Teaching
Statistical Signal Processing
(Rice University 2007)
Michael A Lexa
Research Fellow
Institute for Digital Communications (IDCOM)
School of Engineering
University of Edinburgh
Edinburgh, United Kingdom
michael dot lexa at ed.ac.uk
Research/Projects
Sub-Nyquist sampling of sparse signals, statistical signal processing, quantization theory, and statistical learning theory with applications to communications and distributed systems.
Distributed Quantization for Classification: The Impact of Structure and Nonparametric Estimation
Distributed signal processing refers to any signal processing task (estimation, detection, source coding, sampling, etc.) under the defining constraint that either the data, the processing units, or both are physically separated. Such systems often have the ability to tackle complex problems using a divide and conquer strategy, but at the same time their structure, that is the distributed nature of the system (and data), severely constrains how data are processed and thus may degrade system performance. To mitigate these structural constraints, partial information can be shared (communicated) among a distributed system's processing units while largely retaining its ability to tackle computationally tough problems. (Think of a wireless sensor network for example.) Knowing what, when, and to whom communication should occur in any given problem are fundamental questions surrounding distributed signal processing.
This work begins to answer these questions by examining simple, distributed quantization systems optimized such that the performance of a downstream classifier is maximized. Even for these conceptually simple communicative systems, suboptimal estimation techniques need to be used to avoid overwhelming computational complexity. Thus, priority is placed on understanding how structure and these estimation strategies affect and balance performance and computational complexity.
Publications
M.A. Lexa and D.H. Johnson, Distributed Structures, Sequential Optimization, and Quantization for Detection. IEEE Trans. Signal Processing, vol. 56, no. 4, pp.1740-1745, Apr 2008.
M.A. Lexa and D.H. Johnson, Joint Optimization of Distributed Broadcast Quantization Systems for Classification. IEEE Data Compression Conference (DCC'07) Mar 2007.
M.A. Lexa, C.J. Rozell, S. Sinanovic and D.H. Johnson, To cooperate or not to cooperate: Detection Strategies in Sensor Networks. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'04), Apr 2004.
M.A. Lexa and D.H. Johnson, An Information Processing Approach to Distributed Detection. IEEE Workshop on Statistical Signal Processing (SSP'03), Sep 2003.
M.A. Lexa and D.H. Johnson, Optimizing Binary Decision Systems by Manipulating Transmission Intervals. IEEE International Symposium on Signal Processing and Its Applications (ISSPA'03), July 2003.
M.A. Lexa and D.H. Johnson, A New Look at the Informational Gain of Soft Decisions. IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'03), Apr 2003.
M.A. Lexa and D.H. Johnson, Information Processing Ability of Binary Detectors and Block Decoders. IEEE Digital Signal Processing Workshop (DSP'02), 2002.
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Unpublished Manuscripts
M.A. Lexa, Empirical Quantization Design for Sparse Sampling Systems, Submitted to the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP'10).
M.A. Lexa, Ph.D. Thesis, Sequential Quantization for Classification: The Impact of Structure and Nonparametric Estimates, Aug 2008. Thesis style (pdf) / Report form (pdf) / Errata (pdf)
M.A. Lexa, Useful Facts about the Kullback-Leibler Discrimination Distance, Dec 2004.
M.A. Lexa, Remembering John Napier and His Logarithms, August 2000.