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The aim of this Printed Edition of Special Issue entitled "Recent Advancements in Radar Imaging and Sensing Technology” was to gather the latest research results in the area of modern radar technology using active and/or radar imaging sensing techniques in different applications, including both military use and a broad spectrum of civilian applications. As a result, the 19 papers that have been published highlighted a variety of topics related to modern radar imaging and microwave sensing technology. The sequence of articles included in the Printed Edition of Special Issue dealt with wide aspects of different applications of radar imaging and sensing technology in the area of topics including high-resolution radar imaging, novel Synthetic Apertura Radar (SAR) and Inverse SAR (ISAR) imaging techniques, passive radar imaging technology, modern civilian applications of using radar technology for sensing, multiply-input multiply-output (MIMO) SAR imaging, tomography imaging, among others.
This is the first book to bring together the increasingly complex radar automotive technologies and tools being explored and utilized in the development of fully autonomous vehicles – technologies and tools now understood to be an essential need for the field to fully mature. The book presents state-of-the-art knowledge as shared by the best and brightest experts working in the automotive radar industry today -- leaders who have “been there and done that.” Each chapter is written as a standalone "master class" with the authors, seeing the topic through their eyes and experiences. Where beneficial, the chapters reference one another but can otherwise be read in any order desired, making the book an excellent go-to reference for a particular topic or review you need to understand. You’ll get a big-picture tour of the key radar needs for fully autonomous vehicles, and how achieving these needs is complicated by the automotive environment’s dense scenes, number of possible targets of interest, and mix of very large and very small returns. You’ll then be shown the challenges from – and mitigations to – radio frequency interference (RFI), an ever-increasing challenge as the number of vehicles with radars – and radars per vehicle grow. The book also dives into the impacts of weather on radar performance, providing you with insights gained from extensive real-world testing. You are then taken through the integration and systems considerations, especially regarding safety, computing needs, and testing. Each of these areas is influenced heavily by the needs of fully autonomous vehicles and are open areas of research and development. With this authoritative volume you will understand: * How to engage with radar designers (from a system integrator / OEM standpoint); * How to structure and set requirements for automotive radars; * How to address system safety needs for radars in fully autonomous vehicles; * How to assess weather impact on the radar and its ability to support autonomy; * How to include weather effects into specifications for radars. This is an essential reference for engineers currently in the autonomous vehicle arena and/or working in automotive radar development, as well as engineers and leaders in adjacent radar fields needing to stay abreast of the rapid developments in this exciting and dynamic field of research and development.
This book aims at serving the researchers and practitioners in related fields with a timely dissemination of the recent progress on robotics and artificial intelligence. This book is based on a collection of papers presented at the 9th International Conference on Robot Intelligence Technology and Applications (RiTA), held at KAIST in Daejeon, Korea, in a hybrid format, on December 16–17, 2021. Humankind is getting through the third year of COVID-19 pandemic. While this pandemic has made everyone’s life so challenging, it has also expedited transition of our everyday lives into a new form, often called “the new normal.” Although many people often use the terminology, perhaps we still do not have a consensus about what it is and what is should be like. One thing that is clear is that robotics and artificial intelligence technologies are playing critical roles in this phase transition of our everyday lives. We see last-mile delivery robots on the street, AI-embedded service robots in the restaurants, uninhabited shops, non-face-to-face medical services, conferences and talks in metaverses and AI-based online education programs. For better readability, the total of 53 papers are grouped into four chapters: Chapter I: Motion Planning and Control; Chapter II: Design and Robot Application; Chapter III: Sensing, Perception and Recognition; and Chapter IV: Cognition, Autonomy and Intelligence. For those who have research on robot intelligence technology, we believe this book will help them understand the recent robot technologies and applications and enhance their study.
Orthogonal Frequency Division Multiplexing (OFDM) has been the waveform of choice for most wireless communications systems in the past 25 years. This book addresses the "what comes next? question by presenting the recently proposed waveform known as Orthogonal Time-Frequency-Space (OTFS), which offers a better alternative for high-mobility environments. The OTFS waveform is based on the idea that the mobile wireless channels can be effectively modelled in the delay-Doppler domain. This domain provides a sparse representation closely resembling the physical geometry of the wireless channel. The key physical parameters such as relative velocity and distance of the reflectors with respect to the receiver can be considered roughly invariant in the duration of a frame up to a few milliseconds. This enables the information symbols encoded in the delay-Doppler domain to experience a flat fading channel even when they are affected by multiple Doppler shifts present in high-mobility environments. Delay-Doppler Communications: Principles and Applications covers the fundamental concepts and the underlying principles of delay-Doppler communications. Readers familiar with OFDM will be able to quickly understand the key differences in delay-Doppler domain waveforms that can overcome some of the challenges of high-mobility communications. For the broader readership with a basic knowledge of wireless communications principles, the book provides sufficient background to be self-contained. The book provides a general overview of future research directions and discusses a range of applications of delay-Doppler domain signal processing. - This is the first book on delay-Doppler communications - It is written by three of the leading authorities in the field - It includes a wide range of applications With this book, the reader will be able to: - Recognize the challenges of high-mobility channels affected by both multipath and multiple Doppler shifts in physical layer waveform design and performance - Understand the limitations of current multicarrier techniques such as OFDM in high-mobility channels - Recognize the mathematical and physical relations between the different domains for representing channels and waveforms: time-frequency, time-delay, delay-Doppler - Understand the operation of the key blocks of a delay-Doppler modulator and demodulator both analytically and by hands-on MATLAB examples - Master the special features and advantages of OTFS with regard to detection, channel estimation, MIMO, and multiuser MIMO - Realize the importance of delay-Doppler communications for current and future applications, e.g., 6G and beyond
Injuries due to air turbulence has increased recently, therefore there is considerable concern and interest in understanding and detecting it more accurately. Presently hardly any research deals with air turbulence detection using remote sensing images. Most works use conventional optical remote sensing data with classical methods such as a library spectral signature, band ratio, and principal component analysis without designating new methods and technology. Very little research has attempted to implement optical and microwave remote sensing images for air turbulence detections. This book provides new image processing procedures for air turbulence detection using advanced remote sensing images and quantum image processing. Currently, there is a huge gap between research work in the field of air turbulence detection and advanced remote sensing technology. Most of the theories are not operated in terms of software modules. Most of the software packages in the field of remote sensing images cannot deal with advanced image processing techniques in air turbulence detections due to heavy mathematics work. In this view, this book fills a gap between advanced remote sensing technology and air turbulence detection. For instance, quantum image processing with a new generation of remote sensing technology such as RADARSAT-2 SAR images is also implemented to provide accurate air turbulence detections.
Discover the captivating world of computer vision and deep learning for autonomous driving with our comprehensive and in-depth guide. Immerse yourself in an in-depth exploration of cutting-edge topics, carefully crafted to engage tertiary students and ignite the curiosity of researchers and professionals in the field. From fundamental principles to practical applications, this comprehensive guide offers a gentle introduction, expert evaluations of state-of-the-art methods, and inspiring research directions. With a broad range of topics covered, it is also an invaluable resource for university programs offering computer vision and deep learning courses. This book provides clear and simplified algorithm descriptions, making it easy for beginners to understand the complex concepts. We also include carefully selected problems and examples to help reinforce your learning. Don't miss out on this essential guide to computer vision and deep learning for autonomous driving.
Recent years have seen a vast development in various methodologies for object detection and feature extraction and recognition, both in theory and in practice. When processing images, videos, or other types of multimedia, one needs efficient solutions to perform fast and reliable processing. Computational intelligence is used for medical screening where the detection of disease symptoms is carried out, in prevention monitoring to detect suspicious behavior, in agriculture systems to help with growing plants and animal breeding, in transportation systems for the control of incoming and outgoing transportation, for unmanned vehicles to detect obstacles and avoid collisions, in optics and materials for the detection of surface damage, etc. In many cases, we use developed techniques which help us to recognize some special features. In the context of this innovative research on computational intelligence, the Special Issue “Advanced Computational Intelligence for Object Detection, Feature Extraction and Recognition in Smart Sensor Environments” present an excellent opportunity for the dissemination of recent results and achievements for further innovations and development. It is my pleasure to present this collection of excellent contributions to the research community. - Prof. Marcin Woźniak, Silesian University of Technology, Poland –
Specialized resource providing detailed coverage of recent advances in theory and applications of sparse arrays Sparse Arrays for Radar, Sonar, and Communications discusses various design approaches of sparse arrays, including those seeking to increase the corresponding one-dimensional and two-dimensional virtual array apertures, as well as others that configure the arrays based on solutions of constrained minimization problems. The latter includes statistical bounds and signal-to-interference and noise ratio; in this respect, the book utilizes the recent strides made in convex optimizations and machine learning for sparse array configurability in both fixed and dynamic environments. Similar ideas are presented for sparse array-waveform design. The book also discusses the role of sparse arrays in improving target detection and resolution in radar, improving channel capacity in massive MIMO, and improving underwater target localization in sonar. It covers different sparse array topologies, and provides various approaches that deliver the optimum and semi-optimum sparse array transceivers. . Edited by a world-leading expert in Radar and Signal Processing and contributed to by world-class researchers in their respective fields, Sparse Arrays for Radar, Sonar, and Communications covers topics including: Utilizing sparse arrays in emerging technologies and showing their offerings in various sensing and communications applications Applying sparse arrays to different environments and obtain superior performances over conventional uniform arrays Solving the localization, beamforming, and direction-finding problems using non-uniform array structures for narrowband and wideband signals Designing sparse array structures for both stationary and moving platforms that produce physical and synthesized array apertures. Using deep neural networks that learn the underlying complex nonlinear model and output the sparse array configuration using representations of the input data spatio-temporal observations Solving for optimum sparse array configurations and beamforming coefficients in sensing using iterative convex optimization methods Providing complete coverage of the recent considerable progress in sparse array design and configurations, Sparse Arrays for Radar, Sonar, and Communications is an essential resource on the subject for graduate students and engineers pursuing research and applications in the broad areas of active/passive sensing and communications.