Ulteriori informazioni
Informationen zum Autor 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. Klappentext The only integrative approach to chaos and random fractal theoryChaos 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 moreAdditionally, 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. Zusammenfassung The only integrative approach to chaos and random fractal theoryChaos 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 moreAdditionally, 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. Inhaltsverzeichnis Preface. 1. Introduction. 1.1 Examples of multiscale phenomena. 1.2 Examples of challenging problems to be pursued. 1.3 Outline of the book. 1.4 Bibliographic notes. 2. Overview of fractal and chaos theory. 2.1 Prelude to fractal geometry. 2.2 Prelude to chaos theory. 2.3 Further reading and bibliographic notes. 2.4 Warming up exercises. 3. Basics of probability theory and stochastic processes. 3.1 Basic elements of probability theory. 3.1.1 Probability system. 3.1.2 Random variables. 3.1.3 Expectation. 3.1.4 Characteristic function, moment generating function, Laplace. transform, and probability generating function. 3.2 Commonly used distributions. 3.3 Stochastic processes. 3.3.1 Basic definitions. 3.3.2 Markov proces...
