Smooth extension for nonexpansive wavelet decomposition of finite length signals

Uto, Toshiyuki; Ikehara, Masaaki

doi:10.1109/mwscas.2004.1354106

Cited by 2 publications

(1 citation statement)

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“…Some strategies, such as periodic extension and symmetrical extension, are introduced to eliminate this distortion [12]. But in those extensions, the points besides the edges are only related with the extended signal, not the adjacent data points.…”

Section: De-noising Algorithm and Resultsmentioning

confidence: 99%

Efficiency improvement for data-processing of partial discharge signals using parallel computing

Luo

2010

2010 10th IEEE International Conference on Solid Dielectrics

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Partial discharge (PD) plays a paramount role in determining system reliability. But noise is always a major limitation of PD measurement. Many research work shows that wavelet transform (WT) has a potential to extract PD pulse from noise environment. This procedure is usually realized in MATLAB environment which is time-consuming that cannot be applied in on-line PD data-processing. Parallel computing which processes data concurrently is often used in efficiency improvement. This paper analyzes the feasibilities of parallelism application in WT data-processing. For the same tasks, the operation time of MATLAB programs are more than ten times of the durations of C++ programs. Then the PD de-noising algorithm written in C++ environment is employed to test the practical contaminated PD signal. The solution of boundary distortion is also included. Besides a little difference between PD pulses which due to the level-dependent threshold, the de-noising results using parallelism is the same with sequential method. The operation time of one hundred thousand data points is 16milliseconds. This method is potential to be applied in on-line PD measurement.

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Section: De-noising Algorithm and Resultsmentioning

confidence: 99%