K.C. Chang presents an integrated approach to digital design principles, processes, and implementations to help the reader design increasingly complex systems within shorter design cycles. Chang introduces digital design concepts, VHDL coding, VHDL simulation, synthesis commands, and strategies together.

"Digital Systems Design with VHDL and Synthesis" focuses on the ultimate product of the design cycle: the implementation of a digital design. Many of the design techniques and considerations illustrated in the text are examples of actual real-world designs.

Unique features of the book include the following:

• VHDL code explained line by line to capture the logic behind the design concepts
• Simulation waveforms, synthesized schematics, and results are shown, verified, and analyzed
• VHDL code is synthesized and commands and strategies are discussed
• Variations on the design techniques and common mistakes are addressed
• Demonstrated standard cell, gate array, and FPGA three design processes, each with a complete design case study
• Test bench, post-layout verification, and test vector generation processes are illustrated

This book offers a unique multidisciplinary integration of the physics of turbulence and remote sensing technology. Remote Sensing of Turbulence provides a new vision on the research of turbulence and summarizes the current and future challenges of monitoring turbulence remotely. The book emphasizes sophisticated geophysical applications, detection, and recognition of complex turbulent flows in oceans and the atmosphere. Through several techniques based on microwave and optical/IR observations, the text explores the technological capabilities and tools for the detection of turbulence, their signatures, and variability. FEATURES Covers the fundamental aspects of turbulence problems with a broad geophysical scope for a wide audience of readers Provides a complete description of remote-sensing capabilities for observing turbulence in the earth's environment Establishes the state-of-the-art remote-sensing techniques and methods of data analysis for turbulence detection Investigates and evaluates turbulence detection signatures, their properties, and variability Provides cutting-edge remote-sensing applications for space-based monitoring and forecasts of turbulence in oceans and the atmosphere This book is a great resource for applied physicists, the professional remote sensing community, ecologists, geophysicists, and earth scientists.

This volume does much more than survey modern advanced color processing. Starting with a historical perspective on ways we have classified color, it sets out the latest numerical techniques for analyzing and processing colors, the leading edge in our search to accurately record and print what we see. The human eye perceives only a fraction of available light wavelengths, yet we live in a multicolor world of myriad shining hues. Colors rich in metaphorical associations make us purple with rage or green with envy, andcause us to see red. Defining colors has been the work of centuries, culminating in today s complex mathematical coding that nonetheless remains a work in progress: only recently have we possessed the computing capacity to process the algebraic matrices that reproduce color more accurately. With chapters on dihedral color and image spectrometers, this book provides technicians and researchers with the knowledge they need to grasp the intricacies of today s color imaging."

This volume presents the results of the Neural Information Processing Systems Competition track at the 2018 NeurIPS conference. The competition follows the same format as the 2017 competition track for NIPS. Out of 21 submitted proposals, eight competition proposals were selected, spanning the area of Robotics, Health, Computer Vision, Natural Language Processing, Systems and Physics. Competitions have become an integral part of advancing state-of-the-art in artificial intelligence (AI). They exhibit one important difference to benchmarks: Competitions test a system end-to-end rather than evaluating only a single component; they assess the practicability of an algorithmic solution in addition to assessing feasibility. The eight run competitions aim at advancing the state of the art in deep reinforcement learning, adversarial learning, and auto machine learning, among others, including new applications for intelligent agents in gaming and conversational settings, energy physics, and prosthetics.

This book summarizes the organized competitions held during the first NIPS competition track. It provides both theory and applications of hot topics in machine learning, such as adversarial learning, conversational intelligence, and deep reinforcement learning. Rigorous competition evaluation was based on the quality of data, problem interest and impact, promoting the design of new models, and a proper schedule and management procedure. This book contains the chapters from organizers on competition design and from top-ranked participants on their proposed solutions for the five accepted competitions: The Conversational Intelligence Challenge, Classifying Clinically Actionable Genetic Mutations, Learning to Run, Human-Computer Question Answering Competition, and Adversarial Attacks and Defenses.

Traditionally, scientific fields have defined boundaries, and scientists work on research problems within those boundaries. However, from time to time those boundaries get shifted or blurred to evolve new fields. For instance, the original goal of computer vision was to understand a single image of a scene, by identifying objects, their structure, and spatial arrangements. This has been referred to as image understanding. Recently, computer vision has gradually been making the transition away from understanding single images to analyz ing image sequences, or video understanding. Video understanding deals with understanding of video sequences, e. g., recognition of gestures, activities, fa cial expressions, etc. The main shift in the classic paradigm has been from the recognition of static objects in the scene to motion-based recognition of actions and events. Video understanding has overlapping research problems with other fields, therefore blurring the fixed boundaries. Computer graphics, image processing, and video databases have obvious overlap with computer vision. The main goal of computer graphics is to gener ate and animate realistic looking images, and videos. Researchers in computer graphics are increasingly employing techniques from computer vision to gen erate the synthetic imagery. A good example of this is image-based rendering and modeling techniques, in which geometry, appearance, and lighting is de rived from real images using computer vision techniques. Here the shift is from synthesis to analysis followed by synthesis."

The textbook, Introduction to Wavelet Transforms provides basics of wavelet transforms in a self-contained manner. Applications of wavelet transform theory permeate our daily lives. Therefore it is imperative to have a strong foundation for this subject. Features No prior knowledge of the subject is assumed. Sufficient mathematical background is provided to complete the discussion of different topics. Different topics have been properly segmented for easy learning. This makes the textbook pedagogical and unique. Notation is generally introduced in the definitions. Relatively easy consequences of the definitions are listed as observations, and important results are stated as theorems. Examples are provided for clarity and to enhance reader's understanding of the subject. Each chapter also has a problem section. A majority of the problems are provided with sufficient hints. The textbook can be used either in an upper-level undergraduate or first-year graduate class in electrical engineering, or computer science, or applied mathematics. It can also be used by professionals and researchers in the field who would like a quick review of the basics of the subject. About the Author Nirdosh Bhatnagar works in both academia and industry in Silicon Valley, California. He is also the author of a comprehensive two-volume work: Mathematical Principles of the Internet, published by the CRC Press in the year 2019. Nirdosh earned M.S. in Operations Research, and M.S. and Ph.D. in electrical engineering, all from Stanford University, Stanford, California.

The new multimedia standards (for example, MPEG-21) facilitate the seamless integration of multiple modalities into interoperable multimedia frameworks, transforming the way people work and interact with multimedia data. These key technologies and multimedia solutions interact and collaborate with each other in increasingly effective ways, contributing to the multimedia revolution and having a significant impact across a wide spectrum of consumer, business, healthcare, education, and governmental domains. This book aims to provide a complete coverage of the areas outlined and to bring together the researchers from academic and industry as well as practitioners to share ideas, challenges, and solutions relating to the multifaceted aspects of this field.

This book addresses the problems that hinder image authentication in the presence of noise. It considers the advantages and disadvantages of existing algorithms for image authentication and shows new approaches and solutions for robust image authentication. The state of the art algorithms are compared and, furthermore, innovative approaches and algorithms are introduced. The introduced algorithms are applied to improve image authentication, watermarking and biometry. Aside from presenting new directions and algorithms for robust image authentication in the presence of noise, as well as image correction, this book also: Provides an overview of the state of the art algorithms for image authentication in the presence of noise and modifications, as well as a comparison of these algorithms, Presents novel algorithms for robust image authentication, whereby the image is tried to be corrected and authenticated, Examines different views for the solution of problems connected to image authentication in the presence of noise, Shows examples, how the new techniques can be applied to image authentication, watermarking and biometry. This book is written on the one hand for students, who want to learn about image processing, authentication, watermarking and biometry, and on the other hand for engineers and researchers, who work on aspects of robustness against modifications of secure images.

This book discusses recent advances in wearable technologies and personal monitoring devices, covering topics such as skin contact-based wearables (electrodes), non-contact wearables, the Internet of things (IoT), and signal processing for wearable devices. Although it chiefly focuses on wearable devices and provides comprehensive descriptions of all the core principles of personal monitoring devices, the book also features a section on devices that are embedded in smart appliances/furniture, e.g. chairs, which, despite their limitations, have taken the concept of unobtrusiveness to the next level. Wearable and personal devices are the key to precision medicine, and the medical community is finally exploring the opportunities offered by long-term monitoring of physiological parameters that are collected during day-to-day life without the bias imposed by the clinical environment. Such data offers a prime view of individuals' physical condition, as well as the efficacy of therapy and occurrence of events. Offering an in-depth analysis of the latest advances in smart and pervasive wearable devices, particularly those that are unobtrusive and invisible, and addressing topics not covered elsewhere, the book will appeal to medical practitioners and engineers alike.

This work is dedicated to CMOS based imaging with the emphasis on the noise modeling, characterization and optimization in order to contribute to the design of high performance imagers in general and range imagers in particular. CMOS is known to be superior to CCD due to its flexibility in terms of integration capabilities, but typically has to be enhanced to compete at parameters as for instance noise, dynamic range or spectral response. Temporal noise is an important topic, since it is one of the most crucial parameters that ultimately limits the performance and cannot be corrected. This work gathers the widespread theory on noise and extends the theory by a non-rigorous but potentially computing efficient algorithm to estimate noise in time sampled systems. This work contributed to two generations of LDPD based ToF range image sensors and proposed a new approach to implement the MSI PM ToF principle. This was verified to yield a significantly faster charge transfer, better linearity, dark current and matching performance. A non-linear and time-variant model is provided that takes into account undesired phenomena such as finite charge transfer speed and a parasitic sensitivity to light when the shutters should remain OFF, to allow for investigations of largesignal characteristics, sensitivity and precision. It was demonstrated that the model converges to a standard photodetector model and properly resembles the measurements. Finally the impact of these undesired phenomena on the range measurement performance is demonstrated.

Covers the fundamentals of shape feature extraction from images Discusses different applications of image shape feature in the field of content based image retrieval Includes polygonal approximation techniques of shape features Details moment based, scale space and geometric shape features Different approaches for extracting image shape features are reviewed affecting image retrieval from a large database

The use of synthetic aperture radar (SAR) represents a new era in remote sensing technology. A complete handbook for anyone who must design an SAR system capable of reliably producing high quality image data products, free from image artifacts and calibrated in terms of the target backscatter coefficient. Combines fundamentals underlying the SAR imaging process and the practical system engineering required to produce quality images from a real SAR system. Beginning with a broad overview of SAR technology, it goes on to examine SAR system capabilities and components and detail the techniques required for design and development of the SAR ground data system with emphasis on the correlation processing. Intended for SAR system engineers and researchers, it is generously illustrated for maximum clarity.

This book discusses computer vision, a noncontact as well as a nondestructive technique involving the development of theoretical and algorithmic tools for automatic visual understanding and recognition which finds huge applications in agricultural productions. It also entails how rendering of machine learning techniques to computer vision algorithms is boosting this sector with better productivity by developing more precise systems. Computer vision and machine learning (CV-ML) helps in plant disease assessment along with crop condition monitoring to control the degradation of yield, quality, and severe financial loss for farmers. Significant scientific and technological advances have been made in defect assessment, quality grading, disease recognition, pests, insects, fruits, and vegetable types recognition and evaluation of a wide range of agricultural plants, crops, leaves, and fruits. The book discusses intelligent robots developed with the touch of CV-ML which can help farmers to perform various tasks like planting, weeding, harvesting, plant health monitoring, and so on. The topics covered in the book include plant, leaf, and fruit disease detection, crop health monitoring, applications of robots in agriculture, precision farming, assessment of product quality and defects, pest, insect, fruits, and vegetable types recognition.

This book describes the methods and algorithms for image pre-processing and recognition. These methods are based on a parallel shift technology of the imaging copy, as well as simple mathematical operations to allow the generation of a minimum set of features to describe and recognize the image. This book also describes the theoretical foundations of parallel shift technology and pattern recognition. Based on these methods and theories, this book is intended to help researchers with artificial intelligence systems design, robotics, and developing software and hardware applications.

Statistical Computing in Nuclear Imaging introduces aspects of Bayesian computing in nuclear imaging. The book provides an introduction to Bayesian statistics and concepts and is highly focused on the computational aspects of Bayesian data analysis of photon-limited data acquired in tomographic measurements. Basic statistical concepts, elements of decision theory, and counting statistics, including models of photon-limited data and Poisson approximations, are discussed in the first chapters. Monte Carlo methods and Markov chains in posterior analysis are discussed next along with an introduction to nuclear imaging and applications such as PET and SPECT. The final chapter includes illustrative examples of statistical computing, based on Poisson-multinomial statistics. Examples include calculation of Bayes factors and risks as well as Bayesian decision making and hypothesis testing. Appendices cover probability distributions, elements of set theory, multinomial distribution of single-voxel imaging, and derivations of sampling distribution ratios. C++ code used in the final chapter is also provided. The text can be used as a textbook that provides an introduction to Bayesian statistics and advanced computing in medical imaging for physicists, mathematicians, engineers, and computer scientists. It is also a valuable resource for a wide spectrum of practitioners of nuclear imaging data analysis, including seasoned scientists and researchers who have not been exposed to Bayesian paradigms.

This book is open access under a CC BY-NC 2.5 license. This book presents the VISCERAL project benchmarks for analysis and retrieval of 3D medical images (CT and MRI) on a large scale, which used an innovative cloud-based evaluation approach where the image data were stored centrally on a cloud infrastructure and participants placed their programs in virtual machines on the cloud. The book presents the points of view of both the organizers of the VISCERAL benchmarks and the participants. The book is divided into five parts. Part I presents the cloud-based benchmarking and Evaluation-as-a-Service paradigm that the VISCERAL benchmarks used. Part II focuses on the datasets of medical images annotated with ground truth created in VISCERAL that continue to be available for research. It also covers the practical aspects of obtaining permission to use medical data and manually annotating 3D medical images efficiently and effectively. The VISCERAL benchmarks are described in Part III, including a presentation and analysis of metrics used in evaluation of medical image analysis and search. Lastly, Parts IV and V present reports by some of the participants in the VISCERAL benchmarks, with Part IV devoted to the anatomy benchmarks and Part V to the retrieval benchmark. This book has two main audiences: the datasets as well as the segmentation and retrieval results are of most interest to medical imaging researchers, while eScience and computational science experts benefit from the insights into using the Evaluation-as-a-Service paradigm for evaluation and benchmarking on huge amounts of data.

The book provides copyright protection approaches for videos using watermarking. The various watermarking techniques using various transforms such as discrete cosine transform (DCT), discrete wavelet transform (DWT) and singular value decomposition (SVD) for videos are presented. The book also provides video watermarking approach using compressive sensing (CS) theory. The presented watermarking techniques are designed and implemented using color digital videos. The performance of the presented techniques is evaluated using Peak Signal to Noise Ratio (PSNR) and Normalized Correlation (NC).

An Image Processing Tour of College Mathematics aims to provide meaningful context for reviewing key topics of the college mathematics curriculum, to help students gain confidence in using concepts and techniques of applied mathematics, to increase student awareness of recent developments in mathematical sciences, and to help students prepare for graduate studies. The topics covered include a library of elementary functions, basic concepts of descriptive statistics, probability distributions of functions of random variables, definitions and concepts behind first- and second-order derivatives, most concepts and techniques of traditional linear algebra courses, an introduction to Fourier analysis, and a variety of discrete wavelet transforms - all of that in the context of digital image processing. Features Pre-calculus material and basic concepts of descriptive statistics are reviewed in the context of image processing in the spatial domain. Key concepts of linear algebra are reviewed both in the context of fundamental operations with digital images and in the more advanced context of discrete wavelet transforms. Some of the key concepts of probability theory are reviewed in the context of image equalization and histogram matching. The convolution operation is introduced painlessly and naturally in the context of naive filtering for denoising and is subsequently used for edge detection and image restoration. An accessible elementary introduction to Fourier analysis is provided in the context of image restoration. Discrete wavelet transforms are introduced in the context of image compression, and the readers become more aware of some of the recent developments in applied mathematics. This text helps students of mathematics ease their way into mastering the basics of scientific computer programming.

In view of better results expected from examination of medical datasets (images) with hybrid (integration of thresholding and segmentation) image processing methods, this work focuses on implementation of possible hybrid image examination techniques for medical images. It describes various image thresholding and segmentation methods which are essential for the development of such a hybrid processing tool. Further, this book presents the essential details, such as test image preparation, implementation of a chosen thresholding operation, evaluation of threshold image, and implementation of segmentation procedure and its evaluation, supported by pertinent case studies. Aimed at researchers/graduate students in the medical image processing domain, image processing, and computer engineering, this book: Provides broad background on various image thresholding and segmentation techniques Discusses information on various assessment metrics and the confusion matrix Proposes integration of the thresholding technique with the bio-inspired algorithms Explores case studies including MRI, CT, dermoscopy, and ultrasound images Includes separate chapters on machine learning and deep learning for medical image processing

The aim of this book is to outline the concept of entropy, various types of entropies and their implementation to evaluate a variety of biomedical signals/images. The book emphasizes various entropy-based image pre-processing methods which are essential for the development of suitable computerized examination systems. The recent research works on biomedical signal evaluation confirms that signal analysis provides vital information regarding the physiological condition of the patient, and the efficient evaluation of these signals can help to diagnose the nature and the severity of the disease. This book emphasizes various entropy-based image pre-processing methods which are essential for the development of suitable computerized examination systems for the analysis of biomedical images recorded with a variety of modalities. The work discusses the image pro-processing methods with the Entropies, such as Kapur, Tsallis, Shannon and Fuzzy on a class of RGB-scaled and gray-scaled medical pictures. The performance of the proposed technique is justified with the help of suitable case studies, which involves x-ray image analysis, MRI analysis and CT analysis. This book is intended for medical signal/image analysts, undergraduate and postgraduate students, researchers, and medical scientists interested in biomedical data evaluation.

A New Way of Analyzing Object Data from a Nonparametric Viewpoint Nonparametric Statistics on Manifolds and Their Applications to Object Data Analysis provides one of the first thorough treatments of the theory and methodology for analyzing data on manifolds. It also presents in-depth applications to practical problems arising in a variety of fields, including statistics, medical imaging, computer vision, pattern recognition, and bioinformatics. The book begins with a survey of illustrative examples of object data before moving to a review of concepts from mathematical statistics, differential geometry, and topology. The authors next describe theory and methods for working on various manifolds, giving a historical perspective of concepts from mathematics and statistics. They then present problems from a wide variety of areas, including diffusion tensor imaging, similarity shape analysis, directional data analysis, and projective shape analysis for machine vision. The book concludes with a discussion of current related research and graduate-level teaching topics as well as considerations related to computational statistics. Researchers in diverse fields must combine statistical methodology with concepts from projective geometry, differential geometry, and topology to analyze data objects arising from non-Euclidean object spaces. An expert-driven guide to this approach, this book covers the general nonparametric theory for analyzing data on manifolds, methods for working with specific spaces, and extensive applications to practical research problems. These problems show how object data analysis opens a formidable door to the realm of big data analysis.

The vision of a world in which privacy persists and security is ensured but the full potential of the technology is nevertheless tapped guides this work. It is argued that security and privacy can be ensured using technical safeguards if the whole RFID system is designed properly. The challenge is immense since many constraints exist for providing security and privacy in RFID systems: technically and economically but also ethically and socially. Not only security and privacy needs to be provided but the solutions also need to be inexpensive, practical, reliable, scalable, flexible, inter-organizational, and lasting.

After analyzing the problem area in detail, this work introduces a number of new concepts and protocols that provide security and ensure privacy in RFID systems by technical means. The classic RFID model is extended and considerations in new directions are taken. This leads to innovative solutions with advantageous characteristics. Finally, a comprehensive framework including required protocols for operation is proposed. It can be used within a global scope, supports inter-organizational cooperation and data sharing, and adheres to all the architectural guidelines derived in this work. Security and privacy is provided by technical means in an economic manner. Altogether, the goal of building scalable and efficient RFID systems on a global, inter-organizational scale without neglecting security and privacy has been achieved well.

This book covers the technology of digital image processing in various fields with big data and their applications. Readers will understand various technologies and strategies used in digital image processing as well as handling big data, using machine-learning techniques. This book will help to improve the skills of students and researchers in such fields as engineering, agriculture, and medical imaging. There is a need to be able to understand and analyse the latest developments of digital image technology. As such, this book will cover: * Applications such as biomedical science and biometric image processing, content-based image retrieval, remote sensing, pattern recognition, shape and texture analysis * New concepts in color interpolation to produce the full color from the sub-pattern bare pattern color prevalent in today's digital cameras and other imaging devices * Image compression standards that are needed to serve diverse applications * Applications of remote sensing, medical science, traffic management, education, innovation, and analysis in agricultural design and image processing * Both soft and hard computing approaches at great length in relation to major image processing tasks * The direction and development of current and future research in many areas of image processing * A comprehensive bibliography for additional research (integrated within the framework of the book) This book focuses not only on theoretical and practical knowledge in the field but also on the traditional and latest tools and techniques adopted in image processing and data science. It also provides an indispensable guide to a wide range of basic and advanced techniques in the fields of image processing and data science.

Content-Based Image Classification: Efficient Machine Learning Using Robust Feature Extraction Techniques is a comprehensive guide to research with invaluable image data. Social Science Research Network has revealed that 65% of people are visual learners. Research data provided by Hyerle (2000) has clearly shown 90% of information in the human brain is visual. Thus, it is no wonder that visual information processing in the brain is 60,000 times faster than text-based information (3M Corporation, 2001). Recently, we have witnessed a significant surge in conversing with images due to the popularity of social networking platforms. The other reason for embracing usage of image data is the mass availability of high-resolution cellphone cameras. Wide usage of image data in diversified application areas including medical science, media, sports, remote sensing, and so on, has spurred the need for further research in optimizing archival, maintenance, and retrieval of appropriate image content to leverage data-driven decision-making. This book demonstrates several techniques of image processing to represent image data in a desired format for information identification. It discusses the application of machine learning and deep learning for identifying and categorizing appropriate image data helpful in designing automated decision support systems. The book offers comprehensive coverage of the most essential topics, including: Image feature extraction with novel handcrafted techniques (traditional feature extraction) Image feature extraction with automated techniques (representation learning with CNNs) Significance of fusion-based approaches in enhancing classification accuracy MATLAB (R) codes for implementing the techniques Use of the Open Access data mining tool WEKA for multiple tasks The book is intended for budding researchers, technocrats, engineering students, and machine learning/deep learning enthusiasts who are willing to start their computer vision journey with content-based image recognition. The readers will get a clear picture of the essentials for transforming the image data into valuable means for insight generation. Readers will learn coding techniques necessary to propose novel mechanisms and disruptive approaches. The WEKA guide provided is beneficial for those uncomfortable coding for machine learning algorithms. The WEKA tool assists the learner in implementing machine learning algorithms with the click of a button. Thus, this book will be a stepping-stone for your machine learning journey. Please visit the author's website for any further guidance at https://www.rikdas.com/