This book brings together papers from the 2018 International Conference on Communications, Signal Processing, and Systems, which was held in Dalian, China on July 14-16, 2018. Presenting the latest developments and discussing the interactions and links between these multidisciplinary fields, the book spans topics ranging from communications, signal processing and systems. It is aimed at undergraduate and graduate electrical engineering, computer science and mathematics students, researchers and engineers from academia and industry as well as government employees.

The sixth generation of wireless communication (6G), succeeding 5G cellular technology, opens up several possibilities in terms of technology and its offered services. 6G is expected to allow usage of available higher frequency spectrums to cater to increased capacity, throughput, and low latency (<1 µs). 6G will witness the unification of various technologies, such as artificial intelligence (AI), machine learning (ML), augmented/virtual reality (AR/VR), etc., to provide an immersive user experience. It is foreseen as the accelerator of transformation and innovation globally. To make this book a fundamental resource, we have invited world-renowned experts in 6G from the industry and academia to pen down their ideas on different aspects of 6G research. The chapters in this book cover a broader scope and various related and unrelated verticals. Specifically, this book covers the following topics: 6G use cases, requirements, and enabling technologies new spectrums and their challenges for 6G privacy preservation in 6G networks aerial infrastructure for 6G networks economic challenges associated with 6G wireless networks. The encompassing intent of this book is to explore the evolution from current 5G networks towards the future 6G networks from a service, air interface, and network perspective, thereby laying out a vision for 6G networks.

This book is timely and discusses the effects from the pandemic. Written for longevity, and may be useful to compare this pandemic and the response to future events. The book is written for academia: social sciences, public health, information science, emergency management, and policy fields, and is easier informational reading for the layperson.

This book cover wireless communication, security issues, advanced wireless sensor networks, routing protocols of WSNs with cross-layer solutions, emerging trends in the advanced WSNs, power management, distributed sensing and data gathering techniques for WSNs, WSNs Security, applications, research of advanced WSNs with simulation results, and simulation tools for WSNs. Features: Covers technologies supporting advanced wireless communication system, sensor networks and the conceptual development of the subject. Discusses advanced data gathering and sharing/ distributed sensing techniques with its business applicability. Includes numerous worked-out mathematical equations/formulas, and essential principles including figures, illustrations, algorithms, and flow charts are included in the book. Provides pervasive background knowledge including both wireless communications and wireless sensor networks Covers wireless networks as well as sensor network models in detailed. This is aimed at graduate students, researchers and academicians working in the field of computer science, wireless communication technology, and advanced wireless sensor networks.

This book gathers papers presented at the 9th International Conference on Computer Engineering and Networks (CENet2019), held in Changsha, China, on October 18-20, 2019. It examines innovations in the fields of computer engineering and networking and explores important, state-of-the-art developments in areas such as Information Security, Information Hiding and Cryptography, Cyber Security, and Intelligent Computing and Applications. The book also covers emerging topics in computer engineering and networking, along with their applications, discusses how to improve productivity by using the latest advanced technologies, and examines innovation in the fields of computer engineering and networking, particularly in intelligent computing and security.

Optimization techniques have been widely adopted to implement various data mining algorithms. In addition to well-known Support Vector Machines (SVMs) (which are based on quadratic programming), different versions of Multiple Criteria Programming (MCP) have been extensively used in data separations. Since optimization based data mining methods differ from statistics, decision tree induction, and neural networks, their theoretical inspiration has attracted many researchers who are interested in algorithm development of data mining.

"Optimization based Data Mining: Theory and Applications," mainly focuses on MCP and SVM especially their recent theoretical progress and real-life applications in various fields. These include finance, web services, bio-informatics and petroleum engineering, which has triggered the interest of practitioners who look for new methods to improve the results of data mining for knowledge discovery.

Most of the material in this book is directly from the research and application activities that the authors' research group has conducted over the last ten years. Aimed at practitioners and graduates who have a fundamental knowledge in data mining, it demonstrates the basic concepts and foundations on how to use optimization techniques to deal with data mining problems.

This book intersects the distributed ledger technology (DLT) community with the international security community. Given the increasing application of blockchain technology in the fields of business and international development, there is a growing body of study on other use cases. For instance, can blockchain have a significant role in preserving and improving international security? This book explores this question in the context of preventing the proliferation of some of the most dangerous materials in the world-items that if not secured can lend to the development of weapons of mass destruction. It considers how blockchain can increase efficiencies in the global trade of nuclear and chemical materials and technology, thereby increasing assurances related to compliance with international nonproliferation and disarmament treaties.

This book systematically summarizes the fundamentals and various technologies in both terrestrial radio wireless networks and underwater acoustic networks (UWANs). It addresses the basic issues frequently investigated in terrestrial radio wireless networks and the key technologies suitable for the newly developing research area of UWANs. Starting with a review of our current understanding of wireless networks, it then introduces the principles of the main technologies, including error control, medium access control (MAC) protocols, routing protocols, end-to-end transmission control and mobility issues as well as network security for terrestrial radio wireless networks, and offers detailed surveys of these technologies for UWANs. Providing readers with the basic knowledge of terrestrial radio wireless networking technologies and raising readers' awareness of the developing topic of UWANs in ocean , it is a valuable resource for researchers and practitioners in terrestrial radio wireless networks and UWANs.

This book introduces novel solutions to the rendezvous problem in distributed systems, a fundamental problem that underpins the construction of many important functions in distributed systems and networks. The book covers rendezvous theories, distributed rendezvous algorithms, and rendezvous applications in practical systems, presents state-of-the-art rendezvous results and highlights the latest methods of rendezvous in distributed systems. It provides in particular an in-depth treatment of the blind rendezvous and oblivious blind rendezvous problems and their solutions. Further, it sheds new light on rendezvous applications in cognitive radio networks and rendezvous search in graphs. As such, it will also be of interest to readers from other research fields such as robotics, wireless sensor networks, and game theory.

This book constitutes the refereed proceedings of the 6th International Conference on Computer, Communication, and Signal Processing, ICCSP 2022, held in Chennai, India, in February 2022.* The 21 full and 2 short papers presented in this volume were carefully reviewed and selected from 111 submissions. The papers are categorized into topical sub-headings: artificial intelligence and machine learning; Cyber security; and internet of things. *The conference was held as a virtual event due to the COVID-19 pandemic.

Global Information Society: Operating Information Systems in a Dynamic Global Business Environment addresses the importance of information technology management and issues in operating information systems in the global dynamic business environment. It embraces discussions of the global information technology theory, frameworks and IT architecture, discovery of global knowledge management, improvement of the global information systems development methodologies, and applications of the latest technologies such as mobile technology and web services in the global information systems development and operations. The book features a collection of new ideas, latest technology applications and experiences in the global information systems development and operations.

This much-needed update to the bestselling guide on the extensive changes to the local area networks (LAN) switching technologies explains why LAN switching technologies are critical to network design. This in-depth guide covers the capabilities, application, and design of LAN switches and switched internetworks and examines the significant changes that have taken place since the publication of the first edition seven years ago. You're sure to appreciate the witty writing style and easy-to-follow format on such an often-complicated subject matter.

This handbook provides comprehensive knowledge and includes an overview of the current state-of-the-art of Big Data Privacy, with chapters written by international world leaders from academia and industry working in this field. The first part of this book offers a review of security challenges in critical infrastructure and offers methods that utilize acritical intelligence (AI) techniques to overcome those issues. It then focuses on big data security and privacy issues in relation to developments in the Industry 4.0. Internet of Things (IoT) devices are becoming a major source of security and privacy concern in big data platforms. Multiple solutions that leverage machine learning for addressing security and privacy issues in IoT environments are also discussed this handbook. The second part of this handbook is focused on privacy and security issues in different layers of big data systems. It discusses about methods for evaluating security and privacy of big data systems on network, application and physical layers. This handbook elaborates on existing methods to use data analytic and AI techniques at different layers of big data platforms to identify privacy and security attacks. The final part of this handbook is focused on analyzing cyber threats applicable to the big data environments. It offers an in-depth review of attacks applicable to big data platforms in smart grids, smart farming, FinTech, and health sectors. Multiple solutions are presented to detect, prevent and analyze cyber-attacks and assess the impact of malicious payloads to those environments. This handbook provides information for security and privacy experts in most areas of big data including; FinTech, Industry 4.0, Internet of Things, Smart Grids, Smart Farming and more. Experts working in big data, privacy, security, forensics, malware analysis, machine learning and data analysts will find this handbook useful as a reference. Researchers and advanced-level computer science students focused on computer systems, Internet of Things, Smart Grid, Smart Farming, Industry 4.0 and network analysts will also find this handbook useful as a reference.

The book discusses the fundamentals of high-performance computing. The authors combine visualization, comprehensibility, and strictness in their material presentation, and thus influence the reader towards practical application and learning how to solve real computing problems. They address both key approaches to programming modern computing systems: multithreading-based parallelizing in shared memory systems, and applying message-passing technologies in distributed systems. The book is suitable for undergraduate and graduate students, and for researchers and practitioners engaged with high-performance computing systems. Each chapter begins with a theoretical part, where the relevant terminology is introduced along with the basic theoretical results and methods of parallel programming, and concludes with a list of test questions and problems of varying difficulty. The authors include many solutions and hints, and often sample code.

This book covers all the relevant dictionary learning algorithms, presenting them in full detail and showing their distinct characteristics while also revealing the similarities. It gives implementation tricks that are often ignored but that are crucial for a successful program. Besides MOD, K-SVD, and other standard algorithms, it provides the significant dictionary learning problem variations, such as regularization, incoherence enforcing, finding an economical size, or learning adapted to specific problems like classification. Several types of dictionary structures are treated, including shift invariant; orthogonal blocks or factored dictionaries; and separable dictionaries for multidimensional signals. Nonlinear extensions such as kernel dictionary learning can also be found in the book. The discussion of all these dictionary types and algorithms is enriched with a thorough numerical comparison on several classic problems, thus showing the strengths and weaknesses of each algorithm. A few selected applications, related to classification, denoising and compression, complete the view on the capabilities of the presented dictionary learning algorithms. The book is accompanied by code for all algorithms and for reproducing most tables and figures. Presents all relevant dictionary learning algorithms - for the standard problem and its main variations - in detail and ready for implementation; Covers all dictionary structures that are meaningful in applications; Examines the numerical properties of the algorithms and shows how to choose the appropriate dictionary learning algorithm.

This book presents the features and advantages offered by complex networks in the machine learning domain. In the first part, an overview on complex networks and network-based machine learning is presented, offering necessary background material. In the second part, we describe in details some specific techniques based on complex networks for supervised, non-supervised, and semi-supervised learning. Particularly, a stochastic particle competition technique for both non-supervised and semi-supervised learning using a stochastic nonlinear dynamical system is described in details. Moreover, an analytical analysis is supplied, which enables one to predict the behavior of the proposed technique. In addition, data reliability issues are explored in semi-supervised learning. Such matter has practical importance and is not often found in the literature. With the goal of validating these techniques for solving real problems, simulations on broadly accepted databases are conducted. Still in this book, we present a hybrid supervised classification technique that combines both low and high orders of learning. The low level term can be implemented by any classification technique, while the high level term is realized by the extraction of features of the underlying network constructed from the input data. Thus, the former classifies the test instances by their physical features, while the latter measures the compliance of the test instances with the pattern formation of the data. We show that the high level technique can realize classification according to the semantic meaning of the data. This book intends to combine two widely studied research areas, machine learning and complex networks, which in turn will generate broad interests to scientific community, mainly to computer science and engineering areas.

1. Covers latest concepts in intelligent analytics for industry 4.0. 2. Presents the applications of intelligent analytics for various industry 4.0 domains. 3. Covers latest research topics in the field. 4. Written in a comprehensive and simple manner. 5. The text is accompanied by tables and illustrative figures for better understanding of the topic.

This comprehensive treatment of network information theory and its applications provides the first unified coverage of both classical and recent results. With an approach that balances the introduction of new models and new coding techniques, readers are guided through Shannon's point-to-point information theory, single-hop networks, multihop networks, and extensions to distributed computing, secrecy, wireless communication, and networking. Elementary mathematical tools and techniques are used throughout, requiring only basic knowledge of probability, whilst unified proofs of coding theorems are based on a few simple lemmas, making the text accessible to newcomers. Key topics covered include successive cancellation and superposition coding, MIMO wireless communication, network coding, and cooperative relaying. Also covered are feedback and interactive communication, capacity approximations and scaling laws, and asynchronous and random access channels. This book is ideal for use in the classroom, for self-study, and as a reference for researchers and engineers in industry and academia.

This book provides insights on how to approach and utilize data science tools, technologies and methodologies related to artificial intelligence (AI) in industrial contexts. It explains the essence of distributed computing and AI-technologies, and their inter-connections. Description of various technology and methodology approaches, and their purpose and benefits when developing AI-solution in industrial contexts is included. In addition, it summarizes experiences from AI technology deployment projects from several industrial sectors. Features: Presents compendium of methodologies and technologies in Industrial AI and digitalization. Illustrates sensor to actuation approach showing complete cycle, that defines and differences AI and digitalization concept. Covers a broad range of academic and industrial issues within the field of asset management. Discusses impact of Industry 4.0 in other sectors. Includes a dedicated chapter on real-time case studies. This book is aimed at researchers and professionals in industrial and software engineering, network security, AI and ML, engineering managers, operational and maintenance specialists, asset managers, digital and AI manufacturing specialists.

Gives broad perspective on 5G communications with a focus on smart cities Discusses artificial intelligence in future wireless communication and its applications Provides a systemic and comprehensive coverage of 6G technologies, challenges and use cases Explores role of future wireless in safety, health, and transport in smart cities Includes case studies of future wireless communications

This volume publishes new trends and findings in hot topics related to ubiquitous computing/networking. It is the outcome of UNet - ainternational scientific event that took place on September 08-10, 2015, in the fascinating city of Casablanca, Morocco. UNet'15 is technically sponsored by IEEE Morocco Section and IEEE COMSOC Morocco Chapter.

This book introduces novel methods and approaches for semantic integration. In addition to developing ground-breaking new methods for ontology alignment, the author provides extensive explanations of up-to-date case studies. It includes a thorough investigation of the foundations and provides pointers to future steps in ontology alignment with conclusion linking this work to the knowledge society.

Rediscover fundamental and advanced topics in IPAM, DNS, DHCP and other core networking technologies with this updated one-stop reference The thoroughly revised second edition of IP Address Management is the definitive reference for working with core IP management technologies, like address allocation, assignment, and network navigation via DNS. Accomplished professionals and authors Timothy Rooney and Michael Dooley offer readers coverage of recent IPAM developments in the world of cloud computing, Internet of Things (IoT), and security, as well as a comprehensive treatment of foundational concepts in IPAM. The new edition addresses the way that IPAM needs and methods have evolved since the publication of the first edition. The book covers the impact of mainstream use of private and public cloud services, the maturation of IPv6 implementations, new DNS security approaches, and the proliferation of IoT devices. The authors have also reorganized the flow of the book, with much of the technical reference material appearing at the end and making for a smoother and simpler reading experience. The 2nd edition of IP Address Management also covers topics like such as: Discussions about the fundamentals of Internet Protocol Address Management (IPAM), including IP addressing, address allocation and assignment, DHCP, and DNS An examination of IPAM practices, including core processes and tasks, deployment strategies, IPAM security best-practices, and DNS security approaches A treatment of IPAM in the modern context, including how to adapt to cloud computing, the Internet of Things, IPv6, and new trends in IPAM A one-stop reference for IPAM topics, including IP addressing, DHCP, DNS, IPv6, and DNS security Perfect for IP network engineers and managers, network planners, network architects, and security engineers, the second edition of IP Address Management also belongs on the bookshelves of senior undergraduate and graduate students studying in networking, information technology, and computer security-related courses and programs.

Focusses on prospective scenarios in health to foresight possible futures Addresses the urgent needs of the key population, socio-technical, and health themes Covers health innovative practices as 3D models for surgeries, big data to treat rare diseases, AI robot for heart treatments Explores telemedicine using big data, deep learning, robotics, mobile and remote applications Reviews public health based on predictive analytics and disease trends