Model Driven development (MDD) is a software and systems development model that involves the application of visual modeling principles and best practices.

The surge of multicore processors coming into the market and on users’ desktops has made parallel computing the focus of attention once again. This time, however, it is led by the industry, which ensures that multicore computing is here to stay. Nevertheless, there is still so much research work to be done in multicore hardware-software designs before consumer applications can leverage the benefits of this new paradigm. This ebook is being put forward as a platform for immediate collection of state-of-the-art technologies in both hardware and software designs for multicore computing. With the burgeoning prevalence of multicore processors in embedded systems, real-time systems, multimedia systems, bioinformatics systems, network systems, to list a few, this ebook attempts to cover the design and verification issues related to different application domains as a singular source of reference to the state-of-the-art techniques in multicore processor design and software programming that covers multiple application domains. This ebook will be of immense help to system and software engineers, including both experts and non-experts in parallel computing.

Multi-Objective Optimization in Theory and Practice is a traditional two-part approach to solving multi-objective optimization (MOO) problems namely the use of classical methods and evolutionary algorithms. This first book is devoted to classical methods including the extended simplex method by Zeleny and preference-based techniques. This part covers three main topics through nine chapters. The first topic focuses on the design of such MOO problems, their complexities including nonlinearities and uncertainties, and optimality theory. The second topic introduces the founding solving methods including the extended simplex method to linear MOO problems and weighting objective methods. The third topic deals with particular structures of MOO problems, such as mixed-integer programming, hierarchical programming, fuzzy logic programming, and bimatrix games. Multi-Objective Optimization in Theory and Practice is a user-friendly book with detailed, illustrated calculations, examples, test functions, and small-size applications in Mathematica® (among other mathematical packages) and from scholarly literature. It is an essential handbook for students and teachers involved in advanced optimization courses in engineering, information science, and mathematics degree programs.

Multi-Objective Optimization in Theory and Practice is a simplified two-part approach to multi-objective optimization (MOO) problems. This second part focuses on the use of metaheuristic algorithms in more challenging practical cases. The book includes ten chapters that cover several advanced MOO techniques. These include the determination of Pareto-optimal sets of solutions, metaheuristic algorithms, genetic search algorithms and evolution strategies, decomposition algorithms, hybridization of different metaheuristics, and many-objective (more than three objectives) optimization and parallel computation. The final section of the book presents information about the design and types of fifty test problems for which the Pareto-optimal front is approximated. For each of them, the package NSGA-II is used to approximate the Pareto-optimal front. It is an essential handbook for students and teachers involved in advanced optimization courses in engineering, information science and mathematics degree programs.

Agent technology has generated lots of excitement in the past decade. Currently, multi-agent systems (MAS) composed of autonomous agents representing individuals or organizations and capable of reaching mutually beneficial agreements through negotiation and argumentation are becoming increasingly important and pervasive. Research on both automated negotiation and argumentation in MAS has a vigorous, exciting tradition. However, efforts to integrate both areas have received only selective attention in the academia and the practitioner literature. A symbiotic relationship could significantly strengthen each area’s progress and trigger new R&D challenges and prospects toward the advancement of automated negotiators and argumentation tools. Negotiation and Argumentation in Multi-Agent Systems presents the current state-of-the-art on the theory and practice of automated negotiation and argumentation in MAS. The eBook encourages the interaction between these two areas in data modelling and attempts to converge them toward mutual enhancement and synergism. Equally, the monograph brings together researchers and industry practitioners specialized in these areas to share R&D results and discuss existing and emerging theoretical and applied problems. This book is intended as a textbook for graduate courses and a reference book for researchers, advanced-level students in Computers Science, and IT practitioners.

This book dives into the heart of how to design distributed control architectures for heterogeneous teams of humans, robots, and automated systems, enabling them to achieve greater cooperation and autonomy through the use of network technologies. It provides a wide range of practical, proven strategies for pervasive communication and collaborative problem solving abilities of humans, robots, and their environments. Each chapter consists of a presentation of findings from the latest research in networked robots and ambient intelligence. The chapters also detail how to allow robots to achieve universal access to the extended functionality of the environment that brings various cost effective services to those in need. Readers can envision a realistic view of what can be expected from a networked human robot cooperative environment in the next decade.

The book examines several aspects of Orthogonal Frequency Division Multiplexing (OFDM) employing linear diversity techniques such as inter-carrier interference, bit error rate, peak to average power and inter-block interference. It should be a useful reference for readers interested in modern wireless communication systems.

This E-book is a collection of articles that describe advances in speech recognition technology. Robustness in speech recognition refers to the need to maintain high speech recognition accuracy even when the quality of the input speech is degraded, or when the acoustical, articulate, or phonetic characteristics of speech in the training and testing environments differ. Obstacles to robust recognition include acoustical degradations produced by additive noise, the effects of linear filtering, nonlinearities in transduction or transmission, as well as impulsive interfering sources, and diminished accuracy caused by changes in articulation produced by the presence of high-intensity noise sources. Although progress over the past decade has been impressive, there are significant obstacles to overcome before speech recognition systems can reach their full potential. Automatic speech recognition (ASR) systems must be robust to all levels, so that they can handle background or channel noise, the occurrence on unfamiliar words, new accents, new users, or unanticipated inputs. They must exhibit more 'intelligence' and integrate speech with other modalities, deriving the user's intent by combining speech with facial expressions, eye movements, gestures, and other input features, and communicating back to the user through multimedia responses. Therefore, as speech recognition technology is transferred from the laboratory to the marketplace, robustness in recognition becomes increasingly significant. This E-book should be useful to computer engineers interested in recent developments in speech recognition technology.

Face recognition has several applications, including security, such as (authentication and identification of device users and criminal suspects), and in medicine (corrective surgery and diagnosis). Facial recognition programs rely on algorithms that can compare and compute the similarity between two sets of images. This eBook explains some of the similarity measures used in facial recognition systems in a single volume. Readers will learn about various measures including Minkowski distances, Mahalanobis distances, Hansdorff distances, cosine-based distances, among other methods. The book also summarizes errors that may occur in face recognition methods. Computer scientists "facing face" and looking to select and test different methods of computing similarities will benefit from this book. The book is also useful tool for students undertaking computer vision courses.

This brief textbook explains the principles of social network analysis. The book goes beyond theoretical concepts and gives the reader complete knowledge about how to apply analytical techniques using Pajek to perform a large-scale network analysis. The book covers the topic in 2 sections – the first detailing fundamentals of research design and the next one about methods and applications. Readers can then apply the techniques in this book to other online communities, such as Facebook and Twitter. The book is intended for networking students and general readers who want to learn the basics without going deep into mathematical methods. It is also useful for researchers and professionals from other fields seeking to understand the basics of large-scale social network analysis.