- This book covers the production of dissertations in an area where students are much more comfortable writing code than writing academically. - With practical examples of bachelor dissertations and practical research methods utilised in the field of computer science and computer games such as survey methodologies, experimental methodologies, case studies, analysis techniques and reporting techniques, this book will break down the sometimes complicated-seeming nature of the dissertation. - Written to be concise yet comprehensive and with easily accessible language and examples, this book will take the mystery out of undergraduate dissertations in this field.

1. Understand the audit culture, challenges, and benefits of the CAE role in digitally transforming business environment in smart cities 2. Identify ways to advance the value of Internal Audit in digital era 3. Use and control the resources of the city efficiently, and to ensure that the system units work properly in an integrated way.

Unique selling point: The Internet of Things (IoT), AI, and analytics are studied on how they can combat pandemics Core audience: Researchers and medical informatics professionals Place in the market: Academic reference title on timely topic also appealing to professionals

Key Selling Points The first book to showcase physical representations of data, and the first to discuss the creative process behind them. Approaches the topic from a multidisciplinary perspective, showcasing a range of creative approaches from computer science, data science, graphic design, art, craft, and architecture. The book is heavily visual and illustrates each project and the process of creating it via rich photos and sketches, which are accessible and inspiring for both enthusiasts and experts.

This book is designed to be usable as a textbook for an undergraduate course or for an advanced graduate course in coding theory as well as a reference for researchers in discrete mathematics, engineering and theoretical computer science. This second edition has three parts: an elementary introduction to coding, theory and applications of codes, and algebraic curves. The latter part presents a brief introduction to the theory of algebraic curves and its most important applications to coding theory.

This fully updated new edition of a uniquely accessible textbook/reference provides a general introduction to probabilistic graphical models (PGMs) from an engineering perspective. It features new material on partially observable Markov decision processes, causal graphical models, causal discovery and deep learning, as well as an even greater number of exercises; it also incorporates a software library for several graphical models in Python. The book covers the fundamentals for each of the main classes of PGMs, including representation, inference and learning principles, and reviews real-world applications for each type of model. These applications are drawn from a broad range of disciplines, highlighting the many uses of Bayesian classifiers, hidden Markov models, Bayesian networks, dynamic and temporal Bayesian networks, Markov random fields, influence diagrams, and Markov decision processes. Topics and features: Presents a unified framework encompassing all of the main classes of PGMs Explores the fundamental aspects of representation, inference and learning for each technique Examines new material on partially observable Markov decision processes, and graphical models Includes a new chapter introducing deep neural networks and their relation with probabilistic graphical models Covers multidimensional Bayesian classifiers, relational graphical models, and causal models Provides substantial chapter-ending exercises, suggestions for further reading, and ideas for research or programming projects Describes classifiers such as Gaussian Naive Bayes, Circular Chain Classifiers, and Hierarchical Classifiers with Bayesian Networks Outlines the practical application of the different techniques Suggests possible course outlines for instructors This classroom-tested work is suitable as a textbook for an advanced undergraduate or a graduate course in probabilistic graphical models for students of computer science, engineering, and physics. Professionals wishing to apply probabilistic graphical models in their own field, or interested in the basis of these techniques, will also find the book to be an invaluable reference. Dr. Luis Enrique Sucar is a Senior Research Scientist at the National Institute for Astrophysics, Optics and Electronics (INAOE), Puebla, Mexico. He received the National Science Prize en 2016.

The book gives a broad overview of the Internet of Things (IoT) concept from various angles. The book provides rationale for: the concept development; its regulatory and technical background associated aspects such as the ambient and edge intelligence; fog computing; capillary networks and machine-type communications; etc. Each of these items is then extended in further respective chapters that deal with technicalities behind them. Chapters: 2-5, 8, 10-11 are addressed to those who seek expository IoT-related information on aspects such as the pathloss calculation, narrowband radio interfaces, radiation masks, spectrum matters, medium access control, and a transmission frame construction. That section ends with an exhaustive description of the six most popular IoT systems: LoRa, Weightless, SigFox, NB-IoT, LTE-M(TC) and EC-GSM IoT. Specialists and network designers may find chapters 6 and 7 interesting where a novel methodology is proposed for testing narrowband IoT systems performance for immunity to electromagnetic interference (EMI) and multipath propagation, both emulated in artificial conditions of the anechoic and the reverberation chamber.

This book addresses a broad range of topics concerning machine learning, big data, the Internet of things (IoT), and security in the IoT. Its goal is to bring together several innovative studies on these areas, in order to help researchers, engineers, and designers in several interdisciplinary domains pursue related applications. It presents an overview of the various algorithms used, focusing on the advantages and disadvantages of each in the fields of machine learning and big data. It also covers next-generation computing paradigms that are expected to support wireless networking with high data transfer rates and autonomous decision-making capabilities. In turn, the book discusses IoT applications (e.g. healthcare applications) that generate a huge amount of sensor data and imaging data that must be handled correctly for further processing. In the traditional IoT ecosystem, cloud computing offers a solution for the efficient management of huge amounts of data, thanks to its ability to access shared resources and provide a common infrastructure in a ubiquitous manner. Though these new technologies are invaluable, they also reveal serious IoT security challenges. IoT applications are vulnerable to various types of attack such as eavesdropping, spoofing and false data injection, the man-in-the-middle attack, replay attack, denial-of-service attack, jamming attack, flooding attack, etc. These and other security issues in the Internet of things are explored in detail. In addition to highlighting outstanding research and recent advances from around the globe, the book reports on current challenges and future directions in the IoT. Accordingly, it offers engineers, professionals, researchers, and designers an applied-oriented resource to support them in a broad range of interdisciplinary areas.

This book provides a concise overview of VR systems and their cybersickness effects, giving a description of possible reasons and existing solutions to reduce or avoid them. Moreover, the book explores the impact that understanding how efficiently our brains are producing a coherent and rich representation of the perceived outside world would have on helping VR technics to be more efficient and friendly to use. Getting Rid of Cybersickness will help readers to understand the underlying technics and social stakes involved, from engineering design to autonomous vehicle motion sickness to video games, with the hope of providing an insight of VR sickness induced by the emerging immersive technologies. This book will therefore be of interest to academics, researchers and designers within the field of VR, as well as industrial users of VR and driving simulators.

This book looks at the growing segment of Internet of Things technology (IoT) known as Internet of Medical Things (IoMT), an automated system that aids in bridging the gap between isolated and rural communities and the critical healthcare services that are available in more populated and urban areas. Many technological aspects of IoMT are still being researched and developed, with the objective of minimizing the cost and improving the performance of the overall healthcare system. This book focuses on innovative IoMT methods and solutions being developed for use in the application of healthcare services, including post-surgery care, virtual home assistance, smart real-time patient monitoring, implantable sensors and cameras, and diagnosis and treatment planning. It also examines critical issues around the technology, such as security vulnerabilities, IoMT machine learning approaches, and medical data compression for lossless data transmission and archiving. Internet of Medical Things is a valuable reference for researchers, students, and postgraduates working in biomedical, electronics, and communications engineering, as well as practicing healthcare professionals.

Features The first book to unify the lumped-element modelling techniques for various inductively-coupled pulsed accelerator implementations. Discussion of modelling different accelerators in a coherent, rigorous manner, demonstrating the similarities and differences for each type. Authored by authorities in the field.

Provides strong and accessible theoretical bases to swarm intelligence algorithms, from particle optimization to bioinspired and meta-heuristic algorithms Presents emerging meta-heuristic algorithms and applications Provides overviews on Python and R based computing libraries for swarm intelligence and meta-heuristic algorithms Presenting real-world applications, especially on Industry, Medicine and Biology.

This book surveys some of the important research work carried out by Indian scientists in the field of pure and applied probability, quantum probability, quantum scattering theory, group representation theory and general relativity. It reviews the axiomatic foundations of probability theory by A.N. Kolmogorov and how the Indian school of probabilists and statisticians used this theory effectively to study a host of applied probability and statistics problems like parameter estimation, convergence of a sequence of probability distributions, and martingale characterization of diffusions. It will be an important resource to students and researchers of Physics and Engineering, especially those working with Advanced Probability and Statistics.

Provides strong and accessible theoretical bases to swarm intelligence algorithms, from particle optimization to bioinspired and meta-heuristic algorithms Presents emerging meta-heuristic algorithms and applications Provides overviews on Python and R based computing libraries for swarm intelligence and meta-heuristic algorithms Presenting real-world applications, especially on Industry, Medicine and Biology.

1. Understand the audit culture, challenges, and benefits of the CAE role in digitally transforming business environment in smart cities 2. Identify ways to advance the value of Internal Audit in digital era 3. Use and control the resources of the city efficiently, and to ensure that the system units work properly in an integrated way.

The idea behind this book is to simplify the journey of aspiring readers and researchers to understand the convergence of Big Data with the Cloud. This book presents the latest information on the adaptation and implementation of Big Data technologies in various cloud domains and Industry 4.0. Synergistic Interaction of Big Data with Cloud Computing for Industry 4.0 discusses how to develop adaptive, robust, scalable, and reliable applications that can be used in solutions for day-to-day problems. It focuses on the two frontiers - Big Data and Cloud Computing - and reviews the advantages and consequences of utilizing Cloud Computing to tackle Big Data issues within the manufacturing and production sector as part of Industry 4.0. The book unites some of the top Big Data experts throughout the world who contribute their knowledge and expertise on the different aspects, approaches, and concepts related to new technologies and novel findings. Based on the latest technologies, the book offers case studies and covers the major challenges, issues, and advances in Big Data and Cloud Computing for Industry 4.0. By exploring the basic and high-level concepts, this book serves as a guide for those in the industry, while also helping beginners and more advanced learners understand both basic and more complex aspects of the synergy between Big Data and Cloud Computing.

Land management issues, such as mapping tree species, recognizing invasive plants, and identifying key geologic features, require an understanding of complex technical issues before the best decisions can be made. Hyperspectral remote sensing is one the technologies that can help with reliable detection and identification. Presenting the fundamentals of remote sensing at an introductory level, Hyperspectral Remote Sensing: Principles and Applications explores all major aspects of hyperspectral image acquisition, exploitation, interpretation, and applications. The book begins with several chapters on the basic concepts and underlying principles of remote sensing images. It introduces spectral radiometry concepts, such as radiance, irradiance, flux, and blackbody radiation; covers imaging spectrometers, examining spectral range, full width half maximum (FWHM), resolution, sampling, signal-to-noise ratio (SNR), and multispectral and hyperspectral sensor systems; and addresses atmospheric interactions. The book then discusses information extraction, with chapters covering the underlying physics principles that lead to the creation of an image and the interpretation of the image's information. The final chapters describe case studies that illustrate the use of hyperspectral remote sensing in agriculture, environmental monitoring, forestry, and geology. After reading this book, you will have a better understanding of how to evaluate different approaches to hyperspectral analyses and to determine which approaches will work for your applications.

This book presents new findings in industrial cyber-physical system design and control for various domains, as well as their social and economic impacts on society. Industry 4.0 requires new approaches in the context of secure connections, control, and maintenance of cyber-physical systems as well as enhancing their interaction with humans. The book focuses on open issues of cyber-physical system control and its usage, discussing implemented breakthrough systems, models, programs, and methods that could be used in industrial processes for the control, condition assessment, diagnostics, prognostication, and proactive maintenance of cyber-physical systems. Further, it addresses the topic of ensuring the cybersecurity of industrial cyber-physical systems and proposes new, reliable solutions. The authors also examine the impact of university courses on the performance of industrial complexes, and the organization of education for the development of cyber-physical systems. The book is intended for practitioners, enterprise representatives, scientists, students, and Ph.D. and master's students conducting research in the area of cyber-physical system development and implementation in various domains.

This book covers the theory, modeling, and implementation of different RF energy harvesting systems. RF energy harvesting is the best choice among the existing renewable energy sources, in terms of availability, cost, size, and integration with other systems. The device used for harvesting RF energy is called rectenna. A rectenna can work at the microwave, millimeter-wave, and terahertz waves. It also has the capability to operate at optical frequencies to be used for 6G and beyond communication systems. This book covers all aspects of wireless power transfer (WPT)/wireless energy harvesting (WEH), basics, theoretical concepts, and advanced developments occurring in the field of energy harvesting. It also covers the design theory for different types of antenna, rectifier, and impedance matching circuits used in RF energy harvesting systems. Different future and present applications, such as charging of vehicles, smart medical health care, self-driven e-vehicles, self-sustainable home automation system, and wireless drones, have also been discussed in detail.

The proceeding is a collection of research papers presented at the 11th International Conference on Robotics, Vision, Signal Processing & Power Applications (RoViSP 2021). The theme of RoViSP 2021 "Enhancing Research and Innovation through the Fourth Industrial Revolution (IR 4.0)" served as a platform for researchers, scientists, engineers, academicians as well as industrial professionals from all around the globe to present and exchange their research findings and development activities through oral presentations. The book covers various topics of interest, including: Robotics, Control, Mechatronics and Automation Telecommunication Systems and Applications Electronic Design and Applications Vision, Image and Signal Processing Electrical Power, Energy and Industrial Applications Computer and Information Technology Biomedical Engineering and Applications Intelligent Systems Internet-of-things Mechatronics Mobile Technology

Discusses the requirements and establishment of a layered protocol architecture. Highlights the importance of cable media in getting a high-speed network. Covers the fundamental concepts and advanced topics such as metro ethernet, and ethernet first mile in the field of networking. Presents important topics such as multiprotocol label switching, cloud computing in networking, and the internet of things. Explores the necessity of software-defined networking and network functions virtualization.

Facilitates good understanding of concepts of emerging 2D materials and its applications Covers details of highly sensitive sensors using 2D materials for environmental monitoring Outlines the role of 2D materials in improvement of energy harvesting and storage Details application in biosensing and healthcare for the realization of next-generation biotechnologies for personalized health monitoring and so forth Provides exclusive coverage of inorganic 2D MXenes compounds

Internet of Things (IoT)-enabled spaces have made revolutionary advances in the utility grid. Among these advances, intelligent and energy-efficient services are gaining considerable interest. The use of the smart grid is increasing day after day around us and is not only used in saving energy but also in our daily life for intelligent health, traffic, and even farming systems. The grid enabled with IoT features is also expected to communicate with cellular networks smoothly in the next-generation networks (6G and beyond). This will open the door for other interesting research areas. In this book, we consider the most significant and emergent research topics in this domain, addressing major issues and challenges in IoT-based solutions proposed for the smart grid. The chapters provide insight on comprehensive topics in IoT-based smart grids, combining technical aspects with the most up-to-date theory. It investigates the grid under varying and potential emerging paradigms such as edge/fog computing, in addition to big data aspects considerations in the IoT era. With comprehensive surveys and case studies, this book explores basic and high-level grid aspects in the emerging smart city paradigm, which makes it especially attractive to researchers, academics, and higher-level students. This authored book can be used by computer science undergraduate and postgraduate students, researchers and practitioners, city administrators, policymakers, and government regulators.

This book covers the technology of digital image processing in various fields with big data and their applications. Readers will understand various technologies and strategies used in digital image processing as well as handling big data, using machine-learning techniques. This book will help to improve the skills of students and researchers in such fields as engineering, agriculture, and medical imaging. There is a need to be able to understand and analyse the latest developments of digital image technology. As such, this book will cover: * Applications such as biomedical science and biometric image processing, content-based image retrieval, remote sensing, pattern recognition, shape and texture analysis * New concepts in color interpolation to produce the full color from the sub-pattern bare pattern color prevalent in today's digital cameras and other imaging devices * Image compression standards that are needed to serve diverse applications * Applications of remote sensing, medical science, traffic management, education, innovation, and analysis in agricultural design and image processing * Both soft and hard computing approaches at great length in relation to major image processing tasks * The direction and development of current and future research in many areas of image processing * A comprehensive bibliography for additional research (integrated within the framework of the book) This book focuses not only on theoretical and practical knowledge in the field but also on the traditional and latest tools and techniques adopted in image processing and data science. It also provides an indispensable guide to a wide range of basic and advanced techniques in the fields of image processing and data science.

This is an open access book. Important tasks must be completed on time and with guaranteed quality; that is the consensus reached by system designers and users. However, for too long, important tasks have often been given unnecessary urgency, and people intuitively believe that important tasks should be executed first so that their performance can be guaranteed. Actually, in most cases, their performance can be guaranteed even if they are executed later, and the "early" resources can be utilized for other, more urgent tasks. Therefore, confusing importance with urgency hinders the proper use of system resources. In 2007, mixed criticality was proposed to indicate that a system may contain tasks of various importance levels. Since then, system designers and users have distinguished between importance and urgency. In the industrial field, due to the harsh environment they operate in, industrial wireless networks' quality of service (QoS) has always been a bottleneck restricting their applications. Therefore, this book introduces criticality to label important data, which is then allocated more transmission resources, ensuring that important data's QoS requirements can be met to the extent possible. To help readers understand how to apply mixed-criticality data to industrial wireless networks, the content is divided into three parts. First, we introduce how to integrate the model of mixed-criticality data into industrial wireless networks. Second, we explain how to analyze the schedulability of mixed-criticality data under existing scheduling algorithms. Third, we present a range of novel scheduling algorithms for mixed-criticality data. If you want to improve the QoS of industrial wireless networks, this book is for you.