This book in its third edition additionally explores how the ubiquitous electronic spreadsheet can be utilized for wavelet based signal and image processing. Many of the intriguing properties of wavelet and scaling functions can be easily observed in the spreadsheets. New to this Edition :
• Inclusion of a separate and elaborate chapter on Multiwavelet theory.
• Theory of parametric wavelet filters design appended in respective chapters.
• Parametric and non-parametric biorthogonal wavelet design explained in more detail.
• Chapter on M-band wavelet included with simplified design procedures.
The accompanying CD contains worksheets that demonstrate the power of spreadsheet packages as a computational and visualization tool.
Key Features :
Describes wavelet concepts from both the signal expansion and filter theory points of view
Gives clear and concise explanation of biorthogonality and biorthogonal wavelet analysis
Deals with design of wavelets in both time and frequency domain
Explains lifting-scheme based wavelet analysis and design in detail
Covers wavelet applications in computer graphics, signal denoising and compression
Includes latest developments in Groebner basis method of wavelet design, Multiwavelet theory and curvelet transforms
Intended to cater to the postgraduate students of computer science, electrical/electronic and communication engineering, the textbook will also meet the needs of undergraduate and postgraduate students of mathematics and physics.About the AuthorK. P. SOMAN (Ph.D., IIT Kharagpur) is Head, Centre for Excellence in Computational Engineering and Networking, Amrita Vishwa Vidyapeetham, Coimbatore. He has published over 125 papers in international journals and conferences. His areas of interest include high performance computing, Machine learning, wavelet transform, Computational Linear Algebra, Theory of Convex Optimization and Software Defined Radio. Dr Soman has co-authored two other books, Insight into Data Mining: Theory and Practice and Machine Learning with SVM and other Kernel Methods, both published by PHI Learning.|N.G. RESMI (M.Tech.) is a Research Associate in Centre for Excellence in Computational Engineering and Networking, Amrita Vishwa Vidyapeetham, Coimbatore. Her primary research interests are wavelet theory and kernel methods.|K. I. RAMACHANDRAN (Ph.D., IIT Madras) is Professor, Centre for Excellence in Computational Engineering and Networking, Amrita Vishwa Vidyapeetham, Coimbatore. He has published several papers in national and international journals and has also co-authored a book. His research interests include computational fluid mechanics, non-linear dynamics, virtual instrumentation, wavelets and fractals.
