Reliability has always been a major concern in designing computing systems. However, the increasing complexity of such systems has led to a situation where efforts for assuring reliability have become extremely costly, both for the design of solutions for the mitigation of possible faults, and for the reliability assessment of such techniques. Cross-layer reliability is fast becoming the preferred solution. In a cross-layer resilient system, physical and circuit level techniques can mitigate low-level faults. Hardware redundancy can be used to manage errors at the hardware architecture layer. Eventually, software implemented error detection and correction mechanisms can manage those errors that escaped the lower layers of the stack. This book presents state-of-the-art solutions for increasing the resilience of computing systems, both at single levels of abstraction and multi-layers. The book begins by addressing design techniques to improve the resilience of computing systems, covering the logic layer, the architectural layer and the software layer. The second part of the book focuses on cross-layer resilience, including coverage of physical stress, reliability assessment approaches, fault injection at the ISA level, analytical modelling for cross-later resiliency, and stochastic methods. Cross-Layer Reliability of Computing Systems is a valuable resource for researchers, postgraduate students and professional computer architects focusing on the dependability of computing systems.

This book highlights recent research advances on biometrics using new methods such as deep learning, nonlinear graph embedding, fuzzy approaches, and ensemble learning. Included are special biometric technologies related to privacy and security issues, such as cancellable biometrics and soft biometrics. The book also focuses on several emerging topics such as big data issues, internet of things, medical biometrics, healthcare, and robot-human interactions. The authors show how these new applications have triggered a number of new biometric approaches. They show, as an example, how fuzzy extractor has become a useful tool for key generation in biometric banking, and vein/heart rates from medical records can also be used to identify patients. The contributors cover the topics, their methods, and their applications in depth.

Imaging sensors are crucial for electronic imaging systems, including digital cameras, camera modules, medical imaging equipment, night vision equipment, radar and sonar, drones, and many others. This contributed book covers a wide range of frequency, sensing modalities and applications, including x-ray beam imaging sensors, optical scattering sensors, smart visual sensors in robotic systems, tuneable diode Laser absorption spectroscopy (TDLAS) sensors, light detection and ranging (LiDAR) sensors, microwave imaging sensors, electro-magnetic imaging with ultra-wideband (UWB) sensors, synthetic aperture radar (SAR), electrical resistance tomography (ERT) sensors, electrical tomography for medical applications, electro-magnetic tomography (EMT) sensors, micro sensors for cell and blood imaging, and ultrasound imaging sensors. Bringing together information on state-of-the-art research in the field, this book is a valuable resource for engineers, researchers, designers and developers, and advanced students and lecturers working on sensing, imaging, optics, photonics, medical imaging, instrumentation, measurement and electronics.

Dielectric Metamaterials: Fundamentals, Designs, and Applications links fundamental Mie scattering theory with the latest dielectric metamaterial research, providing a valuable reference for new and experienced researchers in the field. The book begins with a historical, evolving overview of Mie scattering theory. Next, the authors describe how to apply Mie theory to analytically solve the scattering of electromagnetic waves by subwavelength particles. Later chapters focus on Mie resonator-based metamaterials, starting with microwaves where particles are much smaller than the free space wavelengths. In addition, several chapters focus on wave-front engineering using dielectric metasurfaces and the nonlinear optical effects, spontaneous emission manipulation, active devices, and 3D effective media using dielectric metamaterials.

III-Nitride Electronic Devices, Volume 102, emphasizes two major technical areas advanced by this technology: radio frequency (RF) and power electronics applications. The range of topics covered by this book provides a basic understanding of materials, devices, circuits and applications while showing the future directions of this technology. Specific chapters cover Electronic properties of III-nitride materials and basics of III-nitride HEMT, Epitaxial growth of III-nitride electronic devices, III-nitride microwave power transistors, III-nitride millimeter wave transistors, III-nitride lateral transistor power switch, III-nitride vertical devices, Physics-Based Modeling, Thermal management in III-nitride HEMT, RF/Microwave applications of III-nitride transistor/wireless power transfer, and more.

Time domain modeling is a fascinating world which brings together several complex phenomena and methods of essential interest to engineers. This book is a reference guide which discusses the most advanced time-domain modeling methods and applications in electromagnetics and electrical engineering. The book starts by clearly explaining why time-domain modeling may be worth doing; then, it provides guidelines about why some choices must be made among the principal modeling approaches and next guides the reader through the state of the art in time domain modeling, concerning either numerical and analytical methods, and applications. Finally, it highlights areas for future time-domain modeling research. The book is a collection of chapters written by leading research groups in the fields, following a logical development set out by the editor. Topics covered include finite element methods in time domain with applications to low-frequency problems; transient analysis of scattering from composite objects using late-time stable TDIEs; the transmission-line modeling method, partial element equivalent circuit method in time-domain; unconditionally stable time-domain methods; time-domain linear macromodeling, analytical techniques for transient analysis; the application of the finite-difference time-domain (FDTD) technique to lightning studies; modeling of lightning and its interaction with overhead conductors; transient behaviour of grounding systems; and statistics of electromagnetic reverberation chambers and their simulation through time domain modeling.

Photonic Crystal Metasurface Optoelectronics, Volume 101, covers an emerging area of nanophotonics that represents a new range of optoelectronic devices based on free-space coupled photonic crystal structures and dielectric metasurfaces. Sections in this new release include Free-space coupled nanophotonic platforms, Fano resonances in nanophotonics, Fano resonances in photonic crystal slabs, Transition from photonic crystals to dielectric metamaterials, Photonic crystals for absorption control and energy applications, Photonic crystal membrane reflector VCSELs, Fano resonance filters and modulators, and Fano resonance photonic crystal sensors.

Advances in Nonvolatile Memory and Storage Technology, Second Edition, addresses recent developments in the non-volatile memory spectrum, from fundamental understanding, to technological aspects. The book provides up-to-date information on the current memory technologies as related by leading experts in both academia and industry. To reflect the rapidly changing field, many new chapters have been included to feature the latest in RRAM technology, STT-RAM, memristors and more. The new edition describes the emerging technologies including oxide-based ferroelectric memories, MRAM technologies, and 3D memory. Finally, to further widen the discussion on the applications space, neuromorphic computing aspects have been included. This book is a key resource for postgraduate students and academic researchers in physics, materials science and electrical engineering. In addition, it will be a valuable tool for research and development managers concerned with electronics, semiconductors, nanotechnology, solid-state memories, magnetic materials, organic materials and portable electronic devices.

Source Separation and Machine Learning presents the fundamentals in adaptive learning algorithms for Blind Source Separation (BSS) and emphasizes the importance of machine learning perspectives. It illustrates how BSS problems are tackled through adaptive learning algorithms and model-based approaches using the latest information on mixture signals to build a BSS model that is seen as a statistical model for a whole system. Looking at different models, including independent component analysis (ICA), nonnegative matrix factorization (NMF), nonnegative tensor factorization (NTF), and deep neural network (DNN), the book addresses how they have evolved to deal with multichannel and single-channel source separation.