Anis Koubaa - Böcker

Unmanned Aerial Systems: Theoretical Foundation and Applications presents some of the latest innovative approaches to drones from the point-of-view of dynamic modeling, system analysis, optimization, control, communications, 3D-mapping, search and rescue, surveillance, farmland and construction monitoring, and more. With the emergence of low-cost UAS, a vast array of research works in academia and products in the industrial sectors have evolved. The book covers the safe operation of UAS, including, but not limited to, fundamental design, mission and path planning, control theory, computer vision, artificial intelligence, applications requirements, and more.

This book provides a unique reference of the state-of-the-art research and development of unmanned aerial systems, making it an essential resource for researchers, instructors and practitioners.

Covers some of the most innovative approaches to drones Provides the latest state-of-the-art research and development surrounding unmanned aerial systems Presents a comprehensive reference on unmanned aerial systems, with a focus on cutting-edge technologies and recent research trends in the area

Edge Intelligence: Advanced Deep Transfer Learning for IoT Security presents a comprehensive exploration into the critical intersection of cybersecurity, edge computing, and deep learning, offering practitioners, researchers, and cybersecurity professionals a definitive guide to protect IoT/IIoT systems. This book delves into the synergistic potential of edge computing and advanced machine/deep learning algorithms, providing insights into lightweight and resource-efficient models with a special focus on resource-constrained edge devices. The rapidly evolving nature of cyberattacks underscores the need for updated and integrated resources that address the intersection of cybersecurity, edge computing, and deep learning. The authors address this issue by offering practical insights, lightweight models, and proactive defense mechanisms tailored to the unique challenges of securing edge devices and networks. This book is not only written to provide its audience effective strategies to detect and mitigate network intrusions by leveraging edge intelligence and advanced deep transfer learning techniques but also to provide practical insights and implementation guidelines tailored to resource-constrained edge devices.Examines the potential of edge computing and deep transfer learning, offering in-depth insights into how edge intelligence can be leveraged to enhance IoT and IIoT securityEmphasizes the development of lightweight and resource-efficient models suitable for deployment on edge devices, ensuring that security measures can be effectively implemented without imposing undue computational burden or network overheadPresents practical examples, case studies, and implementation guidelines that demonstrate how advanced deep transfer learning techniques can be applied to address real-world security challenges in IoT and IIoT deployments

Building on the successful first and second volumes, this book is the third volume of the Springer book on the Robot Operating System (ROS): The Complete Reference. Further, the ROS is being integrated into various robots and systems and is becoming an embedded technology in emerging robotics platforms.

This book presents extensive research on two main problems in robotics: the path planning problem and the multi-robot task allocation problem.

This is the fourth volume of the successful series Robot Operating Systems: The Complete Reference, providing a comprehensive overview of robot operating systems (ROS), which is currently the main development framework for robotics applications, as well as the latest trends and contributed systems. The book is divided into four parts: Part 1 features two papers on navigation, discussing SLAM and path planning. Part 2 focuses on the integration of ROS into quadcopters and their control. Part 3 then discusses two emerging applications for robotics: cloud robotics, and video stabilization. Part 4 presents tools developed for ROS; the first is a practical alternative to the roslaunch system, and the second is related to penetration testing. This book is a valuable resource for ROS users and wanting to learn more about ROS capabilities and features.

This is the fourth volume of the successful series Robot Operating Systems: The Complete Reference, providing a comprehensive overview of robot operating systems (ROS), which is currently the main development framework for robotics applications, as well as the latest trends and contributed systems.

The objective of the book is to provide the reader with comprehensive coverage on the Robot Operating System (ROS), which is currently considered to be the primary development framework for robotics applications, and the latest trends and contributing systems.

The objective of the book is to provide the reader with comprehensive coverage on the Robot Operating System (ROS), which is currently considered to be the primary development framework for robotics applications, and the latest trends and contributing systems.

ROS is currently considered as the primary development framework for robotics applications.There are seven chapters organized into three parts. Part I presents two chapters on the emerging ROS 2.0 framework; The second chapter presents a detailed tuning guide on ROS navigation and the last chapter discusses SLAM for ROS applications.

ROS is currently considered as the primary development framework for robotics applications.There are seven chapters organized into three parts. Part I presents two chapters on the emerging ROS 2.0 framework; The second chapter presents a detailed tuning guide on ROS navigation and the last chapter discusses SLAM for ROS applications.

This book is used at the graduate or advanced undergraduate level and many others. Manned and unmanned ground, aerial and marine vehicles enable many promising and revolutionary civilian and military applications that will change our life in the near future. These applications include, but are not limited to, surveillance, search and rescue, environment monitoring, infrastructure monitoring, self-driving cars, contactless last-mile delivery vehicles, autonomous ships, precision agriculture and transmission line inspection to name just a few. These vehicles will benefit from advances of deep learning as a subfield of machine learning able to endow these vehicles with different capability such as perception, situation awareness, planning and intelligent control. Deep learning models also have the ability to generate actionable insights into the complex structures of large data sets.In recent years, deep learning research has received an increasing amount of attention from researchers in academia, government laboratories and industry. These research activities have borne some fruit in tackling some of the challenging problems of manned and unmanned ground, aerial and marine vehicles that are still open. Moreover, deep learning methods have been recently actively developed in other areas of machine learning, including reinforcement training and transfer/meta-learning, whereas standard, deep learning methods such as recent neural network (RNN) and coevolutionary neural networks (CNN).  The book is primarily meant for researchers from academia and industry, who are working on in the research areas such as engineering, control engineering, robotics, mechatronics, biomedical engineering, mechanical engineering and computer science.The book chapters deal with the recent research problems in the areas of reinforcement learning-based control of UAVs and deep learning for unmanned aerial systems (UAS)The book chapters present various techniques of deep learning for robotic applications. The book chapters contain a good literature survey with a long list of references.The book chapters are well written with a good exposition of the research problem, methodology, block diagrams and mathematical techniques.The book chapters are lucidly illustrated with numerical examples and simulations.The book chapters discuss details of applications and future research areas.

This book is used at the graduate or advanced undergraduate level and many others. Manned and unmanned ground, aerial and marine vehicles enable many promising and revolutionary civilian and military applications that will change our life in the near future. These applications include, but are not limited to, surveillance, search and rescue, environment monitoring, infrastructure monitoring, self-driving cars, contactless last-mile delivery vehicles, autonomous ships, precision agriculture and transmission line inspection to name just a few. These vehicles will benefit from advances of deep learning as a subfield of machine learning able to endow these vehicles with different capability such as perception, situation awareness, planning and intelligent control. Deep learning models also have the ability to generate actionable insights into the complex structures of large data sets.In recent years, deep learning research has received an increasing amount of attention from researchers in academia, government laboratories and industry. These research activities have borne some fruit in tackling some of the challenging problems of manned and unmanned ground, aerial and marine vehicles that are still open. Moreover, deep learning methods have been recently actively developed in other areas of machine learning, including reinforcement training and transfer/meta-learning, whereas standard, deep learning methods such as recent neural network (RNN) and coevolutionary neural networks (CNN).  The book is primarily meant for researchers from academia and industry, who are working on in the research areas such as engineering, control engineering, robotics, mechatronics, biomedical engineering, mechanical engineering and computer science.The book chapters deal with the recent research problems in the areas of reinforcement learning-based control of UAVs and deep learning for unmanned aerial systems (UAS)The book chapters present various techniques of deep learning for robotic applications. The book chapters contain a good literature survey with a long list of references.The book chapters are well written with a good exposition of the research problem, methodology, block diagrams and mathematical techniques.The book chapters are lucidly illustrated with numerical examples and simulations.The book chapters discuss details of applications and future research areas.

In Part II, three chapters present new contributions of ROS frameworks and applications, including micro-ROS, Autonomous 3D Thermal Mapping of Disaster Environments, and Lab-scale Smart Factory Implementation Using ROS.

In Part II, three chapters present new contributions of ROS frameworks and applications, including micro-ROS, Autonomous 3D Thermal Mapping of Disaster Environments, and Lab-scale Smart Factory Implementation Using ROS.

This book addresses many applications of artificial intelligence in robotics, namely AI using visual and motional input. Robotic technology has made significant contributions to daily living, industrial uses, and medicinal applications. Machine learning, in particular, is critical for intelligent robots or unmanned/autonomous systems such as UAVs, UGVs, UUVs, cooperative robots, and so on. Humans are distinguished from animals by capacities such as receiving visual information, adjusting to uncertain circumstances, and making decisions to take action in a complex system. Significant progress has been made in robotics toward human-like intelligence; yet, there are still numerous unresolved issues. Deep learning, reinforcement learning, real-time learning, swarm intelligence, and other developing approaches such as tiny-ML have been developed in recent decades and used in robotics.Artificial intelligence is being integrated into robots in order to develop advanced robotics capable of performing multiple tasks and learning new things with a better perception of the environment, allowing robots to perform critical tasks with human-like vision to detect or recognize various objects. Intelligent robots have been successfully constructed using machine learning and deep learning AI technology. Robotics performance is improving as higher quality, and more precise machine learning processes are used to train computer vision models to recognize different things and carry out operations correctly with the desired outcome.We believe that the increasing demands and challenges offered by real-world robotic applications encourage academic research in both artificial intelligence and robotics. The goal of this book is to bring together scientists, specialists, and engineers from around the world to present and share their most recent research findings and new ideas on artificial intelligence in robotics.

This book addresses many applications of artificial intelligence in robotics, namely AI using visual and motional input. Robotic technology has made significant contributions to daily living, industrial uses, and medicinal applications. Machine learning, in particular, is critical for intelligent robots or unmanned/autonomous systems such as UAVs, UGVs, UUVs, cooperative robots, and so on. Humans are distinguished from animals by capacities such as receiving visual information, adjusting to uncertain circumstances, and making decisions to take action in a complex system. Significant progress has been made in robotics toward human-like intelligence; yet, there are still numerous unresolved issues. Deep learning, reinforcement learning, real-time learning, swarm intelligence, and other developing approaches such as tiny-ML have been developed in recent decades and used in robotics.Artificial intelligence is being integrated into robots in order to develop advanced robotics capable of performing multiple tasks and learning new things with a better perception of the environment, allowing robots to perform critical tasks with human-like vision to detect or recognize various objects. Intelligent robots have been successfully constructed using machine learning and deep learning AI technology. Robotics performance is improving as higher quality, and more precise machine learning processes are used to train computer vision models to recognize different things and carry out operations correctly with the desired outcome.We believe that the increasing demands and challenges offered by real-world robotic applications encourage academic research in both artificial intelligence and robotics. The goal of this book is to bring together scientists, specialists, and engineers from around the world to present and share their most recent research findings and new ideas on artificial intelligence in robotics.

This is a book that covers different aspects of UAV technology, including design and development, applications, security and communication, and legal and regulatory challenges. The book is divided into 13 chapters, grouped into four parts.

This is a book that covers different aspects of UAV technology, including design and development, applications, security and communication, and legal and regulatory challenges. The book is divided into 13 chapters, grouped into four parts.

This book contains contemporary research that outlines and addresses security, privacy challenges and decision-making in IoT environments. The authors provide a variety of subjects related to the following Keywords: IoT, security, AI, deep learning, federated learning, intrusion detection systems, and distributed computing paradigms. This book also offers a collection of the most up-to-date research, providing a complete overview of security and privacy-preserving in IoT environments. It introduces new approaches based on machine learning that tackles security challenges and  provides the field with new research material that’s not covered in the primary literature. The Internet of Things (IoT) refers to a network of tiny devices linked to the Internet or other communication networks. IoT is gaining popularity, because it opens up new possibilities for developing many modern applications. This would include smart cities, smart agriculture, innovative healthcare services and more. The worldwide IoT market surpassed $100 billion in sales for the first time in 2017, and forecasts show that this number might reach $1.6 trillion by 2025. However, as IoT devices grow more widespread, threats, privacy and security concerns are growing. The massive volume of data exchanged highlights significant challenges to preserving individual privacy and securing shared data. Therefore, securing the IoT environment becomes difficult for research and industry stakeholders.Researchers, graduate students and educators in the fields of computer science, cybersecurity, distributed systems and artificial intelligence will want to purchase this book. It will also be a valuable companion for users and developers interested in decision-making and security risk management in IoT environments.

This book contains contemporary research that outlines and addresses security, privacy challenges and decision-making in IoT environments.

Artificial Intelligence (AI) technologies hold immense promise for developing countries by offering innovative solutions to longstanding challenges.

Artificial Intelligence (AI) technologies hold immense promise for developing countries by offering innovative solutions to longstanding challenges.

From revolutionizing healthcare diagnostics and advancing natural language processing for low-resource languages to enhancing software development and promoting environmental sustainability, this book explores the cutting-edge advancements and practical applications of generative AI and large language models (LLMs).

This book presents a groundbreaking compilation of cutting-edge research at the forefront of innovation, where artificial intelligence, machine learning, and advanced technologies converge to address real-world challenges with realistic solutions. This comprehensive book bridges theory and practice, offering transformative insights across diverse domains—from healthcare and cybersecurity to smart cities and language processing.Dive into pioneering advancements in Technology such as Arabic text-to-gloss machine translation, transformer-based image captioning, and GAN-enhanced privacy policies for constrained environments. Learn how deep learning revolutionizes cancer detection in medical imaging, optimizes traffic flow prediction, and refines speaker diarization in forensic audio. Immerse into ethical AI discussions, UAV navigation in GPS-denied terrains, and blockchain-secured voting systems, all while uncovering novel approaches to IoT security, cloud computing, and industrial efficiency.With contributions from leading researchers and practitioners, this book addresses critical topics like AI-driven sentiment analysis for business intelligence, self-supervised graph neural networks, and ethical implications of emerging technologies. Each chapter combines rigorous technical depth with actionable applications, making it an indispensable resource for researchers, engineers, and students eager to harness AI’s potential in tackling modern global challenges.

This peer-reviewed proceedings book presents rigorously selected papers and real-world case studies, with a distinctive focus on high-impact AI for low-resource and developing-country contexts. Readers get methods and evaluation insights they can adapt quickly.It covers healthcare, education, digital economy, biometrics/cybersecurity, robotics, energy and resource management, agriculture, culture and accessibility, generative AI, and networking/cloud systems. New in this edition is broader international participation plus stronger coverage of generative AI, sustainability, and AI-enabled cultural heritage preservation. For researchers, postgraduate students, engineers, industry practitioners, and policymakers, it is a reference for literature review, teaching, and evidence-based AI adoption.

This peer-reviewed proceedings book presents rigorously selected papers and real-world case studies, with a distinctive focus on high-impact AI for low-resource and developing-country contexts. Readers get methods and evaluation insights they can adapt quickly.It covers healthcare, education, digital economy, biometrics/cybersecurity, robotics, energy and resource management, agriculture, culture and accessibility, generative AI, and networking/cloud systems. New in this edition is broader international participation plus stronger coverage of generative AI, sustainability, and AI-enabled cultural heritage preservation. For researchers, postgraduate students, engineers, industry practitioners, and policymakers, it is a reference for literature review, teaching, and evidence-based AI adoption.