ghoraani behnaz (5 risultati)

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Taschenbuch. Condizione: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Signal processing can be found in many applications and its primary goal is to provide underlying information for the purpose of decision making. However, most of the real-world signals are challenging in nature as they are nonstationary, i.e., their statistics are time variant. Therefore, time or frequency descriptions, alone, are insufficient to provide comprehensive information about these challenging signals. On the contrary, extracting joint time-varying frequency characteristics of signals has an immense potential for revealing the nonstationary behaviour of signals better. This book is an attempt to develop a generalized time-frequency (TF) analysis methodology that exploits the benefits of TF distribution in pattern classification systems as related to discriminant feature detection and feature classification. This book describes the traditional TF feature analysis along with recently proposed approaches. The theoretical properties of these methods are examined. Additionally, advantages of these approaches in various applications such as feature extraction and automated-decision making systems are demonstrated through examples of different synthetic and real-world signals. 328 pp. Englisch.

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Ghoraani BehnazDr. Ghoraani is a PhD in Electrical and Computer Engineering. Her research interests center on time-frequency feature analysis, and Multimedia and Biomedical signal processing. Professor Krishnan is a Canada Resear.

Taschenbuch. Condizione: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Signal processing can be found in many applications and its primary goal is to provide underlying information for the purpose of decision making. However, most of the real-world signals are challenging in nature as they are nonstationary, i.e., their statistics are time variant. Therefore, time or frequency descriptions, alone, are insufficient to provide comprehensive information about these challenging signals. On the contrary, extracting joint time-varying frequency characteristics of signals has an immense potential for revealing the nonstationary behaviour of signals better. This book is an attempt to develop a generalized time-frequency (TF) analysis methodology that exploits the benefits of TF distribution in pattern classification systems as related to discriminant feature detection and feature classification. This book describes the traditional TF feature analysis along with recently proposed approaches. The theoretical properties of these methods are examined. Additionally, advantages of these approaches in various applications such as feature extraction and automated-decision making systems are demonstrated through examples of different synthetic and real-world signals.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 328 pp. Englisch.

Taschenbuch. Condizione: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Signal processing can be found in many applications and its primary goal is to provide underlying information for the purpose of decision making. However, most of the real-world signals are challenging in nature as they are nonstationary, i.e., their statistics are time variant. Therefore, time or frequency descriptions, alone, are insufficient to provide comprehensive information about these challenging signals. On the contrary, extracting joint time-varying frequency characteristics of signals has an immense potential for revealing the nonstationary behaviour of signals better. This book is an attempt to develop a generalized time-frequency (TF) analysis methodology that exploits the benefits of TF distribution in pattern classification systems as related to discriminant feature detection and feature classification. This book describes the traditional TF feature analysis along with recently proposed approaches. The theoretical properties of these methods are examined. Additionally, advantages of these approaches in various applications such as feature extraction and automated-decision making systems are demonstrated through examples of different synthetic and real-world signals.