Provides an overview and background of cost-effectiveness analysis and how it's used Discusses cost-effective in relation to systems engineering Links cost-effectiveness with military issues and problems Explores the usage of cost-effectiveness as it relates to systems architecting and the re-engineering of office systems Compares cost-effective analysis to everyday life when dealing with purchasing small home devices such as phones, and large devices such as automobiles.

The field of information technology continues to advance at a brisk pace, including the use of Remote Laboratory (RL) systems in education and research. To address the needs of remote laboratory development for such purposes, the authors present a new state-of-the-art unified framework for RL system development. Included are solutions to commonly encountered RL implementation issues such as third-party plugin, traversing firewalls, cross platform running, and scalability, etc. Additionally, the book introduces a new application architecture of remote lab for mobile-optimized RL application development for Mobile Learning (M-Learning). It also shows how to design and organize the remote experiments at different universities and make available a framework source code. The book is intended to serve as a complete guide for remote lab system design and implementation for an audience comprised of researchers, practitioners and students to enable them to rapidly and flexibly implement RL systems for a range of fields.

In the last decade, both scholars and practitioners have sought novel ways to address the problem of cybersecurity. Innovative outcomes have included applications such as blockchain as well as creative methods for cyber forensics, software development, and intrusion prevention. Accompanying these technological advancements, discussion on cyber matters at national and international levels has focused primarily on the topics of law, policy, and strategy. The objective of these efforts is typically to promote security by establishing agreements among stakeholders on regulatory activities. Varying levels of investment in cyberspace, however, comes with varying levels of risk; in some ways, this can translate directly to the degree of emphasis for pushing substantial change. At the very foundation or root of cyberspace systems and processes are tenets and rules governed by principles in mathematics. Topics such as encrypting or decrypting file transmissions, modeling networks, performing data analysis, quantifying uncertainty, measuring risk, and weighing decisions or adversarial courses of action represent a very small subset of activities highlighted by mathematics. To facilitate education and a greater awareness of the role of mathematics in cyber systems and processes, a description of research in this area is needed. Mathematics in Cyber Research aims to familiarize educators and young researchers with the breadth of mathematics in cyber-related research. Each chapter introduces a mathematical sub-field, describes relevant work in this field associated with the cyber domain, provides methods and tools, as well as details cyber research examples or case studies. Features One of the only books to bring together such a diverse and comprehensive range of topics within mathematics and apply them to cyber research. Suitable for college undergraduate students or educators that are either interested in learning about cyber-related mathematics or intend to perform research within the cyber domain. The book may also appeal to practitioners within the commercial or government industry sectors. Most national and international venues for collaboration and discussion on cyber matters have focused primarily on the topics of law, policy, strategy, and technology. This book is among the first to address the underpinning mathematics.

This unique books looks at a cost-efficient, fast and accurate means of facial reconstruction--from segmented, decomposed, or skeletal remains--using computer-graphic and computational means.
Computer-Graphic Facial Reconstruction is designed as a valuable resource for those scientists designing new research projects and protocols, as well as a practical handbook of methods and techniques for medico-legal practitioners who actually identify the faceless victims of crime. It looks at a variety of approaches: artificial intelligence using neural networks, case-based reasoning, Baysian belief systems, along with a variety of imaging methods: radiological, CT, MRI and the use of imaging devices.
The methods described in this book complement, or may even replace, the less-reliable, more traditional means of securing identification by presumptive means, i.e., recognition of clothing, personal effects and clay reconstruction.
- Covers cutting-edge technologies in the context of historical forensic reconstruction methods
- Features stellar authors from around the globe
- Bridges the areas of computer graphics, animation, and forensic anthropology

1) The book provides a basic understanding to readers who want to start their career in web application development. The book develops an understanding of the basics concepts and then takes a broader turn to enlighten the readers about the horizons which can be accessed using JavaScript. 2) JavaScript is exhaustively used in almost all kinds of application development, be it the web applications, cross platform mobile applications, server backend applications. JavaScript is now being used for full stack development and is thus used to write independent applications. Since the need for JavaScript web developers is high, the numbers of students should be high as well. 3) As compared to other books in the market, the proposed book would cover more topics (or, better, levels) in a single volume.

1) Focuses on the concepts and implementation strategies of various Deep Learning algorithms through properly curated examples. 2) The subject area will be valid for the next 10 years or so, as Deep Learning theory/algorithms and their applications will not be outdated easily. Hence there will be demand for such a book in the market. 3) In comparison to other titles, this book rigorously covers mathematical and conceptual details of relevant topics.

The aim of this book is to provide an internationally respected collection of scientific research methods, technologies and applications in the area of data science. This book can prove useful to the researchers, professors, research students and practitioners as it reports novel research work on challenging topics in the area surrounding data science. In this book, some of the chapters are written in tutorial style concerning machine learning algorithms, data analysis, information design, infographics, relevant applications, etc. The book is structured as follows: * Part I: Data Science: Theory, Concepts, and Algorithms This part comprises five chapters on data Science theory, concepts, techniques and algorithms. * Part II: Data Design and Analysis This part comprises five chapters on data design and analysis. * Part III: Applications and New Trends in Data Science This part comprises four chapters on applications and new trends in data science.

Provides a comprehensive introduction to multi-robot systems planning and task allocation; Explores multi robot aerial planning, flight planning, orienteering and coverage, and deployment, patrolling, and foraging; Includes real-world case studies; Treats different aspects of cooperation in multi-agent systems.

Modelling Transitions shows what computational, formal and data-driven approaches can and could mean for sustainability transitions research, presenting the state-of-the-art and exploring what lies beyond. Featuring contributions from many well-known authors, this book presents the various benefits of modelling for transitions research. More than just taking stock, it also critically examines what modelling of transformative change means and could mean for transitions research and for other disciplines that study societal changes. This includes identifying a variety of approaches currently not part of the portfolios of transitions modellers. Far from only singing praise, critical methodological and philosophical introspection are key aspects of this important book. This book speaks to modellers and non-modellers alike who value the development of robust knowledge on transitions to sustainability, including colleagues in congenial fields. Be they students, researchers or practitioners, everyone interested in transitions should find this book relevant as reference, resource and guide.

Data science is a multi-disciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured (labeled) and unstructured (unlabeled) data. It is the future of Artificial Intelligence (AI) and a necessity of the future to make things easier and more productive. In simple terms, data science is the discovery of data or uncovering hidden patterns (such as complex behaviors, trends, and inferences) from data. Moreover, Big Data analytics/data analytics are the analysis mechanisms used in data science by data scientists. Several tools, such as Hadoop, R, etc., are used to analyze this large amount of data to predict valuable information and for decision-making. Note that structured data can be easily analyzed by efficient (available) business intelligence tools, while most of the data (80% of data by 2020) is in an unstructured form that requires advanced analytics tools. But while analyzing this data, we face several concerns, such as complexity, scalability, privacy leaks, and trust issues. Data science helps us to extract meaningful information or insights from unstructured or complex or large amounts of data (available or stored virtually in the cloud). Data Science and Data Analytics: Opportunities and Challenges covers all possible areas, applications with arising serious concerns, and challenges in this emerging field in detail with a comparative analysis/taxonomy. FEATURES Gives the concept of data science, tools, and algorithms that exist for many useful applications Provides many challenges and opportunities in data science and data analytics that help researchers to identify research gaps or problems Identifies many areas and uses of data science in the smart era Applies data science to agriculture, healthcare, graph mining, education, security, etc. Academicians, data scientists, and stockbrokers from industry/business will find this book useful for designing optimal strategies to enhance their firm's productivity.

Legal Data and Information in Practice provides readers with an understanding of how to facilitate the acquisition, management, and use of legal data in organizations such as libraries, courts, governments, universities, and start-ups. Presenting a synthesis of information about legal data that will furnish readers with a thorough understanding of the topic, the book also explains why it is becoming crucial that data analysis be integrated into decision-making in the legal space. Legal organizations are looking at how to develop data-driven insights for a variety of purposes and it is, as Sutherland shows, vital that they have the necessary skills to facilitate this work. This book will assist in this endeavour by providing an international perspective on the issues affecting access to legal data and clearly describing methods of obtaining and evaluating it. Sutherland also incorporates advice about how to critically approach data analysis. Legal Data and Information in Practice will be essential reading for those in the law library community who are based in English-speaking countries with a common law tradition. The book will also be useful to those with a general interest in legal data, including students, academics engaged in the study of information science and law.

Cybersecurity is an extremely important area which is rapidly evolving, necessarily, to meet current and future threats. Anyone who studies within this domain requires a particular skillset and way of thinking, balancing technical knowledge and human insight. It is vital to recognize both sides of this complex area and integrate the two. This book looks at the technical fields progressively, building up in layers before expanding into more advanced topics. Each area is looked at succinctly, describing the main elements and problems in each area and reinforcing these concepts with practical coding examples, questions and ideas for further research. The book builds on an overview of basic architecture of systems and networks, setting a context for how information is vulnerable. Cryptography is explained in detail with examples, showing the steady progress in this area over time through to the possibilities of quantum encryption. Steganography is also explained, showing how this can be used in a modern-day context through multimedia and even Virtual Reality. A large section of the book is given to the technical side of hacking, how such attacks occur, how they can be avoided and what to do after there has been an intrusion of some description. Cyber countermeasures are explored, along with automated systems of defense, whether created by the programmer or through firewalls and suchlike. The human aspect of cyber security is detailed along with the psychology and motivations for launching attacks. Social engineering is focused on and with the various techniques looked at - revealing how an informed individual, organization or workplace can protect themselves against incursions and breaches. Finally, there is a look the latest developments in the field, and how systems, such as the IoT are being protected. The book is intended for advanced undergraduate and postgraduate courses on cybersecurity but is also useful for those studying IT or Computer Science more generally.

This is a how-to book for solving geometric problems robustly or error free in actual practice. The contents and accompanying source code are based on the feature requests and feedback received from industry professionals and academics who want both the descriptions and source code for implementations of geometric algorithms. The book provides a framework for geometric computing using several arithmetic systems and describes how to select the appropriate system for the problem at hand. Key Features: A framework of arithmetic systems that can be applied to many geometric algorithms to obtain robust or error-free implementations Detailed derivations for algorithms that lead to implementable code Teaching the readers how to use the book concepts in deriving algorithms in their fields of application The Geometric Tools Library, a repository of well-tested code at the Geometric Tools website, https://www.geometrictools.com, that implements the book concepts

Industry 4.0 is a challenge for today's businesses. It's a concept that encompasses the technological innovations of automation, control, and information technology, as it's applied to manufacturing processes. It's a new topic that recently emerged in academia and industry, with few books that target both management and engineering. This book will cover the new advances and the way to manage competitive organizations. The chapters will include terms of theory, evidence, and/or methodology, and significantly advance social scientific research. This book: Focuses on the latest and most recent research findings occurring on the topic of Industry 4.0 Presents the ways companies around the world are facing today's technological challenges Assists researchers and practitioners in selecting the correct options and strategies to manage competitive organizations Provides recent advances in international studies Encompasses the main technological innovations in the fields of automation, control, and information technology applied to the manufacturing processes Industry 4.0: Challenges, Trends, and Solutions in Manangment and Engineering is designed to increase the knowledge and effectiveness of all managers and engineers in all organizations and activity sectors Carolina Machado has been teaching in the Human Resources Management subjects since 1989 at University of Minho, Portugal. She has been an associate professor since 2004, with experience and research interest areas in the field of Human Resource Management, International Human Resource Management, Human Resource Management in SMEs, Training and Development, Emotional Intelligence, Management Change, Knowledge Management, and Management/HRM in the Digital Age. She is head of the Department of Management and head of the Human Resources Management Work Group at University of Minho, as well as chief editor of the International Journal of Applied Management Sciences and Engineering (IJAMSE). J. Paulo Davim is a professor at the Department of Mechanical Engineering of the University of Aveiro, Portugal. He has more than 30 years of teaching and research experience in Manufacturing, Materials, Mechanical, and Industrial Engineering, with special emphasis in Machining & Tribology. He has also interest in Management, Engineering Education, and Higher Education for Sustainability. He has worked as evaluator of projects for ERC (European Research Council) and other international research agencies.

Nature-based algorithms play an important role among artificial intelligence algorithms. Among them are global optimization algorithms called swarm intelligence algorithms. These algorithms that use the behavior of simple agents and various ways of cooperation between them, are used to solve specific problems that are defined by the so-called objective function. Swarm intelligence algorithms are inspired by the social behavior of various animal species, e.g. ant colonies, bird flocks, bee swarms, schools of fish, etc. The family of these algorithms is very large and additionally includes various types of modifications to enable swarm intelligence algorithms to solve problems dealing with areas other than those for which they were originally developed. This book presents 24 swarm algorithms together with their modifications and practical applications. Each chapter is devoted to one algorithm. It contains a short description along with a pseudo-code showing the various stages of its operation. In addition, each chapter contains a description of selected modifications of the algorithm and shows how it can be used to solve a selected practical problem. This book should also be useful for undergraduate and postgraduate students studying nature-based optimization algorithms, and can be a helpful tool for learning these algorithms, along with their modifications and practical applications. In addition, it can be a useful source of knowledge for scientists working in the field of artificial intelligence, as well as for engineers interested in using this type of algorithms in their work. If the reader wishes to expand his knowledge beyond the basics of swarm intelligence algorithms presented in this book and is interested in more detailed information, we recommend the book "Swarm Intelligence Algorithms: A Tutorial" (Edited by A. Slowik, CRC Press, 2020). It contains a detailed explanation of how each algorithm works, along with relevant program codes in Matlab and the C ++ programming language, as well as numerical examples illustrating step-by-step how individual algorithms work.

The book begins with an overview of automation history and followed by chapters on PLC, DCS, and SCADA -describing how such technologies have become synonymous in process instrumentation and control. The book then introduces the niche of Fieldbuses in process industries. It then goes on to discuss wireless communication in the automation sector and its applications in the industrial arena. The book also discusses theall-pervading IoT and its industrial cousin,IIoT, which is finding increasing applications in process automation and control domain. The last chapter introduces OPC technology which has strongly emerged as a defacto standard for interoperable data exchange between multi-vendor software applications and bridges the divide between heterogeneous automation worlds in a very effective way. Key features: Presents an overall industrial automation scenario as it evolved over the years Discusses the already established PLC, DCS, and SCADA in a thorough and lucid manner and their recent advancements Provides an insight into today's industrial automation field Reviews Fieldbus communication and WSNs in the context of industrial communication Explores IIoT in process automation and control fields Introduces OPC which has already carved out a niche among industrial communication technologies with its seamless connectivity in a heterogeneous automation world Dr. Chanchal Dey is Associate Professor in the Department of Applied Physics, Instrumentation Engineering Section, University of Calcutta. He is a reviewer of IEEE, Elsevier, Springer, Acta Press, Sage, and Taylor & Francis Publishers. He has more than 80 papers in international journals and conference publications. His research interests include intelligent process control using conventional, fuzzy, and neuro-fuzzy techniques. Dr. Sunit Kumar Sen is an ex-professor, Department of Applied Physics, Instrumentation Engineering Section, University of Calcutta. He was a coordinator of two projects sponsored by AICTE and UGC, Government of India. He has published around70 papers in international and national journals and conferences and has published three books - the last one was published by CRC Press in 2014. He is a reviewer of Measurement, Elsevier. His field of interest is new designs of ADCs and DACs.

Exam board: AQA Level: A-level Subject: Computer Science First teaching: September 2015 First exams: Summer 2016 (AS); Summer 2017 (A-level) Strengthen your students' understanding and upgrade their confidence with our AQA Computer Science workbooks, full of self-contained exercises to consolidate knowledge and improve performance. Written by an experienced Computer Science author and teacher, these full colourworkbooks provide stimulus materials on a number of AS and A-level topics, followed by sets of questions designed to develop and test skills in the unit. * With consolidation questions to reinforce knowledge and test understanding, these workbooks will raise your students' chances of achieving the highest grades. * Helps students identify their revision needs and see how to target the top grades using online answers for each question. * Saves valuable preparation time and expense, with self-contained exercises that don't need photocopying and provide instant lesson and homework solutions for specialist and non-specialist teachers. * Encourages ongoing revision throughout the course as students progressively develop their skills in class and at home.

Presents original method of enhanced ant colony optimization in feature selection, based on mathematical experiments and modelling. Provides a theoretical concept in iris features searching and detection as part of feature extraction process. Demonstrates the iris features selection and detection using the proposed design methodology with enhanced ant colony optimization for iris recognition.

Covers deep learning fundamentals; Focuses on applications; Covers human emotion analysis and deep learning; Explains how to use web based techniques for deep learning applications; Includes coverage of autonomous vehicles and deep learning

Distributed and peer-to-peer (P2P) applications are increasing daily, and cyberattacks are constantly adopting new mechanisms to threaten the security and privacy of users in these Internet of Things (IoT) environments. Blockchain, a decentralized cryptographic-based technology, is a promising element for IoT security in manufacturing, finance, healthcare, supply chain, identity management, e-governance, defence, education, banking, and trading. Blockchain has the potential to secure IoT through repetition, changeless capacity, and encryption. Blockchain for Information Security and Privacy provides essential knowledge of blockchain usage in the mainstream areas of security, trust, and privacy in decentralized domains. This book is a source of technical information regarding blockchain-oriented software and applications. It provides tools to researchers and developers in both computing and software engineering to develop solutions and automated systems that can promote security, trust, and privacy in cyberspace. FEATURES Applying blockchain-based secured data management in confidential cyberdefense applications Securing online voting systems using blockchain Safeguarding electronic healthcare record (EHR) management using blockchain Impacting security and privacy in digital identity management Using blockchain-based security and privacy for smart contracts By providing an overview of blockchain technology application domains in IoT (e.g., vehicle web, power web, cloud internet, and edge computing), this book features side-by-side comparisons of modern methods toward secure and privacy-preserving blockchain technology. It also examines safety objectives, efficiency, limitations, computational complexity, and communication overhead of various applications using blockchain. This book also addresses the combination of blockchain and industrial IoT. It explores novel various-levels of information sharing systems.

The fusion of AI and IoT enables the systems to be predictive, prescriptive, and autonomous, and this convergence has evolved the nature of emerging applications from being assisted to augmented, and ultimately to autonomous intelligence. This book discusses algorithmic applications in the field of machine learning and IoT with pertinent applications. It further discusses challenges and future directions in the machine learning area and develops understanding of its role in technology, in terms of IoT security issues. Pertinent applications described include speech recognition, medical diagnosis, optimizations, predictions, and security aspects. Features: Focuses on algorithmic and practical parts of the artificial intelligence approaches in IoT applications. Discusses supervised and unsupervised machine learning for IoT data and devices. Presents an overview of the different algorithms related to Machine learning and IoT. Covers practical case studies on industrial and smart home automation. Includes implementation of AI from case studies in personal and industrial IoT. This book aims at Researchers and Graduate students in Computer Engineering, Networking Communications, Information Science Engineering, and Electrical Engineering.

Do you have creative ideas that you wish you could transform into code? Do you want to boost your problem solving and logic skills? Do you want to enhance your career by adopting an algorithmic mindset? In our increasingly digital world, coding is an essential skill. Communicating an algorithm to a machine to perform a set of tasks is vital. Beginner's Guide to Code Algorithms: Experiments to Enhance Productivity and Solve Problems written by Deepankar Maitra teaches you how to think like a programmer. The author unravels the secret behind writing code - building a good algorithm. Algorithmic thinking leads to asking the right question and enables a shift from issue resolution to value creation. Having this mindset will make you more marketable to employers. This book takes you on a problem-solving journey to expand your mind and increase your willingness to experiment with code. You will: Learn the art of building an algorithm through hands-on exercises Understand how to develop code for inspiring productivity concepts Build a mentality of developing algorithms to solve problems Develop, test, review, and improve code through guided experimentation This book is designed to develop a culture of logical thinking through intellectual stimulation. It will benefit students and teachers of programming, business professionals, as well as experienced users of Microsoft Excel who wish to become proficient with macros.

Data is an intrinsic part of our daily lives. Everything we do is a data point. Many of these data points are recorded with the intent to help us lead more efficient lives. We have apps that track our workouts, sleep, food intake, and personal finance. We use the data to make changes to our lives based on goals we have set for ourselves. Businesses use vast collections of data to determine strategy and marketing. Data scientists take data, analyze it, and create models to help solve problems. You may have heard of companies having data management teams or chief information officers (CIOs) or chief data officers (CDOs), etc. They are all people who work with data, but their function is more related to vetting data and preparing it for use by data scientists. The jump from personal data usage for self-betterment to mass data analysis for business process improvement often feels bigger to us than it is. In turn, we often think big data analysis requires tools held only by advanced degree holders. Although advanced degrees are certainly valuable, this book illustrates how it is not a requirement to adequately run a data science project. Because we are all already data users, with some simple strategies and exposure to basic analytical software programs, anyone who has the proper tools and determination can solve data science problems. The process presented in this book will help empower individuals to work on and solve data-related challenges. The goal of this book is to provide a step-by-step guide to the data science process so that you can feel confident in leading your own data science project. To aid with clarity and understanding, the author presents a fictional restaurant chain to use as a case study, illustrating how the various topics discussed can be applied. Essentially, this book helps traditional businesspeople solve data-related problems on their own without any hesitation or fear. The powerful methods are presented in the form of conversations, examples, and case studies. The conversational style is engaging and provides clarity.

Many mathematical problems in science and engineering are defined by ordinary or partial differential equations with appropriate initial-boundary conditions. Among the various methods, boundary integral equation method (BIEM) is probably the most effective. It's main advantage is that it changes a problem from its formulation in terms of unbounded differential operator to one for an integral/integro-differential operator, which makes the problem tractable from the analytical or numerical point of view. Basically, the review/study of the problem is shifted to a boundary (a relatively smaller domain), where it gives rise to integral equations defined over a suitable function space. Integral equations with singular kernels areamong the most important classes in the fields of elasticity, fluid mechanics, electromagnetics and other domains in applied science and engineering. With the advancesin computer technology, numerical simulations have become important tools in science and engineering. Several methods have been developed in numerical analysis for equations in mathematical models of applied sciences. Widely used methods include: Finite Difference Method (FDM), Finite Element Method (FEM), Finite Volume Method (FVM) and Galerkin Method (GM). Unfortunately, none of these are versatile. Each has merits and limitations. For example, the widely used FDM and FEM suffers from difficulties in problem solving when rapid changes appear in singularities. Even with the modern computing machines, analysis of shock-wave or crack propagations in three dimensional solids by the existing classical numerical schemes is challenging (computational time/memory requirements). Therefore, with the availability of faster computing machines, research into the development of new efficient schemes for approximate solutions/numerical simulations is an ongoing parallel activity. Numerical methods based on wavelet basis (multiresolution analysis) may be regarded as a confluence of widely used numerical schemes based on Finite Difference Method, Finite Element Method, Galerkin Method, etc. The objective of this monograph is to deal with numerical techniques to obtain (multiscale) approximate solutions in wavelet basis of different types of integral equations with kernels involving varieties of singularities appearing in the field of elasticity, fluid mechanics, electromagnetics and many other domains in applied science and engineering.

The Foundations of Computational Mathematics meetings are a platform for cross-fertilisation between numerical analysis, mathematics and computer science. This volume is a collection of articles based on plenary presentations, given at the 2011 meeting, by some of the world's foremost authorities in computational mathematics. The topics covered reflect the breadth of research within the area as well as the richness of interactions between seemingly unrelated branches of pure and applied mathematics. As a result this volume will be of interest to researchers in the field of computational mathematics and also to non-experts who wish to gain some insight into the state of the art in this active and significant field.