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Computational Intelligence and Neuroscience
Volume 2012 (2012), Article ID 209590, 20 pages
http://dx.doi.org/10.1155/2012/209590
Research Article

Channel Identification Machines

Department of Electrical Engineering, Columbia University, New York, NY 10027, USA

Received 8 March 2012; Revised 29 June 2012; Accepted 16 July 2012

Academic Editor: Cheng-Jian Lin

Copyright © 2012 Aurel A. Lazar and Yevgeniy B. Slutskiy. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

We present a formal methodology for identifying a channel in a system consisting of a communication channel in cascade with an asynchronous sampler. The channel is modeled as a multidimensional filter, while models of asynchronous samplers are taken from neuroscience and communications and include integrate-and-fire neurons, asynchronous sigma/delta modulators and general oscillators in cascade with zero-crossing detectors. We devise channel identification algorithms that recover a projection of the filter(s) onto a space of input signals loss-free for both scalar and vector-valued test signals. The test signals are modeled as elements of a reproducing kernel Hilbert space (RKHS) with a Dirichlet kernel. Under appropriate limiting conditions on the bandwidth and the order of the test signal space, the filter projection converges to the impulse response of the filter. We show that our results hold for a wide class of RKHSs, including the space of finite-energy bandlimited signals. We also extend our channel identification results to noisy circuits.