Drift Correction Methods for gas Chemical Sensors in Artificial Olfaction Systems: Techniques and Challenges

In this chapter the authors introduce the main challenges faced when developing drift correction techniques and will propose a deep overview of state-of-the-art methodologies that have been proposed in the scientific literature trying to underlying pros and cons of these techniques and focusing on challenges still open and waiting for solution

Design Issues and Challenges of File Systems for Flash Memories

This chapter discusses how to properly address the issues of using NAND flash memories as mass-memory devices from the native file system standpoint. We hope that the ideas and the solutions proposed in this chapter will be a valuable starting point for designers of NAND flash-based mass-memory devices

Pattern Analysis & Applications (2002)5:306–315 Ownership and Copyright © 2002 Springer-Verlag London Limited Dynamic Cluster Recognition with Multiple Self-Organising Maps

Abstract: A neural architecture, based on several self-organising maps, is presented which counteracts the parameter drift problem for an array of conducting polymer gas sensors when used for odour sensing. The neural architecture is named mSom, where m is the number of odours to be recognised, and is mainly constituted of m maps; each one approximates the statistical distribution of a given odour. Competition occurs both within each map and between maps for the selection of the minimum map distance in the Euclidean space. The network (mSom) is able to adapt itself to new changes of the input probability distribution by repetitive self-training processes based on its experience. This architecture has been tested and compared with other neural architectures, such as RBF and Fuzzy ARTMAP. The network shows long-term stable behaviour, and is completely autonomous during the testing phase, where re-adaptation of the neurons is needed due to the changes of the input probability distribution of the given data set