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James R. Burnham

dblp:89/7449 · DBLP profile ↗
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1ranked-venue papers
1as first author
0since 2021 · last 2009
—ORCID · none

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

Systems, architecture and hardware · 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.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Performance modeling and evaluation · 62% Integrated circuit design · 38%

Topics — the 2 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Integrated circuit design › analog and mixed-signal circuits › mixed-signal circuit design
delay-locked loop
0.012009
A stochastic jitter model for analyzing digital timing-recovery circuits · DAC 2009
Integrated circuit design › analog and mixed-signal circuits
mixed-signal circuit design
0.012009
A stochastic jitter model for analyzing digital timing-recovery circuits · DAC 2009

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

stochastic modeling · 0.1markov chain · 0.1
YearPublicationVenuePosition
2009 A stochastic jitter model for analyzing digital timing-recovery circuits
abstract
This paper describes a stochastic jitter model for analyzing the performance and bit error rate (BER) of digital timing recovery circuits. The model uses parallel interconnected Markov chains to simulate the behavior of the system in response to both random and deterministic jitter. Unlike conventional Markov-chain models that require the system to be stationary, the parallel-chain model approximates deterministic changes in conditions with transitions between sub-chains. To verify the accuracy of the model, an analysis was performed on a digital delay-locked loop, and the results compared to measured data. The resulting transition probabilities and BER predicted by the proposed model are more than three orders of magnitude more accurate than those predicted by a conventional Markov-chain model.
James R. Burnham, Chih-Kong Ken Yang, Haitham A. Hindi
DAC1