Signal Processing for Radiation Detectors

Phosphors for Radiation Detector Phosphors for Radiation Detectors Discover a comprehensive overview of luminescence phosphors for radiation detection In Phosphors for Radiation Detection, accomplished researchers Takayuki Yanagida and Masanori Koshimizu deliver a state-of-the-art exploration of the use of phosphors in radiation detection. The internationally recognized contributors discuss the fundamental physics and detector functions associated with the technology with a focus on real-world applications. The book discusses all forms of luminescence phosphors for radiation detection used in a variety of fields, including medicine, security, resource exploration, environmental monitoring, and high energy physics. Readers will discover discussions of dosimeter materials, including thermally stimulated luminescent materials, optically stimulated luminescent materials, and radiophotoluminescence materials. The book also covers transparent ceramics and glasses and a broad range of devices used in this area. Phosphors for Radiation Detection also includes: Thorough introductions to ionizing radiation induced luminescence, organic scintillators, and inorganic oxide scintillators Comprehensive explorations of luminescent materials, including discussions of materials synthesis and their use in gamma-ray, neutron, and charged particle detection Practical discussions of semiconductor scintillators, including treatments of organic-inorganic layered perovskite materials for scintillation detectors In-depth examinations of thermally stimulated luminescent materials, including discussions of the dosimetric properties for photons, charged particles, and neutrons Relevant for research physicists, materials scientists, and electrical engineers, Phosphors for Radiation Detection is an also an indispensable resource for postgraduate and senior undergraduate students working in detection physics.

A comprehensive and invaluable guide to 5G technology, implementation and practice in one single volume. For all things 5G, this book is a must-read. Signal processing techniques have played the most important role in wireless communications since the second generation of cellular systems. It is anticipated that new techniques employed in 5G wireless networks will not only improve peak service rates significantly, but also enhance capacity, coverage, reliability , low-latency, efficiency, flexibility, compatibility and convergence to meet the increasing demands imposed by applications such as big data, cloud service, machine-to-machine (M2M) and mission-critical communications. This book is a comprehensive and detailed guide to all signal processing techniques employed in 5G wireless networks. Uniquely organized into four categories, New Modulation and Coding, New Spatial Processing, New Spectrum Opportunities and New System-level Enabling Technologies, it covers everything from network architecture, physical-layer (down-link and up-link), protocols and air interface, to cell acquisition, scheduling and rate adaption, access procedures and relaying to spectrum allocations. All technology aspects and major roadmaps of global 5G standard development and deployments are included in the book. Key Features: Offers step-by-step guidance on bringing 5G technology into practice, by applying algorithms and design methodology to real-time circuit implementation, taking into account rapidly growing applications that have multi-standards and multi-systems. Addresses spatial signal processing for 5G, in particular massive multiple-input multiple-output (massive-MIMO), FD-MIMO and 3D-MIMO along with orbital angular momentum multiplexing, 3D beamforming and diversity. Provides detailed algorithms and implementations, and compares all multicarrier modulation and multiple access schemes that offer superior data transmission performance including FBMC, GFDM, F-OFDM, UFMC, SEFDM, FTN, MUSA, SCMA and NOMA. Demonstrates the translation of signal processing theories into practical solutions for new spectrum opportunities in terms of millimeter wave, full-duplex transmission and license assisted access. Presents well-designed implementation examples, from individual function block to system level for effective and accurate learning. Covers signal processing aspects of emerging system and network architectures, including ultra-dense networks (UDN), software-defined networks (SDN), device-to-device (D2D) communications and cloud radio access network (C-RAN).

This book examines signal processing techniques for cognitive radios. The book is divided into three parts: Part I, is an introduction to cognitive radios and presents a history of the cognitive radio (CR), and introduce their architecture, functionalities, ideal aspects, hardware platforms, and state-of-the-art developments. Dr. Jayaweera also introduces the specific type of CR that has gained the most research attention in recent years: the CR for Dynamic Spectrum Access (DSA). Part II of the book, Theoretical Foundations, guides the reader from classical to modern theories on statistical signal processing and inference. The author addresses detection and estimation theory, power spectrum estimation, classification, adaptive algorithms (machine learning), and inference and decision processes. Applications to the signal processing, inference and learning problems encountered in cognitive radios are interspersed throughout with concrete and accessible examples. Part III of the book, Signal Processing in Radios, identifies the key signal processing, inference, and learning tasks to be performed by wideband autonomous cognitive radios. The author provides signal processing solutions to each task by relating the tasks to materials covered in Part II. Specialized chapters then discuss specific signal processing algorithms required for DSA and DSS cognitive radios.

This book discusses the fundamentals of RFID and the state-of-the-art research results in signal processing for RFID, including MIMO, blind source separation, anti-collision, localization, covert RFID and chipless RFID. Aimed at graduate students as well as academic and professional researchers/engineers in RFID technology, it enables readers to become conversant with the latest theory and applications of signal processing for RFID. Key Features: Provides a systematic and comprehensive insight into the application of modern signal processing techniques for RFID systems Discusses the operating principles, channel models of RFID, RFID protocols and analog/digital filter design for RFID Explores RFID-oriented modulation schemes and their performance Highlights research fields such as MIMO for RFID, blind signal processing for RFID, anti-collision of multiple RFID tags, localization with RFID, covert RFID and chipless RFID Contains tables, illustrations and design examples

The aim of this book is to deal with biometrics in terms of signal and image processing methods and algorithms. This will help engineers and students working in digital signal and image processing deal with the implementation of such specific algorithms. It discusses numerous signal and image processing techniques that are very often used in biometric applications. In particular, algorithms related to hand feature extraction, speech recognition, 2D/3D face biometrics, video surveillance and other interesting approaches are presented. Moreover, in some chapters, Matlab codes are provided so that readers can easily reproduce some basic simulation results. This book is suitable for final-year undergraduate students, postgraduate students, engineers and researchers in the field of computer engineering and applied digital signal and image processing. 1. Introduction to Biometrics, Bernadette Dorizzi. 2. Introduction to 2D Face Recognition, Amine Nait-Ali and Dalila Cherifi. 3. Facial Soft Biometrics for Person Recognition, Antitza Dantcheva, Christelle Yemdji, Petros Elia and Jean-Luc Dugelay. 4. Modeling, Reconstruction and Tracking for Face Recognition, Catherine Herold, Vincent Despiegel, St?phane Gentric,
S?verine Dubuisson and Isabelle Bloch. 5. 3D Face Recognition, Mohsen Ardabilian, Przemyslaw Szeptycki, Di Huang and Liming Chen. 6. Introduction to Iris Biometrics, Kamel Aloui, Amine Nait-Ali, R?gis Fournier and Saber Naceur. 7. Voice Biometrics: Speaker Verification and Identification, Foezur Chowdhury, Sid-Ahmed Selouani
and Douglas O’Shaughnessy. 8. Introduction to Hand Biometrics, R?gis Fournier and Amine Nait-Ali. 9. Multibiometrics, Romain Giot, Baptiste Hemery, Estelle Cherrier and
Christophe Rosenberger. 10. Hidden Biometrics, Amine Nait-Ali, R?gis Fournier, Kamel Aloui and
Noureddine Belgacem. 11. Performance Evaluation of Biometric Systems, Mohamad El-Abed, Romain Giot, Baptiste Hemery, Julien Mahier
and Christophe Rosenberger. 12. Classification Techniques for Biometrics, Amel Bouchemha, Ch?rif Nait-Hamoud, Amine Nait-Ali and
R?gis Fournier. 13. Data Cryptography, Islam Naveed and William Puech. 14. Visual Data Protection, Islam Naveed and William Puech. 15. Biometrics in Forensics, Guillaume Galou and Christophe Lambert.

Multimedia Signal Processing is a comprehensive and accessible text to the theory and applications of digital signal processing (DSP). The applications of DSP are pervasive and include multimedia systems, cellular communication, adaptive network management, radar, pattern recognition, medical signal processing, financial data forecasting, artificial intelligence, decision making, control systems and search engines. This book is organised in to three major parts making it a coherent and structured presentation of the theory and applications of digital signal processing. A range of important topics are covered in basic signal processing, model-based statistical signal processing and their applications. Part 1: Basic Digital Signal Processing gives an introduction to the topic, discussing sampling and quantization, Fourier analysis and synthesis, Z-transform, and digital filters. Part 2: Model-based Signal Processing covers probability and information models, Bayesian inference, Wiener filter, adaptive filters, linear prediction hidden Markov models and independent component analysis. Part 3: Applications of Signal Processing in Speech, Music and Telecommunications explains the topics of speech and music processing, echo cancellation, deconvolution and channel equalization, and mobile communication signal processing. Covers music signal processing, explains the anatomy and psychoacoustics of hearing and the design of MP3 music coder Examines speech processing technology including speech models, speech coding for mobile phones and speech recognition Covers single-input and multiple-inputs denoising methods, bandwidth extension and the recovery of lost speech packets in applications such as voice over IP (VoIP) Illustrated throughout, including numerous solved problems, Matlab experiments and demonstrations Companion website features Matlab and C++ programs with electronic copies of all figures. This book is ideal for researchers, postgraduates and senior undergraduates in the fields of digital signal processing, telecommunications and statistical data analysis. It will also be a valuable text to professional engineers in telecommunications and audio and signal processing industries.

Digital signal processing is essential for improving the accuracy and reliability of a range of engineering systems, including communications, networking, and audio and video applications. Using a combination of programming and mathematical techniques, it clarifies, or standardizes the levels or states of a signal, in order to meet the demands of designing high performance digital hardware. Written by authors with a wealth of practical experience working with digital signal processing, this text is an excellent step-by-step guide for practitioners and researchers needing to understand and quickly implement the technology. Split into six, self-contained chapters, Digital Signal Processing: A Practitioner’s Approach covers: basic principles of signal processing such as linearity, stability, convolution, time and frequency domains, and noise; descriptions of digital filters and their realization, including fixed point implementation, pipelining, and field programmable gate array (FGPA) implementation; Fourier transforms, especially discrete (DFT), and fast Fourier transforms (FFT); case studies demonstrating difference equations, direction of arrival (DoA), and electronic rotating elements, and MATLAB programs to accompany each chapter. A valuable reference for engineers developing digital signal processing applications, this book is also a useful resource for electrical and computer engineering graduates taking courses in signal processing.

With special relation to smart grids, this book provides clear and comprehensive explanation of how Digital Signal Processing (DSP) and Computational Intelligence (CI) techniques can be applied to solve problems in the power system. Its unique coverage bridges the gap between DSP, electrical power and energy engineering systems, showing many different techniques applied to typical and expected system conditions with practical power system examples. Surveying all recent advances on DSP for power systems, this book enables engineers and researchers to understand the current state of the art and to develop new tools. It presents: an overview on the power system and electric signals, with description of the basic concepts of DSP commonly found in power system problems the application of several signal processing tools to problems, looking at power signal estimation and decomposition, pattern recognition techniques, detection of the power system signal variations description of DSP in relation to measurements, power quality, monitoring, protection and control, and wide area monitoring a companion website with real signal data, several Matlab codes with examples, DSP scripts and samples of signals for further processing, understanding and analysis Practicing power systems engineers and utility engineers will find this book invaluable, as will researchers of electrical power and energy systems, postgraduate electrical engineering students, and staff at utility companies.

Electroencephalograms (EEGs) are becoming increasingly important measurements of brain activity and they have great potential for the diagnosis and treatment of mental and brain diseases and abnormalities. With appropriate interpretation methods they are emerging as a key methodology to satisfy the increasing global demand for more affordable and effective clinical and healthcare services. Developing and understanding advanced signal processing techniques for the analysis of EEG signals is crucial in the area of biomedical research. This book focuses on these techniques, providing expansive coverage of algorithms and tools from the field of digital signal processing. It discusses their applications to medical data, using graphs and topographic images to show simulation results that assess the efficacy of the methods. Additionally, expect to find: explanations of the significance of EEG signal analysis and processing (with examples) and a useful theoretical and mathematical background for the analysis and processing of EEG signals; an exploration of normal and abnormal EEGs, neurological symptoms and diagnostic information, and representations of the EEGs; reviews of theoretical approaches in EEG modelling, such as restoration, enhancement, segmentation, and the removal of different internal and external artefacts from the EEG and ERP (event-related potential) signals; coverage of major abnormalities such as seizure, and mental illnesses such as dementia, schizophrenia, and Alzheimer’s disease, together with their mathematical interpretations from the EEG and ERP signals and sleep phenomenon; descriptions of nonlinear and adaptive digital signal processing techniques for abnormality detection, source localization and brain-computer interfacing using multi-channel EEG data with emphasis on non-invasive techniques, together with future topics for research in the area of EEG signal processing. The information within EEG Signal Processing has the potential to enhance the clinically-related information within EEG signals, thereby aiding physicians and ultimately providing more cost effective, efficient diagnostic tools. It will be beneficial to psychiatrists, neurophysiologists, engineers, and students or researchers in neurosciences. Undergraduate and postgraduate biomedical engineering students and postgraduate epileptology students will also find it a helpful reference.