Instrumentation thrusts and achievements are reported in the field of simulation of aerospace dynamics. Quantified mapping techniques and measurements in research in unsteady fluid mechanics phenomena are described and the frontiers of speed and flight simulation are extended."

Many methods and models have been proposed for solving difficult problems such as prediction, planning and knowledge discovery in application areas such as bioinformatics, speech and image analysis. Most, however, are designed to deal with static processes which will not change over time. Some processes - such as speech, biological information and brain signals - are not static, however, and in these cases different models need to be used which can trace, and adapt to, the changes in the processes in an incremental, on-line mode, and often in real time. This book presents generic computational models and techniques that can be used for the development of evolving, adaptive modelling systems. The models and techniques used are connectionist-based (as the evolving brain is a highly suitable paradigm) and, where possible, existing connectionist models have been used and extended. The first part of the book covers methods and techniques, and the second focuses on applications in bioinformatics, brain study, speech, image, and multimodal systems. It also includes an extensive bibliography and an extended glossary. Evolving Connectionist Systems is aimed at anyone who is interested in developing adaptive models and systems to solve challenging real world problems in computing science or engineering. It will also be of interest to researchers and students in life sciences who are interested in finding out how information science and intelligent information processing methods can be applied to their domains.

This is Volume III of a three volume set constituting the refereed proceedings of the Third International Symposium on Neural Networks, ISNN 2006. 616 revised papers are organized in topical sections on neurobiological analysis, theoretical analysis, neurodynamic optimization, learning algorithms, model design, kernel methods, data preprocessing, pattern classification, computer vision, image and signal processing, system modeling, robotic systems, transportation systems, communication networks, information security, fault detection, financial analysis, bioinformatics, biomedical and industrial applications, and more.

The genesis of this volume was the participation of the editors in an ACMlSIGIR (Association for Computing Machinery/Special Interest Group on Information Retrieval) workshop entitled "Beyond Word Relations" (Hetzler, 1997). This workshop examined a number of relationship types with significance for information retrieval beyond the conventional topic-matching relationship. From this shared participation came the idea for an edited volume on relationships, with chapters to be solicited from researchers and practitioners throughout the world. Ultimately, one volume became two volumes. The first volume, Relationships in the Organization of Knowledge (Bean & Green, 200 I), examines the role of relationships in knowledge organization theory and practice, with emphasis given to thesaural relationships and integration across systems, languages, cultures, and disciplines. This second volume examines relationships in a broader array of contexts. The two volumes should be seen as companions, each informing the other. As with the companion volume, we are especially grateful to the authors who willingly accepted challenges of space and time to produce chapters that summarize extensive bodies of research. The value of the volume clearly resides in the quality of the individual chapters. In naming this volume The Semantics of Relationships: An Interdisciplinary Perspective, we wanted to highlight the fact that relationships are not just empty connectives. Relationships constitute important conceptual units and make significant contributions to meaning.