Advances in Imaging and Electron Physics, Volume 218 merges two long-running serials, Advances in Electronics and Electron Physics and Advances in Optical and Electron Microscopy. The series features articles on the physics of electron devices (especially semiconductor devices), particle optics at high and low energies, microlithography, image science, digital image processing, electromagnetic wave propagation, electron microscopy and the computing methods used in all these domains. Specific chapters in this release cover Phase retrieval methods applied to coherent imaging, X-ray phase-contrast imaging: a broad overview of some fundamentals, Graphene and borophene as nanoscopic materials for electronics - with review of the physics, and more.

This book takes the reader through the actual manufacturing process of making a typical chip, from start to finish, including a detailed discussion of each step, in plain language. The evolution of today's technology is added to the story, as seen through the eyes of the engineers who solved some of the problems. The authors are well suited to that discussion since they are three of those same engineers. They have a broad exposure to the industry and its technology that extends all the way back to Shockley Laboratories, the first semiconductor manufacturer in Silicon Valley.
The CMOS (Complementary Metal-Oxide-Semiconductor) process flow is the focus of the discussion and is covered in ten chapters. The vast majority of chips made today are fabricated using this general method. In order to ensure that all readers are comfortable with the vocabulary, the first chapter carefully and clearly introduces the science concepts found in later chapters. A chapter is devoted to pointing out the differences in other manufacturing methods, such as the gallium arsenide technology that produces chips for cell phones. In addition, a chapter describing the nature of the semiconductor industry from a business perspective is included.
"The entire process of making a chip is surprisingly easy to understand. The part of the story that defies belief is the tiny dimensions: the conducting wires and other structures on a chip are more than a hundred times thinner than a hair - and getting thinner with every new chip design."
* Included CD gives the reader a much greater comprehension of the process than a strictly print book with static illustrations provides
* Authors are actual engineers who have a broad range of exposure and experience with chip technology
* Contains a unique chapter describing the nature of the semiconductor industry from a business perspective

Quantitative Atomic-Resolution Electron Microscopy, Volume 217, the latest release in the Advances in Imaging and Electron Physics series merges two long-running serials, Advances in Electronics and Electron Physics and Advances in Optical and Electron Microscopy. The series features extended articles on the physics of electron devices (especially semiconductor devices), particle optics at high and low energies, microlithography, image science, digital image processing, electromagnetic wave propagation, electron microscopy, and the computing methods. Chapters in this release include Statistical parameter estimation theory, Efficient fitting algorithm, Statistics-based atom counting , Atom column detection, Optimal experiment design for nanoparticle atom-counting from ADF STEM images, and more.

Thinking Machines: Machine Learning and Its Hardware Implementation covers the theory and application of machine learning, neuromorphic computing and neural networks. This is the first book that focuses on machine learning accelerators and hardware development for machine learning. It presents not only a summary of the latest trends and examples of machine learning hardware and basic knowledge of machine learning in general, but also the main issues involved in its implementation. Readers will learn what is required for the design of machine learning hardware for neuromorphic computing and/or neural networks. This is a recommended book for those who have basic knowledge of machine learning or those who want to learn more about the current trends of machine learning.

Machine Learning, Big Data, and IoT for Medical Informatics focuses on the latest techniques adopted in the field of medical informatics. In medical informatics, machine learning, big data, and IOT-based techniques play a significant role in disease diagnosis and its prediction. In the medical field, the structure of data is equally important for accurate predictive analytics due to heterogeneity of data such as ECG data, X-ray data, and image data. Thus, this book focuses on the usability of machine learning, big data, and IOT-based techniques in handling structured and unstructured data. It also emphasizes on the privacy preservation techniques of medical data. This volume can be used as a reference book for scientists, researchers, practitioners, and academicians working in the field of intelligent medical informatics. In addition, it can also be used as a reference book for both undergraduate and graduate courses such as medical informatics, machine learning, big data, and IoT.

Big Data in Psychiatry and Neurology provides an up-to-date overview of achievements in the field of big data in Psychiatry and Medicine, including applications of big data methods to aging disorders (e.g., Alzheimer's disease and Parkinson's disease), mood disorders (e.g., major depressive disorder), and drug addiction. This book will help researchers, students and clinicians implement new methods for collecting big datasets from various patient populations. Further, it will demonstrate how to use several algorithms and machine learning methods to analyze big datasets, thus providing individualized treatment for psychiatric and neurological patients. As big data analytics is gaining traction in psychiatric research, it is an essential component in providing predictive models for both clinical practice and public health systems. As compared with traditional statistical methods that provide primarily average group-level results, big data analytics allows predictions and stratification of clinical outcomes at an individual subject level.

Intelligent Systems and Learning Data Analytics in Online Education provides novel artificial intelligence (AI) and analytics-based methods to improve online teaching and learning. This book addresses key problems such as attrition and lack of engagement in MOOCs and online learning in general. This book explores the state of the art of artificial intelligence, software tools and innovative learning strategies to provide better understanding and solutions to the various challenges of current e-learning in general and MOOC education. In particular, Intelligent Systems and Learning Data Analytics in Online Education shares stimulating theoretical and practical research from leading international experts. This publication provides useful references for educational institutions, industry, academic researchers, professionals, developers, and practitioners to evaluate and apply.

Machine Learning and Data Science in the Oil and Gas Industry explains how machine learning can be specifically tailored to oil and gas use cases. Petroleum engineers will learn when to use machine learning, how it is already used in oil and gas operations, and how to manage the data stream moving forward. Practical in its approach, the book explains all aspects of a data science or machine learning project, including the managerial parts of it that are so often the cause for failure. Several real-life case studies round out the book with topics such as predictive maintenance, soft sensing, and forecasting. Viewed as a guide book, this manual will lead a practitioner through the journey of a data science project in the oil and gas industry circumventing the pitfalls and articulating the business value.

The rapid development of artificial intelligence technology in medical data analysis has led to the concept of radiomics. This book introduces the essential and latest technologies in radiomics, such as imaging segmentation, quantitative imaging feature extraction, and machine learning methods for model construction and performance evaluation, providing invaluable guidance for the researcher entering the field. It fully describes three key aspects of radiomic clinical practice: precision diagnosis, the therapeutic effect, and prognostic evaluation, which make radiomics a powerful tool in the clinical setting. This book is a very useful resource for scientists and computer engineers in machine learning and medical image analysis, scientists focusing on antineoplastic drugs, and radiologists, pathologists, oncologists, as well as surgeons wanting to understand radiomics and its potential in clinical practice.

This timely book presents a detailed analysis of the role of law and regulation in the utilisation of Artificial Intelligence (AI) in the media sector. As well as contributing to the wider discussion on law and AI, the book also digs deeper by exploring pressing issues at the intersections of AI, media, and the law. Chapters critically re-examine various rights and responsibilities from the perspectives of incentives for accountable utilisation of AI in the industry. Featuring chapters from leading scholars in the field, Artificial Intelligence and the Media provides a timely and in-depth research-based contribution to complex themes - especially at the interface of new technology (including AI) with media and regulation. Analysing both legislative and ethical solutions, chapters explore what "AI" and "accountability" mean in terms of media practices, principles, and power relations, as well as how to address the AI revolution with informed law and policy in order to incentivise accountable utilisation of AI and to reduce negative societal impacts. Offering ideas for further research in the area, this book is key reading for academics and researchers in the fields of information and media law, regulation, and technology law. It may also interest media law practitioners, with research-based guidance for everyday practices and tools to prepare for future developments in the area.

The key parameter that needs to be considered when planning the management of resources in futuristic wireless networks is a balanced approach to resource distribution. A balanced approach is necessary to provide an unbiased working environment for the distribution, sharing, allocation, and supply of resources among the devices of the wireless network. Equal resource distribution also maintains balance and stability between the operations of communication systems and thus improves the performance of wireless networks. Managing Resources for Futuristic Wireless Networks is a pivotal reference source that presents research related to the control and management of key parameters of bandwidth, spectrum sensing, channel selection, resource sharing, and task scheduling, which is necessary to ensure the efficient operation of wireless networks. Featuring topics that include vehicular ad-hoc networks, resource management, and the internet of things, this publication is ideal for professionals and researchers working in the field of networking, information and knowledge management, and communication sciences. Moreover, the book will provide insights and support executives concerned with the management of expertise, knowledge, information, and organizational development in different types of work communities and environments.

Wireless communication is continuously evolving to improve and be a part of our daily communication. This leads to improved quality of services and applications supported by networking technologies. We are now able to use LTE, LTE-Advanced, and other emerging technologies due to the enormous efforts that are made to improve the quality of service in cellular networks. As the future of networking is uncertain, the use of deep learning and big data analytics is a point of focus as it can work in many capacities at a variety of levels for wireless communications. Implementing Data Analytics and Architectures for Next Generation Wireless Communications addresses the existing and emerging theoretical and practical challenges in the design, development, and implementation of big data algorithms, protocols, architectures, and applications for next generation wireless communications and their applications in smart cities. The chapters of this book bring together academics and industrial practitioners to exchange, discuss, and implement the latest innovations and applications of data analytics in advanced networks. Specific topics covered include key encryption techniques, smart home appliances, fog communication networks, and security in the internet of things. This book is valuable for technologists, data analysts, networking experts, practitioners, researchers, academicians, and students.

The Definitive Guide to Arm (R) Cortex (R)-M23 and Cortex-M33 Processors focuses on the Armv8-M architecture and the features that are available in the Cortex-M23 and Cortex- M33 processors. This book covers a range of topics, including the instruction set, the programmer's model, interrupt handling, OS support, and debug features. It demonstrates how to create software for the Cortex-M23 and Cortex-M33 processors by way of a range of examples, which will enable embedded software developers to understand the Armv8-M architecture. This book also covers the TrustZone (R) technology in detail, including how it benefits security in IoT applications, its operations, how the technology affects the processor's hardware (e.g., memory architecture, interrupt handling, etc.), and various other considerations in creating secure software.

Predictive Filtering for Microsatellite Control Systems introduces technological design, modeling, stability analysis, predictive filtering, state estimation problem and real-time operation of spacecraft control systems in aerospace engineering. The book gives a systematically and almost self-contained description of the many facets of envisaging, designing, implementing or experimentally exploring predictive filtering for spacecraft control systems, along with the adequate designs of integrated modeling, dynamics, state estimation, and signal processing of spacecrafts and nonlinear systems.

SECURITY AND PRIVACY VISION IN 6G Prepare for the future of mobile communication with this comprehensive study 6G is the next frontier in mobile communication, with development of 6G standards slated to begin as early as 2026. As telecommunications networks become faster and more intelligent, security and privacy concerns are critical. In an increasingly connected world, there is an urgent need for user data to be safeguarded and system security enhanced against a new generation of threats. Security and Privacy Vision in 6G provides a comprehensive survey of these threats and the emerging techniques for safeguarding against them. It includes mechanisms for prediction, detection, mitigation, and prevention, such that threats to privacy and security can be forestalled at any stage. Fully engaged with proposed 6G architectures, it is an essential resource for mobile communications professionals looking for a head start on the technology of the future. Security and Privacy Vision in 6G readers will also find: Detailed coverage of topics including edge intelligence and cloudification, industrial automation, collaborative robots, and more Treatment balancing the practical and the theoretical An editorial team with decades of international network technology experience in both industry and academia Security and Privacy Vision in 6G is a vital reference for network security professionals and for postgraduate and advanced undergraduate students in mobile communications and network security-related fields.

Advances in Delay-Tolerant Networks: Architecture and Enhanced Performance, Second Edition provides an important overview of delay-tolerant networks (DTNs) for researchers in electronics, computer engineering, telecommunications and networking for those in academia and R&D in industrial sectors. Part I reviews the technology involved and the prospects for improving performance, including different types of DTN and their applications, such as satellite and deep-space communications and vehicular communications. Part II focuses on how the technology can be further improved, addressing topics, such as data bundling, opportunistic routing, reliable data streaming, and the potential for rapid selection and dissemination of urgent messages. Opportunistic, delay-tolerant networks address the problem of intermittent connectivity in a network where there are long delays between sending and receiving messages, or there are periods of disconnection.

Intelligent machines are populating our social, economic and political spaces. These intelligent machines are powered by Artificial Intelligence technologies such as deep learning. They are used in decision making. One element of decision making is the issue of rationality. Regulations such as the General Data Protection Regulation (GDPR) require that decisions that are made by these intelligent machines are explainable. Rational Machines and Artificial Intelligence proposes that explainable decisions are good but the explanation must be rational to prevent these decisions from being challenged. Noted author Tshilidzi Marwala studies the concept of machine rationality and compares this to the rationality bounds prescribed by Nobel Laureate Herbert Simon and rationality bounds derived from the work of Nobel Laureates Richard Thaler and Daniel Kahneman. Rational Machines and Artificial Intelligence describes why machine rationality is flexibly bounded due to advances in technology. This effectively means that optimally designed machines are more rational than human beings. Readers will also learn whether machine rationality can be quantified and identify how this can be achieved. Furthermore, the author discusses whether machine rationality is subjective. Finally, the author examines whether a population of intelligent machines collectively make more rational decisions than individual machines. Examples in biomedical engineering, social sciences and the financial sectors are used to illustrate these concepts.

Online high school education is challenging with limited resources for teachers to turn to. In most cases, teachers rely on trial-and-error. This research-based and practitioner-focused text provides best practice techniques and utilizes analogies from brick-and-mortar education to provide a conceptual framework to a better understanding of how online education functions and how to be engage students and how to build and maintain a positive digital culture. This book provides real-world solutions to online and hybrid educators. The aim of this is to train educators to develop online culture, healthy and inclusive communication, and how to use the online classroom environment in parallel or stand-alone with a face-to-face classroom. Engagement strategies will be discussed as well as the use of multi-tiered systems of support to engage students. The desired impact is to increase learning, growth and to prepare high school students for the next step in their academic career.

Semantic computing is critical for the development of semantic systems and applications that must utilize semantic analysis, semantic description, semantic interfaces, and semantic integration of data and services to deliver their objectives. Semantic computing has enormous capabilities to enhance the efficiency and throughput of systems that are based on key emerging concepts and technologies such as semantic web, internet of things, blockchain technology, and knowledge graphs. Thus, research that expounds advanced concepts, methods, technologies, and applications of semantic computing for solving challenges in real-world domains is vital. Advanced Concepts, Methods, and Applications in Semantic Computing is a scholarly reference book that provides a sound theoretical foundation for the application of semantic methods, concepts, and technologies for practical problem solving. It is designed as a comprehensive and reliable resource on how semantic-oriented approaches can be used to aid new emergent technologies and tackle real-world problems. Covering topics that include deep learning, machine learning, blockchain technology, and semantic web services, this book is ideal for professionals, academicians, researchers, and students working in the field of semantic computing in various disciplines, including but not limited to software engineering, systems engineering, knowledge engineering, electronic commerce, computer science, and information technology.