Sukhpal Singh Gill – författare

Autonomous and Connected Heavy Vehicle Technology presents the fundamentals, definitions, technologies, standards and future developments of autonomous and connected heavy vehicles. This book provides insights into various issues pertaining to heavy vehicle technology and helps users develop solutions towards autonomous, connected, cognitive solutions through the convergence of Big Data, IoT, cloud computing and cognition analysis. Various physical, cyber-physical and computational key points related to connected vehicles are covered, along with concepts such as edge computing, dynamic resource optimization, engineering process, methodology and future directions.

The book also contains a wide range of case studies that help to identify research problems and an analysis of the issues and synthesis solutions. This essential resource for graduate-level students from different engineering disciplines such as automotive and mechanical engineering, computer science, data science and business analytics combines both basic concepts and advanced level content from technical experts.

Quantum computing is a field in which advanced technologies like quantum communication, artificial intelligence and machine learning can be used to secure and speed up connectivity using quantum computers, quantum drones or quantum satellites. This book serve as a foundation for researchers and scientists in this field. Future technologies, such as quantum drone delivery systems, quicker internet and climate change mitigation, will need quantum information processing and quantum computation. This book deeply explores the importance of quantum computing in real-time applications. It may be used as a reference book for students in higher education, including undergraduate and graduate students, as well as researchers.

Key features:

Quantum computing is a field in which advanced technologies like quantum communication, artificial intelligence and machine learning can be used to secure and speed up connectivity using quantum computers, quantum drones or quantum satellites. This book serve as a foundation for researchers and scientists in this field. Future technologies, such as quantum drone delivery systems, quicker internet and climate change mitigation, will need quantum information processing and quantum computation. This book deeply explores the importance of quantum computing in real-time applications. It may be used as a reference book for students in higher education, including undergraduate and graduate students, as well as researchers.

Key features:

Applying artificial intelligence (AI) to new fields has made AI and data science indispensable to researchers in a wide range of fields. The proliferation and successful deployment of AI algorithms are fuelling these changes, which can be seen in fields as disparate as healthcare and emerging Internet of Things (IoT) applications. Machine learning techniques, and AI more broadly, are expected to play an ever-increasing role in the modelling, simulation, and analysis of data from a wide range of fields by the interdisciplinary research community. Ideas and techniques from multidisciplinary research are being utilised to enhance AI; hence, the connection between the two fields is a two-way street at a crossroads. Algorithms for inference, sampling, and optimisation, as well as investigations into the efficacy of deep learning, frequently make use of methods and concepts from other fields of study. Cloud computing platforms may be used to develop and deploy several AI models with high computational power. The intersection between multiple fields, including math, science, and healthcare, is where the most significant theoretical and methodological problems of AI may be found. To gather, integrate, and synthesise the many results and viewpoints in the connected domains, refer to it as interdisciplinary research. In light of this, the theory, techniques, and applications of machine learning and AI, as well as how they are utilised across disciplinary boundaries, are the main areas of this research topic.

Applying artificial intelligence (AI) to new fields has made AI and data science indispensable to researchers in a wide range of fields. The proliferation and successful deployment of AI algorithms are fuelling these changes, which can be seen in fields as disparate as healthcare and emerging Internet of Things (IoT) applications. Machine learning techniques, and AI more broadly, are expected to play an ever-increasing role in the modelling, simulation, and analysis of data from a wide range of fields by the interdisciplinary research community. Ideas and techniques from multidisciplinary research are being utilised to enhance AI; hence, the connection between the two fields is a two-way street at a crossroads. Algorithms for inference, sampling, and optimisation, as well as investigations into the efficacy of deep learning, frequently make use of methods and concepts from other fields of study. Cloud computing platforms may be used to develop and deploy several AI models with high computational power. The intersection between multiple fields, including math, science, and healthcare, is where the most significant theoretical and methodological problems of AI may be found. To gather, integrate, and synthesise the many results and viewpoints in the connected domains, refer to it as interdisciplinary research. In light of this, the theory, techniques, and applications of machine learning and AI, as well as how they are utilised across disciplinary boundaries, are the main areas of this research topic.

This book explores how advances in graphic processing units (GPUs), programmable logic devices (TPUs), and field-programmable gate arrays have altered the serverless computing landscape (FPGAs). Distributed system architectures and implementations have undergone significant changes due to the popularity of serverless computing. Making and releasing product applications, doing market research, and maintaining customer interactions might all benefit from the reduced infrastructure expenses made possible by serverless computing. This book is a great resource for teachers and students interested in learning more about serverless computing. Some of the main questions surrounding serverless technology, such as scalability and performance distribution, are answered. Concepts and fundamentals of computing performance such as cost-free operation, good time and resource management, fairness, and interoperability are discussed. Serverless is at the forefront of this shift, which has made data-intensive, distributed applications, and open-source platforms essential for any modern computer to function. Data-centric queuing, real-time logging and monitoring, querying, and alarms are all examples of serverless services.