Computer Vision Systems

Mem-elements for Neuromorphic Circuits with Artificial Intelligence Applications illustrates recent advances in the field of mem-elements (memristor, memcapacitor, meminductor) and their applications in nonlinear dynamical systems, computer science, analog and digital systems, and in neuromorphic circuits and artificial intelligence. The book is mainly devoted to recent results, critical aspects and perspectives of ongoing research on relevant topics, all involving networks of mem-elements devices in diverse applications. Sections contribute to the discussion of memristive materials and transport mechanisms, presenting various types of physical structures that can be fabricated to realize mem-elements in integrated circuits and device modeling.As the last decade has seen an increasing interest in recent advances in mem-elements and their applications in neuromorphic circuits and artificial intelligence, this book will attract researchers in various fields.

電腦視覺是研究如何使人工系統從圖像或多維資料中“感知”的科學。本書是電腦視覺領域的經典教材，內容涉及攝像機的幾何模型、光照及陰影、顏色、線性濾波、局部圖像特徵、紋理、立體視覺、運動結構、聚類分割、分組與模型擬合、跟蹤、配准、平滑表面及其輪廓、深度資料、圖像分類、物體檢測與識別、基於圖像的建模與渲染、人形研究、圖像搜索與檢索、優化技術等。與前一版相比，本書簡化了部分主題，增加了應用示例，重寫了關於現代特徵的內容，詳述了現代圖像編輯技術與物體識別技術。

Handbook of Computational Intelligence in Biomedical Engineering and Healthcare helps readers analyze and conduct advanced research in specialty healthcare applications surrounding oncology, genomics and genetic data, ontologies construction, bio-memetic systems, biomedical electronics, protein structure prediction, and biomedical data analysis. The book provides the reader with a comprehensive guide to advanced computational intelligence, spanning deep learning, fuzzy logic, connectionist systems, evolutionary computation, cellular automata, self-organizing systems, soft computing, and hybrid intelligent systems in biomedical and healthcare applications. Sections focus on important biomedical engineering applications, including biosensors, enzyme immobilization techniques, immuno-assays, and nanomaterials for biosensors and other biomedical techniques.Other sections cover gene-based solutions and applications through computational intelligence techniques and the impact of nonlinear/un

We often hear that AI is revolutionising the financial sector, like no other technology has done before. This book looks beyond these clich s and explores all aspects of this transformation at a deep level. It spells out a vision for the future and answers many questions that are routinely ignored.What do we mean by Artificial Intelligence in finance? How do we move past the myths and misconceptions to reveal the key driving forces?What are the industry trends that align with this transformation? Is it the explosion of digital touchpoints in retail, the reduced risk taking by investment banks, or the ascent of passive funds in asset management?How do we develop concrete use cases from idea generation to production? How do we engineer systems to make accurate predictions, offer recommendations to clients, or analyse unstructured news data?How do we build a successful data-driven organisation? What are the key pitfalls to avoid? Is it about culture, data governance, or management vision?

Dealing with all aspects of Monte Carlo simulation of complex physical systems encountered in condensed matter physics and statistical mechanics, this book provides an introduction to computer simulations in physics. The 5th edition contains extensive new material describing numerous powerful algorithms and methods that represent recent developments in the field. New topics such as active matter and machine learning are also introduced. Throughout, there are many applications, examples, recipes, case studies, and exercises to help the reader fully comprehend the material. This book is ideal for graduate students and researchers, both in academia and industry, who want to learn techniques that have become a third tool of physical science, complementing experiment and analytical theory.

Discover how everyday tech works while developing your coding skills with Create the Code!Each title in this series explains the key computer code and systems that are used to create and control the t

Symbolic dynamics is a mature yet rapidly developing area of dynamical systems. It has established strong connections with many areas, including linear algebra, graph theory, probability, group theory, and the theory of computation, as well as data storage, statistical mechanics, and $C^*$-algebras. This Second Edition maintains the introductory character of the original 1995 edition as a general textbook on symbolic dynamics and its applications to coding. It is written at an elementary level and aimed at students, well-established researchers, and experts in mathematics, electrical engineering, and computer science. Topics are carefully developed and motivated with many illustrative examples. There are more than 500 exercises to test the reader's understanding. In addition to a chapter in the First Edition on advanced topics and a comprehensive bibliography, the Second Edition includes a detailed Addendum, with companion bibliography, describing major developments and new research di

An invaluable text for the teaching, design, and development of gas sensor technology. This excellent resource synthesizes the fundamental principles of spectroscopy, laser physics, and photonics technology and engineering to enable the reader to fully understand the key issues and apply them in the design of optical gas absorption sensors. It provides a straightforward introduction to low-cost and highly versatile near-IR systems, as well as an extensive review of mid-IR systems. Fibre laser systems for spectroscopy are also examined in detail, especially the emerging technique of frequency comb spectroscopy. Featuring many examples of real-world application and performance, as well as MATLAB computer programs for modeling and simulation, this exceptional work is ideal for postgraduate students, researchers, and professional engineers seeking to gain an in-depth understanding of the principles and applications of fibre-optic and laser-based gas sensors.

Discover how everyday tech works while developing your coding skills with Create the Code!Each title in this series explains the key computer code and systems that are used to create and control the t

This book introduces Raspberry Pi, using real world applications in computer vision, machine learning, and deep learning. It provides a detailed, step-by-step, approach to application development for

This book will help readers understand fundamental and advanced statistical models and deep learning models for robust speaker recognition and domain adaptation. This useful toolkit enables readers to apply machine learning techniques to address practical issues, such as robustness under adverse acoustic environments and domain mismatch, when deploying speaker recognition systems. Presenting state-of-the-art machine learning techniques for speaker recognition and featuring a range of probabilistic models, learning algorithms, case studies, and new trends and directions for speaker recognition based on modern machine learning and deep learning, this is the perfect resource for graduates, researchers, practitioners and engineers in electrical engineering, computer science and applied mathematics.

Boolean functions are essential to systems for secure and reliable communication. This comprehensive survey of Boolean functions for cryptography and coding covers the whole domain and all important results, building on the author's influential articles with additional topics and recent results. A useful resource for researchers and graduate students, the book balances detailed discussions of properties and parameters with examples of various types of cryptographic attacks that motivate the consideration of these parameters. It provides all the necessary background on mathematics, cryptography, and coding, and an overview on recent applications, such as side channel attacks on smart cards, cloud computing through fully homomorphic encryption, and local pseudo-random generators. The result is a complete and accessible text on the state of the art in single and multiple output Boolean functions that illustrates the interaction between mathematics, computer science, and telecommunications

Algorithms permeate our lives in numerous ways, performing tasks that until recently could only be carried out by humans. Artificial Intelligence (AI) technologies, based on machine learning algorithms and big-data-powered systems, can perform sophisticated tasks such as driving cars, analyzing medical data, and evaluating and executing complex financial transactions - often without active human control or supervision. Algorithms also play an important role in determining retail pricing, online advertising, loan qualification, and airport security. In this work, Martin Ebers and Susana Navas bring together a group of scholars and practitioners from across Europe and the US to analyze how this shift from human actors to computers presents both practical and conceptual challenges for legal and regulatory systems. This book should be read by anyone interested in the intersection between computer science and law, how the law can better regulate algorithmic design, and the legal ramificatio

Transfer learning deals with how systems can quickly adapt themselves to new situations, tasks and environments. It gives machine learning systems the ability to leverage auxiliary data and models to help solve target problems when there is only a small amount of data available. This makes such systems more reliable and robust, keeping the machine learning model faced with unforeseeable changes from deviating too much from expected performance. At an enterprise level, transfer learning allows knowledge to be reused so experience gained once can be repeatedly applied to the real world. For example, a pre-trained model that takes account of user privacy can be downloaded and adapted at the edge of a computer network. This self-contained, comprehensive reference text describes the standard algorithms and demonstrates how these are used in different transfer learning paradigms. It offers a solid grounding for newcomers as well as new insights for seasoned researchers and developers.

Although computation and the science of physical systems would appear to be unrelated, there are a number of ways in which computational and physical concepts can be brought together in ways that illuminate both. This volume examines fundamental questions which connect scholars from both disciplines: is the universe a computer? Can a universal computing machine simulate every physical process? What is the source of the computational power of quantum computers? Are computational approaches to solving physical problems and paradoxes always fruitful? Contributors from multiple perspectives reflecting the diversity of thought regarding these interconnections address many of the most important developments and debates within this exciting area of research. Both a reference to the state of the art and a valuable and accessible entry to interdisciplinary work, the volume will interest researchers and students working in physics, computer science, and philosophy of science and mathematics.

Nature-based solutions (NBS) are essential to ensure a sustainable society and healthy ecosystem over the coming decades. However, the systems to be managed are both broad and complex, requiring an integrated understanding of both bio-physical systems, such as soils and water, and economic and social systems, such as urban development and human behaviour. This edited book joins these domains of knowledge together from an applied perspective and considers how computer science can help. It takes a strategic look at the benefits and barriers to using modelling within environmental management and planning practice. It delves further by providing an in-depth comparative review of a wide range of models from a variety of scientific disciplines of interest with examples of their use for NBS. As such, this illustrated guide is designed to help students, researchers and practitioners navigate the huge range of modelling options available and develop the common understanding to work inter-discip

With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for grad

Our world and the people within it are increasingly interpreted and classified by automated systems. At the same time, automated classifications influence what happens in the physical world. These entanglements change what it means to interact with governance, and shift what elements of our identity are knowable and meaningful. In this cyber-physical world, or 'world state', what is the role for law? Specifically, how should law address the claim that computational systems know us better than we know ourselves? Monitoring Laws traces the history of government profiling from the invention of photography through to emerging applications of computer vision for personality and behavioral analysis. It asks what dimensions of profiling have provoked legal intervention in the past, and what is different about contemporary profiling that requires updating our legal tools. This work should be read by anyone interested in how computation is changing society and governance, and what it is about p

Network flow theory has been used across a number of disciplines, including theoretical computer science, operations research, and discrete math, to model not only problems in the transportation of goods and information, but also a wide range of applications from image segmentation problems in computer vision to deciding when a baseball team has been eliminated from contention. This graduate text and reference presents a succinct, unified view of a wide variety of efficient combinatorial algorithms for network flow problems, including many results not found in other books. It covers maximum flows, minimum-cost flows, generalized flows, multicommodity flows, and global minimum cuts and also presents recent work on computing electrical flows along with recent applications of these flows to classical problems in network flow theory.

Sustainable businesses create economic and social value while simultaneously protecting the natural environment for future generations. This examination of environmental sustainability through the lens of the family business identifies factors that help family and non-family organizations address the dilemma of balancing short-term productivity, efficiency and profitability objectives, with innovating for long-term sustainable value creation. Exploring the case of the wine industry - an industry characterized by a variety of governance systems - Sanjay and Pramodita Sharma develop fresh insights into influences and drivers for proactive environmental strategies to address major global sustainability challenges. By doing so, the authors are able to demonstrate that family firms with a focus on trans-generational continuity of business, long temporal orientation, shared vision, faster decision-making processes, and the goal of preserving socio-emotional wealth are more likely to make pat

An accessible introduction to the artificial intelligence technology that enables computer vision, speech recognition, machine translation, and driverless cars.Deep learning is an artificial intellige

Computer networks allow people to easily share information. Organizations and entire cities rely on computer networks. Hackers can break into networks to disrupt systems or steal data. Hacking and oth

Proof complexity is a rich subject drawing on methods from logic, combinatorics, algebra and computer science. This self-contained book presents the basic concepts, classical results, current state of the art and possible future directions in the field. It stresses a view of proof complexity as a whole entity rather than a collection of various topics held together loosely by a few notions, and it favors more generalizable statements. Lower bounds for lengths of proofs, often regarded as the key issue in proof complexity, are of course covered in detail. However, upper bounds are not neglected: this book also explores the relations between bounded arithmetic theories and proof systems and how they can be used to prove upper bounds on lengths of proofs and simulations among proof systems. It goes on to discuss topics that transcend specific proof systems, allowing for deeper understanding of the fundamental problems of the subject.

Quantum theory is the soul of theoretical physics. It is not just a theory of specific physical systems, but rather a new framework with universal applicability. This book shows how we can reconstruct the theory from six information-theoretical principles, by rebuilding the quantum rules from the bottom up. Step by step, the reader will learn how to master the counterintuitive aspects of the quantum world, and how to efficiently reconstruct quantum information protocols from first principles. Using intuitive graphical notation to represent equations, and with shorter and more efficient derivations, the theory can be understood and assimilated with exceptional ease. Offering a radically new perspective on the field, the book contains an efficient course of quantum theory and quantum information for undergraduates. It is aimed at researchers, professionals, and students in physics, computer science and philosophy, as well as the curious outsider seeking a deeper understanding of the theo

Electroencephalography (EEG) is an electrophysiological monitoring method used to record the brain activity in brain-computer interface (BCI) systems. It records the electrical activity of the brain,

If you’re a developer familiar with AI, machine learning, and computer vision, this practical book provides you with a foundational understanding of augmented and virtual reality functions. AR a

大數據分析是許多傳統產業的主要驅動力，例如：金融、醫療和能源等等。視覺資料分析（Visual data analysis）和電腦視覺（Computer Vision）則是這場資料革命的一部分。近年來，它們的發展漸趨成熟，在自主導航、遊戲和製造業等領域，創造了許多新穎又令人振奮的應用程式。 本書將帶領讀者建立強大且獨特的電腦視覺應用程式。每一章將從一個專案展開，並深入OpenCV的某個重要面向，例如

Each book goes inside a different machine or technology and explains the key computer code and systems that are controlling them. Step-by-step activities teach you how to create your own versions of t

Gain insights into image-processing methodologies and algorithms, using machine learning and neural networks in Python. This book begins with the environment setup, understanding basic image-processing terminology, and exploring Python concepts that will be useful for implementing the algorithms discussed in the book. You will then cover all the core image processing algorithms in detail before moving onto the biggest computer vision library: OpenCV. You’ll see the OpenCV algorithms and how to use them for image processing. The next section looks at advanced machine learning and deep learning methods for image processing and classification. You’ll work with concepts such as pulse coupled neural networks, AdaBoost, XG boost, and convolutional neural networks for image-specific applications. Later you’ll explore how models are made in real time and then deployed using various DevOps tools. All the concepts in Practical Machine Learning and Image Processing are explained using r

Written in an easy-to-understand manner, this comprehensive textbook brings together both basic and advanced concepts of numerical methods in a single volume. Important topics including error analysis, nonlinear equations, systems of linear equations, interpolation and interpolation for Equal intervals and bivariate interpolation are discussed comprehensively. The textbook is written to cater to the needs of undergraduate students of mathematics, computer science, mechanical engineering, civil engineering and information technology for a course on numerical methods/numerical analysis. The text simplifies the understanding of the concepts through exercises and practical examples. Pedagogical features including solved examples and unsolved exercises are interspersed throughout the book for better understanding.