ISBN 9788120332409,New Directions In Statistical Signal Processing : From Systems To Brains

New Directions In Statistical Signal Processing : From Systems To Brains



Phi Learning Private Ltd

Publication Year 2009

ISBN 9788120332409

ISBN-10 8120332407


Number of Pages 524 Pages
Language (English)

Science & Technology

Signal processing and neural computation have, for long, significantly but separately influenced many disciplines. New researches and the fact that highly sophisticated kinds of signal processing and elaborate computations are performed side by side in the brain, however, show that these two fields have much to teach each other as well. This book discusses the cross-fertilization of these two streams and compiles work of leading researchers from both the areas that promote interaction between both the disciplines. This text is primarily meant for the advanced undergraduate and postgraduate students of bioinformatics and biomedical engineering. However, having evolved from two different fields, the text is also useful for the senior students of electronics and communication engineering, computer science and engineering, and electrical engineering. Table of Contents Series Foreword Preface 1. Modeling the Mind: From Circuits to Systems 2. Empirical Statistics and Stochastic Models for Visual Signals 3. The Machine Cocktail Party Problem 4. Sensor Adaptive Signal Processing of Biological Nanotubes (Ion Channels) at Macroscopic and Nano Scales 5. Spin Diffusion: A New Perspective in Magnetic Resonance Imaging 6. What Makes a Dynamical System Computationally Powerful? 7. A Variational Principle for Graphical Models 8. Modeling Large Dynamical Systems with Dynamical Consistent Neural Networks 9. Diversity in Communication: From Source Coding to Wireless Networks 10. Designing Patterns for Easy Recognition: Information Transmission with Low-Density Parity-Check Codes 11. Turbo Processing 12. Blind Signal Processing Based on Data Geometric Properties 13. Game-Theoretic Learning 14. Learning Observable Operator Models via the Efficient Sharpening Algorithm References Contributors Index