Development of a distributed disaster data and human life sign probe system

Ishihara, Seiya; Kobayashi, Hiroyuki

doi:10.1109/icit.2016.7475052

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Wavelet Transform Theory1

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2016

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“…One is 'small', that is, there is compact support or similar compact support in time domain. The other is 'volatility', which means, DC component is zero and positive and negative is alternative (Ishihara and Kobayashi, 2016).…”

Section: Wavelet Transform Theorymentioning

confidence: 99%

Weak Life Signal Detection Based on Wavelet Transform and Threshold De-noising Theory

2016

Revista Tecnica De La Facultad De Ingenieria Universidad Del Zu

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When natural disasters and man-made disasters occur, people hope to search and rescue as soon as possible to the survivors. The radar echo signal is very weak and hard to extract in the life signal detection. In order to solve this problem, a new method based on wavelet transform and threshold de-noising theory is proposed. Through the studies of wavelet threshold de-noising method, the use of it in weak life signal de-noising in strong noise background, and the verification of simulation by Matlab. The results show that the wavelet threshold de-noising theory and method can effectively eliminate the noise signal from the weak life signal, which is an effective de-noising and extracting method, which is suitable for weak life signal detection. If this method is applied to the signal analysis of detection instruments, it should play an important role in the detection field of weak life signal.

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Section: Wavelet Transform Theorymentioning

confidence: 99%

Weak Life Signal Detection Based on Wavelet Transform and Threshold De-noising Theory

2016

Revista Tecnica De La Facultad De Ingenieria Universidad Del Zu

View full text Add to dashboard Cite

show abstract

Development of a distributed disaster data and human life sign probe system

Cited by 1 publication

References 3 publications

Weak Life Signal Detection Based on Wavelet Transform and Threshold De-noising Theory

Weak Life Signal Detection Based on Wavelet Transform and Threshold De-noising Theory

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