Algorithms for enhancing pattern separability, feature selection and incremental learning with applications to gas sensing electronic nose systems

Three major issues in pattern recognition and data analysis have been addressed in this study and applied to the problem of identification of volatile organic compounds (VOC) for gas sensing applications. Various approaches have been proposed and discussed. These approaches are not only applicable to the VOC identification, but also to a variety of pattern recognition and data analysis problems. In particular, (1) enhancing pattern separability for challenging classification problems, (2) optimum feature selection problem, and (3) incremental learning for neural networks have been investigated;Three different approaches are proposed for enhancing pattern separability for classification of closely spaced, or possibly overlapping clusters. In the neurofuzzy approach, a fuzzy inference system that considers the dynamic ranges of individual features is developed. Feature range stretching (FRS) is introduced as an alternative approach for increasing intercluster distances by mapping the tight dynamic range of each feature to a wider range through a nonlinear function. Finally, a third approach, nonlinear cluster transformation (NCT), is proposed, which increases intercluster distances while preserving intracluster distances. It is shown that NCT achieves comparable, or better, performance than the other two methods at a fraction of the computational burden. The implementation issues and relative advantages and disadvantages of these approaches are systematically investigated;Selection of optimum features is addressed using both a decision tree based approach, and a wrapper approach. The hill-climb search based wrapper approach is applied for selection of the optimum features for gas sensing problems;Finally, a new method, Learn++, is proposed that gives classification algorithms, the capability of incrementally learning from new data. Learn++ is introduced for incremental learning of new data, when the original database is no longer available. Learn++ algorithm is based on strategically combining an ensemble of classifiers, each of which is trained to learn only a small portion of the pattern space. Furthermore, Learn++ is capable of learning new data even when new classes are introduced, and it also features a built-in mechanism for estimating the reliability of its classification decision;All proposed methods are explained in detail and simulation results are discussed along with directions for future work

Biomimetic Based Applications

The interaction between cells, tissues and biomaterial surfaces are the highlights of the book "Biomimetic Based Applications". In this regard the effect of nanostructures and nanotopographies and their effect on the development of a new generation of biomaterials including advanced multifunctional scaffolds for tissue engineering are discussed. The 2 volumes contain articles that cover a wide spectrum of subject matter such as different aspects of the development of scaffolds and coatings with enhanced performance and bioactivity, including investigations of material surface-cell interactions

Schweizerische Präsenz an internationalen Forschungsfronten 1999

Winterhager M, Schwechheimer H. Schweizerische Präsenz an internationalen Forschungsfronten 1999. Center for Science and Technology Studies. Vol 2002,8. Bern: CEST; 2002.Ziel der vorliegenden Untersuchung ist die Identifikation und Analyse der wichtigsten Forschungsfronten, an denen in der Schweiz tätige Forschende 1999 beteiligt waren. Schwerpunkte schweizerischer Forschungsaktivität werden durch ein bibliometrisches Profil auf der Grundlage einer Ko-Zitationsanalyse transparent gemacht. Die mit der Ko-Zitationsanalyse identifizierten Forschungsfronten liefern eine Abbildung der aktuellen Forschungslandschaft, die allein auf der Auswertung der Ströme formaler Kommunikation (Publikationen und Zitationen) beruht. In diesem Sinne ist das Verfahren unabhängig von bestehenden Klassifikationsschemata, disziplinären Zuordnungen und subjektiven Sichtweisen einzelner Experten. Es nutzt lediglich die durch die publizierenden Forscherinnen und Forscher selbst realisierten kognitiven Bezüge, um aktuelle Forschungsfronten zu identifizieren und ihre Relationen zueinander darzustellen. Der Bericht dokumentiert zunächst das Ergebnis der Suche nach den Forschungsfronten mit schweizerischer Beteiligung. Als Datenbasis wurde eine Ko-Zitationsanalyse des Jahrgangs 1999 des Science Citation Index Expanded und des Social Sciences Citation Index herangezogen. Diese Datenbasis besteht aus insgesamt 22942 Forschungsfronten aus allen disziplinären Bereichen. Die Forschungsfronten werden ohne vorgängige disziplinäre Kategorisierungen generiert und sind daher in besonderer Weise geeignet, interdisziplinäre Entwicklungen abzubilden. Aus dem Gesamtdatenbestand aller Fronten des Jahrgangs 1999 wurden diejenigen 2.404 ausgewählt, in deren Kern mindestens eine Publikation schweizerischen Ursprungs enthalten ist