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Ethirajan Janardhanan

dblp:47/10570 · DBLP profile ↗
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2ranked-venue papers
2as first author
0since 2021 · last 1980
—ORCID · none

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

Computer networks · 1 · 1 first-authorTheory 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
2 papers
Coding theory · 90% Information theory · 10%
Computer networks
1 paper
Physical-layer communications · 50% Content delivery and video streaming · 50%

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

TopicWeightPapersLastEvidence papers
Coding theory › source coding › predictive coding
differential pulse-code modulation
0.011980
An analysis of leaky integrator differential PCM systems with stationary first-order Gauss Markov input (Ph.D. Thesis abstr.) · IEEE Trans. Inf. Theory 1980
Coding theory
source coding
0.011980
An analysis of leaky integrator differential PCM systems with stationary first-order Gauss Markov input (Ph.D. Thesis abstr.) · IEEE Trans. Inf. Theory 1980
Content delivery and video streaming › source coding
differential pulse code modulation
0.011979
Differential PCM Systems · IEEE Trans. Commun. 1979
Physical-layer communications
signal processing for communications
0.011979
Differential PCM Systems · IEEE Trans. Commun. 1979
Coding theory › source coding
rate-distortion theory
0.011979
Differential PCM Systems · IEEE Trans. Commun. 1979
Information theory
signal processing
0.011980
An analysis of leaky integrator differential PCM systems with stationary first-order Gauss Markov input (Ph.D. Thesis abstr.) · IEEE Trans. Inf. Theory 1980

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

gauss-markov process analysis · 0.0quantization · 0.0
YearPublicationVenuePosition
1980 An analysis of leaky integrator differential PCM systems with stationary first-order Gauss Markov input (Ph.D. Thesis abstr.)
Ethirajan Janardhanan
IEEE Trans. Inf. Theory1
1979 Differential PCM Systems
abstract
Matched differential PCM systems are analyzed for stationary first order Gauss Markov process inputs. The computational procedure yields convergent solutions for the variance and the probability density function of the point-wise error for all sampling intervals. Time average error between the input signalX(t)and a time continuous estimateX\hat{\approx}(t)is computed and it is used as the basis for finding the optimum number of quantizer levels and hence, the optimum sampling interval for a given bit rate. For time average error values of practical interest, 4 level DPCM is superior; the time average error exceeds the appropriate rate-distortion bound by only 2.5 dB. A comparison with a good PCM system shows that 4 level DPCM is superior for large bit rates and the difference increases with bit rate. The same DPCM systems are analyzed under the assumption that the prediction error is Gaussian and the results are within 10% of the exactly computed results for the region of optimum performance.
Ethirajan Janardhanan
IEEE Trans. Commun.1