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researcher

C. Leung

4 papers hereh-index 17941 citations81 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author4

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • eess.SP2
  • cs.LG1
  • eess.IV1
same name
  • C. Leung — 60 papers, h 32
  • C. Leung — 29 papers, h 10
  • C. Leung — 23 papers, h 14
  • C. Leung — 17 papers, h 9
  • C. Leung — 14 papers, h 4
  • C. Leung — 13 papers, h 10

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedADMM-MCP Framework for Sparse Recovery with Global Convergence

3 citations · 5 across the 4 of their papers we have counts for

collaborators

4 papers

eess.IV2018

Robust Real-time Ellipse Fitting Based on Lagrange Programming Neural Network and Locally Competitive Algorithm

Hao Wang, Chi-Sing Leung, Hing Cheung So +3

Given a set of 2-dimensional (2-D) scattering points, which are usually obtained from the edge detection process, the aim of ellipse fitting is to construct an elliptic equation th…

eess.SP2018★ 2 cited

Robust MIMO Radar Target Localization based on Lagrange Programming Neural Network

Hao Wang, Chi-Sing Leung, Hing Cheung So +3

This paper focuses on target localization in a widely distributed multiple-input-multiple-output (MIMO) radar system. In this system, range measurements, which include the sum of d…

eess.SP2018★ 3 cited

ADMM-MCP Framework for Sparse Recovery with Global Convergence

Hao Wang, Zhanglei Shi, Chi-Sing Leung +1

In compressed sensing, the l0-norm minimization of sparse signal reconstruction is NP-hard. Recent work shows that compared with the best convex relaxation (l1-norm), nonconvex pen…

cs.LG2018

l0-norm Based Centers Selection for Training Fault Tolerant RBF Networks and Selecting Centers

Hao Wang, Chi-Sing Leung, Hing Cheung So +2

The aim of this paper is to train an RBF neural network and select centers under concurrent faults. It is well known that fault tolerance is a very attractive property for neural n…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.