Affective Computing

This book provides an overview of state of the art research in Affective Computing. It presents new ideas, original results and practical experiences in this increasingly important research field. The book consists of 23 chapters categorized into four sections. Since one of the most important means of human communication is facial expression, the first section of this book (Chapters 1 to 7) presents a research on synthesis and recognition of facial expressions. Given that we not only use the face but also body movements to express ourselves, in the second section (Chapters 8 to 11) we present a research on perception and generation of emotional expressions by using full-body motions. The third section of the book (Chapters 12 to 16) presents computational models on emotion, as well as findings from neuroscience research. In the last section of the book (Chapters 17 to 22) we present applications related to affective computing

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

Abstracts on Radio Direction Finding (1899 - 1995)

The files on this record represent the various databases that originally composed the CD-ROM issue of "Abstracts on Radio Direction Finding" database, which is now part of the Dudley Knox Library's Abstracts and Selected Full Text Documents on Radio Direction Finding (1899 - 1995) Collection. (See Calhoun record https://calhoun.nps.edu/handle/10945/57364 for further information on this collection and the bibliography). Due to issues of technological obsolescence preventing current and future audiences from accessing the bibliography, DKL exported and converted into the three files on this record the various databases contained in the CD-ROM. The contents of these files are: 1) RDFA_CompleteBibliography_xls.zip [RDFA_CompleteBibliography.xls: Metadata for the complete bibliography, in Excel 97-2003 Workbook format; RDFA_Glossary.xls: Glossary of terms, in Excel 97-2003 Workbookformat; RDFA_Biographies.xls: Biographies of leading figures, in Excel 97-2003 Workbook format]; 2) RDFA_CompleteBibliography_csv.zip [RDFA_CompleteBibliography.TXT: Metadata for the complete bibliography, in CSV format; RDFA_Glossary.TXT: Glossary of terms, in CSV format; RDFA_Biographies.TXT: Biographies of leading figures, in CSV format]; 3) RDFA_CompleteBibliography.pdf: A human readable display of the bibliographic data, as a means of double-checking any possible deviations due to conversion

Advances in Intelligent Vehicle Control

This book is a printed edition of the Special Issue Advances in Intelligent Vehicle Control that was published in the journal Sensors. It presents a collection of eleven papers that covers a range of topics, such as the development of intelligent control algorithms for active safety systems, smart sensors, and intelligent and efficient driving. The contributions presented in these papers can serve as useful tools for researchers who are interested in new vehicle technology and in the improvement of vehicle control systems

A speaker classification framework for non-intrusive user modeling : speech-based personalization of in-car services

Speaker Classification, i.e. the automatic detection of certain characteristics of a person based on his or her voice, has a variety of applications in modern computer technology and artificial intelligence: As a non-intrusive source for user modeling, it can be employed for personalization of human-machine interfaces in numerous domains. This dissertation presents a principled approach to the design of a novel Speaker Classification system for automatic age and gender recognition which meets these demands. Based on literature studies, methods and concepts dealing with the underlying pattern recognition task are developed. The final system consists of an incremental GMM-SVM supervector architecture with several optimizations. An extensive data-driven experiment series explores the parameter space and serves as evaluation of the component. Further experiments investigate the language-independence of the approach. As an essential part of this thesis, a framework is developed that implements all tasks associated with the design and evaluation of Speaker Classification in an integrated development environment that is able to generate efficient runtime modules for multiple platforms. Applications from the automotive field and other domains demonstrate the practical benefit of the technology for personalization, e.g. by increasing local danger warning lead time for elderly drivers.Die Sprecherklassifikation, also die automatische Erkennung bestimmter Merkmale einer Person anhand ihrer Stimme, besitzt eine Vielzahl von Anwendungsmöglichkeiten in der modernen Computertechnik und Künstlichen Intelligenz: Als nicht-intrusive Wissensquelle für die Benutzermodellierung kann sie zur Personalisierung in vielen Bereichen eingesetzt werden. In dieser Dissertation wird ein fundierter Ansatz zum Entwurf eines neuartigen Sprecherklassifikationssystems zur automatischen Bestimmung von Alter und Geschlecht vorgestellt, welches diese Anforderungen erfüllt. Ausgehend von Literaturstudien werden Konzepte und Methoden zur Behandlung des zugrunde liegenden Mustererkennungsproblems entwickelt, welche zu einer inkrementell arbeitenden GMM-SVM-Supervector-Architektur mit diversen Optimierungen führen. Eine umfassende datengetriebene Experimentalreihe dient der Erforschung des Parameterraumes und zur Evaluierung der Komponente. Weitere Studien untersuchen die Sprachunabhängigkeit des Ansatzes. Als wesentlicher Bestandteil der Arbeit wird ein Framework entwickelt, das alle im Zusammenhang mit Entwurf und Evaluierung von Sprecherklassifikation anfallenden Aufgaben in einer integrierten Entwicklungsumgebung implementiert, welche effiziente Laufzeitmodule für verschiedene Plattformen erzeugen kann. Anwendungen aus dem Automobilbereich und weiteren Domänen demonstrieren den praktischen Nutzen der Technologie zur Personalisierung, z.B. indem die Vorlaufzeit von lokalen Gefahrenwarnungen für ältere Fahrer erhöht wird