This book gathers selected research papers presented at the First International Conference on Embedded Systems and Artificial Intelligence (ESAI 2019), held at Sidi Mohamed Ben Abdellah University, Fez, Morocco, on 2–3 May 2019. Highlighting the latest innovations in Computer Science, Artificial Intelligence, Information Technologies, and Embedded Systems, the respective papers will encourage and inspire researchers, industry professionals, and policymakers to put these methods into practice.
This book comprehensively covers the state-of-the-art security applications of machine learning techniques. The first part explains the emerging solutions for anti-tamper design, IC Counterfeits detection and hardware Trojan identification. It also explains the latest development of deep-learning-based modeling attacks on physically unclonable functions and outlines the design principles of more resilient PUF architectures. The second discusses the use of machine learning to mitigate the risks of security attacks on cyber-physical systems, with a particular focus on power plants. The third part provides an in-depth insight into the principles of malware analysis in embedded systems and describes how the usage of supervised learning techniques provides an effective approach to tackle software vulnerabilities.
The fourth edition of Embedded Systems takes a big leap from the fundamentals of hardware to Edge Computing, Embedded IoT & Embedded AI. The book discusses next generation embedded systems topics, such as embedded SoC, Exascale computing systems and embedded systems’ tensor processing units. This thoroughly updated edition serves as a textbook for engineering students and reference book for students of software-training institutions and embedded-systems-design professionals. Salient Features: 1. New chapters on IoT system architecture and design & Embedded AI 2. Case studies, such as, of Automatic Chocolate Vending Machine and Automobile Cruise Control 3. Bloom’s Taxonomy-based chapter structure 4. Rich Pedagogy o 1000+ Self-assessment questions o 150+ MCQs o 220+ Review questions o 200+ Practice exercises
Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Volume 122 delves into artificial Intelligence and the growth it has seen with the advent of Deep Neural Networks (DNNs) and Machine Learning. Updates in this release include chapters on Hardware accelerator systems for artificial intelligence and machine learning, Introduction to Hardware Accelerator Systems for Artificial Intelligence and Machine Learning, Deep Learning with GPUs, Edge Computing Optimization of Deep Learning Models for Specialized Tensor Processing Architectures, Architecture of NPU for DNN, Hardware Architecture for Convolutional Neural Network for Image Processing, FPGA based Neural Network Accelerators, and much more. Updates on new information on the architecture of GPU, NPU and DNN Discusses In-memory computing, Machine intelligence and Quantum computing Includes sections on Hardware Accelerator Systems to improve processing efficiency and performance
Proceedings of the International Conference on Artificial Intelligence and Applied Mathematics in Engineering (ICAIAME 2020)
Author: Jude Hemanth
Publisher: Springer Nature
This book briefly covers internationally contributed chapters with artificial intelligence and applied mathematics-oriented background-details. Nowadays, the world is under attack of intelligent systems covering all fields to make them practical and meaningful for humans. In this sense, this edited book provides the most recent research on use of engineering capabilities for developing intelligent systems. The chapters are a collection from the works presented at the 2nd International Conference on Artificial Intelligence and Applied Mathematics in Engineering held within 09-10-11 October 2020 at the Antalya, Manavgat (Turkey). The target audience of the book covers scientists, experts, M.Sc. and Ph.D. students, post-docs, and anyone interested in intelligent systems and their usage in different problem domains. The book is suitable to be used as a reference work in the courses associated with artificial intelligence and applied mathematics.
Cyber-physical systems (CPS) have emerged as a unifying name for systems where cyber parts (i.e., the computing and communication parts) and physical parts are tightly integrated, both in design and during operation. Such systems use computations and communication deeply embedded in and interacting with human physical processes as well as augmenting existing and adding new capabilities. As such, CPS is an integration of computation, networking, and physical processes. Embedded computers and networks monitor and control the physical processes, with feedback loops where physical processes affect computations and vice versa. The economic and societal potential of such systems is vastly greater than what has been realized, and major investments are being made worldwide to develop the technology. Artificial Intelligence Paradigms for Smart Cyber-Physical Systems focuses on the recent advances in Artificial intelligence-based approaches towards affecting secure cyber-physical systems. This book presents investigations on state-of-the-art research issues, applications, and achievements in the field of computational intelligence paradigms for CPS. Covering topics that include autonomous systems, access control, machine learning, and intrusion detection and prevention systems, this book is ideally designed for engineers, industry professionals, practitioners, scientists, managers, students, academicians, and researchers seeking current research on artificial intelligence and cyber-physical systems.
Proceedings of the 3rd IFAC Workshop, California, USA, 23-25 September 1991
Author: M.G. Rodd
Category: Technology & Engineering
This set of proceedings contains the most significant papers presented at the third IFAC Workshop on Artificial Intelligence in Real-time Control, which was held from September 23-25, 1991 in the USA. In this workshop, although there were still some "exotic" applications, a more practical view of the applications and limitations of current AI technology dominated the participants' discussions. With its resultant focus on reliability and safety considerations, the workshop posed as many questions as it answered. It provides an excellent mirror of the current state-of-the-art which these proceedings are intended to illustrate.
Learning to perform complex action strategies is an important problem in the fields of artificial intelligence, robotics, and machine learning. Filled with interesting new experimental results, Learning in Embedded Systems explores algorithms that learn efficiently from trial-and error experience with an external world. It is the first detailed exploration of the problem of learning action strategies in the context of designing embedded systems that adapt their behavior to a complex, changing environment; such systems include mobile robots, factory process controllers, and long-term software databases. Kaelbling investigates a rapidly expanding branch of machine learning known as reinforcement learning, including the important problems of controlled exploration of the environment, learning in highly complex environments, and learning from delayed reward. She reviews past work in this area and presents a number of significant new results. These include the intervalestimation algorithm for exploration, the use of biases to make learning more efficient in complex environments, a generate-and-test algorithm that combines symbolic and statistical processing into a flexible learning method, and some of the first reinforcement-learning experiments with a real robot.
Hugo de Man Professor Katholieke Universiteit Leuven Senior Research Fellow IMEC The steady evolution of hardware, software and communications technology is rapidly transforming the PC- and dot.com world into the world of Ambient Intelligence (AmI). This next wave of information technology is fundam- tally different in that it makes distributed wired and wireless computing and communication disappear to the background and puts users to the foreground. AmI adapts to people instead of the other way around. It will augment our consciousness, monitor our health and security, guide us through traffic etc. In short, its ultimate goal is to improve the quality of our life by a quiet, reliable and secure interaction with our social and material environment. What makes AmI engineering so fascinating is that its design starts from studying person to world interactions that need to be implemented as an int- ligent and autonomous interplay of virtually all necessary networked electronic intelligence on the globe. This is a new and exciting dimension for most elect- cal and software engineers and may attract more creative talent to engineering than pure technology does. Development of the leading technology for AmI will only succeed if the engineering research community is prepared to join forces in order to make Mark Weiser’s dream of 1991 come true. This will not be business as usual by just doubling transistor count or clock speed in a microprocessor or increasing the bandwidth of communication.
Author: Management Association, Information Resources
Publisher: IGI Global
Ongoing advancements in modern technology have led to significant developments in artificial intelligence. With the numerous applications available, it becomes imperative to conduct research and make further progress in this field. Artificial Intelligence: Concepts, Methodologies, Tools, and Applications provides a comprehensive overview of the latest breakthroughs and recent progress in artificial intelligence. Highlighting relevant technologies, uses, and techniques across various industries and settings, this publication is a pivotal reference source for researchers, professionals, academics, upper-level students, and practitioners interested in emerging perspectives in the field of artificial intelligence.