Jose Martinez-Carranza – författare

This book highlights applications that include machine learning methods to enhance new developments in complex and unmanned systems. The contents are organized from the applications requiring few methods to the ones combining different methods and discussing their development and hardware/software implementation. The book includes two parts: the first one collects machine learning applications in complex systems, mainly discussing developments highlighting their modeling and simulation, and hardware implementation. The second part collects applications of machine learning in unmanned systems including optimization and case studies in submarines, drones, and robots. The chapters discuss miscellaneous applications required by both complex and unmanned systems, in the areas of artificial intelligence, cryptography, embedded hardware, electronics, the Internet of Things, and healthcare. Each chapter provides guidelines and details of different methods that can be reproduced in hardware/software and discusses future research.

Features

Provides details of applications using machine learning methods to solve real problems in engineering Discusses new developments in the areas of complex and unmanned systems Includes details of hardware/software implementation of machine learning methods Includes examples of applications of different machine learning methods for future lines for research in the hot topic areas of submarines, drones, robots, cryptography, electronics, healthcare, and the Internet of Things

This book can be used by graduate students, industrial and academic professionals to examine real case studies in applying machine learning in the areas of modeling, simulation, and optimization of complex systems, cryptography, electronics, healthcare, control systems, Internet of Things, security, and unmanned systems such as submarines, drones, and robots.

This book highlights applications that include machine learning methods to enhance new developments in complex and unmanned systems. The contents are organized from the applications requiring few methods to the ones combining different methods and discussing their development and hardware/software implementation. The book includes two parts: the first one collects machine learning applications in complex systems, mainly discussing developments highlighting their modeling and simulation, and hardware implementation. The second part collects applications of machine learning in unmanned systems including optimization and case studies in submarines, drones, and robots. The chapters discuss miscellaneous applications required by both complex and unmanned systems, in the areas of artificial intelligence, cryptography, embedded hardware, electronics, the Internet of Things, and healthcare. Each chapter provides guidelines and details of different methods that can be reproduced in hardware/software and discusses future research.

Features

Provides details of applications using machine learning methods to solve real problems in engineering Discusses new developments in the areas of complex and unmanned systems Includes details of hardware/software implementation of machine learning methods Includes examples of applications of different machine learning methods for future lines for research in the hot topic areas of submarines, drones, robots, cryptography, electronics, healthcare, and the Internet of Things

This book can be used by graduate students, industrial and academic professionals to examine real case studies in applying machine learning in the areas of modeling, simulation, and optimization of complex systems, cryptography, electronics, healthcare, control systems, Internet of Things, security, and unmanned systems such as submarines, drones, and robots.

This book highlights applications that include machine learning methods to enhance new developments in complex and unmanned systems. The contents are organized from the applications requiring few methods to the ones combining different methods and discussing their development and hardware/software implementation. The book includes two parts: the first one collects machine learning applications in complex systems, mainly discussing developments highlighting their modeling and simulation, and hardware implementation. The second part collects applications of machine learning in unmanned systems including optimization and case studies in submarines, drones, and robots. The chapters discuss miscellaneous applications required by both complex and unmanned systems, in the areas of artificial intelligence, cryptography, embedded hardware, electronics, the Internet of Things, and healthcare. Each chapter provides guidelines and details of different methods that can be reproduced in hardware/software and discusses future research.FeaturesProvides details of applications using machine learning methods to solve real problems in engineeringDiscusses new developments in the areas of complex and unmanned systemsIncludes details of hardware/software implementation of machine learning methodsIncludes examples of applications of different machine learning methods for future lines for research in the hot topic areas of submarines, drones, robots, cryptography, electronics, healthcare, and the Internet of ThingsThis book can be used by graduate students, industrial and academic professionals to examine real case studies in applying machine learning in the areas of modeling, simulation, and optimization of complex systems, cryptography, electronics, healthcare, control systems, Internet of Things, security, and unmanned systems such as submarines, drones, and robots.

This book highlights applications that include machine learning methods to enhance new developments in complex and unmanned systems. The contents are organized from the applications requiring few methods to the ones combining different methods and discussing their development and hardware/software implementation. The book includes two parts: the first one collects machine learning applications in complex systems, mainly discussing developments highlighting their modeling and simulation, and hardware implementation. The second part collects applications of machine learning in unmanned systems including optimization and case studies in submarines, drones, and robots. The chapters discuss miscellaneous applications required by both complex and unmanned systems, in the areas of artificial intelligence, cryptography, embedded hardware, electronics, the Internet of Things, and healthcare. Each chapter provides guidelines and details of different methods that can be reproduced in hardware/software and discusses future research.FeaturesProvides details of applications using machine learning methods to solve real problems in engineeringDiscusses new developments in the areas of complex and unmanned systemsIncludes details of hardware/software implementation of machine learning methodsIncludes examples of applications of different machine learning methods for future lines for research in the hot topic areas of submarines, drones, robots, cryptography, electronics, healthcare, and the Internet of ThingsThis book can be used by graduate students, industrial and academic professionals to examine real case studies in applying machine learning in the areas of modeling, simulation, and optimization of complex systems, cryptography, electronics, healthcare, control systems, Internet of Things, security, and unmanned systems such as submarines, drones, and robots.

This handbook introduces some of the most relevant techniques used to develop intelligent robotic systems and provides several examples of applications where robots equipped with AI are deployed to solve a task.Handbook of Intelligent Robots: Theory, Methods and Applications is split into two main parts. The first part reviews key methods for developing intelligent robots implemented across various robotic systems, including service robots, micro aerial vehicles, manipulators, and humanoids, among others, deployed in diverse applications. The second part of the book provides several examples of applications where robotics systems are leveraged by AI and machine learning techniques to address real life applications, thus providing insights into the challenges and limitations of deploying robotic systems outside the laboratory. The main goal of the book is to familiarize the reader with the most recent concepts and techniques that are enabling robots to update their learned models online, to perform them efficiently on embedded processors, and to enable sophisticated interaction with the environment using spatial AI techniques such as visual simultaneous localization and mapping. To this end, the reader will delve into techniques such as continual learning, binary neural networks, neural controllers, fuzzy controllers, generation of time-optimal trajectories, generative models, natural language processing for robotics, and robot audition.This book is intended for electrical, computer and mechanical engineers interested in robotics and AI as well as those interested in robots deployed in real life scenarios. It will be useful to postgraduate students seeking reviews of the state of the art regarding AI methods for robotics such as visual SLAM, continual learning, neural networks, and transformers.

This handbook introduces some of the most relevant techniques used to develop intelligent robotic systems and provides several examples of applications where robots equipped with AI are deployed to solve a task.Handbook of Intelligent Robots: Theory, Methods and Applications is split into two main parts. The first part reviews key methods for developing intelligent robots implemented across various robotic systems, including service robots, micro aerial vehicles, manipulators, and humanoids, among others, deployed in diverse applications. The second part of the book provides several examples of applications where robotics systems are leveraged by AI and machine learning techniques to address real life applications, thus providing insights into the challenges and limitations of deploying robotic systems outside the laboratory. The main goal of the book is to familiarize the reader with the most recent concepts and techniques that are enabling robots to update their learned models online, to perform them efficiently on embedded processors, and to enable sophisticated interaction with the environment using spatial AI techniques such as visual simultaneous localization and mapping. To this end, the reader will delve into techniques such as continual learning, binary neural networks, neural controllers, fuzzy controllers, generation of time-optimal trajectories, generative models, natural language processing for robotics, and robot audition.This book is intended for electrical, computer and mechanical engineers interested in robotics and AI as well as those interested in robots deployed in real life scenarios. It will be useful to postgraduate students seeking reviews of the state of the art regarding AI methods for robotics such as visual SLAM, continual learning, neural networks, and transformers.