Multiscale Signal Analysis And Modeling

Multiscale Signal Analysis and Modeling presents recent advances in multiscale analysis and modeling using wavelets and other systems. This book also presents applications in digital signal processing using sampling theory and techniques from various function spaces, filter design, feature extraction and classification, signal and image representation/transmission, coding, nonparametric statistical signal processing, and statistical learning theory.

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.

Paul Butzer, who is considered the academic father and grandfather of many prominent mathematicians, has established one of the best schools in approximation and sampling theory in the world. He is one of the leading figures in approximation, sampling theory, and harmonic analysis. Although on April 15, 2013, Paul Butzer turned 85 years old, remarkably, he is still an active research mathematician. In celebration of Paul Butzer’s 85th birthday, New Perspectives on Approximation and Sampling Theory is a collection of invited chapters on approximation, sampling, and harmonic analysis written by students, friends, colleagues, and prominent active mathematicians. Topics covered include approximation methods using wavelets, multi-scale analysis, frames, and special functions. New Perspectives on Approximation and Sampling Theory requires basic knowledge of mathematical analysis, but efforts were made to keep the exposition clear and the chapters self-contained. This volume will appeal to researchers and graduate students in mathematics, applied mathematics and engineering, in particular, engineers working in signal and image processing.

The book provides details regarding the application of various signal processing and artificial intelligence-based methods for electroencephalography data analysis. It will help readers in understanding the use of electroencephalography signals for different neural information processing and cognitive neuroscience applications. The book: Covers topics related to the application of signal processing and machine learning-based techniques for the analysis and classification of electroencephalography signals Presents automated methods for detection of neurological disorders and other applications such as cognitive task recognition, and brain-computer interface Highlights the latest machine learning and deep learning methods for neural signal processing Discusses mathematical details for the signal processing and machine learning algorithms applied for electroencephalography data analysis Showcases the detection of dementia from electroencephalography signals using signal processing and machine learning-based techniques It is primarily written for senior undergraduates, graduate students, and researchers in the fields of electrical engineering, electronics and communications engineering, and biomedical engineering.

The increasingly competitive environment within which modern industry has to work means that processes have to be operated over a wider range of conditions in order to meet constantly changing performance targets. Add to this the fact that many industrial operations are nonlinear, and the need for on-line control algorithms for nonlinear processes becomes clear. Major progress has been booked in constrained model-based control and important issues of nonlinear process control have been solved. This text surveys the state-of-the-art in nonlinear model-based control technology, by writers who have actually created the scientific profile. A broad range of issues are covered in depth, from traditional nonlinear approaches to nonlinear model predictive control, from nonlinear process identification and state estimation to control-integrated design. Advances in the control of inverse response and unstable processes are presented. Comparisons with linear control are given, and case studies are used for illustration.

The book is an introduction to the methods that deal with problems raised in using multiscale mathematical/statistical models such as wavelets and other multiscale systems. Special emphasis is given to the applications in filter design, sampling and nonparametric statistical methods for signal modeling, detection and recovering as well as learning and prediction. Applications of these methods notably to signal distortion treatment (Gibbs phenomenon), misisng sample identification, pattern recognition and maching learning problems are discussed and illustrated by examples. Both continuous and sampled (digitized) signals are considered. These methods are in contrast to more traditional methods involving mainly Fourier series withwhich they will also be compared. These multiscale methods have better localization properties, but also avoid excessive oscillations often encountered inboth signal and image analysis.