral auditory system: Normal and impaired frequency discrimination}, Journal = {Speech Communication}, Volume = {39}, Number = {1-2}, Pages = {5 - 21}, Year = {2003}, url = {http://dx.doi.org/10.1016/S0167-6393(02)00055-9}, Keywords = {Acoustic signal processing;Signal distortion;Hearing aids;Signal detection;Performance;Computer simulation;}, Abstract = {A theoretical analysis of auditory signal processing and the distortions introduced by various types of hearing impairments can aid in the design and development of digital hearing aids. In this paper, we investigate the differences between normal and impaired auditory processing on a frequency discrimination task by analyzing the responses of a computational auditory model using signal detection theory. Hearing impairments that were simulated included a threshold shift, damage to the outer hair cells, and impaired neural synchrony. We implemented two detectors, one using all of the information in the signal, the other using only the number of neural firings, and used them to generate theoretical predictions of performance. Evaluation of performance differences between theoretical detectors and experimental data allows quantification of both the type of information present in the auditory system and the efficiency of its use. This method of analysis predicted both the trends observed in comparable experimental data and the relation between normal and impaired behavior. Finally, a very simple hearing aid was simulated and the gains in performance in the impaired cases were related to the physiological bases of the impairments. This demonstrates the utility of the proposed approach in the design of more complex hearing aids. © 2002 Elsevier Science B.V. All rights reserved.}, Key = {02517277827} } @article{03487759873, Author = {Zhang, Yan and Collins, Leslie M. and Carin, Lawrence}, Title = {Model-Based Statistical Signal Processing for UXO Discrimination: Performance Results from the JPG-V Demonstration}, Journal = {Proceedings of SPIE - The International Society for Optical Engineering}, Volume = {5089}, Number = {2}, Pages = {1116 - 1126}, Address = {Orlando, FL, United States}, Year = {2003}, url = {http://dx.doi.org/10.1117/12.487181}, Keywords = {Explosives;Sensor data fusion;Clutter (information theory);Electromagnetic field effects;Spurious signal noise;Magnetometers;Data acquisition;Statistical process control;Alarm systems;Feature extraction;Learning algorithms;Mathematical models;}, Abstract = {Detection and remediation of unexploded ordnance (UXO) represents a major challenge. The detection problem is exacerbated by the fact that on sites contaminated with UXO, extensive surface and sub-surface clutter and shrapnel is also present. Traditional methods used for UXO remediation have difficulty distinguishing buried UXO from these anthropic clutter items as well as from naturally occurring magnetic geologic noise, and thus incur prohibitively high false alarm rates. In this research, model-based statistical signal processing techniques are applied to field data from magnetometer and electromagnetic induction (EMI) sensors in order to determine to what degree such an approach results in false alarm mitigation. Features of the target signatures are extracted by inverting the measured sensor data associated with an anomaly using the associated physical, or forward, model. The statistical uncertainty in the feature space is explicitly treated using statistical processors, including generalized likelihood ratio tests and support vector machines, to discriminate targets from clutter. This approach has been evaluated on data collected in a recent field trial that was performed at JPG. Results are presented for one area in which ground truth was known, and for two others in which the ground truth was not known. Substantial reduction of the false alarm rate is achieved for two different platforms, the GEM-3 and the MTADS system. For example, using data from the GEM-3 in one area, the number of false targets was reduced from 181 to 20 with 100% detection of all UXO objects.}, Key = {03487759873} } @article{03487759872, Author = {Tantum, Stacy L. and Collins, Leslie M. and Khadr, Nagi and Barrow, Bruce J.}, Title = {Correcting GPS Measurement Errors Induced by System Motion over Uneven Terrain}, Journal = {Proceedings of SPIE - The International Society for Optical Engineering}, Volume = {5089}, Number = {2}, Pages = {1105 - 1115}, Address = {Orlando, FL, United States}, Year = {2003}, url = {http://dx.doi.org/10.1117/12.487244}, Keywords = {Position measurement;Measurement errors;Adaptive filtering;Parameter estimation;Low pass filters;Signal receivers;Data acquisition;Markov processes;Algorithms;Mathematical models;}, Abstract = {Many cart- and vehicular-based UXO detection systems employ GPS receivers to accurately determine the system's position. However, the unevenness of the terrain often causes the system to tilt during the data collection, introducing errors in the GPS measurements. In this paper, two approaches are considered to correct the errors in the GPS measurements caused by the tilting of the system; low-pass filtering and adaptive filtering using a hidden Markov model (HMM). The low-pass filter smooths the data collection path recorded by the GPS receiver. Although this filter does not explicitly model the system motion, it does remove dramatic, and unrealistic, jumps in the GPS measurements. In contrast, the movement of the system can be explicitly modeled by an HMM. The HMM characterizes the cart motion so that the subsequent filtering is appropriate for the type of motion encountered. The error correction techniques are first applied to simulated data, in which both the sources of error and the ground truth are known so that the performance of the algorithms can be compared. The algorithms are then applied to measured data collected with a cart-based system to evaluate the robustness of their performance.}, Key = {03487759872} } @article{03487759865, Author = {Zhang, Yan and Collins, Leslie and Carin, Lawrence}, Title = {Physics model based unexploded ordnance discrimination using wideband EMI data}, Journal = {Proceedings of SPIE - The International Society for Optical Engineering}, Volume = {5089}, Number = {2}, Pages = {1023 - 1034}, Address = {Orlando, FL, United States}, Year = {2003}, url = {http://dx.doi.org/10.1117/12.487200}, Keywords = {Magnetoelectric effects;Signal processing;Sensors;Performance;Pattern recognition;Algorithms;Waveform analysis;}, Abstract = {Unexploded ordnance (UXO) discrimination is investigated using the wide band electromagnetic induction (EMI) data. The main focus of this paper is on the practical phenomenological modeling for the induced wideband EMI sensor response from different targets. Modeling for the sensor response provides feature vectors to UXO classification algorithms, and it has been proven to be very important for the improvement of the overall remediation performance. A parametric model is discussed with the emphsis on multiple offset dipole centers. The measured data from several actual targets are utilized to validate the model and to demonstrate the advantage of multiple offset dipole centers vs. single dipole center. We further illustrate the application of the model with multiple dipoles in target classifications by numerical examples. We show that the classification performance might be improved substantially. Finally, we state that the nonlinear EMI dipole model can be decomposed into a linear model. Thus it benefits from the rich literature of linear algebra and signal processing. To report one of our efforts, two methods are proposed to detect the number of dipoles blindly by the information theoretic criteria, namely the Akaike information criterion (AIC) and the minimum description length (MDL). The methods are testified using measured EMI data.}, Key = {03487759865} } @article{03487759855, Author = {Schofield, Deborah and Hu, Wei and Collins, Leslie}, Title = {Separation of overlapping signatures in EMI data}, Journal = {Proceedings of SPIE - The International Society for Optical Engineering}, Volume = {5089}, Number = {2}, Pages = {904 - 915}, Address = {Orlando, FL, United States}, Year = {2003}, url = {http://dx.doi.org/10.1117/12.487232}, Keywords = {Sensor data fusion;Ordnance;Object recognition;Mines;Blind source separation;Frequency domain analysis;Independent component analysis;Matrix algebra;Algorithms;}, Abstract = {Due to an object's unique combination of several physical characteristics, including conductivity and permeability, detection systems using the principles of electromagnetic induction (EMI) can be used to detect and classify a characteristic shape or signature for various targets. Subsequently, a library of signatures for targets of interest may be generated for classification purposes to reduce the false alarm rate associated with remediation. However, targets of interest are rarely isolated in the subsurface environment; metallic clutter and/or other targets of interest may be located in close proximity, thereby altering the returned signature and creating the possibility of false alarms. In this paper, we will present data that was collected on UXO and mine-like targets located in close proximity to each other using a prototype frequency-domain EMI sensor, the Geophex GEM-3. We will show that weighted combinations of each object's independent signature can represent the resulting EMI response from two objects, located in close proximity. We will also present simple algorithms to detect the presence of overlapping objects and analyze their performance as a function of object separation and other relevant parameters. The creation and use of target recognition algorithms that consider multiple closely spaced objects will also be discussed.}, Key = {03487759855} } @article{03487759886, Author = {Liao, Yuwei and Nolte, Loren W. and Collins, Leslie}, Title = {Optimal Multisensor Decision Fusion of Mine Detection Algorithms}, Journal = {Proceedings of SPIE - The International Society for Optical Engineering}, Volume = {5089}, Number = {2}, Pages = {1252 - 1260}, Address = {Orlando, FL, United States}, Year = {2003}, url = {http://dx.doi.org/10.1117/12.486834}, Keywords = {Signal detection;Sensor data fusion;Metal detectors;Spurious signal noise;Decision theory;Algorithms;}, Abstract = {Numerous detection algorithms, using various sensor modalities, have been developed for the detection of mines in cluttered and noisy backgrounds. The performance for each detection algorithm is typically reported in terms of the Receiver Operating Characteristic (ROC), which is a plot of the probability of detection versus false alarm as a function of the threshold setting on the output decision variable of each algorithm. In this paper we present multi-sensor decision fusion algorithms that combine the local decisions of existing detection algorithms for different sensors. This offers, in certain situations, an expedient, attractive and much simpler alternative to "starting over" with the redesign of a new algorithm which fuses multiple sensors at the data level. The goal in our multi-sensor decision fusion approach is to exploit complimentary strengths of existing multi-sensor algorithms so as to achieve performance (ROC) that exceeds the performance of any sensor algorithm operating in isolation. Our approach to multi-sensor decision fusion is based on optimal signal detection theory, using the likelihood ratio. We consider the optimal fusion of local decisions for two sensors, GPR (ground penetrating radar) and MD (metal detector). A new robust algorithm for decision fusion is presented that addresses the problem that the statistics of the training data is not likely to exactly match the statistics of the test data. ROC's are presented and compared for real data.}, Key = {03487759886} } @article{03487759893, Author = {Yu, Yongli and Collins, Leslie M.}, Title = {Adaptive Multi-Modality Processing for the Discrimination of Landmines}, Journal = {Proceedings of SPIE - The International Society for Optical Engineering}, Volume = {5089}, Number = {2}, Pages = {1316 - 1323}, Address = {Orlando, FL, United States}, Year = {2003}, url = {http://dx.doi.org/10.1117/12.487216}, Keywords = {Sensors;Ground penetrating radar systems;Adaptive algorithms;Statistical methods;Probability density function;}, Abstract = {As in many application areas, performance of landmine detection algorithms is judged in terms of detection and false alarm rates. It is widely accepted that single sensors cannot simultaneously achieve both high detection rates and low false alarm rates, since every sensor has its advantages and disadvantages when dealing with a large variety of landmines, from large metal-cased mines to small plastic-cased mines. The recent development of high quality sensors in conjunction with statistical signal processing algorithms has shown that there are sensors that can not only discriminate targets from clutter, but can also identify subsurface or obscured targets. Here, we utilize this identification capability in addition to contextual information in a multi-modal adaptive algorithm where the identification capabilities of multiple sensors are utilized to modify the prior probability density functions associated with statistical models being utilized by other sensors. In general, every sensor modality is associated with a specific physics-based feature set that is extracted from the sensor data. Often, the statistics describing these features are assumed to follow a Gaussian mixture density, where in many cases the individual Gaussian distributions that make up the mixture result from different target types or target classes. We utilize identification information from one sensor to modify the weights associated with the probability density functions being utilized by algorithms associated with other sensor modalities. Using both simulated and real data, this approach is shown to be improve sensor performance by reducing the overall false alarm rate.}, Key = {03487759893} } @article{7674619, Author = {Yifang Xu and Collins, L.M.}, Title = {Predicting the threshold of single-pulse electrical stimuli using a stochastic auditory nerve model: the effects of noise}, Journal = {IEEE Trans. Biomed. Eng. (USA)}, Volume = {50}, Number = {7}, Pages = {825 - 35}, Year = {2003}, url = {http://dx.doi.org/10.1109/TBME.2003.813542}, Keywords = {bioelectric phenomena;hearing aids;Markov processes;neurophysiology;noise;physiological models;probability;speech recognition;}, Abstract = {An important factor that may play a role in speech recognition by individuals with cochlear implants is that electrically stimulated nerves respond with a much higher level of synchrony than is normally observed in acoustically stimulated nerves. Recent work has indicated that the addition of noise to an electrical stimulus may result in neural responses whose statistical characteristics are more similar to those observed in acoustically driven neurons. Psychophysical data have indicated that performance on some tasks might also be enhanced by the addition of noise. However, little theoretical work has been done toward predicting the effect of noise on psychoacoustic measurements. In this paper, theoretical predictions of these effects are developed through the use of a stochastic computational model. The effect of additive noise on the input and output characteristics and aggregate threshold behavior of modeled auditory nerves (ANs) is specifically studied. This paper derives the stochastic properties of the model input and output when using adaptive threshold procedures. A closed form solution for the input, or amplitude, probability distribution is obtained via Markov models for both one-down one-up (1D1U) and two-down one-up (2D1U) experimental paradigms. The output statistics are derived by integrating over the noise-free probability mass function (PMF). All theoretical PMFs are verified by simulations with the model. Theoretical threshold is predicted as a function of noise level based on these PMFs and the predictions match simulated performance. The results indicate that threshold may be adversely affected by the presence of high levels of noise}, Key = {7674619} } @article{7799271, Author = {Ferguson, W.D. and Collins, L.M. and Smith, D.W.}, Title = {Psychophysical threshold variability in cochlear implant subjects}, Journal = {Hear. Res. (Netherlands)}, Volume = {180}, Number = {1-2}, Pages = {101 - 13}, Year = {2003}, url = {http://dx.doi.org/10.1016/S0378-5955(03)00111-4}, Keywords = {biomedical electrodes;hearing aids;neurophysiology;psychology;speech recognition;stochastic processes;}, Abstract = {The dramatic differences observed when comparing auditory neural responses to electrical and acoustic stimulation may illustrate one of the important mechanisms underlying the sometimes poor speech recognition abilities of individuals with cochlear implants. Recent research has suggested that the absence of a stochastic component in neural responses to electrical activation may be an important potential mechanism for this degradation in speech recognition performance. There are few psychophysical data, however, demonstrating that this stochastic behavior can be measured directly in implant subjects. In this study, variability in psychophysical threshold was investigated as a measure of the stochastic nature of the underlying neural response in human and non-human subjects implanted with intracochlear electrode arrays. Threshold data collected in both monopolar and bipolar stimulation modes at several phase durations from cat and human subjects are presented. The nature of the neural input/output curve suggests that threshold variability should increase as the slope of the input/output curve is decreased, i.e. as phase duration is increased. These predictions are confirmed by the pattern of psychophysical results measured experimentally in cat and human subjects. Furthermore, the data may suggest that subjects with higher threshold variability, i.e. a relatively greater stochastic component, are more likely to have higher speech recognition scores}, Key = {7799271} } @article{02507274888, Author = {Tantum, Stacy L. and Collins, Leslie M.}, Title = {A parameter transformation for improved decay rate estimation}, Journal = {Proceedings of SPIE - The International Society for Optical Engineering}, Volume = {4742}, Number = {II}, Pages = {812 - 820}, Address = {Orlando, FL, United States}, Year = {2002}, url = {http://dx.doi.org/10.1117/12.479154}, Keywords = {Ordnance;Electromagnetic waves;Sensors;Computer simulation;}, Abstract = {Decay rate estimation has been proposed as an effective method for landmine and unexploded ordnance (UXO) detection and discrimination when electromagnetic induction (EMI) sensors are employed. The phenomenological basis for this strategy is that every object in the target library (i.e., landmine and/or UXO target) possesses a unique set of decay rates that are dependent upon the physical characteristics of the target of interest. In theory, these decay rates can be estimated from the measured EMI response and then utilized for target detection and subsequent discrimination and/or classification. Since the basis for this approach to target detection and identification is that targets are uniquely characterized by their decay rates, discrimination performance is dependent upon decay rate estimation performance. Unfortunately, decay rate estimation is notoriously difficult, and this difficulty adversely impacts target discrimination performance. We propose a parameter transformation to improve both the accuracy and the robustness of decay rate estimation when the decay rates are estimated using nonlinear least squares techniques. We present simulation results showing the improvement in the both the RMS error and the bias of the estimates achieved with the parameter transformation.}, Key = {02507274888} } @article{02507274878, Author = {Collins, Leslie M. and Torrione, Peter and Munshi, Vivek and Throckmorton, Chandra S. and Zhu, Quan and Clodfelter, Fred and Frasier, Shane}, Title = {Algorithms for landmine detection using the NIITEK ground penetrating radar}, Journal = {Proceedings of SPIE - The International Society for Optical Engineering}, Volume = {4742}, Number = {II}, Pages = {709 - 718}, Address = {Orlando, FL, United States}, Year = {2002}, url = {http://dx.doi.org/10.1117/12.479144}, Keywords = {Ground penetrating radar systems;Algorithms;Electromagnetism;Spurious signal noise;Wavelet transforms;}, Abstract = {Ground penetrating radar has been proposed as an alternative sensor to classical electromagnetic induction techniques for the landmine detection problem. The NIITEK-Wichmann antenna provides a high frequency radar signal with very low noise levels following the ground reflection. As a result, the signal from a buried object is not masked by the inherent noise in the system. It has been demonstrated that an operator can learn to interpret the NIITEK-Wichmann radar signal to detect and identify buried targets. The goal of this work is to develop signal processing algorithms to automatically process the radar signals and differentiate between targets and clutter. The algorithms that we are investigating have been tested on data collected at the JUXOCO test grid as well as on data collected in calibration lanes that are used for evaluating the performance of handheld and vehicular landmine detection systems. We have developed algorithms based on principle component analysis, independent component analysis, matched filters, and Bayesian processing of wavelet features. We have also considered several approaches to ground-bounce removal prior to processing. In this paper we discuss the relative performance of each of the techniques as well as the impact of ground bounce removal on processing of the data.}, Key = {02507274878} } @article{02457189387, Author = {Tantum, Stacy L. and Collins, Leslie M.}, Title = {A parameter transformation and cramer-rao bounds for estimating decay rates from exponential signals}, Journal = {International Geoscience and Remote Sensing Symposium (IGARSS)}, Volume = {4}, Pages = {2568 - 2571}, Address = {Toronto, Ont., Canada}, Year = {2002}, url = {http://dx.doi.org/10.1109/IGARSS.2002.1026613}, Keywords = {Computer simulation;Signal to noise ratio;Signal detection;Estimation;}, Abstract = {Weighted sums of decaying exponentials characterize the response of many physical systems. Therefore, accurate decay rate estimation is a goal in many diverse disciplines. In this paper, a parameter transformation which improves decay rate estimation is presented. Simulation results across a wide range of decay rates, signal-to-noise ratios (SNRs), and ratios of decay rates show that nonlinear least squares estimation of the decay rates via the proposed parameter transformation provides estimates with smaller RMS errors and bias than can be obtained without the parameter transformation. In addition, it is shown that the parameter transformation provides decay rate estimates which are closer to achieving the Cramer-Rao bound. Improvement in estimation performance for this class of signals has important ramifications in signal detection performance in several application areas.}, Key = {02457189387} } @article{02287014964, Author = {Huettel, Lisa G. and Collins, Leslie M.}, Title = {A theoretical analysis of the effects of auditory impairment on intensity discrimination}, Journal = {ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings}, Volume = {2}, Pages = {1933-1936 -}, Address = {Orlando, FL}, Year = {2002}, url = {http://dx.doi.org/10.1109/ICASSP.2002.1006147}, Keywords = {Human rehabilitation engineering;Computational methods;Signal detection;Bandwidth;Poisson ratio;}, Abstract = {The effect of cochlear hearing loss on intensity discrimination has been experimentally investigated and reported in the literature. It has been observed that the impairment configuration has a significant effect on a subject's discrimination abilities. In some cases, intensity discrimination for an impaired individual is near-normal at equal SPLs, whereas in other cases, the difference limen is elevated. It has been hypothesized that the near-normal performance of some impaired individuals may be due to either greater intensity resolution (resulting from loudness recruitment) or to the normal spread of excitation. In this paper, we simulate an experiment conducted by Schroder et al. (1994) devised to test these hypotheses. Using a signal detection theory-based approach in combination with a computational auditory model, we are able to replicate their experimental results. Additionally, by manipulating the model, we are able to demonstrate theoretically that the observed behavior results from the spread of excitation.}, Key = {02287014964} } @article{02457189469, Author = {Collins, Leslie M. and Zhang, Yan and Carin, Lawrence}, Title = {Model-based statistical sensor fusion for unexploded ordnance detection}, Journal = {International Geoscience and Remote Sensing Symposium (IGARSS)}, Volume = {3}, Pages = {1556 - 1559}, Address = {Toronto, Ont., Canada}, Year = {2002}, url = {http://dx.doi.org/10.1109/IGARSS.2002.1026180}, Keywords = {Mathematical models;Magnetometers;Sensors;Magnetostatics;Bombs (ordnance);Feature extraction;Monte Carlo methods;Probability;Algorithms;}, Abstract = {Detection and remediation of unexploded ordnance (UXO) represents a major challenge on closed, closing, and transferred military ranges as well as on active installations. The detection problem is exacerbated by the fact that on sites contaminated with UXO, extensive surface and sub-surface clutter and shrapnel is also present. Traditional methods used for UXO remediation have difficulty distinguishing buried UXO from these anthropic clutter items as well as from naturally occurring magnetic geologic noise, and thus incur prohibitively high false alarm rates. The reduction of the false alarm rate has proven to be the greatest challenge for UXO remediation. In this paper, sensor fusion techniques are applied to field data from magnetometer and electromagnetic induction (EMI) sensors in order to determine to what degree such an approach results in false alarm mitigation. The adoption of a model consisting of multiple non co-located dipoles is shown to improve our ability to predict measured signatures. A Monte Carlo fitting procedure in which multiple initial conditions is utilized for the inversion process. The statistical uncertainty in the feature space is explicitly treated using a Bayesian processor to discriminate targets from clutter. Substantial reduction of the false alarm rate is achieved for a recently developed frequency-domain EMI system. Furthermore, we investigate the effects of the processing bandwidth on discrimination performance for the frequency-domain system. The results indicate that performance can be improved by limiting the processing bandwidth to those frequencies that are the most robust to naturally occurring geological noise.}, Key = {02457189469} } @article{02507274887, Author = {Torrione, Peter and Collins, Leslie}, Title = {The performance of matched subspace detectors and support vector machines for induction-based landmine detection}, Journal = {Proceedings of SPIE - The International Society for Optical Engineering}, Volume = {4742}, Number = {II}, Pages = {800 - 811}, Address = {Orlando, FL, United States}, Year = {2002}, url = {http://dx.doi.org/10.1117/12.479153}, Keywords = {Detectors;Electromagnetic waves;Tuning;Signal receivers;}, Abstract = {Wideband electromagnetic induction (EMI) data provides an opportunity to apply statistical signal processing techniques to potentially mitigate false alarm rates in landmine detection. This paper explores the applications of matched subspace detectors and support vector machines (SVMs) to this problem. A library of landmine responses is generated from background-corrected calibration data and a bank of matched subspace detectors, each tuned to a specific mine type, is generated. Support vector machines are implemented based on the full mine responses, decay rate estimates, and the outputs of the matched subspace filter banks. Different training approaches are considered for the support vector machines. Receiver operating characteristics (ROCs) for the matched subspace detectors and support vector machines operating in a blind field test are presented. The results indicate that substantial reductions in the false alarm rates can be achieved using these techniques.}, Key = {02507274887} } @article{02457189474, Author = {Tan, Yingyi and Tantum, Stacy L. and Collins, Leslie M.}, Title = {Landmine detection with nuclear quadrupole resonance}, Journal = {International Geoscience and Remote Sensing Symposium (IGARSS)}, Volume = {3}, Pages = {1575 - 1578}, Address = {Toronto, Ont., Canada}, Year = {2002}, url = {http://dx.doi.org/10.1109/IGARSS.2002.1026185}, Keywords = {Resonance;Bombs (ordnance);Electromagnetic wave interference;Adaptive filtering;Interference suppression;Algorithms;Antennas;Signal to noise ratio;}, Abstract = {Nuclear Quadrupole Resonance (NQR) technology for the detection of explosives is of crucial importance in an increasing number of applications. For landmine detection, NQR has proven to be highly effective if the NQR sensor is not exposed to radio frequency interference (RFI). Since strong nonstationary RFI in the field is unavoidable, a robust detection method is required. With the aid of reference antennas, a frequency domain LMS algorithm is applied to cancel the RFI in field data. An average power detector based on power spectral estimation algorithms is proposed and performance using both the periodogram and MUSIC algorithms is evaluated. The detection performance has been compared with that of a non-adaptive Bayesian detector. The experimental results show that, unlike the non-adaptive Bayesian detector, the average power detector provides perfect detection capability if the data segments involved in the collection process are sufficiently long.}, Key = {02457189474} } @article{02297025525, Author = {Collins, Leslie and Gao, Ping and Schofield, Deborah and Moulton, John P. and Makowsky, Lawrence C. and Reidy, Denis M. and Weaver, Richard C.}, Title = {A statistical approach to landmine detection using broadband electromagnetic induction data}, Journal = {IEEE Transactions on Geoscience and Remote Sensing}, Volume = {40}, Number = {4}, Pages = {950 - 962}, Year = {2002}, url = {http://dx.doi.org/10.1109/TGRS.2002.1006387}, Keywords = {Magnetoelectric effects;Metal detectors;Clutter (information theory);Signal detection;Soil testing;Time domain analysis;Statistical methods;Algorithms;Mathematical models;}, Abstract = {The response of time-domain electromagnetic induction (EMI) sensors, which have been used almost exclusively for landmine detection, is related to the amount of metal present in the object and its distance from the sensor. Unluckily, there is often a significant amount of metallic clutter in the environment that also induces an EMI response. Consequently, EMI sensors employing detection algorithms based solely on metal content suffer from large false alarm rates. To mitigate this false alarm problem for mines with substantial metal content, statistical algorithms have been developed that exploit models of the underlying physics. In such models it is commonly assumed that the soil has a negligible effect on the sensor response, thus the object is modeled in "free space." We report on studies that were performed to test the hypotheses that for broadband EMI sensors: 1) soil cannot be modeled as free space when the buried object has low metal content and 2) advanced signal processing algorithms can be applied to reduce the false alarm rates. Our results show that soil cannot be modeled as free space and that when modeling soil correctly our advanced algorithms reduced the false alarm probability by up to a factor of 10 in blind tests.}, Key = {02297025525} } @article{02507274892, Author = {Collins, Leslie M. and Huettel, Lisa G. and Simpson, William A. and Tantum, Stacy L.}, Title = {Sensor fusion of EMI and GPR data for improved landmine detection}, Journal = {Proceedings of SPIE - The International Society for Optical Engineering}, Volume = {4742}, Number = {II}, Pages = {872 - 879}, Address = {Orlando, FL, United States}, Year = {2002}, url = {http://dx.doi.org/10.1117/12.479159}, Keywords = {Ground penetrating radar systems;Electromagnetic waves;Debris;Sensors;}, Abstract = {It is widely accepted that single sensors cannot simultaneously achieve both high detection rates and low false alarm rates for the landmine detection problem. Thus, in this paper we consider the fusion of two types of sensors, electromagnetic induction (EMI) and ground penetrating radar (GPR). In its most common instantiation, EMI essentially provides metal detection and thus detects mines with high metal content as well as metal debris in the environment. More advanced EMI systems have begun to show potential for discriminating such debris from landmines. GPR is also used for landmine detection since it can detect and identify low-metallic subsurface anomalies. In our previous work, we have shown that a Bayesian detection approach can be applied to EMI and GPR data and provide improvements in false alarm rates. In this paper, we present results that indicate that statistical signal processing techniques can be applied simultaneously to GPR and EMI data and that reductions in false alarm rates can be achieved. We present results for two landmine detection systems, both handheld, and when possible compare the results to those obtained by a human operator who essentially fuses the outputs of the single sensor systems.}, Key = {02507274892} } @article{7439231, Author = {Yifang Xu and Collins, L.M.}, Title = {Threshold prediction for noise-modulated electrical stimuli using a stochastic auditory nerve model: implications for cochlear implants}, Journal = {2002 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.02CH37334)}, Volume = {vol.2}, Pages = {1929 - 32}, Address = {Orlando, FL, USA}, Year = {2002}, url = {http://dx.doi.org/10.1109/ICASSP.2002.1006146}, Keywords = {acoustic noise;auditory evoked potentials;Gaussian noise;hearing aids;Markov processes;}, Abstract = {The effect of a low level additive noise process on the input and output characteristics and threshold behavior of auditory nerves (ANs) is studied by means of a stochastic computational model. This paper derives the stochastic properties of the model input and output for adaptive threshold procedures. A closed form solution for the input, or amplitude, probability distribution is obtained via Markov models. The output statistics are derived by integrating over the noise-free probability mass function (PMF). All theoretical PMFs are verified by simulations. Theoretical threshold predictions as a function of noise level are made based on these PMFs and the results indicate that threshold is adversely affected by the presence of low levels of noise}, Key = {7439231} } @article{02507274880, Author = {Tantum, Stacy L. and Wei, Yuchuan and Munshi, Vivek S. and Collins, Leslie M.}, Title = {A comparison of algorithms for landmine detection and discrimination using ground penetrating radar}, Journal = {Proceedings of SPIE - The International Society for Optical Engineering}, Volume = {4742}, Number = {II}, Pages = {728 - 735}, Address = {Orlando, FL, United States}, Year = {2002}, url = {http://dx.doi.org/10.1117/12.479146}, Keywords = {Ground penetrating radar systems;Algorithms;Electromagnetic waves;Radar target recognition;Hough transforms;}, Abstract = {Ground penetrating radar (GPR) has been proposed as an effective sensing modality for reducing the excessively high false alarm rates often encountered in landmine detection applications. Ground penetrating radar is sensitive to discontinuities in the interrogated medium, rather than the presence of metal, and thus exploits a different phenomenology than electromagnetic induction (EMI) sensors. Thus, unique signals that are dependent on the composition of the targets can be obtained from buried objects. Consequently, the detection of low metal content targets is improved since the radar responds to non-metallic objects, such as wood, plastic, and stone, as well as metallic objects. When the GPR sensor is mounted on a moving platform, the target signatures are hyperbolas in a time-domain data record. Furthermore, the hyperbolas from different targets often exhibit different characteristics. The goal of this work is to develop robust signal processing algorithms which exploit this knowledge to improve target detection and discrimination. Among the algorithms considered are a Bayesian approach and an approach similar to the Hough transform. The algorithms are evaluated using real data collected with fielded GPR sensors, and are compared in terms of their computational requirements as well as their detection and discrimination performance.}, Key = {02507274880} } @article{02287014851, Author = {Zhang, Yan and Wang, Chun-Mei and Collins, Leslie M.}, Title = {Adaptive time delay estimation method with signal selectivity}, Journal = {ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings}, Volume = {2}, Pages = {1477-1480 -}, Address = {Orlando, FL}, Year = {2002}, url = {http://dx.doi.org/10.1109/ICASSP.2002.1006033}, Keywords = {Adaptive algorithms;Estimation;FIR filters;Mathematical models;Sensors;Computer simulation;Spurious signal noise;Signal interference;Signal receivers;Signal distortion;Communication channels (information theory);Random processes;}, Abstract = {A new adaptive time delay estimation method is proposed for highly curruptive environments based on the cyclostationary property of the source signal. The time delay operator is modeled as a finite impulse response filter. The new adaptive scheme is based on parametric modeling between two sensor measurements and employs cyclic statistics of the data. Simulation examples are presented to demonstrate the signal selectivity of the new adaptive method in the presence of noises and interference. The only requirement is that the source signal has a known (or measurable) carrier frequency or keying rate that is distinct from those of interfering signals.}, Key = {02287014851} } @article{02297026168, Author = {Throckmorton, Chandra S. and Collins, Leslie M.}, Title = {The effect of channel interactions on speech recognition in cochlear implant subjects: Predictions from an acoustic model}, Journal = {Journal of the Acoustical Society of America}, Volume = {112}, Number = {1}, Pages = {285 - 296}, Year = {2002}, url = {http://dx.doi.org/10.1121/1.1482073}, Keywords = {Spectrum analysis;Frequencies;Hardware;Computer simulation;}, Abstract = {Acoustic models that produce speech signals with information content similar to that provided to cochlear implant users provide a mechanism by which to investigate the effect of various implant-specific processing or hardware parameters independent of other complicating factors. This study compares speech recognition of normal-hearing subjects listening through normal and impaired acoustic models of cochlear implant speech processors. The channel interactions that were simulated to impair the model were based on psychophysical data measured from cochlear implant subjects and include pitch reversals, indiscriminable electrodes, and forward masking effects. In general, spectral interactions degraded speech recognition more than temporal interactions. These effects were frequency dependent with spectral interactions that affect lower-frequency information causing the greatest decrease in speech recognition, and interactions that affect higher-frequency information having the least impact. The results of this study indicate that channel interactions, quantified psychophysically, affect speech recognition to different degrees. Investigation of the effects that channel interactions have on speech recognition may guide future research whose goal is compensating for psychophysically measured channel interactions in cochlear implant subjects. © 2002 Acoustical Society of America.}, Key = {02297026168} } @article{01436696548, Author = {Huettel, L.G. and Collins, L.M.}, Title = {A theoretical study of information transmission in the auditory system using signal detection theory: Frequency discrimination by normal and impaired systems}, Journal = {ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings}, Volume = {5}, Pages = {3305 - 3308}, Address = {Salt Lake, UT}, Year = {2001}, url = {http://dx.doi.org/10.1109/ICASSP.2001.940365}, Keywords = {Information analysis;Signal filtering and prediction;Signal detection;Frequency allocation;Frequency domain analysis;}, Abstract = {In this paper, we have investigated the differences between normal and impaired auditory processing for a frequency discrimination task by analyzing the responses of a computational auditory model using signal detection theory. Two detectors, one using all of the information in the signal, the other using only the number of neural responses, were implemented. An evaluation of the performance differences between the two theoretical detectors and experimental data may provide insight into quantifying the type of information present in the auditory system as well as whether the human auditory system uses this information efficiently. Results support previous hypotheses that, for low- and mid-range frequencies, the auditory system is able to use temporal information to perform frequency discrimination [8]. The results also suggest that some temporal information is represented in the neural spike train, even at high frequencies. However, the ability of the auditory system to use this information deteriorates at higher frequencies.}, Key = {01436696548} } @article{7177120, Author = {Jain, S. and Workman, R.W. and Collins, L.M. and Ervin, E.C.}, Title = {Development of a high-level supply chain simulation model}, Journal = {Proceeding of the 2001 Winter Simulation Conference (Cat. No.01CH37304)}, Volume = {vol.2}, Pages = {1129 - 37}, Address = {Arlington, VA, USA}, Year = {2001}, url = {http://dx.doi.org/10.1109/WSC.2001.977425}, Keywords = {business data processing;digital simulation;goods dispatch data processing;logistics data processing;}, Abstract = {This paper describes an effort that involved development of a simulation model for evaluating the business processes and inventory control parameters of a logistics and distribution supply chain. A generic simulation tool, rather than a supply chain simulator, was developed for meeting customized needs of the effort. The paper describes, the approaches used to model at the selected level of abstraction, the development of interfaces for data and experimentation and the development and delivery of animation for communicating the approach and results to the client}, Key = {7177120} } @article{02417134212, Author = {Chiang, Pei-Ju and Tantum, Stacy L. and Collins, Leslie M.}, Title = {Signal processing of ground penetrating radar data for subsurface object detection}, Journal = {Proceedings of SPIE - The International Society for Optical Engineering}, Volume = {4394}, Number = {1}, Pages = {470 - 475}, Address = {Orlando, FL, United States}, Year = {2001}, url = {http://dx.doi.org/10.1117/12.445499}, Keywords = {Explosives;Signal processing;Image processing;Sensor data fusion;Algorithms;Electromagnetic wave transmission;Hough transforms;Statistical methods;}, Abstract = {Ground penetrating radar (GPR) generates a cross-sectional profile of the soil by transmitting electromagnetic waves that reflect back in a manner associated with the electrical properties and geometry of the objects buried underground. The responses of the reflected waves are processed using a variety of digital signal processing and image processing techniques. In this paper we compare an energy detector, matched filter, and a proposed Hough Transform approach. The results from each of the algorithms are compared using receiver operating characteristic (ROC) curves. Comparatively, the matched filter method has the lowest false alarm rate, however it is essentially providing a performance bound since for this analysis we derived the matching template from the data to be tested. Thus, in this case the Hough transform method may be more robust when the testing and training sets are separate, as it is inherently integrating over the uncertainty associated with the subsurface object detection problem.}, Key = {02417134212} } @article{02317035855, Author = {Collins, Leslie and Gao, Ping and Tantum, Stacy}, Title = {Model-based statistical signal processing using electromagnetic induction data for landmine detection and classification}, Journal = {IEEE Workshop on Statistical Signal Processing Proceedings}, Pages = {162 - 165}, Address = {Singapore}, Year = {2001}, url = {http://dx.doi.org/10.1109/SSP.2001.955247}, Keywords = {Ordnance;Time domain analysis;Data reduction;Sensors;Independent component analysis;Algorithms;}, Abstract = {Traditionally, electromagnetic induction (EMI) sensors are operated in the time-domain and the response strength is related to the amount of metal present in the object. These sensors have been used almost exclusively for landmine detection. Unfortunately, there is often a significant amount of metallic clutter in the environment that also induces an EMI response. Consequently, EMI sensors employing detection algorithms based solely on metal content suffer from large false alarm rates. A second issue regarding processing of data collected on highly cluttered sites is that anomalies are often in close proximity, and the measured EMI signal consists of a weighted sum of responses from each anomaly. To mitigate the false alarm problem, statistical algorithms have been developed which exploit models of the underlying physics for mines with substantial metal content. In such models it is commonly assumed that the soil has a negligible effect on the sensor response, thus the object is modeled in "free space". To date, such advanced algorithms have not been applied specifically to the problem of detecting of low-metal mines in a cluttered environment. Addressing this problem requires considering the effects of soil on signatures, separating the multiple signatures constituting the measured EMI response as well as discriminating between landmine signatures and clutter signatures. In this paper, we consider statistically based approaches to the landmine detection and classification problem for frequency-domain EMI sensors. We also develop a preliminary statistical approach based on independent components analysis (ICA) for separating the signals of multiple objects that are within the field of view of the sensor and illustrate the performance of this approach on measured data.}, Key = {02317035855} } @article{01246545094, Author = {Collins, L.M. and Zhang, Y. and Li, J. and Wang, H. and Carin, L. and Hart, S.J. and Rose-Pehrsson, S.L. and Nelson, H.H. and McDonald, J.R.}, Title = {A comparison of the performance of statistical and fuzzy algorithms for unexploded ordnance detection}, Journal = {IEEE Transactions on Fuzzy Systems}, Volume = {9}, Number = {1}, Pages = {17 - 30}, Year = {2001}, url = {http://dx.doi.org/10.1109/91.917111}, Keywords = {Ordnance;Algorithms;Sensors;Magnetometers;Maximum likelihood estimation;Neural networks;}, Abstract = {In most field environments, unexploded ordnance (UXO) items are found among extensive surface and subsurface clutter and shrapnel from ordnance. Traditional algorithms for UXO remediation experience severe difficulty distinguishing buried targets from anthropic clutter. Furthermore, naturally occurring magnetic geologic noise often adds to the complexity of the discrimination task. These problems render site remediation a very slow, labor-intensive, and inefficient process. While sensors have improved significantly over the past several years in their ability to detect conducting and/or permeable targets, reduction of the false alarm rate has proven to be a significantly more challenging problem. Our work has focused on the development of signal processing algorithms that incorporate the underlying physics characteristic of the sensor and of the anticipated UXO target, in order to address the false alarm issue. In this paper, we describe several algorithms for discriminating targets from clutter that have been applied to data obtained with the multisensor towed array detection system (MTADS). This sensor suite has been developed by the U.S. Naval Research Laboratory (NRL), and includes both electromagnetic induction (EMI) and magnetometer sensors. We describe four signal processing techniques that incorporate features derived from simple physics-based sensor models: a generalized likelihood ratio technique, a maximum likelihood estimation-based clustering algorithm, a probabilistic neural network, and a subtractive fuzzy clustering technique. These algorithms have been applied to the data measured by MTADS in a magnetically clean test pit and at a field demonstration. We show that overall the subtractive fuzzy technique performs better than the alternative techniques when the training and testing data sets are separate. The results also allow us to quantify the utility of fusing the magnetometer and the EMI data, and we show that performance is improved when both EMI and magnetometer features are utilized. The results indicate that the application of advanced signal processing algorithms could provide up to a factor of two reduction in false alarm probability for the UXO detection problem.}, Key = {01246545094} } @article{02417134252, Author = {Tan, Yingyi and Tantum, Stacy and Collins, Leslie}, Title = {Enhanced signal and auditory processing for landmine detection using EMI sensors}, Journal = {Proceedings of SPIE - The International Society for Optical Engineering}, Volume = {4394}, Number = {2}, Pages = {852 - 858}, Address = {Orlando, FL, United States}, Year = {2001}, url = {http://dx.doi.org/10.1117/12.445413}, Keywords = {Signal processing;Sensors;Acoustic transducers;Optimization;}, Abstract = {Although the ability of EMI sensors to detect landmines has improved significantly, false alarm rate reduction remains a challenging problem. However, experienced operators can often discriminate mines from metallic clutter with the aid of an audio transducer. The goal of this work is to optimize the presentation of information to the operator and to determine whether information as to the presence of metal can be co-presented with information regarding mine/non-mine belief. Traditionally, an energy calculation is provided to the sensor operator via a signal whose loudness and/or frequency is proportional to the energy of the received signal. This information codes information as to the amount of metal present. However, there is information in the unprocessed sensor signal that the operator could use to effect discrimination. We have experimentally investigated the perceptual dimensions that most effectively convey the information in a sensor response to a listener using simulated data. Results indicated that, consistent with the auditory warning literature, pulsed audio signals with a distinct harmonic pattern which rise in fundamental frequency can be used to provide information which provides better performance than simple single-frequency tones. Additionally, the data indicated that the amount of metal could be coded in the rising pitch of the complex, and that the mine/no-mine probabilities could be coded in a separate dimension - The pulse rate. In this paper, we describe these results in detail.}, Key = {02417134252} } @article{02417134262, Author = {Collins, Leslie M. and Huettel, Lisa G.}, Title = {Single sensor processing and sensor fusion for GPR and EMI data}, Journal = {Proceedings of SPIE - The International Society for Optical Engineering}, Volume = {4394}, Number = {2}, Pages = {952 - 958}, Address = {Orlando, FL, United States}, Year = {2001}, url = {http://dx.doi.org/10.1117/12.445423}, Keywords = {Sensor data fusion;Ground penetrating radar systems;Metal detectors;Signal interference;Signal processing;Statistics;}, Abstract = {As in many areas, performance of landmine detection algorithms is judged in terms of detection and false alarm rates. For the landmine detection problem, it is often the case that detectors satisfy one requirement at the cost of poor performance with regard to the other. It is widely accepted that single sensors cannot simultaneously achieve both high detection rates and low false alarm rates, since every sensor has its advantages and disadvantages when dealing with a large variety of landmines, from large metal-cased mines to small plastic-cased mines. Thus, in this paper we consider two types of sensors, EMI and GPR. In its most common instantiation, time-domain EMI is essentially a metal detector and thus detects mines with high metal content as well as metal debris in the environment. More advanced EMI systems have begun to show potential for the discrimination of such debris from mines. GPR is also used for landmine detection since it can detect and identify low-metallic subsurface anomalies. In our previous work, we have shown that Bayesian detection approach can be applied to EMI data and provide promising results. In this paper, we present results that indicate that statistical signal processing technique applied to GPR data can also yield performance improvements. Theoretical results are verified by data collected with a developmental mine-detection system, which consists of colocated metal detectors and GPR sensors. Thus, in addition to discussing individual sensor data processing, we also present result of data fusion of both the EMI and the GPR data using the detection system.}, Key = {02417134262} } @article{01336615556, Author = {Tantum, S.L. and Collins, L.M.}, Title = {A comparison of algorithms for subsurface target detection and identification using time-domain electromagnetic induction data}, Journal = {IEEE Transactions on Geoscience and Remote Sensing}, Volume = {39}, Number = {6}, Pages = {1299 - 1306}, Year = {2001}, url = {http://dx.doi.org/10.1109/36.927453}, Keywords = {Electromagnetic field effects;Object recognition;Signal processing;Parameter estimation;Error detection;Algorithms;Computer simulation;}, Abstract = {In this paper, the performance of subsurface target identification algorithms using data from time-domain electromagnetic induction (EMI) sensors is investigated. The response of time-domain EMI sensors to the presence of a conducting object may be modeled as a weighted sum of decaying exponential signals. Although the weights associated with each of the modes are dependent on the target/sensor orientation, the decay rates are a function of the target's composition and geometry and therefore are intrinsic to the target. Since the decay rates are not dependent on target/sensor orientation or other unobservable parameters, decay rate estimation has previously been proposed as a viable method for target identification. The performance attained with Bayesian target identification algorithms operating on the entire time-domain signal and decay rate estimates is compared through both numerical simulations and application to experimental data. The decay rate estimates utilized in the numerical simulations are assumed to achieve the Cramer-Rao lower bound (CRLB), which provides a lower bound on the variance of an unbiased parameter estimate. The simulations as well as results obtained with experimental data show that processing the entire time-domain signal provides better target identification and discrimination performance than processing decay rate estimates.}, Key = {01336615556} } @article{00105364611, Author = {Tantum, Stacy and Collins, Leslie}, Title = {Physics-based statistical signal processing for improved landmine detection and classification via decay rate estimation}, Journal = {Proceedings of SPIE - The International Society for Optical Engineering}, Volume = {4038 (I)}, Pages = {36 - 44}, Address = {Orlando, FL, USA}, Year = {2000}, url = {http://dx.doi.org/10.1117/12.396260}, Keywords = {Sensors;Signal processing;Statistical methods;Bombs (ordnance);Optimization;Probability distributions;Signal to noise ratio;Computer simulation;}, Abstract = {Target discrimination via decay rate (pole) estimation has been proposed as an effective method for landmine and UXO detection. The physical basis for this strategy is that every object in the target library (i.e., landmine and/or UXO target) possesses a unique set of decay rates, or poles. In theory, these poles can be estimated from the measured EMI response and then utilized for target detection and subsequent discrimination and/or identification. Unfortunately, pole estimation is notoriously difficult and this difficulty adversely impacts target discrimination performance. We present simulation results that show that when the sensor/object orientation is not known, the time-domain signatures of two objects with distinct sets of poles may be indistinguishable. Furthermore, this can occur even when the two sets of poles are not necessarily 'close' to each other. Since the basis for this approach to target detection and identification is that targets are uniquely characterized by their estimated poles, discrimination performance is dependent upon pole estimation performance. The Cramer-Rao lower bound (CRLB), which provides a lower bound on the variance of an unbiased estimator, for the pole and amplitude coefficient estimates is utilized to investigate the fundamental limitations on target discrimination via pole estimation. It is shown how both the sampling strategy (i.e., uniform, geometric, logarithmic sampling) and the number of poles being estimated affect pole estimation performance. Detection and identification results are presented for simulated data.}, Key = {00105364611} } @article{00105364546, Author = {Gao, Ping and Collins, Leslie}, Title = {Time-domain metal detector and GPR data processing and fusion for landmine detection}, Journal = {Proceedings of SPIE - The International Society for Optical Engineering}, Volume = {4038 (II)}, Pages = {847 - 852}, Address = {Orlando, FL, USA}, Year = {2000}, url = {http://dx.doi.org/10.1117/12.396312}, Keywords = {Radar target recognition;Bombs (ordnance);Metal detectors;Time domain analysis;Electromagnetic field effects;Radar clutter;Statistical methods;Signal processing;}, Abstract = {Data taken by the Coleman Research Corporation (CRC) Handheld Standoff Mine Detection System (HSTAMIDS) at Fort A. P. Hill, VA. and Yuma, AZ, it is demonstrated that statistical signal processing techniques show improved performance over the conventional detection methods. The active system of the HSTAMIDS contains co-located metal detector (MD) and GPR sensors, that allows to fuse data from the MD and GPR sensors. Thus in addition to discussing individual sensor data processing, results of data fusion of both the MD and the GPR data using the HSTAMIDS system are presented.}, Key = {00105364546} } @article{00075229107, Author = {Gao, Ping and Collins, Leslie and Garber, Philip M. and Geng, Norbert and Carin, Lawrence}, Title = {Classification of landmine-like metal targets using wideband electromagnetic induction}, Journal = {IEEE Transactions on Geoscience and Remote Sensing}, Volume = {38}, Number = {3}, Pages = {1352 - 1361}, Year = {2000}, url = {http://dx.doi.org/10.1109/36.843029}, Keywords = {Sensors;Mines;Probability;Decision theory;Statistical methods;Algorithms;Pattern recognition;Method of moments;Computer simulation;Electromagnetic field theory;}, Abstract = {In our previous work, we have shown that the detectability of landmines can be improved dramatically by the careful application of signal detection theory to time-domain electromagnetic induction (EMI) data using a purely statistical approach. In this paper, classification of various metallic land-mine-like targets via signal detection theory is investigated using a prototype wideband frequency-domain EMI sensor. An algorithm that incorporates both a theoretical model of the response of such a sensor and the uncertainties regarding the target/sensor orientation is developed. This allows the algorithms to be trained without an extensive data collection. The performance of this approach is evaluated using both simulated and experimental data. The results show that this approach affords substantial classification performance gains over a standard approach, which utilizes the signature obtained when the sensor is centered over the target and located at the mean expected target/sensor distance, and thus ignores the uncertainties inherent in the problem. On the average, a 60% improvement is obtained.}, Key = {00075229107} } @article{00085289898, Author = {Gao, Ping and Collins, Leslie M.}, Title = {Two-dimensional generalized likelihood ratio test for land mine and small unexploded ordnance detection}, Journal = {Signal Processing}, Volume = {80}, Number = {8}, Pages = {1669 - 1686}, Year = {2000}, url = {http://dx.doi.org/10.1016/S0165-1684(00)00100-6}, Keywords = {Probability;Electromagnetic field theory;Sensor data fusion;Mines;Ordnance;Frequencies;Algorithms;Signal to noise ratio;}, Abstract = {The fundamental goals of land-mine and small unexploded ordnance (UXO) detection are to achieve a high probability of detection (P<sub>d</sub>) and a low probability of false alarm (P<sub>fa</sub>). Conventional methods usually fulfill the first goal at the cost of a high P<sub>fa</sub>. In our previous work (Collins et al., IEEE Trans. Geosci. Remote Sensing 37 (2) (March 1998) 811-819; Gao and Collins, Proceedings of SPIE, Orlando, FL, April 1998; Gao, Master's Thesis, Duke University, December, 1997), we have shown that a Bayesian decision theoretic approach can be applied to improve the detectibility of land mines and small UXO targets using a single spatial sample of the electromagnetic induction (EMI) sensor data. In this paper, we present an alternative approach which significantly improves P<sub>d</sub> at a fixed P<sub>fa</sub> by utilizing features that capture the physical nature of EMI data within a statistical signal processing framework. The method we develop is a two-dimensional generalized likelihood ratio test (2-D GLRT) which utilizes spatial information from the sensor output. To illustrate the performance improvement, results obtained with the 2-D GLRT detector are compared to those for the standard threshold test for single-channel time-domain sensor data, as well as the energy detector, the integral detector, and the single location generalized likelihood ratio test (1-D GLRT) detector for multi-channel time-domain EMI sensor data.}, Key = {00085289898} } @article{00105364619, Author = {Tan, Yingyi and Huettel, Lisa and Collins, Leslie}, Title = {Predicting improved human auditory discrimination for landmine detection using EMI sensors}, Journal = {Proceedings of SPIE - The International Society for Optical Engineering}, Volume = {4038 (I)}, Pages = {122 - 129}, Address = {Orlando, FL, USA}, Year = {2000}, url = {http://dx.doi.org/10.1117/12.396238}, Keywords = {Bombs (ordnance);Electromagnetic field effects;Sensors;Audition;Optimization;Decision theory;Statistical tests;Computer simulation;Signal encoding;Algorithms;}, Abstract = {In this study, an attempt is made to optimize the presentation of information to the operator and to predict improved performance prior to extensive experimental testing. It is shown that by supplying the sensor response more appropriately to the listener, discrimination, as opposed to simple detection, could be achieved. Through additional theoretical treatment of the experimental data, it is demonstrated that improvements can be predicted.}, Key = {00105364619} } @article{00105364551, Author = {Tantum, Stacy and Collins, Leslie}, Title = {Detection and classification of landmine-like targets in a non-Gaussian noise environment}, Journal = {Proceedings of SPIE - The International Society for Optical Engineering}, Volume = {4038 (II)}, Pages = {900 - 909}, Address = {Orlando, FL, USA}, Year = {2000}, url = {http://dx.doi.org/10.1117/12.396317}, Keywords = {Statistical methods;White noise;Bombs (ordnance);Maximum likelihood estimation;Sensors;Electromagnetic field effects;Computer simulation;Probability distributions;Algorithms;Signal to noise ratio;}, Abstract = {Many statistical signal processing approaches to target detection and classification assume the measurement is corrupted by independent, identically distributed (i.i.d.) white Gaussian noise. This common assumption often results in simpler, and less computationally intense, mathematical realizations for the processor. However, in many instances it is not clear if this assumptions regarding the statistics of the noise is valid. In this paper, the effects of assuming i.i.d. white Gaussian noise on the performance of likelihood ratio detectors and maximum likelihood classifiers implemented in a non-Gaussian noise environment are discussed. If the assumptions regarding the noise distribution are accurate, the resulting likelihood ratio detector and classifier are optimal. However, if those assumptions are inaccurate, performance may be degraded. We present simulation results illustrating the effects of mismatch between the assumed and actual noise distributions on detection and classification performance for likelihood ratio processors derived under several assumptions regarding the noise distribution. Specifically, target detection and classification utilizing electromagnetic induction (EMI) sensors is considered.}, Key = {00105364551} } @article{04057934037, Author = {Collins, Leslie M. and Throckmorton, Chandra S.}, Title = {Investigating perceptual features of electrode stimulation via a multidimensional scaling paradigm}, Journal = {Journal of the Acoustical Society of America}, Volume = {108}, Number = {5 I}, Pages = {2353 - 2365}, Year = {2000}, url = {http://dx.doi.org/10.1121/1.1314320}, Abstract = {To achieve the most effective speech processing for individuals with cochlear implants, it is important to understand the perceptual features associated with the stimulation parameters. In general, when electrodes are stimulated in order from apex to base, the pitch of the perceived sound changes in an orderly fashion from low to high. Some deviations from this assumed order have been documented. Also, pitch is the dominant perceptual attribute of a sound when the stimuli associated with different electrodes have been accurately loudness balanced. In this study, the results of a multidimensional scaling (MDS) paradigm were compared to the results of a pitch-ranking procedure for six subjects implanted with multichannel cochlear prostheses. Results indicate that there may be multiple percepts that change with electrode location. Not surprisingly, the dominant percept is strongly correlated with pitch. The results also indicate that the structure of the second percept is consistent across subjects, although not interpretable using the data measured in this study. Furthermore, results indicate that MDS data can be used to pinpoint indiscriminable electrodes more accurately than pitch data. The results of this study may have importance for the design of the next generation of speech processors. © 2000 Acoustical Society of America.}, Key = {04057934037} } @article{00105364610, Author = {Gao, Ping and Collins, Leslie and Carin, Lawrence}, Title = {Bayesian optimal classification of metallic objects: a comparison of time-domain and frequency-domain EMI performance}, Journal = {Proceedings of SPIE - The International Society for Optical Engineering}, Volume = {4038 (I)}, Pages = {25 - 35}, Address = {Orlando, FL, USA}, Year = {2000}, url = {http://dx.doi.org/10.1117/12.396251}, Keywords = {Electromagnetic field effects;Bombs (ordnance);Signal processing;Statistical methods;Electric conductivity;Frequencies;Probability density function;Signal to noise ratio;Computer simulation;Performance;}, Abstract = {Traditionally, field EMI sensors are operated in the time-domain. The time-domain (TD) EMI sensor usually is a pulsed system. It contains both a transmitting coil and a receiving coil. After transmitting an excitation pulse, which generates the primary field, the receiving coil records the secondary field in the late time. Since a TD EMI sensor measures only the late-time response, the information contained in the early time response is lost thus limiting the types of objects that can be discriminated. Alternatively, EMI sensors can be operated in the frequency-domain (FD). In this case, the excitations are sinusoidal signals and the sensor measures the static response. The advantages and disadvantages of TD and FD EMI sensors are reviewed in this paper. For landmine and UXO detection, discrimination of targets of interest from clutter is required, since the cost of large false alarm rates is substantial amounts of money, labor and time. In order to discriminate targets from clutter, Bayesian optimal classifiers are derived. Traditional detectors for these applications only utilize the energy of the signal at the position under test or the output of a matched filter that is matched to the signature of the object at a particular (known) depth. Furthermore, in the real world scenario, the depth of the underground objects is uncertain. The optimal classifier that we utilize takes these uncertainties into account also. In this paper, we present classification performance for four metal objects using TD and FD EMI data. Experimental data were taken with the PSS-12, a standard army issued metal detector, and the GEM-3, a prototype frequency-domain EMI sensor. Although the optimal classifier improves performance for both TD and FD data, FD classification rate are higher than those for TD systems. The theoretical basis for this result is explored.}, Key = {00105364610} } @article{00105364543, Author = {Zhang, Yan and Li, Jing and Carin, Lawrence and Collins, Leslie}, Title = {Improved UXO detection via sensor fusion}, Journal = {Proceedings of SPIE - The International Society for Optical Engineering}, Volume = {4038 (II)}, Pages = {816 - 823}, Address = {Orlando, FL, USA}, Year = {2000}, url = {http://dx.doi.org/10.1117/12.396309}, Keywords = {Algorithms;Radar target recognition;Radar clutter;Bombs (ordnance);Signal processing;Magnetometers;Statistical methods;Mathematical models;}, Abstract = {Traditional algorithms for UXO remediation experience severe difficulties distinguishing buried targets from anthropic clutter, and in most cases UXO items are found amongst extensive surface clutter and shrapnel from ordnance operations. These problems render site mediation a very slow, labor intensive, and inefficient process. While sensors have improved significantly over the past several years in their ability to detect conducting and/or permeable targets, reduction of the false alarm rate has proven to be a significantly more challenging problem. Our work has focused on the development of optimal signal processing algorithms that rigorously incorporate the underlying physics characteristic of the sensor and the anticipated UXO target in order to address the false alarm issue. In this paper, we describe several techniques for discriminating targets from clutter that have been applied to data obtained with the Multisensor Towed Array Detection System (MTADS) that has been developed by the Naval Research Laboratory. MTADS includes both EMI and magnetometer sensors. We describe a variety of signal processing techniques which incorporate physics-based models that have been applied to the data measured by MTADS during field demonstrations. We will compare and contrast the performance of the various algorithms as well as discussing tradeoffs, such as training requirements. The results of this analysis quantify the utility of fusing magnetometer and EMI data. For example, in the JPG-IV test, at the False Positive level obtained by NRL, one of our algorithms achieved a 13% improvement in True Positive level over the algorithm traditionally used for processing MTADS data.}, Key = {00105364543} } @article{00105364609, Author = {Collins, Leslie and Tantum, Stacy and Gao, Ping and Moulton, John and Makowsky, Larry and Reidy, Denis and Weaver, Dick}, Title = {Improved detection of low-metallic content landmines using EMI data}, Journal = {Proceedings of SPIE - The International Society for Optical Engineering}, Volume = {4038 (I)}, Pages = {14 - 24}, Address = {Orlando, FL, USA}, Year = {2000}, url = {http://dx.doi.org/10.1117/12.396241}, Keywords = {Electromagnetic field effects;Bombs (ordnance);Algorithms;Statistical methods;Probability;Electromagnetic field theory;Data acquisition;Data reduction;Sensor data fusion;}, Abstract = {EMI sensors are used extensively to detect landmines, and operate by detecting the metal that is present in mines. However, mines vary in their construction from metal-cased varieties with a large mass of metal to plastic-cased varieties with minute amounts of metal. Unfortunately, there is often a significant amount of metallic clutter present in the environment. Consequently, EMI sensors that utilize traditional detection algorithms based solely on metal content suffer from large false alarm rates. We have at least partially mitigated this false alarm problem for high-metal content mines by developing statistical algorithms that exploit phenomenological models of the underlying physics. The Joint UXO Coordination Office (JUXOCO) is sponsoring a series of experiments designed to establish a performance baseline for a variety of sensors. The experiments to date have focused on detection and discrimination of low-metallic content mines. This baseline will be used to measure the potential improvements in performance offered by advanced signal processing algorithms. This paper describes the results of several experiments performed in conjunction with the JUXOCO effort. In our preliminary work, statistical algorithms have been applied specifically to the problem of detection of low-metal mines, and dramatic performance improvements have been observed with respect to the baseline performance. However, these algorithms were statistical in nature, did not incorporate phenomenological models, and exploited spatial information. The tradeoffs among these various factors are explored in this paper, along with the performance of alternative statistical approaches. In addition, approaches to classification of the mine-type are discussed and the performance of such classifiers is presented.}, Key = {00105364609} } @article{00125423727, Author = {Tantum, Stacy L. and Collins, Leslie M.}, Title = {Performance bounds for target identification using decay rates estimates from EMI measurements}, Journal = {International Geoscience and Remote Sensing Symposium (IGARSS)}, Volume = {5}, Pages = {2278 - 2280}, Address = {Honolulu, HI, USA}, Year = {2000}, Keywords = {Magnetoelectric effects;Poles and zeros;Data structures;Demodulation;}, Abstract = {Decay rate estimation has been proposed as an effective method for landmine and unexploded ordnance (UXO) detection. The physical basis for this strategy is that every object in the target library possesses a unique set of decay rates. In theory, the characteristic decay rates can be estimated from the measured electromagnetic induction (EMI) response, and then utilized for target detection and subsequent identification. Unfortunately, decay rate estimation is notoriously difficult and this difficulty adversely impacts target identification performance. Since the basis for this approach to target detection and identification is that targets are uniquely characterized by their decay rates, discrimination performance is dependent upon decay rate estimation performance. The Cramer-Rao lower bound (CRLB) for decay rate and amplitude coefficient estimates is utilized to investigate the fundamental limitations on target identification via decay rate estimation. It is shown how both the temporal sampling strategy and the number of poles being estimated affects pole estimation and target identification performance.}, Key = {00125423727} } @article{00095308829, Author = {Gao, Ping and Collins, Leslie M.}, Title = {Theoretical performance analysis and simulation of time-domain EMI sensor data for land mine detection}, Journal = {IEEE Transactions on Geoscience and Remote Sensing}, Volume = {38}, Number = {4 II}, Pages = {2042 - 2055}, Year = {2000}, url = {http://dx.doi.org/10.1109/36.851785}, Keywords = {Image sensors;Sensor data fusion;Bombs (ordnance);Electromagnetic waves;Computer simulation;Time domain analysis;White noise;Mathematical models;}, Abstract = {In this paper, the physical phenomenology of electromagnetic induction (EMI) sensors is reviewed for application to land mine detection and remediation. The response from time-domain EMI sensors is modeled as an exponential damping as a function of time, characterized by the initial magnitude and decay rate. Currently deployed EMI sensors that are used for the land mine detection process the recorded signal in a variety of ways in order to provide an audio output for the operator to judge whether or not the signal is from a mine. Sensors may sample the decay curve, sum it, or calculate its energy. Based on exponential decay model and the assumption that the sensor response is subject to additive white Gaussian noise, the performance of these, as well as optimal, detectors are derived and compared. Theoretical performance predictions derived using simplifying assumptions are shown to agree closely with simulated performance. It will also be shown that the generalized likelihood ratio test (GLRT) is equivalent to the likelihood ratio test (LRT) for multichannel time-domain EMI sensor data under the additive white Gaussian noise assumption and specific assumptions regarding the statistics of the decay rates of targets and clutter.}, Key = {00095308829} } @article{00105364664, Author = {Liu, Feng and Tantum, Stacy and Collins, Leslie and Carin, Lawrence}, Title = {Statistical signal processing for detection of buried landmines using quadrupole resonance}, Journal = {Proceedings of SPIE - The International Society for Optical Engineering}, Volume = {4038 (I)}, Pages = {572 - 577}, Address = {Orlando, FL, USA}, Year = {2000}, url = {http://dx.doi.org/10.1117/12.396285}, Keywords = {Statistical mechanics;Interference suppression;Nuclear magnetic resonance;Explosives;Electromagnetic field theory;Toluene;Metal detectors;Algorithms;Least squares approximations;}, Abstract = {Quadrupole resonance (QR) is a technique that discriminates mines from clutter by exploiting unique properties of explosives. However, explosives detection via QR is complicated by several issues. This article discusses several signal processing tools developed to further enhance the utility of QR explosives (mine) detection. In particular, with regard to the uncertainties concerning the background environment and sensor height, statistical signal processing strategies are explored to rigorously account for the inherent variability in these parameters.}, Key = {00105364664} } @article{00125423851, Author = {Collins, Leslie and Gao, Ping and Makowsky, Larry and Moulton, John and Reidy, Denis and Weaver, Dick}, Title = {Improving detection of low-metallic content landmines using EMI data}, Journal = {International Geoscience and Remote Sensing Symposium (IGARSS)}, Volume = {4}, Pages = {1631 - 1633}, Address = {Honolulu, HI, USA}, Year = {2000}, Keywords = {Bombs (ordnance);Metal detectors;Algorithms;Sensors;}, Abstract = {EMI, or metal detector, sensors are used extensively to detect landmines. There is often a significant amount of metallic clutter present in the environment, thus EMI sensors that utilize traditional detection algorithms based solely on metal content suffer from large false alarm rates. The focus of this paper is on performance improvements that have been obtained using Bayesian-based statistical algorithms designed to detect low-metallic content landmines in highly cluttered environments. The Joint UXO Coordination Office (JUXOCO) is sponsoring a series of experiments designed to establish a performance baseline for a variety of sensors for the problem of detecting low-metallic content mines. Algorithm developers are blind to the ground truth. This paper describes the results of several experiments performed in conjunction with this effort. Statistical algorithms have been applied specifically to the problem of detection of low-metal mines, and dramatic performance improvements have been observed with respect to the baseline performance. The tradeoffs associated with the amount of spatial information used as well as the incorporation of prior information are explored, along with the performance of alternative approaches. Approaches to classification of the mine-type are also discussed and the performance of such classifiers is presented.}, Key = {00125423851} } @article{00025070007, Author = {Gresham, Lisa C. and Collins, Leslie M.}, Title = {Effect of a Poisson 'internal noise' process on theoretical acoustic signal detectability}, Journal = {IEEE ASSP Workshop on Applications of Signal Processing to Audio and Acoustics}, Pages = {219 - 222}, Address = {New Paltz, NY, USA}, Year = {1999}, url = {http://dx.doi.org/10.1109/ASPAA.1999.810889}, Keywords = {Signal detection;Signal theory;Computer simulation;Audition;Spectrum analysis;Audio acoustics;}, Abstract = {Historically, theoretical predictions of human auditory perception have not agreed with experimental measurements. We have previously demonstrated that using signal detection theory to analyze the outputs of deterministic computational auditory models yields more accurate predictions of experimental performance than traditional approaches. However, discrepancies remained between predicted and actual performance. In this paper, the effects of stimulus uncertainty and neural variability on the detectability of a tone in noise are studied. The results suggest that remarkably accurate predictions of detection performance can be generated when such uncertainty is incorporated into the problem.}, Key = {00025070007} } @article{00035075097, Author = {Gao, Ping and Collins, Leslie and Geng, Norbert and Carin, Lawrence}, Title = {Classification of buried metal objects using wideband frequency-domain electromagnetic induction responses: a comparison of optimal and sub-optimal processors}, Journal = {International Geoscience and Remote Sensing Symposium (IGARSS)}, Volume = {3}, Pages = {1819 - 1822}, Address = {Hamburg, Ger}, Year = {1999}, Keywords = {Sensors;Frequency response;Electromagnetic fields;Mathematical models;Explosives;Computational complexity;Conductive materials;Probability;}, Abstract = {A study is carried out to investigate sub-optimal detectors that continue to incorporate the physical nature of the wideband frequency-domain electromagnetic induction (EMI) signal, but are less computationally burdensome. In addition, a comparison is made on the performance of such suboptimal and optimal processors. Furthermore it is shown that by careful detector design, the sub-optimal processor can achieve nearly the same performance as that of the optimal processor.}, Key = {00035075097} } @article{99084746215, Author = {Gresham, Lisa C. and Collins, Leslie M.}, Title = {Comparison using signal detection theory of the ability of two computational auditory models to predict experimental data}, Journal = {ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings}, Volume = {2}, Pages = {933 - 936}, Address = {Phoenix, AZ, USA}, Year = {1999}, Keywords = {Signal detection;Audition;Mathematical models;}, Abstract = {In order to develop improved remediation techniques for hearing impairment, auditory researchers must gain a greater understanding of the relation between the psychophysics of hearing and the underlying physiology. One approach to studying the auditory system has been to design computational auditory models that predict neurophysiological data such as neural firing rates. To link these physiologically-based models to psychophysics, theoretical bounds on detection performance have been derived using signal detection theory to analyze the simulated data for various psychophysical tasks. Previous efforts, including our own recent work using the Auditory Image Model, have demonstrated the validity of this type of analysis; however, theoretical predictions often exceed experimentally-measured performance. In this paper, we compare predictions of detection performance across several computational auditory models. We reconcile some of the previously observed discrepancies by incorporating phase uncertainty into the optimal detector.}, Key = {99084746215} } @article{99094787776, Author = {Gao, Ping and Tantum, Stacy and Collins, Leslie}, Title = {Single sensor processing and sensor fusion of GPR and EMI data for landmine detection}, Journal = {Proceedings of SPIE - The International Society for Optical Engineering}, Volume = {3710}, Number = {II}, Pages = {1139 - 1148}, Address = {Orlando, FL, USA}, Year = {1999}, url = {http://dx.doi.org/10.1117/12.356994}, Keywords = {Sensor data fusion;Radar systems;Ordnance;Mathematical models;Time domain analysis;Frequency domain analysis;Optimization;Algorithms;Probability;Data acquisition;}, Abstract = {In our previous work, we have shown theoretically that a model-based Bayesian approach to the detection of landmines affords significant performance gains over standard thresholding techniques. These performance gains hold for both time- and frequency-domain electromagnetic induction (EMI) sensors. Our methodology merges physical models of the evoked target response with a probabilistic description of the clutter. Under a specific set of assumptions, our technique provides both an optimal detection algorithm and performance evaluation measures expressed as probability of detection (Pd) and probability of false alarm (Pfa). This approach also provides a formal framework for incorporating target and/or environmental uncertainties into the processing algorithms. The significant performance improvements observed theoretically have been verified on both time-domain and frequency-domain EMI data collected in the field. In this paper, we review our previous theoretical work, and we use actual data collected in the field to illustrate the improvement obtained by appropriately accounting for environmental uncertainties. We present new results in which a suboptimal processor provides nearly identical performance to that of the optimal processor but with much greater computational efficiency. We also present results that indicate that such an approach can be applied successfully to ground penetrating radar (GPR) data. Specifically, we consider data taken by the BRTRC/Wichmann system. In addition to processing the data from each type of sensor individually, we discuss an approach where the data is fused using Bayesian techniques. The performance of each of these sensors individually, as well as the combination of sensors, will be discussed.}, Key = {99094787776} } @article{99094787853, Author = {Tantum, Stacy and Collins, Leslie and Carin, Lawrence and Gorodnitsky, Irina and Hibbs, Andy and Walsh, Dave and Barrell, Geoff and Gregory, Dave and Matthews, Rob and Vierkotter, Stephie}, Title = {Signal processing for NQR discrimination of buried landmines}, Journal = {Proceedings of SPIE - The International Society for Optical Engineering}, Volume = {3710}, Number = {I}, Pages = {474 - 482}, Address = {Orlando, FL, USA}, Year = {1999}, url = {http://dx.doi.org/10.1117/12.357071}, Keywords = {Explosives;Clutter (information theory);Signal to noise ratio;Radio interference;Algorithms;Least squares approximations;}, Abstract = {Nuclear quadrupole resonance (NQR) is a technique that discriminates mines from clutter by exploiting unique properties of explosives, rather than the attributes of the mine that exist in many forms of anthropic clutter (e.g., metal content). After exciting the explosive with a properly designed electromagnetic-induction (EMI) system, one attempts to sense late-time spin echoes, which are characterized by radiation at particular frequencies. It is this narrow-band radiation that indicates the presence of explosives, since this effect is not seen in most clutter, both natural and anthropic. However, this problem is complicated by several issues. First, the late-time radiation is often very weak, particularly for TNT, and therefore the signal-to-noise ratio must be high for extracting the NQR response. Further, the frequency at which the explosive radiates is often a strong function of the background environment (e.g., temperature), and therefore in practice the NQR radiation frequency is not known a priori. Finally, at the frequencies of interest, there is a significant amount of background radiation, which induces radio frequency interference (RFI). In this paper we discuss several signal processing tools we have developed to enhance the utility of NQR explosives (mine) detection. In particular, with regard to the RFI, we explore least-mean-squares (LMS) algorithms which have proven well suited to extracting background interference. Algorithm performance is assessed through consideration of actual measured data. With regard to the detection of the NQR electromagnetic echo, we consider a Bayesian discrimination algorithm. The performance of the Bayesian algorithm is presented, again using measured NQR data.}, Key = {99094787853} } @article{00025009871, Author = {Huettel, Lisa G. and Collins, Leslie M.}, Title = {Using computational auditory models to predict simultaneous masking data: Model comparison}, Journal = {IEEE Transactions on Biomedical Engineering}, Volume = {46}, Number = {12}, Pages = {1432 - 1440}, Year = {1999}, url = {http://dx.doi.org/10.1109/10.804571}, Keywords = {Neurophysiology;Audition;Mathematical models;Speech intelligibility;Psychophysiology;Signal detection;}, Abstract = {In order to develop improved remediation techniques for hearing impairment, auditory researchers must gain a greater understanding of the relation between the psychophysics of hearing and the underlying physiology. One approach to studying the auditory system has been to design computational auditory models that predict neurophysiological data such as neural firing rates. To link these physiologically-based models to psychophysics, theoretical bounds on detection performance have been derived using signal detection theory to analyze the simulated data for various psychophysical tasks. Previous efforts, including our own recent work using the Auditory Image Model, have demonstrated the validity of this type of analysis; however, theoretical predictions often continue to exceed experimentally-measured performance. In this paper, we compare predictions of detection performance across several computational auditory models. We also reconcile some of the previously observed discrepancies by incorporating appropriate signal uncertainty into the optimal detector.}, Key = {00025009871} } @article{00035074731, Author = {Gao, Ping and Tantum, Stacy and Collins, Leslie and Weaver, Dick and Moulton, John and Makowsky, Larry and Reidy, Denis}, Title = {Statistical signal processing techniques for the detection of low-metal landmines using EMI and GPR sensors (topic area: E.1)}, Journal = {International Geoscience and Remote Sensing Symposium (IGARSS)}, Volume = {5}, Pages = {2465 - 2467}, Address = {Hamburg, Ger}, Year = {1999}, Keywords = {Remote sensing;Explosives;Sensors;Signal detection;Statistical methods;Algorithms;Data acquisition;Radar;}, Abstract = {The Joint UXO Coordination Office (JUXOCO) at Ft. Belvoir, VA is sponsoring a series of experiments designed to establish a performance baseline for a variety of sensors. The purpose of these experiments is to determine if advanced algorithms are useful for detecting low-metallic content mines and if ground penetrating radar (GPR) sensors can help mitigate the high false alarm rates associated with electromagnetic induction (EMI) sensors. In this paper, we describe the results of two data collection and algorithm development efforts. One involves a prototype frequency-domain EMI system; the other involves a prototype wide-band GPR system. In both cases, substantial performance improvements over baseline are achieved using statistically-based signal processing algorithms.}, Key = {00035074731} } @article{99054667878, Author = {Collins, Leslie and Gao, Ping and Carin, Lawrence}, Title = {Improved Bayesian decision theoretic approach for land mine detection}, Journal = {IEEE Transactions on Geoscience and Remote Sensing}, Volume = {37}, Number = {2 II}, Pages = {811 - 819}, Year = {1999}, url = {http://dx.doi.org/10.1109/36.752197}, Keywords = {Sensors;Explosives;Signal detection;Time domain analysis;Waveform analysis;Decision theory;}, Abstract = {A rigorous signal detection theoretic analysis is used to improve detectability of land mines. The development is performed for sensors that integrate time-domain information to provide a single data point (standard metal detector), those that provide a sampled portion of the time-domain waveform, and those that operate at several discrete frequencies. This approach is compared to standard thresholding techniques, and it is shown to provide substantial improvements when evaluated on measured data.}, Key = {99054667878} } @article{99094787743, Author = {Throckmorton, Sandy and Tan, Yingyi and Gao, Ping and Gresham, Lisa and Collins, Leslie}, Title = {Enhanced auditory processing for landmine detection using EMI sensors}, Journal = {Proceedings of SPIE - The International Society for Optical Engineering}, Volume = {3710}, Number = {II}, Pages = {787 - 796}, Address = {Orlando, FL, USA}, Year = {1999}, url = {http://dx.doi.org/10.1117/12.357100}, Keywords = {Ordnance;Sensors;Signal encoding;Algorithms;Mathematical models;Statistical methods;Electromagnetic fields;Man machine systems;Behavioral research;Optimization;Decision theory;Computational methods;}, Abstract = {Although the ability of EMI sensors to detect landmines has improved significantly, false alarm rate reduction remains a challenging problem. Improvements have been achieved through development of optimal algorithms that exploit models of the underlying physics along with knowledge of clutter statistics. Moreover, experienced operators can often discriminate mines from clutter with the aid of an audio transducer, the method most commonly used to alert the sensor operator that a target is present. Assuming the basic information needed for discriminating landmines from clutter is largely available from existing sensors, the goal of this work is to optimize the presentation of information to the operator. Traditionally, an energy calculation is provided to the sensor operator via a signal whose loudness or frequency is proportional to the energy of the received signal. Our preliminary work has shown that when the statistic used to make a decision is not simply the signal energy the performance of mine detection systems can be improved dramatically. This finding suggests that the operator could make better use of a signal that is a function of this more accurate test statistic, and that there may be information in the unprocessed sensor signal that the operator could use to effect discrimination. In this paper, we investigate and quantify, through listening experiments, the perceptual dimensions that most effectively convey the information in a sensor response to a listener. Results indicate that by supplying the sensor response more appropriately to the listener, discrimination, as opposed to simple detection, can be achieved.}, Key = {99094787743} } @article{6358419, Author = {Gresham, L.C. and Collins, L.M.}, Title = {A comparison using signal detection theory of the ability of two computational auditory models to predict experimental data}, Journal = {1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258)}, Volume = {vol.2}, Pages = {933 - 6}, Address = {Phoenix, AZ, USA}, Year = {1999}, url = {http://dx.doi.org/10.1109/ICASSP.1999.759825}, Keywords = {acoustic signal detection;audio signal processing;hearing;neurophysiology;optimisation;prediction theory;}, Abstract = {In order to develop improved remediation techniques for hearing impairment, auditory researchers must gain a greater understanding of the relation between the psychophysics of hearing and the underlying physiology. One approach to studying the auditory system has been to design computational auditory models that predict neurophysiological data such as neural firing rates. To link these physiologically-based models to psychophysics, theoretical bounds on detection performance have been derived using signal detection theory to analyze the simulated data for various psychophysical tasks. Previous efforts, including our own recent work using the auditory image model, have demonstrated the validity of this type of analysis; however, theoretical predictions often exceed experimentally-measured performance. In this paper, we compare predictions of detection performance across several computational auditory models. We reconcile some of the previously observed discrepancies by incorporating phase uncertainty into the optimal detector}, Key = {6358419} } @article{99104855162, Author = {Gao, Ping and Collins, Leslie}, Title = {Comparison of optimal and suboptimal processors for classification of buried metal objects}, Journal = {IEEE Signal Processing Letters}, Volume = {6}, Number = {8}, Pages = {216 - 218}, Year = {1999}, url = {http://dx.doi.org/10.1109/97.774871}, Keywords = {Frequency domain analysis;Computational complexity;Decision theory;Data processing;Computer simulation;Method of moments;}, Abstract = {Classification of metal objects is important for landmine and unexploded ordnance applications. Previously, we have investigated optimal classification of landmine-like metal objects using wideband frequency-domain electromagnetic induction data [1]. Here, a suboptimal processor, which is computationally less burdensome than the optimal processor, is discussed. The data is first normalized, exploiting the fact that the level of the response changes significantly while the structure of the magnitude of the response changes only slightly as the target/sensor orientation changes for the class of objects considered. Results indicate that the suboptimal processor performance approaches that of the optimal classifier on normalized data. Thus, normalization mitigates the uncertainty resulting from the target/sensor orientation.}, Key = {99104855162} } @article{04057881966, Author = {Throckmorton, Chandra S. and Collins, Leslie M.}, Title = {Investigation of the effects of temporal and spatial interactions on speech-recognition skills in cochlear-implant subjects}, Journal = {Journal of the Acoustical Society of America}, Volume = {105}, Number = {2}, Pages = {861 - 873}, Year = {1999}, url = {http://dx.doi.org/10.1121/1.426275}, Abstract = {Forward masking was investigated as a measure of spectral and temporal interactions. Such interactions may adversely affect speech recognition in cochlear-implant subjects. Seven subjects, implanted with the Nucleus 22 device, performed a forward-masking task. They also performed an electrode-discrimination task in order to measure spectral interactions without temporal interactions. Correlation analysis indicated a significant relationship between data obtained in the two tasks (p less than or equal 0.1). The two tasks were also correlated with the subjects' scores from five measures of speech recognition. Forward masking and electrode discrimination were strongly correlated with measures requiring consonant and phoneme recognition, respectively. These results indicate that the relationship between forward masking and speech recognition may be due, in part, to a lack of spectral resolution. The data also indicate that consonants may be more readily masked than vowels. Forward-masking data measured for all clinically programmed electrodes in three of the seven subjects were used with a model of the spectral maxima sound processor (SMSP) to estimate the number of electrodes stimulated during a consonant that might be masked by prior presentation of a vowel. These results suggest that temporal interactions across electrodes may be a factor in speech-recognition abilities of some cochlear-implant subjects. © 1999 Acoustical Society of America.}, Key = {04057881966} } @article{6378541, Author = {Pfingst, B.E. and Holloway, L.A. and Zwolan, T.A. and Collins, L.M.}, Title = {Effects of stimulus level on electrode-place discrimination in human subjects with cochlear implants}, Journal = {Hear. Res. (Netherlands)}, Volume = {134}, Number = {1-2}, Pages = {105 - 15}, Year = {1999}, url = {http://dx.doi.org/10.1016/S0378-5955(99)00079-9}, Keywords = {biomedical electrodes;ear;hearing aids;prosthetics;}, Abstract = {Effects of stimulus level on discrimination of one stimulation site from another were examined in 15 human subjects with Nucleus-22 cochlear implant systems. Bipolar stimulation was used in all cases with electrodes in the bipolar pair separated by 1.5 mm (center to center). Subjects were first tested at a medium loudness level, using an adaptive tracking procedure, to determine the regions of the electrode array where electrode-place discrimination was best and the regions where it was poorest. Electrode-place discrimination was then tested at three regions distributed throughout the array, which included the regions of best and poorest discrimination. At each region, electrode-place discrimination was tested at three levels: 25%, 50%, and 75% of the dynamic range. For each of these nine conditions (3 sites×3 levels), the test-electrode pairs were loudness balanced with the reference-electrode pairs. A two-interval forced-choice same-different procedure was then used to determine discriminability of the reference-electrode pair from the nearest, apical, test-electrode pair. If P(C)<sub>max</sub> was <0.707 at all three levels, additional testing was done using the next, more apical, electrode pair as the test-electrode pair. A tendency toward better discrimination at more apical regions of the array was observed. Electrode pairs with poor discrimination typically had smaller dynamic ranges than those with good discrimination. There was a weak tendency toward better discrimination at higher levels of stimulation. However, effects of level on electrode-place discrimination were less pronounced and less consistent than previously observed effects of level on temporal discriminations. These results suggest interactions between current spread and the condition of the implanted cochlea as underlying mechanisms}, Key = {6378541} } @article{98064242388, Author = {Gresham, Lisa C. and Collins, Leslie M.}, Title = {Analysis of the performance of a model-based optimal auditory signal processor}, Journal = {Journal of the Acoustical Society of America}, Volume = {103}, Number = {5 pt 1}, Pages = {2520 -}, Year = {1998}, url = {http://dx.doi.org/10.1121/1.422773}, Key = {98064242388} } @article{98094360057, Author = {Collins, Leslie and Gao, Ping}, Title = {Hypothesis testing for landmine detection with EMI images}, Journal = {IEEE International Conference on Fuzzy Systems}, Volume = {1}, Pages = {237 - 240}, Address = {Archorage, AK, USA}, Year = {1998}, url = {http://dx.doi.org/10.1109/FUZZY.1998.687490}, Keywords = {Ordnance;Probability;Statistical methods;Clutter (information theory);Algorithms;Data reduction;Image analysis;Image quality;}, Abstract = {The fundamental goal of any landmine detection system is to achieve a high probability of detection while at the same time maintaining a low probability of false alarm. For the problem of detection of landmines with electromagnetic induction (EMI) sensors, the performance tends to be limited by the false alarm rate, as opposed to the detection rate. In this paper, we review a statistical Bayesian approach for deriving algorithms to test the `mine' and `no mine' hypotheses which incorporates the physical nature of the response of EMI sensors as well as the statistical nature of the clutter process into the detection framework. Theoretical performance bounds are described, and the performance of such algorithms on data collected in conjunction with the DARPA Backgrounds Clutter Experiment is described.}, Key = {98094360057} } @article{5777286, Author = {Zwolan, T.A. and Collins, L.M. and Wakefield, G.H.}, Title = {Electrode discrimination and speech recognition in postlingually deafened adult cochlear implant subjects}, Journal = {J. Acoust. Soc. Am. (USA)}, Volume = {102}, Number = {6}, Pages = {3673 - 85}, Year = {1997}, url = {http://dx.doi.org/10.1121/1.420401}, Keywords = {hearing aids;prosthetics;speech intelligibility;}, Abstract = {This study investigated the relationship between electrode discrimination and speech recognition in 11 postlingually deafened adult cochlear implant subjects who were implanted with the Nucleus/Cochlear Corporation multichannel device. The discriminability of each electrode included in a subject's clinical map was measured using adaptive and fixed-level discrimination tasks. Considerable variability in electrode discriminability was observed across subjects. Two subjects could discriminate all electrodes, and discrimination performance by the remaining nine subjects varied from near perfect to very poor. In these nine subjects, the results obtained from the discrimination tasks were used to create a map that contained only discriminable electrodes, and subjects' performance on speech recognition tasks using this experimental map was measured. Four different speech recognition tests were administered: a nine-choice closed-set medial vowel recognition task, a 14-choice closed-set medial consonant recognition task, the NU6 Monosyllabic Words Test [T.W. Tillman and T. Carhart, Tech. Rep. No. SAM-TR-66-55, USAF School of Aerospace Medicine, Brooks Air Force Base, Texas (1966)] scored for both words and phonemes correct, and the Central Institute for the Deaf (CID) Everyday Sentences test [H. Davis and S.R. Silverman, Hearing and Deafness (Holt, Rinehart and Winston, New York, 1978)]. Seven of the nine subjects tested with the experimental map showed significant improvement on at least one speech recognition measure, even though the experimental map contained fewer electrodes than the original map. Three subjects' scores improved significantly on the CID Everyday Sentences test, three subjects' scores improved significantly on the NU6 Monosyllabic Words test, and five subjects' scores improved significantly on the NU6 Monosyllabic Words test scored for phonemes correct. None of the subjects' scores improved significantly on either the vowel or consonant tests. No significant correlation was observed between electrode discrimination ability and speech recognition scores or between electrode discrimination ability and improvement in speech recognition scores when programmed with the experimental map. The results of this study suggest that electrode discrimination tasks may be used to improve speech recognition of some cochlear implant subjects, and that each electrode site does not necessarily provide perceptually distinct information}, Key = {5777286} } @article{97023516566, Author = {Collins, Leslie M. and Zwolan, Teresa A. and Wakefield, Gregory H.}, Title = {Comparison of electrode discrimination, pitch ranking, and pitch scaling data in postlingually deafened adult cochlear implant subjects}, Journal = {Journal of the Acoustical Society of America}, Volume = {101}, Number = {1}, Pages = {440 -}, Year = {1997}, url = {http://dx.doi.org/10.1121/1.417989}, Key = {97023516566} } @article{97093813246, Author = {Collins, Leslie M. and George, Vivian and Altshuler, Thomas W. and Nolte, Loren W. and Carlin, Lawrence}, Title = {Improved decision-theoretic approach to the optimum detection of mines}, Journal = {Proceedings of SPIE - The International Society for Optical Engineering}, Volume = {3079}, Pages = {716 - 723}, Address = {Orlando, FL, USA}, Year = {1997}, url = {http://dx.doi.org/10.1117/12.280900}, Keywords = {Detectors;Arsenals;Decision theory;Explosives;Optimization;Probability;Electromagnetic fields;Data acquisition;}, Abstract = {The fundamental goal of mine detection is to achieve a high detection rate along with a low false alarm rate. While many mine detectors achieve the first of these goals, it is often at the cost of a prohibitively large false alarm rate. In this paper, a Bayesian decision-theoretic approach to the detection of mines, which incorporates the physical properties of the target response to an electromagnetic induction device, is presented. This approach merges physical modeling of the evoked target response with a probabilistic description that represents uncertainty in the ground surface, composition of the mine, and its placement in the surrounding environment. This approach provides both an optimal detection scheme, and performance evaluation measures in the form of probability of detection and false alarm rate. We present results in which the model-based, Bayesian approach significantly outperforms the energy detector and matched filter detectors on data obtained from the DARPA backgrounds clutter data collection experiment. In addition, the model-based, Bayesian approach is also shown to outperform a detector which estimates the eddy-current decay rate from the data. Results are also presented to illustrate the amount of sensitivity of the matched filter detector for a known environment to incorrect prior knowledge of uncertain parameters in the demining scenario, as well as the robustness of performance and performance bounds realizable by the optimum detection algorithm that properly accounts for uncertainty within a Bayesian framework.}, Key = {97093813246} } @article{5196102, Author = {Subotic, N.S. and Collins, L.M. and Reiley, M.F. and Gorman, J.D.}, Title = {A multiresolution generalized likelihood ratio detection approach to target screening in synthetic aperture radar data}, Journal = {Proc. SPIE - Int. Soc. Opt. Eng. (USA)}, Volume = {2487}, Pages = {226 - 34}, Address = {Orlando, FL, USA}, Year = {1995}, Keywords = {autoregressive processes;feature extraction;image resolution;interference (signal);radar clutter;radar detection;radar imaging;statistical analysis;synthetic aperture radar;}, Abstract = {We present a detection concept for initial target screening based on features that are derived from a multiresolution decomposition of synthetic aperture radar (SAR) data. The physical motivation of the multiresolution feature based approach is the exploitation of signature oscillations produced by the interference between prominant scatterers in cultural objects when resolution is varied. We develop a generalized likelihood ratio test detector (GLRT) which differentiates between first order autoregressive (AR) multiresolution processes attributed to different spatial areas. We then derive two special cases of this detector motivated by arguments regarding the clutter statistics. We show that these schemes significantly outperform a standard energy detector operating on the finest available SAR resolution only}, Key = {5196102} } @article{95092854012, Author = {Subotic, Nikola S. and Collins, Leslie M. and Gorman, John D. and Thelen, Brian J.}, Title = {Multiresolution target detection in SAR imagery}, Journal = {ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings}, Volume = {4}, Pages = {2157 - 2160}, Address = {Detroit, MI, USA}, Year = {1995}, url = {http://dx.doi.org/10.1109/ICASSP.1995.479902}, Keywords = {Synthetic aperture radar;Signal detection;Radar tracking;Image reconstruction;Optimization;Random processes;White noise;Algorithms;Radar cross section;Radar interference;Mathematical models;Statistics;}, Abstract = {We demonstrate the utility of a multiresolution approach for target detection in SAR imagery. Man-made objects exhibit characteristic phase and amplitude fluctuations as the image resolution is varied, while natural terrain has a random signature. We construct a number of detection strategies: an optimal invariant multiresolution detector based on a derived multiresolution increments process; and a generalized likelihood ratio detector to differentiate between a first-order autoregressive multiresolution increments process and white noise. We show that these schemes significantly outperform a standard energy detector operating on the finest available SAR resolution.}, Key = {95092854012} } @article{95022594966, Author = {Gorman, John D. and Subotic, Nikola S. and Thelen, Brian J. and Collins, Leslie}, Title = {Multiresolution detection of coherent radar targets}, Journal = {IEEE International Conference on Image Processing}, Volume = {1}, Pages = {446 - 450}, Address = {Austin, TX, USA}, Year = {1994}, url = {http://dx.doi.org/10.1109/ICIP.1994.413353}, Keywords = {Synthetic aperture radar;Radar clutter;Wavelet transforms;Algorithms;Object recognition;Mathematical models;Random processes;}, Abstract = {We examine the problem of detecting a known target in clutter and show that a multiresolution-based detector significantly outperforms a more conventional single-resolution detector. We then apply a sampling strategy that allows one to choose, for each fixed n, those n resolutions that maximize detection performance. This optimal multiresolution sampling strategy suggests that wavelet or wavelet packet decompositions which typically use dyadic or triadic resolution splits are not necessarily optimal bases for coherent radar signature representation.}, Key = {95022594966} } @article{94122458282, Author = {Collins, L.M. and Wakefield, G.H. and Feinman, G.R.}, Title = {Temporal pattern discrimination and speech recognition under electrical simulation}, Journal = {Journal of the Acoustical Society of America}, Volume = {96}, Number = {5 pt 1}, Pages = {2731 -}, Year = {1994}, Key = {94122458282} } @article{95062736583, Author = {Subotic, Nikola S. and Collins, Leslie M. and Gorman, John D. and Thelen, Brian J.}, Title = {Multiresolution approach to target detection in synthetic aperture radar data}, Journal = {Conference Record of the Asilomar Conference on Signals, Systems & Computers}, Volume = {1}, Pages = {122 - 126}, Address = {Pacific Grove, CA, USA}, Year = {1994}, url = {http://dx.doi.org/10.1109/ACSSC.1994.471429}, Keywords = {Synthetic aperture radar;Optical resolving power;Brownian movement;Algorithms;Backscattering;Radar cross section;Detectors;Mathematical models;Statistics;Radar clutter;}, Abstract = {We demonstrate the utility of a multiresolution approach for target detection in SAR imagery. In particular, man-made objects exhibit characteristic phase and amplitude fluctuations as the image resolution is varied, while natural terrain (i.e., clutter) has a random signature. We show that the multiresolution clutter process decomposes into a Brownian motion process in resolution. We then construct an optimal invariant multiresolution detector based on a derived multiresolution increments process and show that it significantly outperforms a standard energy detector operating on the finest available SAR resolution.}, Key = {95062736583} } @article{4188910, Author = {Farahat, M.S. and Shipp, D.S. and Collins, L.M. and Strycula, E.C. and Woodley, N.H. and Bauman, D.A.}, Title = {Development and implementation of a harmonic expert system}, Journal = {Record of Conference Papers. IEEE Cement Industry Technical Conference XXXIII (Cat. No.91CH3045-2)}, Pages = {357 - 74}, Address = {Mexico City, Mexico}, Year = {1991}, url = {http://dx.doi.org/10.1109/CITCON.1991.162812}, Keywords = {distribution networks;expert systems;harmonics;industrial plants;microcomputer applications;power engineering computing;}, Abstract = {Solid-state rectification devices for power conversion, nonlinear loads, and power factor correction capacitor banks are being used more and more in industrial plants, and this has led to an increase in the number of harmonics-related problems. The authors discuss the development and implementation of a personal-computer-based expert system employing artificial intelligence methodologies. The system allows engineers to diagnose power system operation problems which may be caused by harmonic voltages and currents. The system integrates a wide variety of data input mechanisms, and generates results and data summaries in several formats. The system is also used for training personnel in the causes, effects, and solutions of harmonic problems}, Key = {4188910} } | |
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