Chuck-Wah Law

dblp:134/5789 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 1977
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Theory of computation · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Theoretical computer science
1 paper
Coding theory · 100%

Topics — the 6 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Coding theory › error-correcting codes
convolutional codes
0.011977
Real-number convolutional codes for speech-like quasi-stationary sources (Corresp.) · IEEE Trans. Inf. Theory 1977
Coding theory › error-correcting codes › convolutional codes
real-number convolutional codes
0.011977
Real-number convolutional codes for speech-like quasi-stationary sources (Corresp.) · IEEE Trans. Inf. Theory 1977
Coding theory
source coding
0.011977
Real-number convolutional codes for speech-like quasi-stationary sources (Corresp.) · IEEE Trans. Inf. Theory 1977
Coding theory › source coding › lossy source coding
speech coding
0.011977
Real-number convolutional codes for speech-like quasi-stationary sources (Corresp.) · IEEE Trans. Inf. Theory 1977
Coding theory › source coding
tree coding
0.011977
Real-number convolutional codes for speech-like quasi-stationary sources (Corresp.) · IEEE Trans. Inf. Theory 1977
Coding theory › error-correcting codes › decoding › trellis decoding
viterbi algorithm
0.011977
Real-number convolutional codes for speech-like quasi-stationary sources (Corresp.) · IEEE Trans. Inf. Theory 1977

Methods — techniques the papers use, named apart from their topics

stack algorithm · 0.0m-algorithm · 0.0empirical optimization · 0.0
YearPublicationVenuePosition
1977 Real-number convolutional codes for speech-like quasi-stationary sources (Corresp.)
abstract
A quasi-stationary source is one which is stationary over short periods, but changes occasionally to a new mode of stationarity; a typical example is the speech source. Convolutional codes are designed for the speech source for use with search algorithms such as the Viterbi, stack, andM-algorithms; the design is first by heuristic means and then by an empirical optimization. These codes have the same generating structure as the usual binary convolutional codes, but employ ordinary arithmetic. It is found by experiment that, at rate 2 bits/sample (which allows telephone quality speech), such codes need not have constraint length longer than five or six, which implies a generating circuit of about 1000 states. Encoding noise becomes white and uncorrelated with simple code searching; this shows that a short fixed code can successfully decorrelate waveforms from different source modes.
Chuck-Wah Law
IEEE Trans. Inf. Theory2