Now showing items 1-10 of 21
Minimum Mean Square Error Estimation of Connectivity in Biological Neural Networks
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 ...
Detection of Binary Sources Over Discrete Channels with Additive Markov Noise
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 ...
Adaptive Wavelet Based Image Coding
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 ...
Optimal Detection of Discrete Markov Sources Over Discrete Memoryless Channels - Applications to Combined Sources-Channel Coding
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 ...
Finite-State Vector Quantization for Noisy Channels
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, ...
Design and Performance Evaluation of a Class of Finite-State Vector Quantizers
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 ...
Extension of the Fixed-Rate Structured Vector quantizer to Vector Sources
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 ...
Feedback Does Not Increase the Capacity of Discrete Channels with Additive Noise
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.
Distributed Hypothesis Testing with Data Compression
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 ...
Trellis-Based Scalar-Vector Quantizer for Memoryless Sources
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 ...