@inproceedings{3cebc76519aa45f9a869e4c1db64357f,
title = "Weak biosignal processing using adaptive wavelet neural network",
abstract = "How to detect the weak signals buried in noises is a fundamental and important problem. Conventional wavelet denoising fails for signals with low signal-to-noise ratio (SNR). This paper discussed an approach which is based on the use of adaptive wavelet probabilistic neural network (AWPNN). The biorthogonal 9-7 wavelet is used to extract the features from original signal, and then the probabilistic neural network (PNN) is used to analyze the meaningful features and perform discrimination tasks. Simulations indicated that the AWPNN is suitable for increasing the SNR of weak signals which commonly have below 0 dB SNR, and our method can deal with the signals with fairly low (approximately -20 dB) SNR.",
keywords = "Adaptive wavelet neural network(AWNN), Denoising, Signal processing, Signal-to-noise ratio (SNR)",
author = "Jiaoying Huang and Haibin Yuan and Hong Lv and Qiusheng Wang and Haiwen Yuan",
year = "2008",
doi = "10.1109/CSSE.2008.757",
language = "英语",
isbn = "9780769533360",
series = "Proceedings - International Conference on Computer Science and Software Engineering, CSSE 2008",
pages = "24--27",
booktitle = "Proceedings - International Conference on Computer Science and Software Engineering, CSSE 2008",
note = "International Conference on Computer Science and Software Engineering, CSSE 2008 ; Conference date: 12-12-2008 Through 14-12-2008",
}