This volume reviews and summarizes some of A. I. McLeod's significant contributions to time series analysis. It also contains original contributions to the field and to related areas by participants of the festschrift held in June 2014 and friends of Dr. McLeod. Covering a diverse range of state-of-the-art topics, this volume well balances applied and theoretical research across fourteen contributions by experts in the field. It will be of interest to researchers and practitioners in time series, econometricians, and graduate students in time series or econometrics, as well as environmental statisticians, data scientists, statisticians interested in graphical models, and researchers in quantitative risk management.

Since process variation and chip performance uncertainties have become more pronounced as technologies scale down into the nanometer regime, accurate and efficient modeling or characterization of variations from the device to the architecture level have become imperative for the successful design of VLSI chips. This book provides readers with tools for variation-aware design methodologies and computer-aided design (CAD) of VLSI systems, in the presence of process variations at the nanometer scale. It presents the latest developments for modeling and analysis, with a focus on statistical interconnect modeling, statistical parasitic extractions, statistical full-chip leakage and dynamic power analysis considering spatial correlations, statistical analysis and modeling for large global interconnects and analog/mixed-signal circuits. Provides readers with timely, systematic and comprehensive treatments of statistical modeling and analysis of VLSI systems with a focus on interconnects, on-chip power grids and clock networks, and analog/mixed-signal circuits; Helps chip designers understand the potential and limitations of their design tools, improving their design productivity; Presents analysis of each algorithm with practical applications in the context of real circuit design; Includes numerical examples for the quantitative analysis and evaluation of algorithms presented. Provides readers with timely, systematic and comprehensive treatments of statistical modeling and analysis of VLSI systems with a focus on interconnects, on-chip power grids and clock networks, and analog/mixed-signal circuits; Helps chip designers understand the potential and limitations of their design tools, improving their design productivity; Presents analysis of each algorithm with practical applications in the context of real circuit design; Includes numerical examples for the quantitative analysis and evaluation of algorithms presented.

This book describes the current state of the art in big-data analytics, from a technology and hardware architecture perspective. The presentation is designed to be accessible to a broad audience, with general knowledge of hardware design and some interest in big-data analytics. Coverage includes emerging technology and devices for data-analytics, circuit design for data-analytics, and architecture and algorithms to support data-analytics. Readers will benefit from the realistic context used by the authors, which demonstrates what works, what doesn’t work, and what are the fundamental problems, solutions, upcoming challenges and opportunities. Provides a single-source reference to hardware architectures for big-data analytics; Covers various levels of big-data analytics hardware design abstraction and flow, from device, to circuits and systems; Demonstrates how non-volatile memory (NVM) based hardware platforms can be a viable solution to existing challenges in hardware architecture for big-data analytics.

This book presents the latest techniques for characterization, modeling and design for nano-scale non-volatile memory (NVM) devices. Coverage focuses on fundamental NVM device fabrication and characterization, internal state identification of memristic dynamics with physics modeling, NVM circuit design and hybrid NVM memory system design-space optimization. The authors discuss design methodologies for nano-scale NVM devices from a circuits/systems perspective, including the general foundations for the fundamental memristic dynamics in NVM devices. Coverage includes physical modeling, as well as the development of a platform to explore novel hybrid CMOS and NVM circuit and system design. * Offers readers a systematic and comprehensive treatment of emerging nano-scale non-volatile memory (NVM) devices; * Focuses on the internal state of NVM memristic dynamics, novel NVM readout and memory cell circuit design and hybrid NVM memory system optimization; * Provides both theoretical analysis and practical examples to illustrate design methodologies; * Illustrates design and analysis for recent developments in spin-toque-transfer, domain-wall racetrack and memristors.

Boundary element methods are very important for solving boundary value problems in PDEs. Many boundary value problems of partial differential equations can be reduced into boundary integral equations by the natural boundary reduction. In this book the natural boundary integral method, suggested and developed by Feng and Yu, is introduced systematically. It is quite different from popular boundary element methods and has many distinctive advantages. The variational principle is conserved after the natural boundary reduction, and some useful properties are also preserved faithfully. Moreover, it can be applied directly and naturally in the coupling method and the domain decomposition method of finite and boundary elements. Most of the material in this book has only appeared in the author's previous papers. Compared with its Chinese edition (Science Press, Beijing, 1993), many new research results such as the domain decomposition methods based on the natural boundary reduction are added.

Along with numerous opportunities in communication and imaging applications, the design of emerging millimeter-wave (mm-wave) and terahertz (THz) electronic circuits and systems in CMOS technology faces new challenges and requires new devices. Design of CMOS Millimeter-Wave and Terahertz Integrated Circuits with Metamaterials provides alternative solutions using CMOS on-chip metamaterials. Unlike conventional metamaterial devices on printed circuit boards (PCBs), the presented CMOS metamaterials can be utilized to build many mm-wave and THz circuits and systems on chip. Leveraging the authors’ extensive expertise and experience with CMOS on-chip metamaterials, this book shows that with the use of metamaterials, one can realize coherent THz signal generation, amplification, transmission, and detection of phase-arrayed CMOS transistors with significantly improved performance. Offering detailed coverage from device to system, the book hereby: Describes integrated circuit design with application of metamaterials in CMOS technology Includes real CMOS integrated circuit examples and chip demonstrations with measurement results Evaluates novel mm-wave and THz communication and imaging systems under CMOS-based system-on-chip integration Design of CMOS Millimeter-Wave and Terahertz Integrated Circuits with Metamaterials reflects the latest research progress and provides a state-of-the-art reference on CMOS-based metamaterial devices and mm-wave and THz systems.

The transition towards exascale computing has resulted in major transformations in computing paradigms. The need to analyze and respond to such large amounts of data sets has led to the adoption of machine learning (ML) and deep learning (DL) methods in a wide range of applications. One of the major challenges is the fetching of data from computing memory and writing it back without experiencing a memory-wall bottleneck. To address such concerns, in-memory computing (IMC) and supporting frameworks have been introduced. In-memory computing methods have ultra-low power and high-density embedded storage. Resistive Random-Access Memory (ReRAM) technology seems the most promising IMC solution due to its minimized leakage power, reduced power consumption and smaller hardware footprint, as well as its compatibility with CMOS technology, which is widely used in industry. In this book, the authors introduce ReRAM techniques for performing distributed computing using IMC accelerators, present ReRAM-based IMC architectures that can perform computations of ML and data-intensive applications, as well as strategies to map ML designs onto hardware accelerators. The book serves as a bridge between researchers in the computing domain (algorithm designers for ML and DL) and computing hardware designers.

This book represents the first comprehensive compilation of information on all aspects of the medicinal plant Panax ginseng, ranging from its botany to applied aspects in medicine and molecular breeding. In contributions by respected experts, it also discusses the genetic background and biochemical profile of this important medicinal plant. Ginsenoside biosynthesis and metabolic dynamics are also described in detail. Given its scope, the book offers a valuable guide for students, educators and scientists in academia and industry interested in medicinal plants and pharmacy.

3D integration is an emerging technology for the design of many-core microprocessors and memory integration. This book, Advances in 3D Integrated Circuits and Systems, is written to help readers understand 3D integrated circuits in three stages: device basics, system level management, and real designs.Contents presented in this book include fabrication techniques for 3D TSV and 2.5D TSI; device modeling; physical designs; thermal, power and I/O management; and 3D designs of sensors, I/Os, multi-core processors, and memory.Advanced undergraduates, graduate students, researchers and engineers may find this text useful for understanding the many challenges faced in the development and building of 3D integrated circuits and systems.

3D integration is an emerging technology for the design of many-core microprocessors and memory integration. This book, Advances in 3D Integrated Circuits and Systems, is written to help readers understand 3D integrated circuits in three stages: device basics, system level management, and real designs.Contents presented in this book include fabrication techniques for 3D TSV and 2.5D TSI; device modeling; physical designs; thermal, power and I/O management; and 3D designs of sensors, I/Os, multi-core processors, and memory.Advanced undergraduates, graduate students, researchers and engineers may find this text useful for understanding the many challenges faced in the development and building of 3D integrated circuits and systems.

Along with numerous opportunities in communication and imaging applications, the design of emerging millimeter-wave (mm-wave) and terahertz (THz) electronic circuits and systems in CMOS technology faces new challenges and requires new devices. Design of CMOS Millimeter-Wave and Terahertz Integrated Circuits with Metamaterials provides alternative solutions using CMOS on-chip metamaterials. Unlike conventional metamaterial devices on printed circuit boards (PCBs), the presented CMOS metamaterials can be utilized to build many mm-wave and THz circuits and systems on chip. Leveraging the authors' extensive expertise and experience with CMOS on-chip metamaterials, this book shows that with the use of metamaterials, one can realize coherent THz signal generation, amplification, transmission, and detection of phase-arrayed CMOS transistors with significantly improved performance. Offering detailed coverage from device to system, the book hereby: Describes integrated circuit design with application of metamaterials in CMOS technology Includes real CMOS integrated circuit examples and chip demonstrations with measurement results Evaluates novel mm-wave and THz communication and imaging systems under CMOS-based system-on-chip integration Design of CMOS Millimeter-Wave and Terahertz Integrated Circuits with Metamaterials reflects the latest research progress and provides a state-of-the-art reference on CMOS-based metamaterial devices and mm-wave and THz systems.

This book describes the current state of the art in big-data analytics, from a technology and hardware architecture perspective. The presentation is designed to be accessible to a broad audience, with general knowledge of hardware design and some interest in big-data analytics. Coverage includes emerging technology and devices for data-analytics, circuit design for data-analytics, and architecture and algorithms to support data-analytics. Readers will benefit from the realistic context used by the authors, which demonstrates what works, what doesn't work, and what are the fundamental problems, solutions, upcoming challenges and opportunities. Provides a single-source reference to hardware architectures for big-data analytics; Covers various levels of big-data analytics hardware design abstraction and flow, from device, to circuits and systems; Demonstrates how non-volatile memory (NVM) based hardware platforms can be a viable solution to existing challenges in hardware architecture for big-data analytics.

This volume reviews and summarizes some of A. I. McLeod's significant contributions to time series analysis. It also contains original contributions to the field and to related areas by participants of the festschrift held in June 2014 and friends of Dr. McLeod. Covering a diverse range of state-of-the-art topics, this volume well balances applied and theoretical research across fourteen contributions by experts in the field. It will be of interest to researchers and practitioners in time series, econometricians, and graduate students in time series or econometrics, as well as environmental statisticians, data scientists, statisticians interested in graphical models, and researchers in quantitative risk management.

Exa-scale computing needs to re-examine the existing hardware platform that can support intensive data-oriented computing. Since the main bottleneck is from memory, we aim to develop an energy-efficient in-memory computing platform in this book. First, the models of spin-transfer torque magnetic tunnel junction and racetrack memory are presented. Next, we show that the spintronics could be a candidate for future data-oriented computing for storage, logic, and interconnect. As a result, by utilizing spintronics, in-memory-based computing has been applied for data encryption and machine learning. The implementations of in-memory AES, Simon cipher, as well as interconnect are explained in details. In addition, in-memory-based machine learning and face recognition are also illustrated in this book.

Boundary element methods are very important for solving boundary value problems in PDEs. Many boundary value problems of partial differential equations can be reduced into boundary integral equations by the natural boundary reduction. In this book the natural boundary integral method, suggested and developed by Feng and Yu, is introduced systematically. It is quite different from popular boundary element methods and has many distinctive advantages. The variational principle is conserved after the natural boundary reduction, and some useful properties are also preserved faithfully. Moreover, it can be applied directly and naturally in the coupling method and the domain decomposition method of finite and boundary elements. Most of the material in this book has only appeared in the author's previous papers. Compared with its Chinese edition (Science Press, Beijing, 1993), many new research results such as the domain decomposition methods based on the natural boundary reduction are added.

This book represents the first comprehensive compilation of information on all aspects of the medicinal plant Panax ginseng, ranging from its botany to applied aspects in medicine and molecular breeding. In contributions by respected experts, it also discusses the genetic background and biochemical profile of this important medicinal plant. Ginsenoside biosynthesis and metabolic dynamics are also described in detail. Given its scope, the book offers a valuable guide for students, educators and scientists in academia and industry interested in medicinal plants and pharmacy.

Flexible Distribution Networks facilitates the enhanced flexibility, efficiency and reliability of distribution networks under complex operating environments delivered by high DG penetration. Considering the high share of DG and flexibility improvement brought by SOP-type power electronic devices, the work aims to solve the issues of optimal operation of flexible distribution networks. Technical solutions for flexible operation problems of FDN are discussed in depth, including efficient analytical methods with high penetration of DG, SOP-based optimal operation methods with uncertainty, and high-performance convex optimization algorithms. Readers will develop sound knowledge and gain insights into the optimal operation of FDN with high penetration of DG.

Microbial Electrochemical and Fuel Cells: Fundamentals and Applications contains the most updated information on bio-electrical systems and their ability to drive an electrical current by mimicking bacterial interactions found in nature to produce a small amount of power. One of the most promising features of the microbial fuel cell is its application to generate power from wastewater, and its use in the treatment of water to remove contaminants, making it a very sustainable source of power generation that can feasibly find application in rural areas where providing more conventional sources of power is often difficult. The book explores, in detail, both the technical aspects and applications of this technology, and was written by an international team of experts in the field who provide an introduction to microbial fuel cells that looks at their electrochemical principles and mechanisms, explains the materials that can be used for the various sections of the fuel cells, including cathode and anode materials, and provides key analysis of microbial fuel cell performance looking at their usage in hydrogen production, waste treatment, and sensors, amongst other applications.

This book presents the latest techniques for machine learning based data analytics on IoT edge devices. A comprehensive literature review on neural network compression and machine learning accelerator is presented from both algorithm level optimization and hardware architecture optimization. Coverage focuses on shallow and deep neural network with real applications on smart buildings. The authors also discuss hardware architecture design with coverage focusing on both CMOS based computing systems and the new emerging Resistive Random-Access Memory (RRAM) based systems. Detailed case studies such as indoor positioning, energy management and intrusion detection are also presented for smart buildings.