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Bingo Wing-Kuen Ling
Guangdong University of Technology
186Publications
8H-index
667Citations
Publications 186
Newest
Nili Tian1
Estimated H-index: 1
(Guangdong University of Technology),
Bingo Wing-Kuen Ling8
Estimated H-index: 8
(Guangdong University of Technology),
Chunmei Qing4
Estimated H-index: 4
(South China University of Technology)
... more
This paper proposes a method for performing the camera identification based on very low bit rate videos with the overall noise patterns having time varying statistics. First, the overall noise pattern of each frame of each video is converted to a vector. Then, the odd order statistic moments of these vectors are computed. By performing the principal component analysis, only the most major component of each statistic moment vector is attained. These components of all the frames form a feature vec...
Ref 20 Source Cite this paper
Qing Miao (Foshan University), Bingo Wing-Kuen Ling8
Estimated H-index: 8
(Guangdong University of Technology)
The Fisher linear discriminant analysis (FLDA)-based method is a common method for jointly optimizing the intraclass separation and the interclass separation of the projected feature vectors by defining the objective function as the ratio of the intraclass separation over the interclass separation. To address the eigenproblem of the FLDA, a quadratic equality constraint is imposed on the square of the \(l_{2}\) norm of the decision vector. However, the constrained optimization problem is highly ...
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Xuan Xiong (Guangdong University of Technology), Bingo Wing-Kuen Ling8
Estimated H-index: 8
(Guangdong University of Technology),
Haiyan Zhang (Guangdong University of Technology)... more
AbstractA coplanar waveguide (CPW)-fed multiple input multiple output (MIMO) antenna with higher isolation and multi-bandwidth circular polarization (CP) is presented. This antenna covers the impedance operations from 2.2 to 6.8 GHz with < −10 dB, < −14 dB and < −10 dB. Employing two orthogonally placed T-shaped patches (TSP) and three L-shaped parasitic patches (LSPP) in the ground, the dual sense CP in the frequency band between 2.28 and 3.48 GHz (42.5%) and meanwhile between 6.03 and 6.28 GHz...
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2018 in sensors
Wai Lok Woo8
Estimated H-index: 8
,
Bin Gao8
Estimated H-index: 8
,
Ahmed Bouridane1
Estimated H-index: 1
... more
This paper presents an unsupervised learning algorithm for sparse nonnegative matrix factor time–frequency deconvolution with optimized fractional β -divergence. The β -divergence is a group of cost functions parametrized by a single parameter β . The Itakura–Saito divergence, Kullback–Leibler divergence and Least Square distance are special cases that correspond to β = 0 , 1 , 2 , respectively. This paper presents a generalized algorithm that uses a flexible range of β that includes fractional ...
Ref 36Cited 1 Source Cite this paper
Bingo Wing-Kuen Ling8
Estimated H-index: 8
(Guangdong University of Technology)
Download Pdf Cite this paper
Peiru Lin (Guangdong University of Technology), Wei-Chao Kuang1
Estimated H-index: 1
(Guangdong University of Technology),
Yuwei Liu1
Estimated H-index: 1
(Guangdong University of Technology)
... more
This paper proposes a threshold-free method for grouping and selecting the singular spectrum analysis (SSA) components for performing the signal denoising via the empirical mode decomposition (EMD) approach. First, the total number of the groups of the SSA components is selected to be the same as the total number of the intrinsic mode functions (IMFs) of the signal. The SSA components are assigned to the group where the absolute correlation coefficient between the IMF and the SSA component is th...
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Faxian Cao3
Estimated H-index: 3
(Guangdong University of Technology),
Zhijing Yang5
Estimated H-index: 5
(Guangdong University of Technology),
Jinchang Ren8
Estimated H-index: 8
... more
Due to its excellent performance in terms of fast implementation, strong generalization capability and straightforward solution, extreme learning machine (ELM) has attracted increasingly attentions in pattern recognition such as face recognition and hyperspectral image (HSI) classification. However, the performance of ELM for HSI classification remains a challenging problem especially in effective extraction of the featured information from the massive volume of data. To this end, we propose in ...
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Wei-Chao Kuang1
Estimated H-index: 1
,
Bingo Wing-Kuen Ling8
Estimated H-index: 8
,
Zhijing Yang5
Estimated H-index: 5
Source Cite this paper
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