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Hui Dang

dblp:160/1932 · DBLP profile ↗
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4ranked-venue papers
1as first author
3since 2021 · last 2026
—ORCID · conflict

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

Artificial intelligence and machine learning · 1 · 1 since 2021Computer networks · 1 · 1 since 2021Databases, data management, data science and information retrieval · 1 · 1 since 2021Theory 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
Information theory · 100%

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

TopicWeightPapersLastEvidence papers
Information theory
ergodic theory
0.212015
The Strong Law of Large Numbers and the Entropy Ergodic Theorem for Nonhomogeneous Bifurcating Markov Chains Indexed by a Binary Tree · IEEE Trans. Inf. Theory 2015
Information theory › probability theory
stochastic processes
0.212015
The Strong Law of Large Numbers and the Entropy Ergodic Theorem for Nonhomogeneous Bifurcating Markov Chains Indexed by a Binary Tree · IEEE Trans. Inf. Theory 2015

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

strong law of large numbers · 0.2limit theorems · 0.2
YearPublicationVenuePosition
2026 Cross-layer feature consistency and dual-transformer residual framework for underwater image enhancement
Xinbin Li, Song Han 0001, Hui Dang, Muge Li
Eng. Appl. Artif. Intell.5
2024 Regression generative adversarial network based on bounded losses for prediction of free calcium oxide in cement clinker
Gaolu Huang, Xiaochen Hao, Hui Dang
Adv. Eng. Informatics5
2024 A distributed relay selection using a fuzzy-BCM based decision making strategy for multi-hop data dissemination in VANETs
Yonas Abate Debalki, Jin Hou, Baye Yemataw Adane, Vittor Gift Mawutor, Hui Dang
Wirel. Networks5
2015 The Strong Law of Large Numbers and the Entropy Ergodic Theorem for Nonhomogeneous Bifurcating Markov Chains Indexed by a Binary Tree
abstract
Guyon (Guyon J. Limit theorems for bifurcating Markov chains. Application to the detection of cellular aging. Ann Appl Probab, 2007, 17: 1538-1569) introduced an important model for homogeneous bifurcating Markov chains indexed by a binary tree taking values in general state space and studied their limit theorems. The results were applied to detect cellular aging. In this paper, we define a discrete form of nonhomogeneous bifurcating Markov chains indexed by a binary tree and discuss the equivalent properties for them. The strong law of large numbers and the entropy ergodic theorem are studied for these Markov chains with finite state space. In contrast to previous work, we use a new approach to prove the main results of this paper.
Hui Dang, Weiguo Yang, Zhiyan Shi
IEEE Trans. Inf. Theory1