Learn the essentials of Networking and Embedded TCP/IP stacks. Part I of this comprehensive book provides a thorough explanation of Micri m's C/TCP-IP stack including its implementation and usage. Part II describes practical, working applications for embedded medical devices built on C/OS-III, C/TCP-IP and Freescale's TWR-K53N512 medical board (ARM Cortex -M4) using IAR developments tools. Each of the included examples feature hands-on working projects, which allow you to get your application running quickly, and can serve as a reference design to develop an embedded system connected to the Internet of Things. This book is the perfect complement to C/OS-III: The Real-Time Kernel for the ARM Cortex -M4 by Jean Labrosse (ISBN 978-0-9823375-2-3), as it uses the same medical application examples but connects them via TCP/IP. This book is written for serious embedded systems programmers, consultants, hobbyists, and students interested in understanding the inner workings of a TCP/IP stack. C/TCP-IP is more than just a great learning platform. It is a full commercial-grade software package, ready to serve as the foundation for a wide range of products. Some of the key topics covered in this book are: Ethernet technology and device drivers IP connectivity Client and Server architecture Socket programming UDP and TCP performance tuning

This holistic book is an invaluable reference for addressing various practical challenges in architecting and engineering Intelligent IoT and eHealth solutions for industry practitioners, academic and researchers, as well as for engineers involved in product development. The first part provides a comprehensive guide to fundamentals, applications, challenges, technical and economic benefits, and promises of the Internet of Things using examples of real-world applications. It also addresses all important aspects of designing and engineering cutting-edge IoT solutions using a cross-layer approach from device to fog, and cloud covering standards, protocols, design principles, reference architectures, as well as all the underlying technologies, pillars, and components such as embedded systems, network, cloud computing, data storage, data processing, big data analytics, machine learning, distributed ledger technologies, and security. In addition, it discusses the effects of Intelligent IoT, which are reflected in new business models and digital transformation. The second part provides an insightful guide to the design and deployment of IoT solutions for smart healthcare as one of the most important applications of IoT. Therefore, the second part targets smart healthcare-wearable sensors, body area sensors, advanced pervasive healthcare systems, and big data analytics that are aimed at providing connected health interventions to individuals for healthier lifestyles.

This book will attempt to give a first synthesis of recent works con cerning reactive system design. The term "reactive system" has been introduced in order to at'oid the ambiguities often associated with by the term "real-time system," which, although best known and more sugges tive, has been given so many different meanings that it is almost in evitably misunderstood. Industrial process control systems, transporta tion control and supervision systems, signal-processing systems, are ex amples of the systems we have in mind. Although these systems are more and more computerized, it is sur prising to notice that the problem of time in computer science has been studied only recently by "pure" computer scientists. Until the early 1980s, time problems were regarded as the concern of performance evalu ation, or of some (unjustly scorned) "industrial computer engineering," or, at best, of operating systems. A second surprising fact, in contrast, is the growth of research con cerning timed systems during the last decade. The handling of time has suddenly become a fundamental goal for most models of concurrency. In particular, Robin Alilner 's pioneering works about synchronous process algebras gave rise to a school of thought adopting the following abstract point of view: As soon as one admits that a system can instantaneously react to events, i. e."

This volume provides a comprehensive introduction to mHealth technology and is accessible to technology-oriented researchers and practitioners with backgrounds in computer science, engineering, statistics, and applied mathematics. The contributing authors include leading researchers and practitioners in the mHealth field. The book offers an in-depth exploration of the three key elements of mHealth technology: the development of on-body sensors that can identify key health-related behaviors (sensors to markers), the use of analytic methods to predict current and future states of health and disease (markers to predictors), and the development of mobile interventions which can improve health outcomes (predictors to interventions). Chapters are organized into sections, with the first section devoted to mHealth applications, followed by three sections devoted to the above three key technology areas. Each chapter can be read independently, but the organization of the entire book provides a logical flow from the design of on-body sensing technology, through the analysis of time-varying sensor data, to interactions with a user which create opportunities to improve health outcomes. This volume is a valuable resource to spur the development of this growing field, and ideally suited for use as a textbook in an mHealth course.

From basic architecture, interconnection, and parallelization to power optimization, this book provides a comprehensive description of emerging multicore systems-on-chip (MCSoCs) hardware and software design. Highlighting both fundamentals and advanced software and hardware design, it can serve as a primary textbook for advanced courses in MCSoCs design and embedded systems. The first three chapters introduce MCSoCs architectures, present design challenges and conventional design methods, and describe in detail the main building blocks of MCSoCs. Chapters 4, 5, and 6 discuss fundamental and advanced on-chip interconnection network technologies for multi and many core SoCs, enabling readers to understand the microarchitectures for on-chip routers and network interfaces that are essential in the context of latency, area, and power constraints. With the rise of multicore and many-core systems, concurrency is becoming a major issue in the daily life of a programmer. Thus, compiler and software development tools are critical in helping programmers create high-performance software. Programmers should make sure that their parallelized program codes will not cause race condition, memory-access deadlocks, or other faults that may crash their entire systems. As such, Chapter 7 describes a novel parallelizing compiler design for high-performance computing. Chapter 8 provides a detailed investigation of power reduction techniques for MCSoCs at component and network levels. It discusses energy conservation in general hardware design, and also in embedded multicore system components, such as CPUs, disks, displays and memories. Lastly, Chapter 9 presents a real embedded MCSoCs system design targeted for health monitoring in the elderly.

This book is intended for a first course on microprocessor-based systems design for engineering and computer science students. It starts with an introduction of the fundamental concepts, followed by a practical path that guides readers to developing a basic microprocessor example, using a step-by-step problem-solving approach. Then, a second microprocessor is presented, and readers are guided to the implementation and programming of microcomputer systems based on it. The numerous worked examples and solved exercises allow a better understanding and a more effective learning. All the examples and exercises were developed on Deeds (Digital Electronics Education and Design Suite), which is freely available online on a website developed and maintained by the authors. The discussed examples can be simulated by using Deeds and the solutions to all exercises and examples can be found on that website. Further, in the last part of this book, different microprocessor-based systems, which have been specifically thought for educational purposes, are extensively developed, simulated and implemented on FPGA-based platforms. This textbook draws on the authors' extensive experience in teaching and developing learning materials for bachelor's and master's engineering courses. It can be used for self-study as well, and even independently from the simulator. Thanks to the learning-by-doing approach and the plentiful examples, no prior knowledge in computer programming is required.

This book presents the basics of both NAND flash storage and machine learning, detailing the storage problems the latter can help to solve. At a first sight, machine learning and non-volatile memories seem very far away from each other. Machine learning implies mathematics, algorithms and a lot of computation; non-volatile memories are solid-state devices used to store information, having the amazing capability of retaining the information even without power supply. This book will help the reader understand how these two worlds can work together, bringing a lot of value to each other. In particular, the book covers two main fields of application: analog neural networks (NNs) and solid-state drives (SSDs). After reviewing the basics of machine learning in Chapter 1, Chapter 2 shows how neural networks can mimic the human brain; to accomplish this result, neural networks have to perform a specific computation called vector-by-matrix (VbM) multiplication, which is particularly power hungry. In the digital domain, VbM is implemented by means of logic gates which dictate both the area occupation and the power consumption; the combination of the two poses serious challenges to the hardware scalability, thus limiting the size of the neural network itself, especially in terms of the number of processable inputs and outputs. Non-volatile memories (phase change memories in Chapter 3, resistive memories in Chapter 4, and 3D flash memories in Chapter 5 and Chapter 6) enable the analog implementation of the VbM (also called "neuromorphic architecture"), which can easily beat the equivalent digital implementation in terms of both speed and energy consumption. SSDs and flash memories are strictly coupled together; as 3D flash scales, there is a significant amount of work that has to be done in order to optimize the overall performances of SSDs. Machine learning has emerged as a viable solution in many stages of this process. After introducing the main flash reliability issues, Chapter 7 shows both supervised and un-supervised machine learning techniques that can be applied to NAND. In addition, Chapter 7 deals with algorithms and techniques for a pro-active reliability management of SSDs. Last but not least, the last section of Chapter 7 discusses the next challenge for machine learning in the context of the so-called computational storage. No doubt that machine learning and non-volatile memories can help each other, but we are just at the beginning of the journey; this book helps researchers understand the basics of each field by providing real application examples, hopefully, providing a good starting point for the next level of development.

This book describes the most frequently used high-speed serial buses in embedded systems, especially those used by FPGAs. These buses employ SerDes, JESD204, SRIO, PCIE, Aurora and SATA protocols for chip-to-chip and board-to-board communication, and CPCIE, VPX, FC and Infiniband protocols for inter-chassis communication. For each type, the book provides the bus history and version info, while also assessing its advantages and limitations. Furthermore, it offers a detailed guide to implementing these buses in FPGA design, from the physical layer and link synchronization to the frame format and application command. Given its scope, the book offers a valuable resource for researchers, R&D engineers and graduate students in computer science or electronics who wish to learn the protocol principles, structures and applications of high-speed serial buses.

The Elixir programming language has become a go-to tool for creating reliable, fault-tolerant, and robust server-side applications. Thanks to Nerves, those same exact benefits can be realized in embedded applications. This book will teach you how to structure, build, and deploy production grade Nerves applications to network-enabled devices. The weather station sensor hub project that you will be embarking upon will show you how to create a full stack IoT solution in record time. You will build everything from the embedded Nerves device to the Phoenix backend and even the Grafana time-series data visualizations. Elixir as a programming language has found its way into many different software domains, largely in part to the rock-solid foundation of the Erlang virtual machine. Thanks to the Nerves framework, Elixir has also found success in the world of embedded systems and IoT. Having access to all of the Elixir and OTP constructs such as concurrency, supervision, and immutability makes for a powerful IoT recipe. Find out how to create fault-tolerant, reliable, and robust embedded applications using the Nerves framework. Build and deploy a production-grade weather station sensor hub using Elixir and Nerves, all while leveraging the best practices established by the Nerves community for structuring and organizing Nerves applications. Capture all of your weather station sensor data using Phoenix and Ecto in a lightweight server-side application. Efficiently store and retrieve the time-series weather data collected by your device using TimescaleDB (the Postgres extension for time-series data). Finally, complete the full stack IoT solution by using Grafana to visualize all of your time-series weather station data. Discover how to create software solutions where the underlying technologies and techniques are applicable to all layers of the project. Take your project from idea to production ready in record time with Elixir and Nerves.

This book pioneers the field of gain-cell embedded DRAM (GC-eDRAM) design for low-power VLSI systems-on-chip (SoCs). Novel GC-eDRAMs are specifically designed and optimized for a range of low-power VLSI SoCs, ranging from ultra-low power to power-aware high-performance applications. After a detailed review of prior-art GC-eDRAMs, an analytical retention time distribution model is introduced and validated by silicon measurements, which is key for low-power GC-eDRAM design. The book then investigates supply voltage scaling and near-threshold voltage (NTV) operation of a conventional gain cell (GC), before presenting novel GC circuit and assist techniques for NTV operation, including a 3-transistor full transmission-gate write port, reverse body biasing (RBB), and a replica technique for optimum refresh timing. Next, conventional GC bitcells are evaluated under aggressive technology and voltage scaling (down to the subthreshold domain), before novel bitcells for aggressively scaled CMOS nodes and soft-error tolerance as presented, including a 4-transistor GC with partial internal feedback and a 4-transistor GC with built-in redundancy.

Amid recent interest in Clifford algebra for dual quaternions as a more suitable method for Computer Graphics than standard matrix algebra, this book presents dual quaternions and their associated Clifford algebras in a new light, accessible to and geared towards the Computer Graphics community. Collating all the associated formulas and theorems in one place, this book provides an extensive and rigorous treatment of dual quaternions, as well as showing how two models of Clifford algebras emerge naturally from the theory of dual quaternions. Each chapter comes complete with a set of exercises to help readers sharpen and practice their knowledge. This book is accessible to anyone with a basic knowledge of quaternion algebra and is of particular use to forward-thinking members of the Computer Graphics community. .

Embedded System Interfacing: Design for the Internet-of-Things (IoT) and Cyber-Physical Systems (CPS) takes a comprehensive approach to the interface between embedded systems and software. It provides the principles needed to understand how digital and analog interfaces work and how to design new interfaces for specific applications. The presentation is self-contained and practical, with discussions based on real-world components. Design examples are used throughout the book to illustrate important concepts. This book is a complement to the author's Computers as Components, now in its fourth edition, which concentrates on software running on the CPU, while Embedded System Interfacing explains the hardware surrounding the CPU.

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

((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."

This book provides a new perspective on modeling cyber-physical systems (CPS), using a data-driven approach. The authors cover the use of state-of-the-art machine learning and artificial intelligence algorithms for modeling various aspect of the CPS. This book provides insight on how a data-driven modeling approach can be utilized to take advantage of the relation between the cyber and the physical domain of the CPS to aid the first-principle approach in capturing the stochastic phenomena affecting the CPS. The authors provide practical use cases of the data-driven modeling approach for securing the CPS, presenting novel attack models, building and maintaining the digital twin of the physical system. The book also presents novel, data-driven algorithms to handle non- Euclidean data. In summary, this book presents a novel perspective for modeling the CPS.

This book is based on the 18 tutorials presented during the 26th workshop on Advances in Analog Circuit Design. Expert designers present readers with information about a variety of topics at the frontier of analog circuit design, with specific contributions focusing on hybrid ADCs, smart sensors for the IoT, sub-1V and advanced-node analog circuit design. This book serves as a valuable reference to the state-of-the-art, for anyone involved in analog circuit research and development.

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.

Provides comprehensive research ideas about Edge-AI technology that can assist doctors in making better data-driven decisions and will provide insights to researchers about healthcare industry, trends and future perspective. Examines how healthcare systems of the future will operate, by augmenting clinical resources and ensuring optimal patient outcomes. Provides insight about how Edge-AI is revolutionizing decision making, early warnings for conditions, and visual inspection in healthcare. Highlight trends, challenges, opportunities and future areas where Healthcare informatics deal with accessing vast data sets of potentially life-saving information.

This reference text presents the usage of artificial intelligence in healthcare and discusses the challenges and solutions of using advanced techniques like wearable technologies and image processing in the sector. Features: Focuses on the use of artificial intelligence (AI) in healthcare with issues, applications, and prospects Presents the application of artificial intelligence in medical imaging, fractionalization of early lung tumour detection using a low intricacy approach, etc Discusses an artificial intelligence perspective on wearable technology Analyses cardiac dynamics and assessment of arrhythmia by classifying heartbeat using electrocardiogram (ECG) Elaborates machine learning models for early diagnosis of depressive mental affliction This book serves as a reference for students and researchers analyzing healthcare data. It can also be used by graduate and post graduate students as an elective course.