A Unified Treatment of Non-Gaussian Processes and Nonlinear Signal Processing <P>Nonlinear signal processing methods are finding numerous applications in such fields as imaging, teletraffic, communications, hydrology, geology, and economics--fields where nonlinear systems and non-Gaussian processe
Nonlinear signal processing: a statistical approach
โ Scribed by Arce, Gonzalo R
- Publisher
- Wiley-Interscience
- Year
- 2005
- Tongue
- English
- Leaves
- 118
- Category
- Library
No coin nor oath required. For personal study only.
โฆ Synopsis
Nonlinear Signal Processing: A Statistical Approach focuses on unifying the study of a broad and important class of nonlinear signal processing algorithms which emerge from statistical estimation principles, and where the underlying signals are non-Gaussian, rather than Gaussian, processes. Notably, by concentrating on just two non-Gaussian models, a large set of tools is developed that encompass a large portion of the nonlinear signal processing tools proposed in the literature over the past several decades.
Key features include:
* Numerous problems at the end of each chapter to aid development and understanding
* Examples and case studies provided throughout the book in a wide range of applications bring the text to life and place the theory into context
* A set of 60+ MATLAB software m-files allowing the reader to quickly design and apply any of the nonlinear signal processing algorithms described in the book to an application of interest is available on the accompanying FTP site
โฆ Subjects
Signal processing;Mathematics;Statistics;Signalverarbeitung.
๐ SIMILAR VOLUMES
Nonlinear Signal Processing: A Statistical Approach focuses on unifying the study of a broad and important class of nonlinear signal processing algorithms which emerge from statistical estimation principles, and where the underlying signals are non-Gaussian, rather than Gaussian, processes. Notably,
Nonlinear Signal Processing: A Statistical Approach focuses on unifying the study of a broad and important class of nonlinear signal processing algorithms which emerge from statistical estimation principles, and where the underlying signals are non-Gaussian, rather than Gaussian, processes. Notably,
A Unified Treatment of Non-Gaussian Processes and Nonlinear Signal Processing Nonlinear signal processing methods are finding numerous applications in such fields as imaging, teletraffic, communications, hydrology, geology, and economics fields where nonlinear systems and non-Gaussian processes emer
A Unified Treatment of Non-Gaussian Processes and Nonlinear Signal Processing <P>Nonlinear signal processing methods are finding numerous applications in such fields as imaging, teletraffic, communications, hydrology, geology, and economics--fields where nonlinear systems and non-Gaussian processe