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In dieser Arbeit werden adaptive Methoden zur Analyse von Anthropomatikdaten entwickelt. Zielsetzung ist die automatische Erkennung von Systemzuständen mit Hidden-Markov-Modellen. Anwendungsbeispiele sind Bohrgeräusche aus der Wirbelsäulenchirurgie, medizinische Ultraschallbilder und menschliche Bewegungsdaten. Neben dem Vergleich mit anderen Klassifikationsverfahren werden Merkmalsgenerierung, geeignete Modellstrukturen, Optimierung der Zustände und Aspekte der Implementierung besprochen.

Adaptive signal processing.

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Chronicling the new field of cognitive radar (CR), this cutting-edge resource provides an accessible introduction to the theory and applications of CR, and presents a comprehensive overview of the latest developments in this emerging area. The first book on the subject, Cognitive Radar covers important breakthroughs in advanced radar systems, and offers new and powerful methods for combating difficult clutter environments. You find details on specific algorithmic and real-time high-performance embedded computing (HPEC) architectures. This practical book is supported with numerous examples that.

Adaptive signal processing. --- Human-computer interaction. --- Radar.

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Adaptive Learning Methods for Nonlinear System Modeling presents some of the recent advances on adaptive algorithms and machine learning methods designed for nonlinear system modeling and identification. Real-life problems always entail a certain degree of nonlinearity, which makes linear models a non-optimal choice. This book mainly focuses on those methodologies for nonlinear modeling that involve any adaptive learning approaches to process data coming from an unknown nonlinear system. By learning from available data, such methods aim at estimating the nonlinearity introduced by the unknown system. In particular, the methods presented in this book are based on online learning approaches, which process the data example-by-example and allow to model even complex nonlinearities, e.g., showing time-varying and dynamic behaviors. Possible fields of applications of such algorithms includes distributed sensor networks, wireless communications, channel identification, predictive maintenance, wind prediction, network security, vehicular networks, active noise control, information forensics and security, tracking control in mobile robots, power systems, and nonlinear modeling in big data, among many others. This book serves as a crucial resource for researchers, PhD and post-graduate students working in the areas of machine learning, signal processing, adaptive filtering, nonlinear control, system identification, cooperative systems, computational intelligence. This book may be also of interest to the industry market and practitioners working with a wide variety of nonlinear systems. Presents the key trends and future perspectives in the field of nonlinear signal processing and adaptive learning. Introduces novel solutions and improvements over the state-of-the-art methods in the very exciting area of online and adaptive nonlinear identification. Helps readers understand important methods that are effective in nonlinear system modelling, suggesting the right methodology to address particular issues.

Adaptive signal processing. --- Signal processing, Adaptive --- Signal processing

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This book presents the design, analysis, and application of nonlinear adaptive filters with the goal of improving efficient performance (ie the convergence speed, steady-state error, and computational complexity). The authors present a nonlinear adaptive filter, which is an important part of nonlinear system and digital signal processing and can be applied to diverse fields such as communications, control power system, radar sonar, etc. The authors also present an efficient nonlinear filter model and robust adaptive filtering algorithm based on the local cost function of optimal criterion to overcome non-Gaussian noise interference. The authors show how these achievements provide new theories and methods for robust adaptive filtering of nonlinear and non-Gaussian systems. The book is written for the scientist and engineer who are not necessarily an expert in the specific nonlinear filtering field but who want to learn about the current research and application. The book is also written to accompany a graduate/PhD course in the area of nonlinear system and adaptive signal processing. Presents recent research results and applications of nonlinear adaptive filters in a variety of areas; Includes the basic models, algorithms, performance analysis and applications of various nonlinear filters in complex environments; Suitable for scientists and engineers who want to learn about nonlinear filtering in fields such as communications, control, power system, radar, etc.

Adaptive signal processing. --- Signal processing, Adaptive --- Signal processing

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The field of signal processing has seen exponential growth during the past decade, as remarkable innovations both in research and application have been made. The applications of signal processing are numerous and include audio signal processing, biomedical engineering, multimedia, video signal processing, pattern analysis, pattern recognition, artificial intelligence, decision making, control systems, and many more. In the past few years, a new wave of advanced signal-processing techniques has delivered exciting results, increasing systems capabilities of efficiently exchanging data and extrac

Signal processing. --- Adaptive signal processing. --- Signal processing, Adaptive --- Signal processing --- Processing, Signal --- Information measurement --- Signal theory (Telecommunication)