scholarly journals Optimal time-frequency deconvolution filter design for nonstationary signal transmission through a fading channel: AF filter bank approach

1998 ◽  
Vol 46 (12) ◽  
pp. 3220-3234 ◽  
Author(s):  
Bor-Sen Chen ◽  
Yue-Chiech Chung ◽  
Der-Feng Huang
Keyword(s):  
Fading Channel ◽  
Filter Bank ◽  
Filter Design ◽  
Signal Transmission ◽  
Optimal Time ◽  
Nonstationary Signal ◽  
Time Frequency

scholarly journals Nonstationary Signal Analysis with Kernel Machines

2010 ◽  
pp. 223-241
Author(s):  
Paul Honeine ◽  
Cédric Richard ◽  
Patrick Flandrin
Keyword(s):  
Machine Learning ◽  
Pattern Recognition ◽  
Signal Analysis ◽  
Selection Criterion ◽  
Reproducing Kernels ◽  
Classification Problem ◽  
Optimal Time ◽  
Nonstationary Signal ◽  
Time Frequency ◽  
Recent Developments

This chapter introduces machine learning for nonstationary signal analysis and classification. It argues that machine learning based on the theory of reproducing kernels can be extended to nonstationary signal analysis and classification. The authors show that some specific reproducing kernels allow pattern recognition algorithm to operate in the time-frequency domain. Furthermore, the authors study the selection of the reproducing kernel for a nonstationary signal classification problem. For this purpose, the kernel-target alignment as a selection criterion is investigated, yielding the optimal time-frequency representation for a given classification problem. These links offer new perspectives in the field of nonstationary signal analysis, which can benefit from recent developments of statistical learning theory and pattern recognition.

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