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.

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.

Blockchain, whether public or private, is capable enough to maintain the integrity of transactions by decentralizing the records for users. Many IoT companies are using blockchain technology to make the world a better-connected place. Businesses and researchers are exploring ways to make this technology increasingly efficient for IoT services. This volume presents the recent advances in these two technologies. Chapters explain the fundamentals of Blockchain and IoT, before explaining how these technologies, when merged together, provide a transparent, reliable, and secure model for data processing by intelligent devices in various domains. Readers will be able to understand how these technologies are making an impact on healthcare, supply chain management and electronic voting, to give a few examples. The 10 peer-reviewed book chapters have been contributed by scholars, researchers, academicians, and engineering professionals, and provide a comprehensive yet easily digestible update on Blockchain on IoT technology.

This reference presents information about different facets of IoT and blockchain systems that have been recently proposed for practical situations. Chapters provide knowledge about how these technologies are applied in functions related to trust management, identity management, security threats, access control and privacy. </p> Key Features: </p> - Introduces the reader to fundamental concepts of IoT and blockchain technology </p> - reports advances in the field of IoT, ubiquitous computing and blockchain computing </p> - includes the applications of different frameworks </p> - explains the role of blockchains in improving IT security </p> - provides examples of smart grids, data transmission models, digital business platforms, agronomics and big data solutions </p> - Includes references for further reading </p> Blockchain Applications for Secure IoT Frameworks: Technologies Shaping the Future is a handy reference for information technology professionals and students who want updated information about applications of IoT and blockchains in secure operational and business processes.

This reference provides the reader with focused information about microstrip antenna design and applications. Readers are first introduced to the basic design of microstrip antennas. Subsequent chapters explain how microstrip antennas are suitable for practical applications. These chapters cover topics such as fractal and defected ground structure antennas, microstrip antenna evaluation, and the use of microstrip antennas in mobile communications and IoT applications. Scholars, researchers, and industrial professionals involved in the fields of electronics and electrical engineering as well as instrumentation will benefit from the information given in this book.