IEEE Signal Processing Magazine: Special Issue on Bootstrap Methods in Signal Processing

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                    IEEE SIGNAL PROCESSING MAGAZINE
        Special Issue on Bootstrap Methods in Signal Processing

Guest Editors:

Abdelhak M. Zoubir
  Signal Processing Group, Institute of Telecommunications
  Technische Universitaet Darmstadt, Darmstadt, Germany
  (zoubir@ieee.org)

D. Robert Iskander
  Institute for Health and Biomedical Innovation,
  Queensland University of Technology, Brisbane, Australia
  (d.iskander@qut.edu.au)


The use of more accurate models in signal processing applications such as communications, radar, sonar, biomedical engineering, speech and image processing and machine learning has become a fundamental requirement. With an improved accuracy the models have become more complex and inferential statistical signal processing required in parameter estimation and signal detection and classification, for example, has become intractable. The signal processing practitioner requires a simple but accurate method for assessing errors of estimates and answering inferential questions.  Asymptotic approximations are useful only when enough data is available, which is not always possible due to time constraints, the nature of the signal or the measurement setting. In place of the formulae and tables of parametric and non-parametric procedures based on complicated mathematics and asymptotic approximations, tools such as the Bootstrap have revolutionized statistics in the last decade and have become powerful for solving complex engineering problems. It is the method of an engineer's choice for solving inferential signal processing problems, such as signal detection, confidence limits estimation and model selection, to mention a few.

This CFP is aimed at researchers that use bootstrap and other resampling techniques in real-life signal processing applications. High-quality tutorial-style papers are sought, in which the theory of bootstrap methods interleaves with real-world signal processing problems. Of particular interest are applications that go beyond the bootstrap methods for standard errors and bootstrap confidence interval estimation. Possible topical areas include, but are not limited to the following:

1. Signal detection
2. Parameter estimation
3. Linear and non-linear model selection
4. Classification and parameter tests
5. Bootstrap methods in artificial neural networks (ANN)
6. Bootstrap methods in speech and image processing
7. Emerging bootstrap related technologies such as the Bayesian Bootstrap, Empirical Likelihood, Boosting and Bagging

Submission Schedule: Prospective authors should submit a two-page white paper to the web submission system at http://apollo.ee.columbia.edu/spm/ , respecting the following strict schedule:

- White paper due:                 September 15, 2006
- Invitation notification:         October 15, 2006
- Manuscript due:                  January 2, 2007
- Acceptance notification:         February 28, 2007
- Final manuscript due:            March 15, 2007
- Publication date:                July 2007 

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