Xiang Zhang

Deep Learning for EEG-based Brain-Computer Interfaces is an exciting book that describes how emerging deep learning improves the future development of Brain-Computer Interfaces (BCI) in terms of representations, algorithms, and applications. BCI bridges humanity's neural world and the physical world by decoding an individuals' brain signals into commands recognizable by computer devices. This book presents a highly comprehensive summary of commonly-used brain signals; a systematic introduction of around 12 subcategories of deep learning models; a mind-expanding summary of 200+ state-of-the-art studies adopting deep learning in BCI areas; an overview of a number of BCI applications and how deep learning contributes, along with 31 public BCI datasets. The authors also introduce a set of novel deep learning algorithms aimed at current BCI challenges such as robust representation learning, cross-scenario classification, and semi-supervised learning. Various real-world deep learning-based BCI applications are proposed and some prototypes are presented. The work contained within proposes effective and efficient models which will provide inspiration for people in academia and industry who work on BCI.

This book is the first overview book on video surveillance issues within airport grounds. It delves into the distinctive design principles underpinning computer vision algorithms tailored specifically for airport ground video surveillance, while also documenting the latest advancements within this domain. The initial section of this book introduces a comprehensive array of airport ground video surveillance datasets, encompassing the AGVS series alongside other pertinent datasets. Subsequently, the second section delves into the ongoing research endeavors on airport-ground-oriented computer vision algorithms, examining segmentation, recognition, and tracking methodologies. Lastly, the book’s third segment elucidates the design methodologies and exemplary use cases of airport ground video surveillance applications. Written by the authors intimately involved in creating the datasets, algorithms, and applications outlined within, this book stands as an indispensable reference for researchers and graduate students operating within the realm of intelligent transportation.

Discover best practices for real world data research with SAS code and examples Real world health care data is common and growing in use with sources such as observational studies, patient registries, electronic medical record databases, insurance healthcare claims databases, as well as data from pragmatic trials. This data serves as the basis for the growing use of real world evidence in medical decision-making. However, the data itself is not evidence. Analytical methods must be used to turn real world data into valid and meaningful evidence. Real World Health Care Data Analysis: Causal Methods and Implementation Using SAS brings together best practices for causal comparative effectiveness analyses based on real world data in a single location and provides SAS code and examples to make the analyses relatively easy and efficient. The book focuses on analytic methods adjusted for time-independent confounding, which are useful when comparing the effect of different potential interventions on some outcome of interest when there is no randomization. These methods include: propensity score matching, stratification methods, weighting methods, regression methods, and approaches that combine and average across these methods methods for comparing two interventions as well as comparisons between three or more interventions algorithms for personalized medicine sensitivity analyses for unmeasured confounding

Discover best practices for real world data research with SAS code and examples Real world health care data is common and growing in use with sources such as observational studies, patient registries, electronic medical record databases, insurance healthcare claims databases, as well as data from pragmatic trials. This data serves as the basis for the growing use of real world evidence in medical decision-making. However, the data itself is not evidence. Analytical methods must be used to turn real world data into valid and meaningful evidence. Real World Health Care Data Analysis: Causal Methods and Implementation Using SAS brings together best practices for causal comparative effectiveness analyses based on real world data in a single location and provides SAS code and examples to make the analyses relatively easy and efficient. The book focuses on analytic methods adjusted for time-independent confounding, which are useful when comparing the effect of different potential interventions on some outcome of interest when there is no randomization. These methods include: propensity score matching, stratification methods, weighting methods, regression methods, and approaches that combine and average across these methods methods for comparing two interventions as well as comparisons between three or more interventions algorithms for personalized medicine sensitivity analyses for unmeasured confounding

This is the first book to systematically state the fundamental theory of integrability and its development of ordinary differential equations with emphasis on the Darboux theory of integrability and local integrability together with their applications. It summarizes the classical results of Darboux integrability and its modern development together with their related Darboux polynomials and their applications in the reduction of Liouville and elementary integrabilty and in the center—focus problem, the weakened Hilbert 16th problem on algebraic limit cycles and the global dynamical analysis of some realistic models in fields such as physics, mechanics and biology. Although it can be used as a textbook for graduate students in dynamical systems, it is intended as supplementary reading for graduate students from mathematics, physics, mechanics and engineering in courses related to the qualitative theory, bifurcation theory and the theory of integrability of dynamical systems.

'A very handy feature of this book includes an appendix section consisting of fifteen parts, each dedicated to listing equations and solution examples for calculating various important quantities for optoelectronic devices. This book is an in-depth technical resource for understanding the principles of various types of optoelectronic devices and systems. Students, as well as working professionals, would find this book useful for calculating quantities needed in the design of optical system components. There is a section, at the end of the book, along with an extension reference list at the end of each chapter that provides problems from each chapter, making this book suitable for an undergraduate or graduate class in electrical engineering on optoelectronic theory.'IEEE Electrical Insulation MagazineThis book provides a comprehensive treatment of the design and applications of optoelectronic devices. Optoelectronic devices such as light emitting diodes (LEDs), semiconductor lasers, photodetectors, optical fibers, and solar cells, are important components for solid state lighting systems, optical communication systems, and power generation systems. Optical fiber amplifiers and fiber lasers are also important for high power industrial applications and sensors. The applications of optoelectronic devices were first studied in the 1970's. Since then, the diversity and scope of optoelectronic device research and applications have been steadily growing.Optoelectronic Devices is self-contained and unified in presentation. It can be used as an advanced textbook by graduate students and practicing engineers. It is also suitable for non-experts who wish to have an overview of optoelectronic devices and systems. The treatments in the book are detailed enough to capture the interest of the curious reader and complete enough to provide the necessary background to explore the subject further.

This is the first book to systematically state the fundamental theory of integrability and its development of ordinary differential equations with emphasis on the Darboux theory of integrability and local integrability together with their applications. It summarizes the classical results of Darboux integrability and its modern development together with their related Darboux polynomials and their applications in the reduction of Liouville and elementary integrabilty and in the center—focus problem, the weakened Hilbert 16th problem on algebraic limit cycles and the global dynamical analysis of some realistic models in fields such as physics, mechanics and biology. Although it can be used as a textbook for graduate students in dynamical systems, it is intended as supplementary reading for graduate students from mathematics, physics, mechanics and engineering in courses related to the qualitative theory, bifurcation theory and the theory of integrability of dynamical systems.

Inorganic Controlled Release Technology: Materials and Concepts for Advanced Drug Formulation provides a practical guide to the use and applications of inorganic controlled release technology (iCRT) for drug delivery and other healthcare applications, focusing on newly developed inorganic materials such as bioresorbable glasses and bioceramics. The use of these materials is introduced for a wide range of applications that cover inorganic drug delivery systems for new drug development and the reformulation of existing drugs. The book describes basic concepts, principles, and industrial practices by discussing materials chemistry, physics, nano/microstructure, formulation, materials processing, and case studies, as well as the evaluation and characterization of iCRT systems commonly investigated during industrial R&D.