Department of Mathematics
 Search | Help | Login | pdf version | printable version

Math @ Duke



Publications [#243794] of Mauro Maggioni

Papers Published

  1. Coifman, RR; Lafon, S; Maggioni, M; Keller, Y; Szlam, AD; Warner, FJ; Zucker, SW, Geometries of sensor outputs, inference and information processing, in Proc. SPIE, edited by Intelligent Integrated Microsystems; Ravindra A. Athale, John C. Zolper; Eds., Smart Structures and Materials 2005: Active Materials: Behavior and Mechanics, vol. 6232 (September, 2006), pp. 623209, SPIE, ISSN 0277-786X [doi]
    (last updated on 2019/02/18)

    We describe signal processing tools to extract structure and information from arbitrary digital data sets. In particular heterogeneous multi-sensor measurements which involve corrupt data, either noisy or with missing entries present formidable challenges. We sketch methodologies for using the network of inferences and similarities between the data points to create robust nonlinear estimators for missing or noisy entries. These methods enable coherent fusion of data from a multiplicity of sources, generalizing signal processing to a non linear setting. Since they provide empirical data models they could also potentially extend analog to digital conversion schemes like "sigma delta".
ph: 919.660.2800
fax: 919.660.2821

Mathematics Department
Duke University, Box 90320
Durham, NC 27708-0320