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New Directions in Statistical Signal Processing : From Systems to Brains.

By: Contributor(s): Material type: TextTextSeries: Neural Information Processing SeriesPublisher: Cambridge : MIT Press, 2006Copyright date: ©2007Edition: 1st edDescription: 1 online resource (525 pages)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9780262256315
Subject(s): Genre/Form: Additional physical formats: Print version:: New Directions in Statistical Signal ProcessingDDC classification:
  • 006.32
LOC classification:
  • QP363.3.N52 2007
Online resources:
Contents:
Intro -- 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.
Summary: Leading researchers in signal processing and neural computation present work aimed at promoting the interaction and cross-fertilization between the two fields.
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Intro -- 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.

Leading researchers in signal processing and neural computation present work aimed at promoting the interaction and cross-fertilization between the two fields.

Description based on publisher supplied metadata and other sources.

Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.

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