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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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