Now showing items 1-5 of 5
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 ...
Quantization Over Discrete Noisy Channels Under Complexity Constraints
A fundamental problem in communication is the transmission of an information source across a communication channel. According to Shannon's separation principle, this problem can be separated (without loss of optimality) ...
Speech Coding over Noisy Channels
This chapter contains a discussion of quantization over noisy channels. The effects of channel noise on the performance of vector quantizers are discussed and algorithms for the design of noisy-channel vector quantizers ...
An Information Geometric Treatment of Maximum Likelihood Criteria and Generalization in Hidden Markov Modeling
It is shown here that several techniques for masimum likelihood training of Hidden Markov Models are instances of the EM algorithm and have very similar descriptions when formulated as instances of the Alternating Minimization ...