© 2008

Signal Processing Techniques for Knowledge Extraction and Information Fusion

  • Danilo Mandic
  • Martin Golz
  • Anthony Kuh
  • Dragan Obradovic
  • Toshihisa Tanaka


  • Presents knowledge extraction and information fusion supported by state of the art background material

  • Brings together cutting edge research, both theoretical and applied, and reflects the state of the art both in terms of theory applied to biomedical, industrial, and environmental problems

  • Includes contributions by editors and contributors who are experts in their areas and are geographically diverse


Table of contents

  1. Front Matter
    Pages I-XXII
  2. Collaborative Signal Processing Algorithms

    1. Beth Jelfs, Phebe Vayanos, Soroush Javidi, Vanessa Su Lee Goh, Danilo Mandic
      Pages 3-21
    2. Yoshito Hirata, Hideyuki Suzuki, Kazuyuki Aihara
      Pages 23-36
  3. Signal Processing for Source Localization

    1. Henning Lenz, Bruno Betoni Parodi, Hui Wang, Andrei Szabo, Joachim Bamberger, Dragan Obradovic et al.
      Pages 97-120
    2. Anders Høst-Madsen, Nicolas Petrochilos, Olga Boric-Lubecke, Victor M. Lubecke, Byung-Kwon Park, Qin Zhou
      Pages 121-140
    3. Dragan Obradovic, Henning Lenz, Markus Schupfner, Kai Heesche
      Pages 141-158
  4. Information Fusion in Imaging

    1. Hamid Aghajan, Chen Wu, Richard Kleihorst
      Pages 181-200
    2. Norimichi Tsumura, Nobutoshi Ojima, Toshiya Nakaguchi, Yoichi Miyake
      Pages 201-220
    3. Vince D. Calhoun, Tülay Adali
      Pages 221-240
  5. Knowledge Extraction in Brain Science

    1. Danilo Mandic, George Souretis, Wai Yie Leong, David Looney, Marc M. Van Hulle, Toshihisa Tanaka
      Pages 243-260
    2. Tomasz M. Rutkowski, Andrzej Cichocki, Danilo Mandic
      Pages 261-273
    3. Jianting Cao, Zhe Chen
      Pages 275-298
  6. Back Matter
    Pages 317-320

About this book


This state-of-the-art resource brings together the latest findings from the cross-fertilization of signal processing, machine learning and computer science.  The emphasis is on demonstrating synergy of different signal processing methods with knowledge extraction and heterogeneous information fusion. Issues related to the processing of signals with low signal-to-noise ratio, solving real-world multi-channel problems, and using adaptive techniques where nonstationarity, uncertainty and complexity play major roles are addressed.  Particular methods include Independent Component Analysis, Support Vector Machines, Distributed and Collaborative Adaptive Filtering, Empirical Mode Decomposition, Self Organizing Maps, Fuzzy Logic, Evolutionary Algorithms and several others used frequently in these fields.  Also included are both important and novel applications from telecommunications, renewable energy and biomedical engineering.

Signal Processing Techniques for Knowledge Extraction and Information Fusion which proposes new techniques for extracting knowledge based on combining heterogeneous information sources is an excellent reference for professionals in signal and image processing, machine learning, data and sensor fusion, computational intelligence, knowledge discovery, pattern recognition, and environmental science and engineering.


Golz adaptive Filter algorithm algorithms blind source separation brain science collaborative filtering detection filtering filters information renewable energy signal processing signal processing techniques system identification

Editors and affiliations

  • Danilo Mandic
    • 1
  • Martin Golz
    • 2
  • Anthony Kuh
    • 3
  • Dragan Obradovic
    • 4
  • Toshihisa Tanaka
    • 5
  1. 1.Imperial College LondonLondonUK
  2. 2.University of Applied SciencesSchmalkaldenGermany
  3. 3.University of HawaiiHonoluluUSA
  4. 4.Siemens AGMunichGermany
  5. 5.Tokyo University of Agriculture and TechnologyJapan

Bibliographic information