Access US Census Bureau geographic and demographic data directly within R. Wrangle Census data with tidyverse tools and work with margins of error in the American Community Survey. Make maps and interactive web visualizations with US Census data. Explore Census data with spatial analysis using the sf package. Integrate Census data into spatial and machine learning models.

This book provides readers with an up-to-date account of the use of machine learning frameworks, methodologies, algorithms and techniques in the context of computer-aided design (CAD) for very-large-scale integrated circuits (VLSI). Coverage includes the various machine learning methods used in lithography, physical design, yield prediction, post-silicon performance analysis, reliability and failure analysis, power and thermal analysis, analog design, logic synthesis, verification, and neuromorphic design. Provides up-to-date information on machine learning in VLSI CAD for device modeling, layout verifications, yield prediction, post-silicon validation, and reliability; Discusses the use of machine learning techniques in the context of analog and digital synthesis; Demonstrates how to formulate VLSI CAD objectives as machine learning problems and provides a comprehensive treatment of their efficient solutions; Discusses the tradeoff between the cost of collecting data and prediction accuracy and provides a methodology for using prior data to reduce cost of data collection in the design, testing and validation of both analog and digital VLSI designs. From the Foreword As the semiconductor industry embraces the rising swell of cognitive systems and edge intelligence, this book could serve as a harbinger and example of the osmosis that will exist between our cognitive structures and methods, on the one hand, and the hardware architectures and technologies that will support them, on the other....As we transition from the computing era to the cognitive one, it behooves us to remember the success story of VLSI CAD and to earnestly seek the help of the invisible hand so that our future cognitive systems are used to design more powerful cognitive systems. This book is very much aligned with this on-going transition from computing to cognition, and it is with deep pleasure that I recommend it to all those who are actively engaged in this exciting transformation. Dr. Ruchir Puri, IBM Fellow, IBM Watson CTO & Chief Architect, IBM T. J. Watson Research Center

Unlike other books, courses and training that expect an analyst to piece together individual instructions into a cohesive investigation, Investigating Windows Systems provides a walk-through of the analysis process, with descriptions of the thought process and analysis decisions along the way. Investigating Windows Systems will not address topics which have been covered in other books, but will expect the reader to have some ability to discover the detailed usage of tools and to perform their own research. The focus of this volume is to provide a walk-through of the analysis process, with descriptions of the thought process and the analysis decisions made along the way. A must-have guide for those in the field of digital forensic analysis and incident response.

((keine o-Punkte, sondern 2 accents aigus auf dem o in Szokefalvi, s. auch Titel ))

In August 1999, an international conference was held in Szeged, Hungary, in honor of Bela Szokefalvi-Nagy, one of the founders and main contributors of modern operator theory. This volume contains some of the papers presented at the meeting, complemented by several papers of experts who were unable to attend. These 35 refereed articles report on recent and original results in various areas of operator theory and connected fields, many of them strongly related to contributions of Sz.-Nagy. The scientific part of the book is preceeded by fifty pages of biographical material, including several photos."

Building a data-driven organization (DDO) is an enterprise-wide initiative that may consume and lock up resources for the long term. Understandably, any organization considering such an initiative would insist on a roadmap and business case to be prepared and evaluated prior to approval. This book presents a step-by-step methodology in order to create a roadmap and business case, and provides a narration of the constraints and experiences of managers who have attempted the setting up of DDOs. The emphasis is on the big decisions - the key decisions that influence 90% of business outcomes - starting from decision first and reengineering the data to the decisions process-chain and data governance, so as to ensure the right data are available at the right time, every time. Investing in artificial intelligence and data-driven decision making are now being considered a survival necessity for organizations to stay competitive. While every enterprise aspires to become 100% data-driven and every Chief Information Officer (CIO) has a budget, Gartner estimates over 80% of all analytics projects fail to deliver intended value. Most CIOs think a data-driven organization is a distant dream, especially while they are still struggling to explain the value from analytics. They know a few isolated successes, or a one-time leveraging of big data for decision making does not make an organization data-driven. As of now, there is no precise definition for data-driven organization or what qualifies an organization to call itself data-driven. Given the hype in the market for big data, analytics and AI, every CIO has a budget for analytics, but very little clarity on where to begin or how to choose and prioritize the analytics projects. Most end up investing in a visualization platform like Tableau or QlikView, which in essence is an improved version of their BI dashboard that the organization had invested into not too long ago. The most important stakeholders, the decision-makers, are rarely kept in the loop while choosing analytics projects. This book provides a fail-safe methodology for assured success in deriving intended value from investments into analytics. It is a practitioners' handbook for creating a step-by-step transformational roadmap prioritizing the big data for the big decisions, the 10% of decisions that influence 90% of business outcomes, and delivering material improvements in the quality of decisions, as well as measurable value from analytics investments. The acid test for a data-driven organization is when all the big decisions, especially top-level strategic decisions, are taken based on data and not on the collective gut feeling of the decision makers in the organization.

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.

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.

Covers different technologies like AI, IoT and Signal Processing in the context of biomedical applications Reviews medical image analysis, disease detection, and prediction Comprehends the advantage of recent technologies for medical record keeping through electronics health records (EHRs) Presents state of art research in the field of biomedical engineering using various physiological signals Explores different Bio Sensors used in Healthcare Applications using IoT

A heterogeneous network is a network which connects computers and other devices with different operating systems, protocols, or access technologies. By definition, managing heterogenous networks is more difficult that homogenous networks. Confidentiality, integrity, availability (CIA) remain the foundation of security. This book sheds light upon security threats, defenses, and remediation on various networking and data processing domains, including wired networks, wireless networks, mobile ad-hoc networks, wireless sensor networks, and social networks through the prisms of confidentiality, integrity, availability, authentication, and access control. The book is broken into different chapters that explore central subjects and themes in the development of the heterogenous networks we see today. The chapters look at: Access control methods in cloud-enabled Internet of Things Secure routing algorithms for mobile ad-hoc networks Building security trust in mobile ad-hoc networks using soft computing methods The use and development of Blockchain technology, with a particular focus on the nonce-free hash generation in Blockchain Password authentication and keystroke biometrics Health care data analytics over Big Data Bluetooth: and its open issues for managing security services in heterogenous networks Managing Security Services in Heterogenous Networks will be a valuable resource for a whole host of undergraduate and postgraduate students studying related topics, as well as career professionals who have to effectively manage heterogenous networks in the workplace.

This book serves three basic purposes: (1) a tutorial-type reference for complex systems engineering (CSE) concepts and associated terminology, (2) a recommendation of a proposed methodology showing how the evolving practice of CSE can lead to a more unified theory, and (3) a complex systems (CSs) initiative for organizations to invest some of their resources toward helping to make the world a better place. A wide variety of technical practitioners-e.g., developers of new or improved systems (particularly systems engineers), program and project managers, associated staff/workers, funders and overseers, government executives, military officers, systems acquisition personnel, contract specialists, owners of large and small businesses, professional society members, and CS researchers-may be interested in further exploring these topics. Readers will learn more about CS characteristics and behaviors and CSE principles and will therefore be able to focus on techniques that will better serve them in their everyday work environments in dealing with complexity. The fundamental observation is that many systems inherently involve a deeper complexity because stakeholders are engaged in the enterprise. This means that such CSs are more difficult to invent, create, or improve upon because no one can be in total control since people cannot be completely controlled. Therefore, one needs to concentrate on trying to influence progress, then wait a suitable amount of time to see what happens, iterating as necessary. With just three chapters in this book, it seems to make sense to provide a tutorial introduction that readers can peruse only as necessary, considering their background and understanding, then a chapter laying out the suggested artifacts and methodology, followed by a chapter emphasizing worthwhile areas of application.

Based on the technical accumulation and practice of Huawei iLab in the Cloud VR field, this book systematically describes the advantages of Cloud VR technologies; technical requirements on clouds, networks, and terminals as well as solution implementation; Cloud VR experience evaluation baselines and methods; and current business practices. Cloud VR introduces cloud computing and cloud rendering to VR services. With fast and stable networks, cloud-based display output and audio output are coded, compressed, and transmitted to user terminals, implementing cloud-based VR service content and content rendering. Cloud VR has stringent requirements on bandwidth and latency, making it a proficient application for 5G and gigabit home broadband networks in the era of "dual G". As the first advocate of Cloud VR, Huawei iLab developed the first prototype of the Cloud VR technical solution, initiated the industry's first Cloud VR industry cooperation plan - VR OpenLab with partners - and incubated the world's first Cloud VR commercial project with China Mobile Fujian. Cloud VR: Technology and Application is the first official publication of Huawei iLab's research and practice achievements. It systematically and thoroughly introduces the Cloud VR concept, solution architecture, key technologies, and business practices and is of great value in academic and social applications. This book is easy to understand, practical, and suitable for VR vendors, VR technology enthusiasts, carriers, network vendors, cloud service providers, universities, and other enterprises and scientific research institutes.

This book focuses on domain-specific heterogeneous reconfigurable architectures, demonstrating for readers a computing platform which is flexible enough to support multiple standards, multiple modes, and multiple algorithms. The content is multi-disciplinary, covering areas of wireless communication, computing architecture, and circuit design. The platform described provides real-time processing capability with reasonable implementation cost, achieving balanced trade-offs among flexibility, performance, and hardware costs. The authors discuss efficient design methods for wireless communication processing platforms, from both an algorithm and architecture design perspective. Coverage also includes computing platforms for different wireless technologies and standards, including MIMO, OFDM, Massive MIMO, DVB, WLAN, LTE/LTE-A, and 5G.

Technology Innovation discusses the fundamental aspects of processes and structures of technology innovation. It offers a new perspective concerning fundamentals aspects not directly involved in the complex relations existing between technology and the socio-economic system. By considering technology and its innovation from a scientific point of view, the book presents a novel definition of technology as a set of physical, chemical, and biological phenomena, producing an effect exploitable for human purposes. Expanding on the general model of technology innovation by linking the model of technology, based on a structure of technological operations, with the models of the structures for technology innovation, based on organization of fluxes of knowledge and capitals, the book considers various technological processes and the stages of the innovation process. Offers a perspective on the evolution of technology in the frame of an industrial platform network Explains a novel definition of technology as a set of physical, chemical, and biological phenomena producing an effect exploitable for human purposes Discusses technology innovation as result of structures organizing fluxes of knowledge and capitals Provides a technology model simulating the functioning of technology with its optimization Presents a technology innovation model explaining the territorial technology innovation process The book is intended for academics, graduate students, technology developers who are involved in operations management and research, innovation, and technology development.

The book describes state-of-the-art advances in simulators and emulators for quantum computing. It introduces the main concepts of quantum computing, defining q-bits, explaining the parallelism behind any quantum computation, describing measurement of the quantum state of information and explaining the process of quantum bit entanglement, collapsed state and cloning. The book reviews the concept of quantum unitary, binary and ternary quantum operators as well as the computation implied by each operator. It provides details of the quantum processor, providing its architecture, which is validated via execution simulation of some quantum instructions.

Dependence Analysis may be considered to be the second edition of the author's 1988 book, Dependence Analysis for Supercomputing. It is, however, a completely new work that subsumes the material of the 1988 publication. This book is the third volume in the series Loop Transformations for Restructuring Compilers. This series has been designed to provide a complete mathematical theory of transformations that can be used to automatically change a sequential program containing FORTRAN-like do loops into an equivalent parallel form. In Dependence Analysis, the author extends the model to a program consisting of do loops and assignment statements, where the loops need not be sequentially nested and are allowed to have arbitrary strides. In the context of such a program, the author studies, in detail, dependence between statements of the program caused by program variables that are elements of arrays. Dependence Analysis is directed toward graduate and undergraduate students, and professional writers of restructuring compilers. The prerequisite for the book consists of some knowledge of programming languages, and familiarity with calculus and graph theory. No knowledge of linear programming is required.

Addressing Cybersecurity through the lens of a war-time set of varying battlefields is unique. Tying those to Zero Trust is also unique. It has that unique POV that hasn't been covered before combined with a highly credible view of and explanation of Zero Trust.

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.

It's All Analytics! The Foundations of AI, Big Data and Data Science Landscape for Professionals in Healthcare, Business, and Government (978-0-367-35968-3, 325690) Professionals are challenged each day by a changing landscape of technology and terminology. In recent history, especially in the last 25 years, there has been an explosion of terms and methods that automate and improve decision-making and operations. One term, "analytics," is an overarching description of a compilation of methodologies. But AI (artificial intelligence), statistics, decision science, and optimization, which have been around for decades, have resurged. Also, things like business intelligence, online analytical processing (OLAP) and many, many more have been born or reborn. How is someone to make sense of all this methodology and terminology? This book, the first in a series of three, provides a look at the foundations of artificial intelligence and analytics and why readers need an unbiased understanding of the subject. The authors include the basics such as algorithms, mental concepts, models, and paradigms in addition to the benefits of machine learning. The book also includes a chapter on data and the various forms of data. The authors wrap up this book with a look at the next frontiers such as applications and designing your environment for success, which segue into the topics of the next two books in the series.

The COVID-19 pandemic upended the lives of many and taught us the critical importance of taking care of one's health and wellness. Technological advances, coupled with advances in healthcare, has enabled the widespread growth of a new area called mobile health or mHealth that has completely revolutionized how people envision healthcare today. Just as smartphones and tablet computers are rapidly becoming the dominant consumer computer platforms, mHealth technology is emerging as an integral part of consumer health and wellness management regimes. The aim of this book is to inform readers about the this relatively modern technology, from its history and evolution to the current state-of-the-art research developments and the underlying challenges related to privacy and security issues. The book's intended audience includes individuals interested in learning about mHealth and its contemporary applications, from students to researchers and practitioners working in this field. Both undergraduate and graduate students enrolled in college-level healthcare courses will find this book to be an especially useful companion and will be able to discover and explore novel research directions that will further enrich the field.

This book provides insights into contemporary issues and challenges in multi-criteria decision models. It is a useful guide for identifying, understanding and categorising multi-criteria decision models, and ultimately implementing the analysis for effective decision-making. The use of multi-criteria decision models in software reliability engineering is a relatively new field of study, and this book collects all the latest methodologies, tools and techniques in one single volume. It covers model selection, assessment, resource allocation, release management, up-grade planning, open-source systems, bug tracking system management and defect prediction. Multi-Criteria Decision Models in Software Reliability: Methods and Applications will cater to researchers, academicians, post-graduate students, software developers, software reliability engineers and IT managers.

This book provides an insightful guide to the design, testing and optimization of micro-electrode-dot-array (MEDA) digital microfluidic biochips. The authors focus on the characteristics specific for MEDA biochips, e.g., real-time sensing and advanced microfluidic operations like lamination mixing and droplet shape morphing. Readers will be enabled to enhance the automated design and use of MEDA and to develop a set of solutions to facilitate the full exploitation of design complexities that are possible with standard CMOS fabrication techniques. The book provides the first set of design automation and test techniques for MEDA biochips. The methods described in this book have been validated using fabricated MEDA biochips in the laboratory. Readers will benefit from an in-depth look at the MEDA platform and how to combine microfluidics with software, e.g., applying biomolecular protocols to software-controlled and cyberphysical microfluidic biochips.

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.

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

Unique selling point: A project management book that focuses on managing projects in business Core audience: Project and business managers Place in the market: Professional book that can be used for continuing education