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Bingo Wing-Kuen Ling
Guangdong University of Technology
207Publications
15H-index
866Citations
Publications 207
Newest
Published on Jan 1, 2019in IEEE Transactions on Nanobioscience 1.93
Libo Huang , Bingo Wing-Kuen Ling15
Estimated H-index: 15
(Guangzhou Higher Education Mega Center)
+ 3 AuthorsYao Chen
In recent years, the signal processing opportunities with the multi-channel recording and the high precision detection provided by the development of new extracellular multi-electrodes are increasing. Hence, designing new spike sorting algorithms are both attractive and challenging. These algorithms are used to distinguish the individual neurons’ activity from the dense and simultaneously recorded neural action potentials with high accuracy. However, since the overlapping phenomenon often inevit...
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Published on Jun 1, 2019in Signal Processing 4.09
Chuqi Yang1
Estimated H-index: 1
(GDUT: Guangdong University of Technology),
Bingo Wing-Kuen Ling15
Estimated H-index: 15
(GDUT: Guangdong University of Technology)
+ 1 AuthorsJialiang Gu1
Estimated H-index: 1
(GDUT: Guangdong University of Technology)
Abstract A signal can be represented as the sum of the intrinsic mode functions via performing the empirical mode decomposition. For the discrete time signals, the lengths of the intrinsic mode functions are equal to the lengths of the input signals. As there is usually more than one intrinsic mode function, the total numbers of discrete points of all the intrinsic mode functions are usually more than the lengths of the input signals. In other words, the empirical mode decomposition is an oversa...
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Published on May 25, 2019in Signal, Image and Video Processing 1.89
Nili Tian1
Estimated H-index: 1
,
Bingo Wing-Kuen Ling15
Estimated H-index: 15
+ 2 AuthorsKok Lay Teo44
Estimated H-index: 44
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Published on Mar 22, 2019in Signal, Image and Video Processing 1.89
Nili Tian (Guangzhou Higher Education Mega Center), Xiaoling Wang (Guangzhou Higher Education Mega Center)+ 1 AuthorsMustafa Sakalli (Marmara University)
An approximated empirical mode decomposition generates a set of approximated intrinsic mode functions via a linear, nonadaptive but iterative approach. The decomposition was found to be very useful for a content-independent pattern recognition application. As the process is characterized by a system kernel matrix and performed iteratively, the approximated intrinsic mode functions can be understood as the original signals processed by a set of mask operations. Here, some properties of the decomp...
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Published on Feb 22, 2019in Circuits Systems and Signal Processing 1.92
Xiaoling Wang (GDUT: Guangdong University of Technology), Bingo Wing-Kuen Ling15
Estimated H-index: 15
(GDUT: Guangdong University of Technology)
Time series usually consist of an underlying trend and the irregularities. Therefore, the underlying trend extraction plays an important role in the analysis of the time series. This paper proposes a method which combines the ensemble intrinsic timescale decomposition (EITD) algorithm and the matching pursuit (MP) approach for performing the underlying trend extraction. In order to extract the underlying trend, the EITD algorithm is applied to obtain a set of components. Then, the first componen...
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Published on Jan 1, 2019in IEEE Access 4.10
Qing Pan (GDUT: Guangdong University of Technology), Chao Mei (GDUT: Guangdong University of Technology)+ 3 AuthorsZhijing Yang9
Estimated H-index: 9
(GDUT: Guangdong University of Technology)
In order to address the problem of achieving the poor performance of a single-channel receiver operated at a low signal-to-noise ratio (SNR) based on the existing source number estimation method, an effective source enumeration approach for the single-channel receiver operated at a low SNR is proposed in this paper. The proposed method is based on the empirical mode decomposition (EMD) with the auto-correlation coefficient matrix (ACCM) and the jackknifing method. First, the received single-chan...
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