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Download Biosignal and Biomedical Image Processing: MATLAB Based Applications (SIGNAL PROCESSING AND COMMUNICATIONS) epub

by John L. Semmlow




Relying heavily on MATLAB[registered] problems and examples, as well as simulated data, this text/reference surveys a vast array of signal and image processing tools for biomedical applications - providing a working knowledge of the technologies addressed while showcasing valuable implementation procedures, common pitfalls, and essential application concepts.
Download Biosignal and Biomedical Image Processing: MATLAB Based Applications (SIGNAL PROCESSING AND COMMUNICATIONS) epub
ISBN: 0824750683
ISBN13: 978-0824750688
Category: Other
Subcategory: Medicine & Health Sciences
Author: John L. Semmlow
Language: English
Publisher: TAYLOR & FRANCIS INC; 1 edition (January 15, 2004)
Pages: 550 pages
ePUB size: 1599 kb
FB2 size: 1609 kb
Rating: 4.7
Votes: 480
Other Formats: doc rtf lit mbr

Legend 33
Never got into the Medical Image Processing part of the book. Used very heavily the 1D signal Matlab methods. Showed very good examples with explanations of why these different techniques are used. I recommend using "Biomedical Signal Processing and Signal Modeling," by Eugene N. Bruce in conjunction with this book. This book focuses very heavily on the Matlab implementation, but not so much the math behind it.
Ylonean
This book assumes that you have both a prior knowledge of Matlab and of signal processing concepts. It spends the first three chapters going over measurement and transducer systems, basic signals and systems, and classical methods of spectral analysis. Even though these chapters are meant to be a quick review, there are some Matlab implementations of basic algorithms in each chapter. Chapter four introduces digital filters and shows Matlab implementations of both IIR and FIR filters. A special treat of chapter four is that some time is spent introducing the reader to the Matab signal processing toolkit. Now that the basics of digital filtering have been introduced, more advanced signal processing techniques are tackled. These include modern methods of spectral analysis and also time-frequency analysis using such methods as the Wigner-Ville distribution. Again, in all cases, the equations are concise, the prose is very accessible, and all concepts are demonstrated using Matlab programs. There is a separate chapter devoted to the wavelet transform and to its use in filter banks, denoising, and feature detection. Quite frankly, I found this chapter far more accessible than entire books that have been devoted to the subject, especially if you are interested in getting to the heart of the matter and using wavelets to perform a task. Two more chapters round out the section of the book on general signal processing techniques- one is about adaptive filters and another on principal component analysis. The final four chapters of the book concern themselves with image processing and Matlab, and the Matlab image processing toolkit in particular. You should already be familiar with the basic concepts behind image processing, and as with the signal processing portion of this book, the point is to have a single text with all of the relevant signal processing techniques briefly described along with Matlab code for the purpose of biosignal processing. However, even if you are not a biomedical engineer, and I am not, you should find this book helpful in the general sense of producing implementations of signal processing concepts. This book would also be helpful for biometric professionals since it goes into great detail on how to turn biological features into measurements that can be processed and compared.