Demo Data from EDBT 2026 / reviewers in the wild / expert
James R. Burnham
dblp:89/7449
· DBLP profile ↗
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
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Integrated circuit design › analog and mixed-signal circuits › mixed-signal circuit design
delay-locked loop |
0.0 | 1 | 2009 | A stochastic jitter model for analyzing digital timing-recovery circuits · DAC 2009 |
Integrated circuit design › analog and mixed-signal circuits
mixed-signal circuit design |
0.0 | 1 | 2009 | 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
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2009 | A stochastic jitter model for analyzing digital timing-recovery circuitsabstractThis 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 |
DAC | 1 |