Intelligent Systems for Machine Olfaction

Manel Martinez-Ramon, Evor Hines, Mark Leeson, editors

INTRODUCTION


Intelligent systems are those that, given some data, are able to learn from that data. This ability makes it possible for complex systems to be modeled and/or for performance to be predicted. In turn it is possible to control their functionality through learning/training, without the need for a priori knowledge of the system s structure.

In the early years of research into intelligent systems, neural networks were very popular because they were the first such systems to be able to produce nonlinear behavior and adaptive characteristics through online training. Nevertheless, some of these early systems resulted in a high degree of complexity that could not be controlled, and a large number of free parameters need to be adjusted, resulting in overfitting and a high computational burden. Since then, a vast array of literature has been written on this and related areas, and a number of modern alternatives have been introduced that overcome almost all the drawbacks of the classical techniques. These alternatives include genetic and evolutionary algorithms, fuzzy algorithms, kernels or support vector machines; thus making it possible to use intelligent systems to solve a very wide range of practical applications.

OBJECTIVE OF THE BOOK


This work will introduce new and state-of-the art applications of intelligent systems to researchers and developers in the area of machine olfaction who may benefit from the use of these intelligent systems techniques. The material will be presented in a series of chapters that support the reader via the introduction of theoretical material and application examples. The book will also reach potential readers in other research areas such as chemistry, biology, medicine and other related areas where intelligent systems have a great potential that has only barely been explored.

In addition this work will serve as a reference of the main theory, state of the art intelligent systems and possible technology trends in intelligent systems for machine olfaction. Also, the book will contain the key references that are needed for further reading. The book will be a source of application examples that can be readily implemented. Hence the publication will serve as a practical guide for the implementation of solutions in other scenarios.

Publisher

This book is scheduled to be published by IGI Global (formerly Idea Group Inc.), publisher of the Information Science Reference (formerly Idea Group Reference), Medical Information Science Reference, and IGI Publishing imprints. For additional information regarding the publisher, please visit www.igi-global.com. It is anticipated that this publication will be released in the second half of 2010.

Important Dates:

April 17, 2009:

April 24, 2009:

June 25, 2009:

Aug. 30, 2009:

Oct. 30, 2009:

Proposal Submission

Notification of Acceptance

Full Chapter Submission

Review Result Returned

Final Chapter Submission

Inquiries and submissions can be forwarded electronically (Word document) or by mail to:

Manel Martinez-Ramon

Department of Signal Theory and Communications

Universidad Carlos III de Madrid

E-mail: manel@tsc.uc3m.es

Evor Hines

School of Engineering

The University of Warwick

E-mail: E.L.Hines@warwick.ac.uk

Mark Leeson

School of Engineering

The University of Warwick

E-mail: Mark-Leeson@warwick.ac.uk

TARGET AUDIENCE

This book will be an invaluable text for engineers and others who do not have a background in intelligent systems and are in need of an accessible starting point. It will also be an attractive resource for educators who may find it suitable for final year undergraduates and graduates for their courses as a means of illustrating challenging aspects of real world intelligent systems applications.

Recommended topics include, but are not limited to, the following:


Intelligent Systems/Computational Intelligence/Soft Computing with Evolutionary Programming such as Genetic Algorithms, Genetic Programming

Intelligent Systems with Kernel Methods

Intelligent Systems/Computational Intelligence/Soft Computing with Novel neural networks, swarm intelligence approaches

Intelligent Systems/Computational Intelligence/Soft Computing with hybrid systems: such as neuro-fuzzy, evolutionary-fuzzy etc

SUBMISSION PROCEDURE

Researchers and practitioners are invited to submit on or before April 17, 2009, a 2-3 page chapter proposal clearly explaining the mission and concerns of his or her proposed chapter. Authors of accepted proposals will be notified by April 24, 2009 about the status of their proposals and sent chapter guidelines. Full chapters are expected to be submitted by June 25, 2009. All submitted chapters will be reviewed on a double-blind review basis. Contributors may also be requested to serve as reviewers for this project. Additional information for this project can be found at: www.igi-global.com/requests/details.asp?ID=610

EXISTING COMPETING PUBLICATIONS

There are a number of books devoted to the theory and applications of Intelligent Systems, but, to our best knowledge, there are no publications that are concerned with a set of applications covering the main areas of engineering in computational aspects of machine olfaction yet. The most cited and authoritative edited book on machine olfaction is probably [1]. This book has a part (Part C) dedicated to Signal Processing and Pattern Analysis. Two of the editors of the present book are authors also in [1]. The fundamental difference between our book and [1] is that [1] is mainly devoted to an audience of sensor people, while we primarily target Intelligent Systems practitioners who have an interest in applications; as well as any such aspiring practitioners. This will have a clear impact on the presentation style. Also the content of the chapters will be different. We will have a more focused, detailed and advanced presentation of state of the art pattern recognition topics (feature selection and classification), while in [1] a single chapter (Pattern Analysis for Electronic Noses, by Hines et al.) contained a basic review of all analysis techniques, from PCA to cluster analysis to linear discriminants. Moreover we will concentrate on the importance of advanced computational approaches for different applications, while -again- in [1] mostly the sensor and sensor system contribution was stressed.

[1] Handbook of Machine Olfaction: Electronic Nose Technology, edited by Tim C. Pearce, Susan S. Schiffman, H. Troy Nagle, Julian W. Gardner. Wiley-VCH , 2003

TENTATIVE TABLE OF CONTENTS

Introduction (Hines, Leeson, Martinez-Ramon).

Section 1. Genetic Algorithms (Editor: E. Hines)

1.1 Introduction to GA

1.2 to 1.6, Case Studies

Section 2. Evolutionary Programming (Editor: M. Leeson)

2.1 Introduction to evolutionary programming

2.2 to 2.6, Case Studies.

Section 3 Kernel Methods (Editor: M. Martinez-Ramon)

3.1 Introduction to Kernel Methods

3.2 to 3.6, Case Studies.