Multiscale Analysis of Complex Time Series
Integration of Chaos and Random Fractal Theory, and Beyond
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This book introduces a number of key concepts from random fractal theory
by emphasizing their usage in signal processing. A number of applications
will be discussed in depth. These include DNA sequence analysis, network traffic
modelling, analysis of neuron inter-spike interval data, and study of heart rate variability and ambiguous visual perception.
by emphasizing their usage in signal processing. A number of applications
will be discussed in depth. These include DNA sequence analysis, network traffic
modelling, analysis of neuron inter-spike interval data, and study of heart rate variability and ambiguous visual perception.
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 withsource 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.
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 withsource 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.