Demo Data from EDBT 2026 / reviewers in the wild / expert
Willis C. Kellogg
dblp:134/3839
· DBLP profile ↗
1ranked-venue papers
1as first author
0since 2021 · last 1967
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Theory of computation · 1 · 1 first-author
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 · 70% Information theory · 30% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Coding theory › error-correcting codes › block codes › linear code
parity-check matrix |
0.0 | 1 | 1967 | Information rates in sampling and quantization · IEEE Trans. Inf. Theory 1967 |
Coding theory › source coding
quantization |
0.0 | 1 | 1967 | Information rates in sampling and quantization · IEEE Trans. Inf. Theory 1967 |
Coding theory › source coding
rate-distortion theory |
0.0 | 1 | 1967 | Information rates in sampling and quantization · IEEE Trans. Inf. Theory 1967 |
Information theory › signal processing › signal representation
sampling and quantization |
0.0 | 1 | 1967 | Information rates in sampling and quantization · IEEE Trans. Inf. Theory 1967 |
Information theory
channel capacity |
0.0 | 1 | 1967 | Information rates in sampling and quantization · IEEE Trans. Inf. Theory 1967 |
Methods — techniques the papers use, named apart from their topics
numerical convolution · 0.0bivariate normal integration · 0.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 1967 | Information rates in sampling and quantizationabstractA computer-aided analysis of a digital communications system has been conducted. Salient results compare the performance of approximately optimum PCM systems with the rate-distortion function, under the assumptions of Gaussian inputs and mean-square-error distortion measure. The calculated distortion is greater than the theoretical minimum by two or three decibels in most cases. The results presented also illuminate the tradeoff between sampling rate and number of quantization levels in a channel of fixed capacity. The presampling and reconstruction filters are represented in the computer by their impulse responses; that is, filtering of signals is accomplished by evaluating the convolution integrals numerically. Impulse responses with durations of20sample times or fewer are long enough to model most known results in optimum filtering of sampled stationary random functions. Procedures are described for computing the autocorrelation function of the quantizer output, and the cross correlation between the quantizer output and a signal correlated with the input. The procedure adopted is the naive one of integrating numerically the appropriate bivariate normal distribution. Willis C. Kellogg |
IEEE Trans. Inf. Theory | 1 |