A Guide to MATLAB Object-Oriented Programming is the first book to deliver broad coverage of the documented and undocumented object-oriented features of MATLAB®. Unlike the typical approach of other resources, this guide explains why each feature is important, demonstrates how each feature is used, and promotes an understanding of the interactions between features. Assuming an intermediate level of MATLAB programming knowledge, the book not only concentrates on MATLAB coding techniques but also discusses topics critical to general software development. It introduces fundamentals first before integrating these concepts into example applications. In the first section, the book discusses eight basic functions: constructor, subsref, subsasgn, display, struct, fieldnames, get, and set. Building on the previous section, it explores inheritance topics and presents the Class Wizard, a powerful MATLAB class generation tool. The final section delves into advanced strategies, including containers, static variables, and function fronts. With more than 20 years of experience designing and implementing object-oriented software, the expert author has developed an accessible and comprehensive book that aids readers in creating effective object-oriented software using MATLAB.

Although some IoT systems are built for simple event control where a sensor signal triggers a corresponding reaction, many events are far more complex, requiring applications to interpret the event using analytical techniques to initiate proper actions. Artificial intelligence of things (AIoT) applies intelligence to the edge and gives devices the ability to understand the data, observe the environment around them, and decide what to do best with minimum human intervention. With the power of AI, AIoT devices are not just messengers feeding information to control centers. They have evolved into intelligent machines capable of performing self-driven analytics and acting independently. A smart environment uses technologies such as wearable devices, IoT, and mobile internet to dynamically access information, connect people, materials and institutions, and then actively manages and responds to the ecosystem's needs in an intelligent manner.

In this edited book, the contributors present challenges, technologies, applications and future trends of AIoT in realizing smart and intelligent environments, including frameworks and methodologies for applying AIoT in monitoring devices and environments, tools and practices most applicable to product or service development to solve innovation problems, advanced and innovative techniques, and practical implementations to enhance future smart environment systems. Chapters cover a broad range of applications including smart cities, smart transportation and smart agriculture.

This book is a valuable resource for industry and academic researchers, scientists, engineers and advanced students in the fields of ICTs and networking, IoT, AI and machine and deep learning, data science, sensing, robotics, automation and smart technologies and smart environments.

First designed to generate personalized recommendations to users in the 90s, recommender systems apply knowledge discovery techniques to users' data to suggest information, products, and services that best match their preferences. In recent decades, we have seen an exponential increase in the volumes of data, which has introduced many new challenges. Divided into two volumes, this comprehensive set covers recent advances, challenges, novel solutions, and applications in big data recommender systems. Volume 1 contains 14 chapters addressing foundations, algorithms and architectures, approaches for big data, and trust and security measures. Volume 2 covers a broad range of application paradigms for recommender systems over 22 chapters.

Big Data Analytics and Software Defined Networking (SDN) are helping to drive the management of data and usage of the extraordinary increase of computer processing power provided by Cloud Data Centres (CDCs). SDN helps CDCs run their services more efficiently by enabling managers to configure, manage, secure, and optimize the network resources very quickly. Big-Data Analytics in turn has entered CDCs to harvest the massive computing powers and deduct information that was never reachable by conventional methods. Big Data and Software Defined Networks investigates areas where Big-Data and SDN can help each other in delivering more efficient services. SDN can help Big-Data applications overcome one of their major challenges: message passing among cooperative nodes.Through proper bandwidth allocation and prioritization, critical surges of Big-Data flows can be better handled to effectively reduce their impacts on CDCs. Big-Data, in turn, can help SDN controllers better analyze collected network information and make more efficient decisions about the allocation of resources to different network flows.

Blockchain technology is a powerful, cost-effective method for network security. Essentially, it is a decentralized ledger for storing all committed transactions in trustless environments by integrating several core technologies such as cryptographic hash, digital signature and distributed consensus mechanisms. Over the past few years, blockchain technology has been used in a variety of network interaction systems such as smart contracts, public services, Internet of Things (IoT), social networks, reputation systems and security and financial services. With its widespread adoption, there has been increased focus on utilizing blockchain technologies to address network security concerns and vulnerabilities as well as understanding real-world security implications. The book begins with an introduction to blockchains, covering key principles and applications. Further chapters cover blockchain system architecture, applications and research issues; blockchain consensuses and incentives; blockchain applications, projects and implementations; blockchain for internet of things; blockchain in 5G and 6G networks; edgechain to provide security in organization based multi agent systems; blockchain driven privacy-preserving machine learning; performance evaluation of differential privacy mechanisms in blockchain based smart metering; scaling-out blockchains with sharding; blockchain for GIS; and finally blockchain applications in remote sensing big data management and production.

This book describes how smart cities can be designed with data at their heart, moving from a broad vision to a consistent city-wide collaborative configuration of activities. The authors present a comprehensive framework of techniques to help decision makers in cities analyse their business strategies, design data infrastructures to support these activities, understand stakeholders' expectations, and translate this analysis into a competitive strategy for creating a smart city data infrastructure. Readers can take advantage of unprecedented insights into how cities and infrastructures function and be ready to overcome complex challenges. The framework presented in this book has guided the design of several urban platforms in the European Union and the design of the City Data Strategy of the Mayor of London, UK.

E-learning has become an important part of our educational life with the development of e-learning systems and platforms and the need for online and remote learning. ICT and computational intelligence techniques are being used to design more intelligent and adaptive systems. However, the art of designing good real-time e-learning systems is difficult as different aspects of learning need to be considered including challenges such as learning rates, involvement, knowledge, qualifications, as well as networking and security issues. The earlier concepts of standalone integrated virtual e-learning systems have been greatly enhanced with emerging technologies such as cloud computing, mobile computing, big data, Internet of Things (IoT), AI and machine learning, and AR/VT technologies. With this book, the editors and authors wish to help researchers, scholars, professionals, lecturers, instructors, developers, and designers understand the fundamental concepts, challenges, methodologies and technologies for the design of performant and reliable intelligent and adaptive real time e-learning systems and platforms. This edited volume covers state of the art topics including user modeling for e-learning systems and cloud, IOT, and mobile-based frameworks. It also considers security challenges and ethical conduct using Blockchain technology.

Cloud computing has evolved as a cost-effective, easy-to-use, elastic and scalable computing paradigm to transform today's business models. 5G, Industrial IoT, Industry 4.0 and China-2050 frameworks and technologies are introducing new challenges that cannot be solved efficiently using current cloud architectures. To handle the collected information from such a vast number of devices and actuators, and address these issues, novel concepts have been proposed to bring cloud-like resources closer to end users at the edge of the network, a technology called edge computing. From architectures to models, technologies and applications, this book focuses on the Edge Computing paradigm due to its unique characteristics where heterogeneous devices can be equipped with decision making processes and automation procedures to carry out applications across widely geographically distributed areas. This book provides valuable insights for ICTs engineers, scientists, researchers, developers and practitioners who are involved in developing and implementing edge and cloud-based solutions ranging from sensors and actuators to cloud-based back-end systems.

The practice of Model-based Systems Engineering is becoming more widely adopted in industry, academia and commerce and, as the use of modelling matures in the real world, so the need for more guidance on how to model effectively and efficiently becomes more prominent. This book describes a number of systems-level 'patterns' (pre-defined, reusable sets of views) that may be applied using the systems modelling language SysML for the development of any number of different applications and as the foundations for a system model. Topics covered include: what is a pattern? Interface definition pattern; traceability pattern; test pattern; epoch pattern; life cycle pattern; evidence pattern; description pattern; context pattern; analysis pattern; model maturity pattern; requirements modelling; expanded requirements modelling; process modelling; competence modelling; life cycle modelling; defining patterns; and using patterns for model assessment, model definition and for model retro-fitting. This book forms a companion volume to both 'SysML for Systems Engineering - a model-based approach' and 'Model-based Requirements Engineering', both published by the IET. Whereas the previous volumes presented the case for modelling and provided an in-depth overview of SysML, this book focusses on a set of 'patterns' as the basis of an MBSE model and their use in today's systems engineering community.

Big Data analytics is the complex process of examining big data to uncover information such as correlations, hidden patterns, trends and user and customer preferences, to allow organizations and businesses to make more informed decisions. These methods and technologies have become ubiquitous in all fields of science, engineering, business and management due to the rise of data-driven models as well as data engineering developments using parallel and distributed computational analytics frameworks, data and algorithm parallelization, and GPGPU programming. However, there remain potential issues that need to be addressed to enable big data processing and analytics in real time. In the first volume of this comprehensive two-volume handbook, the authors present several methodologies to support Big Data analytics including database management, processing frameworks and architectures, data lakes, query optimization strategies, towards real-time data processing, data stream analytics, Fog and Edge computing, and Artificial Intelligence and Big Data. The second volume is dedicated to a wide range of applications in secure data storage, privacy-preserving, Software Defined Networks (SDN), Internet of Things (IoTs), behaviour analytics, traffic predictions, gender based classification on e-commerce data, recommender systems, Big Data regression with Apache Spark, visual sentiment analysis, wavelet Neural Network via GPU, stock market movement predictions, and financial reporting. The two-volume work is aimed at providing a unique platform for researchers, engineers, developers, educators and advanced students in the field of Big Data analytics.