This book focuses on the model and algorithm aspects of securing Unmanned Aerial Vehicle Networks (UAV). To equip readers with the essential knowledge required for conducting research in this field, it covers the foundational concepts of this topic as well. This book also offers a detailed insight into UAV networks from the physical layer security point of view. The authors discuss the appropriate channel models for characterizing various propagation environments that UAV networks are exposed. The state-of-the-art technologies, such as UAV trajectory design, cooperative jamming and reconfigurable intelligent surfaces are covered. The corresponding algorithms for significantly improving the security of UAV networks, along with practical case studies on topics such as energy-efficient and secure UAV networks, elevation angle-distance trade-off for securing UAV networks and securing UAV networks with the aid of reconfigurable intelligent surfaces are presented as well. Last, this book outlines the future challenges and research directions to facilitate further studies on secure UAV networks. This book is suitable reading for graduate students and researchers who are interested in the research areas of UAV networking and communications, IoT security, and physical layer security in wireless networks. Professionals working within these related fields will also benefit from this book.
The automotive industry is transforming to a greater degree that has occurred since Henry Ford introduced mass production of the automobile with the Model T in 1913. Advances in computing, data processing, and artificial intelligence (deep learning in particular) are driving the development of new levels of automation that will impact all aspects of our lives including our vehicles. What are Connected and Automated Vehicles (CAVs)? What are the underlying technologies that need to mature and converge for them to be widely deployed? Fundamentals of Connected and Automated Vehicles is written to answer these questions, educating the reader with the information required to make informed predictions of how and when CAVs will impact their lives. Topics covered include: History of Connected and Automated Vehicles, Localization, Connectivity, Sensor and Actuator Hardware, Computer Vision, Sensor Fusion, Path Planning and Motion Control, Verification and Validation, and Outlook for future of CAVs.
Haavelmo’s 1944 monograph, The Probability Approach in Econometrics, is widely acclaimed as the manifesto of econometrics. This book challenges Haavelmo’s probability approach, shows how its use is delivering defective and inefficient results, and argues for a paradigm shift in econometrics towards a full embrace of machine learning, with its attendant benefits. Machine learning has only come into existence over recent decades, whereas the universally accepted and current form of econometrics has developed over the past century. A comparison between the two is, however, striking. The practical achievements of machine learning significantly outshine those of econometrics, confirming the presence of widespread inefficiencies in current econometric research. The relative efficiency of machine learning is based on its theoretical foundation, and particularly on the notion of Probably Approximately Correct (PAC) learning. Careful examination reveals that PAC learning theory delivers the goals of applied economic modelling research far better than Haavelmo’s probability approach. Econometrics should therefore renounce its outdated foundation, and rebuild itself upon PAC learning theory so as to unleash its pent-up research potential. The book is catered for applied economists, econometricians, economists specialising in the history and methodology of economics, advanced students, philosophers of social sciences.
This book presents the spatial and temporal dynamics of land use and land cover in the central Tibetan Plateau during the last two decades, based on various types of satellite data, long-term field investigation and GIS techniques. Further, it demonstrates how remote sensing can be used to map and characterize land use, land cover and their dynamic processes in mountainous regions, and to monitor and model relevant biophysical parameters. The Tibetan Plateau, the highest and largest plateau on the Earth and well known as “the roof of the world,” is a huge mountainous area on the Eurasian continent and covers millions of square kilometers, with an average elevation of over 4000 m. After providing an overview of the background and an introduction to land use and land cover change, the book analyzes the current land use status, dynamic changes and spatial distribution patterns of different land-use types in the study area, using various types of remotely sensed data, digital elevation models and GIS spatial analysis methods to do so. In turn, it discusses the main driving forces, based on the main physical environment variables and socioeconomic data, and provides a future scenario analysis of land use change using a Markov chain model. Given its scope, it provides a valuable reference guide for researchers, scientists and graduate students working on environmental change in mountainous regions around the globe, and for practitioners working at government and non-government agencies.
Written from the Haavelmo-Cowles Commission econometric perspective, this book provides an account of the advances in the field of econometrics since the 1970s.
This book is concerned with the bifurcation theory, the study of the changes in the structures of the solution of ordinary differential equations as parameters of the model vary.
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