The global smart cities market size was valued at USD 1,226.9 billion in 2022 and is expected to register a compound annual growth rate (CAGR) of 25.8% from 2023 to 2030. Firstly and visually, a smart city is a city that has no traffic jams. In the smart cities, both information and communication technologies are integrated for exchanging real-time data between their citizens, governments, and organisations. Blockchain has been considered as the key driver for development of smart cities. Blockchain technology can provide high security for large communications and transactions between many stakeholders in the smart cities. In addition, digital twins are also considered as the starting key for construction of smart cities. Digital twin refers to a simulation of physical products in virtual space. This simulation makes full use of physical models/wireless sensor networks/ historical data of city operation to integrate big information (digital twin cities) under multi-discipline, multi-physical quantities, multi-scale, and multi-probability. Digital Twin, Blockchain, and Sensor Networks in the Healthy and Mobile City explores how digital twins and blockchain can be used in smart cities. Section 1 deals with their promising applications for safe and healthy cities, respectively. Section 2 covers other promising applications and current perspectives of blockchain and digital twin for future smart cities. Together with its companion volume, Digital Twin, Blockchain, and Sensor Networks in the City, this book will help us make sense of the vast amount of data around the city, and will guide us to use that data to create happy, healthy, safe and productive lives.
Blockchain and Digital Twins for Smart Healthcare describes the role of blockchain and digital twins in smart healthcare, covering the ecosystem of the Internet of Medical Things, how data can be gathered using a sensor network, which is securely stored, updated, and managed with blockchain for efficient and private medical data exchange. Medical data is collected real-time from devices and systems in smart hospitals: the internet of medical things. This data is integrated to provide insight from the analytics or machine learning software using digital twins. Security and transparency are brought through a combination of digital twin and blockchain technologies.
The smart hospital framework involves three main layers: data, insight and access. Medical data is collected real-time from devices and systems in a smart hospitals: the internet of medical things. This data is integrated to provide insight from the analytics or machine learning software using digital twins. Security and transparency are brought through a combination of digital twin and blockchain technologies. Blockchain and Digital Twins for Smart Healthcare describes the role of blockchain and digital twins in smart healthcare. It describes the ecosystem of the Internet of Medical Things, how data can be gathered using a sensor network, which is securely stored, updated and managed with blockchain for efficient and private medical data exchange. The end goal is insight that provides faster, smarter decisions with more efficiency to improve care for the patient.
Sensor Networks for Smart Hospitals shows how the use of sensors to gather data on a patient's condition and the environment in which their care takes place can allow healthcare professionals to monitor well-being and make informed decisions about treatment. Written by experts in the field, this book is an invaluable resource for researchers and healthcare practitioners in their drive to use technology to improve the lives of patients. Data from sensor networks via the smart hospital framework is comprised of three main layers: data, insights, and access.Medical data is collected in real-time from an array of intelligent devices/systems deployed within the hospital. This data offers insight from the analytics or machine learning software that is accessible to healthcare staff via a smartphone or mobile device to facilitate swifter decisions and greater efficiency.
RISC-V Microprocessor System-On-Chip Design is written to be accessible to an advanced undergraduate audience with limited background. It explains concepts from operating systems, VLSI, and memory systems as necessary, and High school mathematics is sufficient preparation for most of the book, although the floating point and division chapters will be primarily of interest to those with a curiosity about computer arithmetic. Like Harris and Harris’s Digital Design and Computer Architecture textbooks, this book will appeal to students with easy-to-read and complete explanations, sidebars, and occasional humor and cartoons.It comes with an open-source implementation and will include end-of-chapter problems to extend the RISC-V processor in various ways. Ancillary materials include a GitHub repository with complete open-source SystemVerilog code, validation code in C and assembly language, and code for benchmarking and booting Linux.
Computer Architecture: A Quantitative Approach, has been considered essential reading by instructors, students and practitioners of computer design for nearly 30 years. The seventh edition of this classic textbook from John Hennessy and David Patterson, winners of the 2017 ACM A.M. Turing Award recognizing contributions of lasting and major technical importance to the computing field, along with new author Christos Kozyrakis, is fully revised with the latest developments in processor and system architecture. True to its original mission of demystifying computer architecture, this edition continues the longstanding tradition of focusing on areas where the most exciting computing innovation is happening, while always keeping an emphasis on good engineering design.
Advanced Sensors for Smart Healthcare provides an invaluable resource for researchers and healthcare practitioners who are eager to use technology to improve the lives of patients. Sections highlight data from sensor networks via the smart hospital framework, including data, insights, and access. This book shows how the use of sensors to gather data on a patient's condition and the environment their care takes place in can allow healthcare professionals to monitor well-being and make informed decisions about treatment.
Iris and Periocular Recognition using Deep Learning systematically explains the fundamental and most advanced techniques for ocular imprint-based human identification, with many applications in sectors such as healthcare, online education, e-business, metaverse, and entertainment. This is the first-ever book devoted to iris recognition that details cutting-edge techniques using deep neural networks. This book systematically introduces such algorithmic details with attractive illustrations, examples, experimental comparisons, and security analysis. It answers many fundamental questions about the most effective iris and periocular recognition techniques.
Modern Assembly Language Programming with the ARM Processor, Second Edition is a tutorial-based book on assembly language programming using the ARM processor. It presents the concepts of assembly language programming in different ways, slowly building from simple examples towards complex programming on bare-metal embedded systems. The ARM processor was chosen as it has fewer instructions and irregular addressing rules to learn than most other architectures, allowing more time to spend on teaching assembly language programming concepts and good programming practice.Careful consideration is given to topics that students struggle to grasp, such as registers vs. memory and the relationship between pointers and addresses, recursion, and non-integral binary mathematics. A whole chapter is dedicated to structured programming principles. Concepts are illustrated and reinforced with many tested and debugged assembly and C source listings. The book also covers advanced topics such as fixed- and floating-point mathematics, optimization, and the ARM VFP and NEONTM extensions.
Embedded Systems: ARM Programming and Optimization, Second Edition combines an exploration of the ARM architecture with an examination of the facilities offered by the Linux operating system to explain how various features of program design can influence processor performance. The book demonstrates methods by which a programmer can optimize program code in a way that does not impact its behavior but instead improves its performance. Several applications, including image transformations, fractal generation, image convolution, computer vision tasks, and now machine learning are used to describe and demonstrate these methods. From this, the reader will gain insight into computer architecture and application design, as well as practical knowledge in embedded software design for modern embedded systems. The second edition has been expanded to include more topics of interest to upper level undergraduate courses in embedded systems.