Multiscale Analysis of Complex Time Series: Integration of Chaos and Random Fractal Theory, and Beyond
Author(s): Jianbo Gao (Author), Yinhe Cao (Author), Wen-wen Tung (Author), Jing Hu (Author)
Publisher: Wiley-Interscience
Publication Date: September 17, 2007
Edition: 1st
Language: English
Print length: 368 pages
ISBN-10: 0471654701
ISBN-13: 9780471654704
Book Description
The only integrative approach to chaos and random fractal theory
Chaos and random fractal theory are two of the most important theories developed for data analysis. Until now, there has been no single book that encompasses all of the basic concepts necessary for researchers to fully understand the ever-expanding literature and apply novel methods to effectively solve their signal processing problems. Multiscale Analysis of Complex Time Series fills this pressing need by presenting chaos and random fractal theory in a unified manner.
Adopting a data-driven approach, the book covers:
DNA sequence analysis
EEG analysis
Heart rate variability analysis
Neural information processing
Network traffic modeling
Economic time series analysis
And more
Additionally, the book illustrates almost every concept presented through applications and a dedicated Web site is available with source codes written in various languages, including Java, Fortran, C, and MATLAB, together with some simulated and experimental data. The only modern treatment of signal processing with chaos and random fractals unified, this is an essential book for researchers and graduate students in electrical engineering, computer science, bioengineering, and many other fields.
Editorial Reviews
From the Inside Flap
The only integrative approach to chaos and random fractal theory
Chaos and random fractal theory are two of the most important theories developed for data analysis. Until now, there has been no single book that encompasses all of the basic concepts necessary for researchers to fully understand the ever-expanding literature and apply novel methods to effectively solve their signal processing problems. Multiscale Analysis of Complex Time Series fills this pressing need by presenting chaos and random fractal theory in a unified manner.
Adopting a data-driven approach, the book covers:
DNA sequence analysis
EEG analysis
Heart rate variability analysis
Neural information processing
Network traffic modeling
Economic time series analysis
And more
Additionally, the book illustrates almost every concept presented through applications and a dedicated Web site is available with source codes written in various languages, including Java, Fortran, C, and MATLAB, together with some simulated and experimental data. The only modern treatment of signal processing with chaos and random fractals unified, this is an essential book for researchers and graduate students in electrical engineering, computer science, bioengineering, and many other fields.
From the Back Cover
The only integrative approach to chaos and random fractal theory
Chaos and random fractal theory are two of the most important theories developed for data analysis. Until now, there has been no single book that encompasses all of the basic concepts necessary for researchers to fully understand the ever-expanding literature and apply novel methods to effectively solve their signal processing problems. Multiscale Analysis of Complex Time Series fills this pressing need by presenting chaos and random fractal theory in a unified manner.
Adopting a data-driven approach, the book covers:
DNA sequence analysis
EEG analysis
Heart rate variability analysis
Neural information processing
Network traffic modeling
Economic time series analysis
And more
Additionally, the book illustrates almost every concept presented through applications and a dedicated Web site is available with source codes written in various languages, including Java, Fortran, C, and MATLAB, together with some simulated and experimental data. The only modern treatment of signal processing with chaos and random fractals unified, this is an essential book for researchers and graduate students in electrical engineering, computer science, bioengineering, and many other fields.
About the Author
Jianbo Gao is an Assistant Professor of the Department of Electrical and Computer Engineering at the University of Florida.
Yinhe Cao is the CEO of BioSieve.
Wen-wen Tung is an Assistant Professor of the Department of Earth and Atmospheric Sciences at Purdue University, West Lafayette, Indiana.
Jing Hu is a Research Engineer of the Department of Electrical and Computer Engineering at the University of Florida.