Demo Data from VLDB 2026 / reviewers in the wild / expert
Hong Jik Kim
dblp:94/2073
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2008
—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 |
Information theory · 100% | |
| Computer networks
1 paper |
Physical-layer communications · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Information theory › communication channels › channel models › noisy channel
dependent noise |
0.1 | 1 | 2008 | Locally Optimum Detection of Signals in Multiplicative and First-Order Markov Additive Noises · IEEE Trans. Inf. Theory 2008 |
Information theory
hypothesis testing |
0.1 | 1 | 2008 | Locally Optimum Detection of Signals in Multiplicative and First-Order Markov Additive Noises · IEEE Trans. Inf. Theory 2008 |
Information theory › hypothesis testing › signal detection › weak signal detection
locally optimum detection |
0.1 | 1 | 2008 | Locally Optimum Detection of Signals in Multiplicative and First-Order Markov Additive Noises · IEEE Trans. Inf. Theory 2008 |
Physical-layer communications
interference |
0.0 | 1 | 2008 | Locally Optimum Detection of Signals in Multiplicative and First-Order Markov Additive Noises · IEEE Trans. Inf. Theory 2008 |
Methods — techniques the papers use, named apart from their topics
markov additive noise models · 0.2asymptotic performance analysis · 0.2
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2008 | Locally Optimum Detection of Signals in Multiplicative and First-Order Markov Additive NoisesabstractIn most previously reported studies on locally optimum detection of signals, independent observations have been assumed in various noise environments. The use of an independent observation model may cause a considerable performance degradation in detection applications of modern high data-rate communication systems exhibiting dependence among interference components. In this paper, the detection of weak known and random signals is addressed in observations corrupted by multiplicative and first-order Markov additive noises. The asymptotic and finite sample-size performance of several detectors are obtained and compared: it is confirmed that the dependence of noise components need to be taken into account to maintain detection performance appropriately. Jumi Lee, Iickho Song, Hyoungmoon Kwon, Hong Jik Kim |
IEEE Trans. Inf. Theory | 4 |