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- W4247011736 abstract "Processing algorithms have roots in fundamental digital signal processing concepts for sensor network theories. Regarding the techniques in machine learning, most of the existing algorithms rely on a central system for fusing and aggregating the collected information. The (often multichannel) signals related to body electrical activities, mainly those from central nervous system, such as electroencephalograph, electrocardiograph, electromyograph, and those from electromagnetic signatures, such as magnetoencephalograph, are called instantaneous in time since there is no time lag in travelling the waveforms from their sources to different sensors. The domain of subspace signal analysis is extended to the analysis of one-dimensional signals by means of singular spectrum analysis (SSA). SSA can be effectively used for the prediction and forecasting of various data modalities. Wavelet transform is another alternative for time-frequency analysis. Synchro-squeezing wavelet transform (SSWT) has been introduced as a post-processing technique to enhance the time-frequency spectrum obtained by applying wavelet transform." @default.
- W4247011736 created "2022-05-12" @default.
- W4247011736 date "2020-06-10" @default.
- W4247011736 modified "2023-10-04" @default.
- W4247011736 title "Signal Processing for Sensor Networks" @default.
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- W4247011736 doi "https://doi.org/10.1002/9781119390060.ch10" @default.
- W4247011736 hasPublicationYear "2020" @default.
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