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A minimum mean square error (MMSE) estimation scheme is employed to identify the synaptic connectivity in neural networks. This new approach can substantially reduce the amount of data and the computational cost involved ...

We consider the problem of directly transmitting a binary source with an inherent redundancy over a binary channel with additive stationary ergodic Markov noise. Out objective is to design an optimum receiver which fully ...

New schemes for classification of images are suggested. An application of these methods in adaptive DCT of images is considered. A new method to combine classification and bit allocation is introduces. Also, an efficient ...

We consider the problem of detecting a discrete Markov source which is transmitted across a discrete memoryless channel. The detection is based upon the maximum a posteriori (MAP) criterion which yields the minimum probability ...

Under noiseless channel conditions and for sources with memory, finite-state vector quantizers (FSVQs) exhibit improvements over memoryless vector quantizers. It is shown, however, that in the presence of channel noise, ...

The finite-state vector quantizer (FSVQ), introduced by Foster, Dunham and Gray, is a finite-state machine that can be viewed as a collection of memoryless full-searched vector quantizers, where each input vector is encoded ...

The fixed-rate structured vector quantizer (SVQ) derived from a variable-length scalar quantizer was originally proposed for quantizing stationary memoryless sources. In this paper, the SVQ has been extended to a specific ...

We consider discrete channels with stationary additive noise. We show that output feedback does not increase the capacity of such channels. This is shown for both ergodic and non-ergodic additive stationary channels.

We evaluate the performance of several multiterminal detection systems, each of which comprises a central detector and a network of remote sensors. The function of the sensors is to collect data on a random signal source ...

This paper describes a structured vector quantization approach for stationary memoryless sources that combines the scalar-vector quantizer (SVQ) ideas (Laroia and Farvardin) with trellis coded quantization (Marcellin and ...