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NEW DIRECTIONS IN STATISTICAL SIGNAL PROCESSING: FROM SYSTEMS TO BRAINS 2007
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△看放大圖
ISBN: 9780262083485
類別: 電子/電機工程Electrical / Electronic Engineering
出版社: MIT PRESS
作者: HAYKIN
年份: 2007
裝訂別: 精裝
頁數: 544
定價: 1,390
售價: 1,251
原幣價: USD 50.00
狀態: 正常
Signal processing and neural computation have separately and significantly influenced many disciplines, but the cross-fertilization of the two fields has begun only recently. Research now shows that each has much to teach the other, as we see highly sophisticated kinds of signal processing and elaborate hierachical levels of neural computation performed side by side in the brain. In New Directions in Statistical Signal Processing, leading researchers from both signal processing and neural computation present new work that aims to promote interaction between the two disciplines.

The book's 14 chapters, almost evenly divided between signal processing and neural computation, begin with the brain and move on to communication, signal processing, and learning systems. They examine such topics as how computational models help us understand the brain's information processing, how an intelligent machine could solve the "cocktail party problem" with "active audition" in a noisy environment, graphical and network structure modeling approaches, uncertainty in network communications, the geometric approach to blind signal processing, game-theoretic learning algorithms, and observable operator models (OOMs) as an alternative to hidden Markov models (HMMs).

Table Of Contents

Series Foreword vii

1. Modeling the Mind: From Circuits to Systems
Suzanna Becker 1

2. Empirical Statistics and Stochastic Models for Visual Signals
David Mumford 23

3. The Machine Cocktail Party Problem
Simon Haykin and Zhe Chen 51

4. Sensor Adaptive Signal Processing of Biological Nanotubes (Ion Channels) at Macroscopic and Nano Scales
Vikram Krishnamurthy 77

5. Spin Diffusion: A New Perspective in Magnetic Resonance Imaging
Timothy R. Field 119

6. What Makes a Dynamical System Computationally Powerful?
Robert Legenstein and Wolfgang Maass 127

7. A Variational Principle for Graphical Models
Martin J. Wainwright and Michael I. Jordan 155

8. Modeling Large Dynamical Systems with Dynamical Consistent Neural Networks
Hans-Georg Zimmermann, Ralph Grothmann, Anton Maximilian Schafer and Christoph Tietz 203

9. Diversity in Communication: From Source Coding to Wireless Networks
Suhas N. Diggavi 243

10. Designing Patterns for Easy Recognition: Information Transmission with Low-Density Parity-Check Codes
Frank R. Kschischang and Masoud Ardakani 287

11. Turbo Processing
Claude Berrou, Charlotte Langlais and Fabrice Seguin 307

12. Blind Signal Processing Based on Data Geometric Properties
Konstantinos Diamantaras 379

13. Game-Theoretic Learning
Geoffrey J. Gordon 379

14. Learning Observable Operator Models via the Efficient Sharpening Algorithm
Herbert Jaeger, Mingjie Zhao, Klaus Kretzschmar, Tobias Oberstein, Dan Popovici and Andreas Kolling 417

References 465

Contributors 509

Index 513
Springer 國外現貨
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