Automatic control engineering Books

Book SynopsisThe classic text, now completely up to date This Second Edition of State Variables for Engineers is completely updated to reflect both the many changes in the field of systems and control and the fact that today''s first-year graduate students are well prepared in the background skills and techniques needed to handle this material. The book begins with an introduction to the basic concepts behind time domain techniques, comparisons between state variable feedback and classical output feedback, and a discussion of the concepts of observability and controllability. The authors stress the importance of studying matrices and linear spaces by offering state variable representations for continuous linear systems in matrix form along with the solution to the resulting linear matrix differential equation. This treatment demonstrates how these basic linear algebra tools are related to the state variable analysis of linear systems. This new edition retains thorough coverage of the eiTrade Review"...a welcome addition to the set of books on this subject." (International Journal of Robust and Linear Controls, Vol. 12, 2002)Table of ContentsTime Domain Techniques. State Variable Representation of Systems. Matrices, Linear Spaces, and Linear Systems. State Variables and Linear Continuous Systems. State Variables and Linear Discrete-Time Systems. Canonical Forms for Representing Linear Systems. Observers and Controllers. Identification and Estimation. Introduction to Stability Theory and Lyapunov's Method. Appendices. Index.

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Book SynopsisThe use of hydraulic control is rapidly growing and the objective of this book is to present a rational and well--balanced treatment of its components and systems. Coverage includes a review of applicable topics in fluid mechanisms; components encountered in hydraulic servo controlled systems; systems oriented issues and much more.Table of ContentsHydraulic Fluids. Fluid Flow Fundamentals. Hydraulic Pumps and Motors. Hydraulic Control Valves. Hydraulic Power Elements. Electrohydraulic Servovalves. Electrohydraulic Servomechanisms. Hydromechanical Servomechanisms. Nonlinearities in Control Systems. Pressure and Flow Control Valves. Hydraulic Power Supplies. Index.

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Book SynopsisThis text focuses on the management of complex automated systems encountered in industrial research and covers specific technologies and application domains that are cross-disciplinary. It offers broad discussions on the many related perspectives and presents detailed case studies.Table of ContentsIntroduction: Complexity Management for Automation and Control (T. Samad). AUTOMATION AND PEOPLE. Advanced Technology in Complex Systems: Automation, People, Culture (E. Cochran & P. Bullemer). The Human Factor in Complexity (C. Miller). Perceived Complexity and Mental Models in Human-Computer Interaction (V. Riley). SENSING AND CONTROL. Active Multimodeling for Autonomous Systems (T. Samad). Randomized Algorithms for Control and Optimization (R. Kulhav?). Complexity Management via Biology (B. Morton & T. Samad). Sensors in Control Systems (J. Zook, et al.). SOFTWARE AND COMPLEX SYSTEMS. Managing the Complexity of Software (J. Krueger). Agents for Complex Control Systems (R. Sanz). System Health Management for Complex Systems (G. Hadden, et al.). COMPLEXITY MANAGEMENT AND NETWORKS. Current and Future Developments in Air Traffic Control (S. Green & J. Jackson). Complex Adaptive Systems: Concepts and Power Industry Applications (A. Wildberger). National Infrastructure as Complex Interactive Networks (M. Amin). Multiscale Networking, Robustness, and Rigor (J. Doyle). Conclusions: Automation, Control, and Complexity (J. Weyrauch). Current Affiliations and Addresses of Contributors. Indexes.

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Book SynopsisFocuses on the quantitative approaches necessary to computer-integrated manufacturing systems, and integrates major topics covering all phases of the production control cycle: production information processing and flow, production planning, forecasting, material requirements planning and monetary control, and scheduling. This new edition features a compendium set of 11 user-friendly computer programs for the IBM PC that enhance the teaching power of the text, allowing readers to solve real-life problems. Among programs included are growth forecasting, aggregate planning, material requirements planning, lot sizing and inventory control, and limited-resource scheduling. The chapters on scheduling give particularly thorough coverage on this difficult subject. Solutions are clearly presented, with many examples and exercises included in the text.Table of ContentsRole of Production. Production Control Information Flow. CAD/CAM and Production Control. Forecasting--The Key to PC. Aggregate Planning. Material Requirement Planning. Lost Sizing Concepts. Sequence Scheduling. Linear Balancing--A Key to Automation. Project Planning and Resource Constrained Scheduling. Personnel Scheduling. Appendices.

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Book SynopsisAn integrated presentation of dynamics, vibrations, and control theory, emphasizing the fundamentals of dynamics. It also includes examples, problems and applications.Table of ContentsConcepts from Linear System Theory. Kinematics. Dynamics of a Particle. Response of First-Order and Second-Order Systems. Dynamics of Systems of Particles. Dynamics of Rigid Bodies. Elements of Analytical Dynamics. Vibration of Linear Multi-Degree-of-Freedom Systems. Introduction to System Stability. Computational Techniques for the Response. Feedback Control Systems. Appendix. Bibliography.

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Book SynopsisA comprehensive and extensive study of the latest research in control systems for marine vehicles. Demonstrates how the implementation of mathematical models and modern control theory can reduce fuel consumption and improve reliability and performance. Coverage includes ocean vehicle modeling, environmental disturbances, the dynamics and stability of ships, sensor and navigation systems. Numerous examples and exercises facilitate understanding.Table of ContentsModeling of Marine Vehicles. Environmental Disturbances. Stability and Control of Underwater Vehicles. Dynamics and Stability of Ships. Automatic Control of Ships. Control of High-Speed Craft. Appendices. Bibliography. Index.

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Book SynopsisWith a strong emphasis on applications of intelligent control, this extremely accessible book covers the fundamentals, methodologies, architectures and algorithms of automatic control systems.Table of ContentsFundamental Techniques for Intelligent Control. Learning Strategies and Algorithms. System Modeling and Estimation. Dynamic Controls. Optimization Control Techniques. Multivariate Statistics and Quality Control. Fault Detection and Diagnosis. Appendix. Bibliography. Index.

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Book SynopsisThe concept of a system as an entity in its own right has emergedwith increasing force in the past few decades in, for example, theareas of electrical and control engineering, economics, ecology,urban structures, automaton theory, operational research andindustry. The more definite concept of a large-scale system isimplicit in these applications, but is particularly evident infields such as the study of communication networks, computernetworks and neural networks. The Wiley-Interscience Series inSystems and Optimization has been established to serve the needs ofresearchers in these rapidly developing fields. It is intended forworks concerned with developments in quantitative systems theory,applications of such theory in areas of interest, or associatedmethodology. This is the first book-length treatment of risk-sensitive control,with many new results. The quadratic cost function of the standardLQG (linear/quadratic/Gaussian) treatment is replaced by theexponential of a quadratTable of ContentsBASICS. Deterministic Models. Stochastic Models. BEYOND. Risk-Sensitive and H infinity Criteria. Time-Integral Methods and Optimal Stationary Policies. Near-Determinism and Large Deviation Theory. Appendices. References. Index.

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Book SynopsisThe main objective of the authors is to deliver specifications and underlying concepts for future computer-aided tools for the design and the control of flexible manufacturing systems for mechanical and electro-mechanical assemblies. This book presents an integrated computer-aided method which supports a concurrent engineering approach for assembled products. This integrated method is divided in several modules which analyse the ease of assembly of a design, the assembly order, the design of an assembly workshop, and the simulation of the workshop taking into account scheduling and flow control. Automatic, semi-automatic and manual utilisations are presented for each module. Communication between design and manufacturing has been emphasised. The environment in this book is a real concurrent engineering one and for the first time the concurrent engineering steps are integrated in a CAD system. The method has been implemented in one of the world s most used CAD systems: CATIA.Table of ContentsThe CAD Method for Industrial Assembly and ConcurrentEngineering. Proposed Architecture for the New CAD Method. Product Design for Assembly. Assembly Planning. Resource Planning. The Simulation Module. The Scheduling Module. The Flow Control Module. Integration Aspects of the CAD Method. Introducing the Integrated CAD Method into Companies. Conclusions. Index.

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Book SynopsisMultistage Fuzzy Control a model-based approach to fuzzy control and decision making Fuzzy techniques are used to cope with imprecision in the control process. This authoritative book explains the essential principles of fuzzy logic and describes both the theoretical and practical advantages of the new model-based, prescriptive approach. Professor Kacprzyk offers a comprehensive and in depth examination of the issues underlying multistage control and decision analysis, addressing in particular fuzzy dynamic systems, fuzzy events, fuzzy probabilities and fuzzy quantifiers. The text also comprises an introduction to the basic concepts of fuzzy sets, fuzzy logic and fuzzy systems, complemented by real-world examples of the use of the model-based prescriptive approach to improve the efficiency of fuzzy control systems. Highly experienced in fuzzy control research, the author identifies new trends in the development of fuzzy sets and their direct application to decision-making processes. FuTable of ContentsBasic Elements of Fuzzy Sets and Fuzzy Systems. A General Setting for Multistage Control Under Fuzziness. Control Processes with a Fixed and Specified TerminationTime. Control Processes with an Implicitly Specified TerminationTime. Control Processes with a Fuzzy Termination Time. Control Processes with an Infinite Termination Time. Examples of Applications. Concluding Remarks. Bibliography. Index.

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Book SynopsisThis text examines synthetic and dynamical properties of fuzzy control systems in a quantitative manner. It includes fuzzy dynamical systems, controllability and sensitivity analysis and how these affect parameters in membership functions, fuzzification, defuzzification and inferencing.Trade Review"Design and control engineers will value the advanced control techniques, new design and analysis tools presented. Post-graduates...a useful reference." (Engineering Design, July 2000) "...a good read...it boldly tackles the stability issue of fuzzy control systems..." (Measurement and Control, October 2000) "Design and control engineers will value the advanced control techniques and new design and analysis tools presented. Postgraduates studying fuzzy control will find this book a useful reference...." (European Power Electronics & Drives Journal September 2001)Table of ContentsMODELING. Information Granularity in the Analysis and Design of Fuzzy Controllers. Fuzzy Modeling for Predictive Control. Adaptive and Learning Schemes for Fuzzy Modeling. Fuzzy System Identification with General Parameter Radial Basis Function Neural Network. ANALYSIS. Lyapunov Stability Analysis of Fuzzy Dynamic Systems. Passivity and Stability of Fuzzy Control Systems. Frequency Domain Analysis of MIMO Fuzzy Control Systems. Analytical Study of Structure of a Mamdani Fuzzy Controller with Three Input Variables. An Approach to the Analysis of Robust Stability of Fuzzy Control Systems. Fuzzy Control Systems Stability Analysis with Application to Aircraft Systems. SYNTHESIS. Observer-Based Controller Synthesis for Model-Based Fuzzy Systems via Linear Matrix Inequalities. LMI-Based Fuzzy Control: Fuzzy Regulator and Fuzzy Observer Design via LMIs. A Framework for the Synthesis of PDC-Type Takagi-Sugano Fuzzy Control Systems: An LMI Approach. On Adaptive Fuzzy Logic Control on Non-linear Systems--Synthesis and Analysis. Stabilization of Direct Adaptive Fuzzy Control Systems: Two Approaches. Gain Scheduling Based Control of a Class of TSK Systems. Output Tracking Using Fuzzy Neural Networks. Fuzzy Life-Extending Control of Mechanical Systems. Epilogue. Index.

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Book SynopsisThis volume''s focus on the design of computer controlled systems features computational tools that can be applied directly and are explained with simple paper-and-pencil calculations. The use of computational tools is balanced by strong emphasis on control system principles and ideas. Extensive pedagogical aids include worked examples, MATLAB macros, and a solutions manual.

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Book SynopsisThe first comprehensive and up-to-date reference on mechatronics, Robert Bishop''s The Mechatronics Handbook was quickly embraced as the gold standard in the field. With updated coverage on all aspects of mechatronics, The Mechatronics Handbook, Second Edition is now available as a two-volume set. Each installment offers focused coverage of a particular area of mechatronics, supplying a convenient and flexible source of specific information. This seminal work is still the most exhaustive, state-of-the-art treatment of the field available.Focusing on the most rapidly changing areas of mechatronics, this book discusses signals and systems control, computers, logic systems, software, and data acquisition. It begins with coverage of the role of control and the role modeling in mechatronic design, setting the stage for the more fundamental discussions on signals and systems. The volume reflects the profound impact the development of not just the computer, but the microcomputer, embeTable of ContentsThe Role of Controls in Mechatronics. The Role of Modeling in Mechatronics Design. Signals and Systems in Mechatronics. State Space Analysis and System Properties. Response of Dynamic Systems. Root Locus Method. Frequency Response Methods. Kalman Filters as Dynamic System State Observers. Digital Signal Processing for Mechatronic Applications. Control System Design via H2 Optimization. Adaptive and Nonlinear Control Design. Neural Networks and Fuzzy Systems. Advanced Control of an Electro-Hydraulic Axis. Design Optimization of Mechatronic Systems.

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Book SynopsisThis book explores how to use generative adversarial networks in a variety of applications and emphasises their substantial advancements over traditional generative models. This book's major goal is to concentrate on cutting-edge research in deep learning and generative adversarial networks, which includes creating new tools and methods for processing text, images, and audio.A Generative Adversarial Network (GAN) is a class of machine learning framework and is the next emerging network in deep learning applications. Generative Adversarial Networks(GANs) have the feasibility to build improved models, as they can generate the sample data as per application requirements. There are various applications of GAN in science and technology, including computer vision, security, multimedia and advertisements, image generation, image translation,text-to-images synthesis, video synthesis, generating high-resolution images, drug discovery, etc.Features: Presents a comprehensive guide on how to use GAN for images and videos. Includes case studies of Underwater Image Enhancement Using Generative Adversarial Network, Intrusion detection using GAN Highlights the inclusion of gaming effects using deep learning methods Examines the significant technological advancements in GAN and its real-world application. Discusses as GAN challenges and optimal solutions The book addresses scientific aspects for a wider audience such as junior and senior engineering, undergraduate and postgraduate students, researchers, and anyone interested in the trends development and opportunities in GAN and Deep Learning.The material in the book can serve as a reference in libraries, accreditation agencies, government agencies, and especially the academic institution of higher education intending to launch or reform their engineering curriculum

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Book SynopsisThe process of developing predictive models includes many stages. Most resources focus on the modeling algorithms but neglect other critical aspects of the modeling process. This book describes techniques for finding the best representations of predictors for modeling and for nding the best subset of predictors for improving model performance. A variety of example data sets are used to illustrate the techniques along with R programs for reproducing the results. Trade Review"The book is timely and needed. The interest in all things 'data science' morphed into everybody pretending to do, or know, Machine Learning. Kuhn and Johnson happen to actually know this—as evidenced by their earlier and still-popular tome entitled ‘Applied Predictive Modeling.’ The proposed ‘Feature Engineering and Selection’ builds on this and extends it. I expect it to become as popular with a wide reach as both a textbook, self-study material, and reference."~Dirk Eddelbuettel, University of Illinois at Urbana-Champaign"As a reviewer, it has been exciting and edifying to see this book develop into what is likely to become one of the foundational works on feature engineering. It is launching propitiously on the current tide of interest in both interpretable models and AutoML."~Robert Horton, Microsoft"In recent years, the statistics literature has featured new developments in modeling and predictive analytics. Approaches such as cross-validation and statistical/machine learning techniques have become widespread. The author's previous book ("Applied Predictive Modeling", APM) provided a wide-ranging introduction and integration of these methods and suggested a workflow in R to carry out exploratory and confirmation analyses. With this project, the authors have identified an important and interesting component of these methods that describes building better models by focusing on the predictors (feature engineering)…The authors focus on the variables that go into the model (and how they are represented) and argue that such issues are as important (or more important) than the particular methods that are applied to an analysis...The proposed book is likely to serve as a textbook (for a number of undergraduate and graduate courses in a variety of disciplines) and reference (for a large number of statisticians seeking principled and well-organized modeling)."~Nicholas Horton, Amherst College"I think this book is great and a joy to read…I like the pragmatic and practical approach taken in the book, and the examples given are very illustrative. The emphasis on how and when to use resampling is refreshing and something that the community needs to hear more." ~Andreas C. Muller, Columbia University"The book is timely and needed. The interest in all things 'data science' morphed into everybody pretending to do, or know, Machine Learning. Kuhn and Johnson happen to actually know this—as evidenced by their earlier and still-popular tome entitled ‘Applied Predictive Modeling.’ The proposed ‘Feature Engineering and Selection’ builds on this and extends it. I expect it to become as popular with a wide reach as both a textbook, self-study material, and reference."~Dirk Eddelbuettel, University of Illinois at Urbana-Champaign"As a reviewer, it has been exciting and edifying to see this book develop into what is likely to become one of the foundational works on feature engineering. It is launching propitiously on the current tide of interest in both interpretable models and AutoML."~Robert Horton, Microsoft"In recent years, the statistics literature has featured new developments in modeling and predictive analytics. Approaches such as cross-validation and statistical/machine learning techniques have become widespread. The author's previous book ("Applied Predictive Modeling", APM) provided a wide-ranging introduction and integration of these methods and suggested a workflow in R to carry out exploratory and confirmation analyses. With this project, the authors have identified an important and interesting component of these methods that describes building better models by focusing on the predictors (feature engineering)…The authors focus on the variables that go into the model (and how they are represented) and argue that such issues are as important (or more important) than the particular methods that are applied to an analysis...The proposed book is likely to serve as a textbook (for a number of undergraduate and graduate courses in a variety of disciplines) and reference (for a large number of statisticians seeking principled and well-organized modeling)."~Nicholas Horton, Amherst College"I think this book is great and a joy to read…I like the pragmatic and practical approach taken in the book, and the examples given are very illustrative. The emphasis on how and when to use resampling is refreshing and something that the community needs to hear more." ~Andreas C. Muller, Columbia UniversityTable of Contents1. Introduction. 2. Illustrative Example: Predicting Risk of Ischemic Stroke. 3. A Review of the Predictive Modeling Process. 4. Exploratory Visualizations. 5. Encoding Categorical Predictors. 6. Engineering Numeric Predictors. 7. Detecting Interaction Effects. 8. Handling Missing Data. 9. Working with Profile Data. 10. Feature Selection Overview. 11. Greedy Search Methods. 12. Global Search Methods.

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Book SynopsisDerived from industry-training classes that the author teaches at the Embedded Systems Institute at Eindhoven, the Netherlands and at Buskerud University College at Kongsberg in Norway, Systems Architecting: A Business Perspective places the processes of systems architecting in a broader context by juxtaposing the relationship of the systems architect with enterprise and management. This practical, scenario-driven guide fills an important gap, providing systems architects insight into the business processes, and especially into the processes to which they actively contribute.The book uses a simple reference model to enable understanding of the inside of a system in relation to its context. It covers the impact of tool selection and brings balance to the application of the intellectual tools versus computer-aided tools. Stressing the importance of a clear strategy, the authors discuss methods and techniques that facilitate the architect's contributioTable of ContentsProcess and Organization. Role and Task of the Systems Architect. From Customer Understanding to Requirements. Systems Architect Methods and Means. Strategy.Harvesting Synergy, Product Families. Supporting Processes. Systems and Software. Boardroom Presentation. Human Side. Reflection and Wrap-Up. References. Pictorial Index.

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Book SynopsisDeepfakes is a synthetic media that leverage powerful Artificial Intelligence (AI) and machine learning (ML) techniques to generate fake visual and audio content that are extremely realistic, thus making it very hard for a human to distinguish from the original ones. Apart from technological introduction to the Deepfakes concept, the book details algorithms to detect Deepfakes, techniques for identifying manipulated content and identifying face swap, generative adversarial neural networks, media forensic techniques, deep learning architectures, forensic analysis of DeepFakes and so forth. Provides a technical introduction to DeepFakes, its benefits, and the potential harms Presents practical approaches of creation and detection of DeepFakes using Deep Learning (DL) Techniques Draws attention towards various challenging issues and societal impact of DeepFakes with their existing solutions Includes research analysis in the domain of DL fakes for assisting the creation and detection of DeepFakes applications Discusses future research directions with emergence of DeepFakes technology This book is aimed at graduate students, researchers and professionals in data science, artificial intelligence, computer vision, and machine learning.Table of Contents1. Introduction to DeepFake technologies 2. DeepFakes: A Systematic Review and Bibliometric Analysis 3. Deep Learning Techniques for Creation of DeepFakes 4. Analyzing DeepFakes Videos by face warping artifacts 5. Development of image translating model to counter Adversarial attacks 6. Detection of DeepFakes using local features and Convolutional Neural Network 7. DeepFakes: Positive Cases 8. Threats and challenges by DeepFake Technology 9. DeepFakes, media, and societal impacts 10. Fake News Detection using Machine Learning 11. Future of DeepFakes & Ectypes

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Book SynopsisQuantum computing is a field in which advanced technologies like quantum communication, artificial intelligence and machine learning can be used to secure and speed up connectivity using quantum computers, quantum drones or quantum satellites. This book serve as a foundation for researchers and scientists in this field. Future technologies, such as quantum drone delivery systems, quicker internet and climate change mitigation, will need quantum information processing and quantum computation. This book deeply explores the importance of quantum computing in real-time applications. It may be used as a reference book for students in higher education, including undergraduate and graduate students, as well as researchers.Key features: Provides a clear insight into the Internet of Drones for academicians, postdoc fellows, research scholars, graduate and postgraduate students, industry fellows and software engineers Useful to professionals who seek information abouTable of Contents1. Quantum information processes: Role of quantum logic gates. 2. A brief study on Quantum walks and Quantum mechanics. 3. A keen study on Quantum Information Systems. 4. Prologue to quantum computing and blockchain technology. 5. Quantum Computing Application for Satellites and Satellite Image Processing. 6. Evolution of Deep Quantum Learning Models based on Comprehensive Survey on Effective Malware Identification and Analysis. 7. Healthcare System 4.0 Driven by Quantum Computing and Its Use Cases: A COVID 19 Perspective. 8. An Overview of Future Applications of Quantum Computing. 9. Authentication and Authorization for Electronic Health Records using Merkle Tree - Attribute Based Encryption. 10. Artificial intelligence, machine learning and smartphone-internet of things (s-iot) for advanced student network and learning. 11. Role of IoT Based Smart Mask to Combat COVID-19. 12. Encryption Algorithms for Cloud Computing and Quantum Blockchain: A Futuristic Technology Roadmap. 13. Quantum Artificial Intelligence for the Science of Climate Change. 14.Quantum Computing- Based Optimization in Depression Detection Using Speech.

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Book SynopsisComputer vision-based crack-like object detection has many useful applications, such as inspecting/monitoring pavement surface, underground pipeline, bridge cracks, railway tracks etc. However, in most contexts, cracks appear as thin, irregular long-narrow objects, and often are buried in complex, textured background with high diversity which make the crack detection very challenging. During the past a few years, deep learning technique has achieved great success and has been utilized for solving a variety of object detection problems.This book discusses crack-like object detection problem comprehensively. It starts by discussing traditional image processing approaches for solving this problem, and then introduces deep learning-based methods. It provides a detailed review of object detection problems and focuses on the most challenging problem, crack-like object detection, to dig deep into the deep learning method. It includes examples of real-world problems, which are easy tTable of ContentsIntroduction. Crack Detection with Deep Classification Network. Crack Detection with Fully Convolutional Network. Crack Detection with Generative Adversarial Learning. Self-Supervised Structure Learning for Crack Detection. Deep Edge Computing. Conclusion and Discussion.

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Book SynopsisThis book highlights the developments, discoveries, and practical and advanced experiences related to responsive distributed computing and how it can support the deployment of trajectory-based applications in smart systems. Smart Trajectories: Metamodeling, Reactive Architecture for Analytics and Smart Applications deals with the representation and manipulation of smart trajectories in various applications and scenarios. Presented in three parts, the book first discusses the foundation and principles for spatial information systems, complex event processing, and building a reactive architecture. Next, the book discusses modeling and architecture in relation to smart trajectory metamodeling, mining and big trajectory data, and clustering trajectories. The final section discusses advanced applications and trends in the field, including congestion trajectory analytics and real-time Big Data analytics in cloud ecosystems. Metamodeling, distributed architectures, reactive Table of Contents1. Intelligent Distributed Computing Paradigm. 2. Multi Micro-Agent System Middleware Model based on Event Sourcing and CQRS Patterns. 3. Intelligent Distributed Computing Paradigm: Emergence, Challenges and Future Research Directions. 4. Emerging Paradigm of Urban Computing: Challenges, Applications, and Future Research Directions. 5. Complex Event Processing Architectures for Smart City Applications. 6. Portunus: Enhancing smart city application connectivity with complex space-time events distributed system. 7. Smart Trajectories Metamodeling. 8. A Type Level Trajectory Framework. 9. A Distributed Reactive Trajectory Framework for Nearby Events Discovery. 10. A Multidimensional Trajectory Model in the Context of Mobile Crowd Sensing. 11. Trajectory mining based on Process mining in RORO terminals: Performance-Driven Analysis to support trajectories redesign. 12. Aspects from mobility data in fog/cloud era: Directions from a pilot case study of Hazmat transportation telemonitoring in urban area. 13. Utility assessment of line-of-sight traffic jam and queue detection in urban environments for intelligent road vehicles. 14. Risky Trajectory Prediction for Safe Walkability under Intuitionistic Fuzzy Environment. 15. A real-time reactive Service Oriented Architecture for safe urban walkability. 16. Safest Trajectories for Pedestrian using Distributed Architecture based on Spatial Risk Analysis and Voronoï Spatial Accessibility. 17. Towards a predictive simulation framework of accidents risks for pedestrians based on distributed artificial intelligence and intuitionist fuzzy modeling. 18. Trajectory to a new shape of organizational structure, Enterprise Architect and Organizational Audit for Governance of Information Systems Processes. 19. Dynamic detection of fuzzy sub-congested urban traffic networks. 20. Multi-Agent Modeling for Pedestrian Risk Assessment.

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Book SynopsisPython for Scientific Computing andArtificial Intelligence is split into 3 parts: in Section 1, the reader is introduced to the Python programming language and shown how Python can aid in the understanding of advanced High School Mathematics. In Section 2, the reader is shown how Python can be used to solve real-world problems from a broad range of scientific disciplines. Finally, in Section 3, the reader is introduced to neural networks and shown how TensorFlow (written in Python) can be used to solve a large array of problems in Artificial Intelligence (AI).This book was developed from a series of national and international workshops that the author has been delivering for over twenty years. The book is beginner friendly and has a strong practical emphasis on programming and computational modelling.Features: No prior experience of programming is required Online GitHub repository available with codes for readers to practice Table of ContentsSection I. An Introduction to Python. 1. The IDLE Integrated Development Learning Environment. 2. Anaconda, Spyder and the Libraries NumPy, Matplotlib and SymPy. 3. Jupyter Notebooks and Google Colab. 4. Python for AS-Level (High School) Mathematics. 5. Python for A-Level (High School) Mathematics. Section II. Python for Scientific Computing. 6. Biology. 7. Chemistry. 8. Data Science. 9. Economics. 10. Engineering. 11. Fractals and Multifractals. 12. Image Processing. 13. Numerical Methods for Ordinary and Partial Differential Equations. 14. Physics. 15. Statistics. Section III. Artificial Intelligence. 16. Brain Inspired Computing. 17. Neural Networks and Neurodynamics. 18. TensorFlow and Keras. 19. Recurrent Neural Networks. 20. Convolutional Neural Networks, TensorBoard, and Further Reading. 21. Answers and Hints to Exercises.

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Book SynopsisSolid requirements engineering has increasingly been recognized as the key to improved, on-time, and on-budget delivery of software and systems projects. New software tools are emerging that are empowering practicing engineers to improve their requirements engineering habits. However, these tools are not usually easy to use without significant training. Requirements Engineering for Software and Systems, Fourth Edition is intended to provide a comprehensive treatment of the theoretical and practical aspects of discovering, analyzing, modeling, validating, testing, and writing requirements for systems of all kinds, with an intentional focus on software-intensive systems. It brings into play a variety of formal methods, social models, and modern requirements writing techniques to be useful to practicing engineers. The book is intended for professional software engineers, systems engineers, and senior and graduate students of software or systems engineering.Since the first edition, there have been made many changes and improvements to this textbook. Feedback from instructors, students, and corporate users was used to correct, expand, and improve the materials. The fourth edition features two newly added chapters: On Non-Functional Requirements and Requirements Engineering: Road Map to the Future. The latter provides a discussion on the relationship between requirements engineering and such emerging and disruptive technologies as Internet of Things, Cloud Computing, Blockchain, Artificial Intelligence, and Affective Computing.All chapters of the book were significantly expanded with new materials that keep the book relevant to current industrial practices. Readers will find expanded discussions on new elicitation techniques, agile approaches (e.g., Kanpan, SAFe, and DEVOps), requirements tools, requirements representation, risk management approaches, and functional size measurement methods. The fourth edition also has significant additions of vignettes, exercises, and references. Another new feature is scannable QR codes linked to sites containing updates, tools, videos, and discussion forums to keep readers current with the dynamic field of requirements engineering. Table of ContentsPreface. Acknowledgments. Authors. 1 Introduction to Requirements Engineering. 2 Preparing for Requirements Elicitation. 3 Requirements Elicitation. 4 Writing the Requirements Document. 5 On Nonfunctional Requirements. 6 Requirements Validations and Verifications. 7 Formal Methods. 8 Requirements Specification and Agile Methodologies. 9 Tool Support for Requirements Engineering. 10 Requirements Management. 11 Value Engineering of Requirements. 12 Requirements Engineering: A Road Map to the Future. Appendix A: Software Requirements Specification for a Smart Home. Appendix B: Software Requirements for a Wastewater Pumping Station Wet-Well Control System. Appendix C: Unified Modeling Language (UML). Appendix D: User Stories. Appendix E: Use Cases. Appendix F: IBM DOORS Requirements Management Tool. Glossary. Index.

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Book SynopsisBecause of the growing reliance on software, concerns are growing as to how reliable a system is before it is commissioned for use, how high the level of reliability is in the system, and how many vulnerabilities exist in the system before its operationalization. Equally pressing issues include how to secure the system from internal and external security threats that may exist in the face of resident vulnerabilities. These two problems are considered increasingly important because they necessitate the development of tools and techniques capable of analyzing dependability and security aspects of a system. These concerns become more pronounced in the cases of safety-critical and mission-critical systems.System Reliability and Security: Techniques and Methodologies focuses on the use of soft computing techniques and analytical techniques in the modeling and analysis of dependable and secure systems. It examines systems and applications having complex distTable of Contents1. A GNN Approach for Software Reliability, 2. Software Reliability Prediction Using Neural Networks: A Non-parametric Approach, 3. Analysis and Modelling of Software Reliability Using Deep Learning Methods, 4. Fixed-Design Local Polynomial Approach for Estimation and Hypothesis Testing of Reliability Measures, 5. Reliability Analysis of Relation between Urbanization, Vegetation Health, and Heat Island Through Markov Chain Model, 6. Modeling and IoT (Internet of Things) Analysis for Smart Precision Agriculture, 7. Engineering Challenges in the Development of Artificial Intelligence and Machine Learning Software Systems, 8. Study and Analysis of Testing Effort Functions for Software Reliability Modeling, 9. Summary of NHPP-Based Software Reliability Modeling With Lindley-Type Distributions, 10. Artificial Intelligence and Machine Learning Problems and Challenges in Software Testing, 11. Software Quality Prediction by CatBoost: Feed-Forward Neural Network in Software Engineering, 12. Software Security, 13. Definitive Guide to Software Security Metrics, 14. Real-Time Supervisory Control and Data Acquisition (SCADA) Model for Resourceful Distribution and Use of Public Water

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Book SynopsisGet ready to be at the forefront of the future of urban development!As cities continue to rapidly grow, the demand for sustainable and efficient infrastructure becomes more urgent. That's where What Every Engineer Should Know About Smart Cities comes in, offering a comprehensive guide to the concepts and technologies driving the transformation of our cities.Delve into the world of smart cities and discover how information and communication technologies are revolutionizing urban environments. With clear definitions and a focus on real-world applications, this book explores the benefits and challenges of smart cities. It also highlights interdisciplinary topics such as smart buildings, autonomous cars, and urban emergency management systems.This book is not just a theoretical exploration of smart cities. It goes beyond that by providing an in-depth look at the key technologies that are essential to creating smart cities. From the Internet of Things and blocTable of ContentsChapter 1 Defining Smart Cities: An OverviewChapter 2 Smart Cities and the Internet of Things: A Synergetic PartnershipChapter 3 Smart Cities as Complex Systems: A Systems-of-Systems ApproachChapter 4 Modeling and Simulation for Smart City DevelopmentChapter 5 Digital Twin for Smart Cities TransformationChapter 6 Blockchain for Secure and Transparent Smart City TransactionsChapter 7 Smart Mobility for Liveable Cities: Opportunities and ChallengesChapter 8 Building Healthy Cities with Smart TechnologiesChapter 9 Agribusiness in the Era of Smart Cities: Opportunities and ChallengesChapter 10 A Smarter Education for a Better Future: Opportunities and ChallengesChapter 11 Smart City in Action: A Case Study of the City of Santa Rosa, Brazil

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Book SynopsisHybrid Computational Intelligent Systems Modeling, Simulation and Optimization unearths the latest advances in evolving hybrid intelligent modeling and simulation of human-centric data-intensive applications optimized for real-time use, thereby enabling researchers to come up with novel breakthroughs in this ever-growing field.Salient features include the fundamentals of modeling and simulation with recourse to knowledge-based simulation, interaction paradigms, and human factors, along with the enhancement of the existing state of art in a high-performance computing setup. In addition, this book presents optimization strategies to evolve robust and failsafe intelligent system modeling and simulation.The volume also highlights novel applications for different engineering problems including signal and data processing, speech, image, sensor data processing, innovative intelligent systems, and swarm intelligent manufacturing systems.Features:A Table of ContentsChapter 1 Creating ratings of agricultural universities based on their digital footprint Chapter 2 Mechatronic Complex’s Fuzzy System for Fixating Moving Objects Chapter 3 Quad Sensor-based Soil-Moisture Prediction using Machine Learning Chapter 4 Stability Analysis for a Diffusive Ratio-dependent Predator-prey Model involving two Delays Chapter 5 Analysis and Prediction of Physical Fitness Test Data of College Students Based on Grey Model Chapter 6 Analysis and Research on Book Borrowing Tendency Based on Apriori Algorithm Chapter 7 Performance Evaluation of Cargo Inspection Systems with the Function of Materials Recognition Chapter 8 Automated Medical Report Generation on Chest X-Ray Images using Co-Attention mechanism Chapter 9 An Energy Efficient Secured Arduino based Home Automation using Android Interface Chapter 10 A Multithreaded Android App to Notify Available `CoWIN’ Vaccination Slots to Multiple Recipients Chapter 11 Binary MMBAIS for Feature Selection Problem Chapter 12 Audio to Indian Sign Language Interpreter (AISLI) using Machine Translation and NLP Techniques Chapter 13 Fragile Medical Image Watermarking using Auto-generated Adaptive Key based Encryption Chapter 14 Designing of a Solution Model for Global Warming and Climate Change using Machine Learning and Data Engineering Techniques Chapter 15 Human Age Estimation using sit-to-stand exercise Data-driven Decision Making by Neural Network Chapter 16 Feature Based Suicide Ideation Detection from Twitter Data Using Machine Learning Techniques Chapter 17 Analyzing the role of Indian Media during the second wave of COVID using Topic Modeling Chapter 18 Hardware Efficient FIR Filter Design using Fast Converging Flower Pollination Algorithm - A Case Study of denoising PCG Signal Chapter 19 Voice Recognition System Using Deep Learning Chapter 20 Modified Harris Hawk Optimization Algorithm for Multi-level Image Thresholding Chapter 21 An automatic probabilistic framework for detection and segmentation of tumor in brain MRI images Chapter 22 Comparative Study of Generative Adversarial Networks for Sensor Data Generation based Remaining Useful Life Classification Chapter 23 Towards a Framework for Implementation of Quantum-Inspired Evolutionary Algorithm on Noisy Intermediate Scale Quantum Devices (IBMQ) for Solving Knapsack Problems

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Book SynopsisBlockchain is an emerging platform for developing decentralized applications and data storage, over and beyond its role as a platform for cryptocurrencies. This reference text provides a comprehensive discussion on blockchain technology from research and application perspective. Discusses different approaches for building distributed applications (DAPPS). Provides detailed listing and discussion of blockchain technology applications in solving real life problems. Covers proof of work (PoW) based blockchain consensus, and proof of stake (PoS) based blockchain consensus. Discusses blockchain algorithms including practical byzantine fault tolerance (PBFT) and simplified byzantine fault tolerance (SBFT). It comprehensively covers important topics including blockchain consensus algorithms, Ethereum, Hyperledger, blockchain scalability, smart contracts with solidity, ERC20 standards, building DApp with Golang, building DApp using Hyperledger, building PoCs with Hyperledger fabric, blockchain as a server, blockchain security and privacy. The text will serve as a useful text for senior undergraduate and graduate students in interdisciplinary areas including electronics and communications engineering, electrical engineering, computer science, and information technology.

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Book SynopsisThis book covers aspects of data science and predictive analytics used in the oil and gas industry by looking into the challenges of data processing and data modelling unique to this industry. It includes upstream management, intelligent/digital wells, value chain integration, crude basket forecasting, and so forth. It further discusses theoretical, methodological, well-established, and validated empirical work dealing with various related topics. Special focus has been given to experimental topics with various case studies.Features: Provides an understanding of the basics of IT technologies applied in the oil and gas sector Includes deep comparison between different artificial intelligence techniques Analyzes different simulators in the oil and gas sector as well as discussion of AI applications Focuses on in-depth experimental and applied topics Details different case studies for upstream and downstream This book Table of Contents1. Understanding the Oil & Gas Sector and its Processes: Upstream and Downstream 2. IT technologies Impacting the Petroleum Sector 3. Data Handling Techniques in Petroleum Sector 4. Predictive Modelling Concepts in Petroleum Sector 5. Supply Chain Management in Oil and Gas Business 6. Prescriptive Analytics and its Application in Oil and Gas Business 7. Future Challenges in Petroleum Sector 8. Oil & Gas Industry in context of Industry 4.0

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Book SynopsisMechatronics is a mongrel, a crossbreed of classic mechanical engineering, the relatively young pup of computer science, the energetic electrical engineering, the pedigree mathematics and the bloodhound of Control Theory.All too many courses in control theory consist of a diet of Everything you could ever need to know about the Laplace Transform' rather than answering What happens when your servomotor saturates?' Topics in this book have been selected to answer the questions that the mechatronics student is most likely to raise.That does not mean that the mathematical aspects have been left out, far from it. The diet here includes matrices, transforms, eigenvectors, differential equations and even the dreaded z transform. But every effort has been made to relate them to practical experience, to make them digestible. They are there for what they can do, not to support pages of mathematical rigour that defines their origins.The theme running throughout the Table of Contents1. Why Do You Need Control Theory? 2. Modelling Time .3. A Simulation Environment 4. Step Length Considerations. 5. Modelling a Second-Order System .6. The Complication of Motor Drive Limits. 7. Practical Controller Design 8. Adding Dynamics to the Controller 9. Sensors and Actuators. 10. Analogue Simulation. 11. Matrix State Equations. 12. Putting It into Practice. 13. Observers 14. More about the Mathematics 15. Transfer Functions 16. Solving the State Equations 17. Discrete Time and the z Operator. 18. Root locus. 19. More about the Phase Plane. 20. Optimisation and an Experiment. 21. Problem Systems. 22. Final Comments.

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Book SynopsisThis book is written in a clear and thorough way to cover both the traditional and modern uses of artificial intelligence and soft computing. It gives an in-depth look at mathematical models, algorithms, and real-world problems that are hard to solve in MATLAB. The book is intended to provide a broad and in-depth understanding of fuzzy logic controllers, genetic algorithms, neural networks, and hybrid techniques such as ANFIS and the GA-ANN model.Features: A detailed description of basic intelligent techniques (fuzzy logic, genetic algorithm and neural network using MATLAB) A detailed description of the hybrid intelligent technique called the adaptive fuzzy inference technique (ANFIS) Formulation of the nonlinear model like analysis of ANOVA and response surface methodology Variety of solved problems on ANOVA and RSM Case studies of above mentioned intelligent techniques on the different process control systems This book can be used as a handbook and a guide for students of all engineering disciplines, operational research areas, computer applications, and for various professionals who work in the optimization area.

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Book SynopsisMachine Learning Toolbox for Social Scientists covers predictive methods with complementary statistical tools that make it mostly self-contained. The inferential statistics is the traditional framework for most data analytics courses in social science and business fields, especially in Economics and Finance. The new organization that this book offers goes beyond standard machine learning code applications, providing intuitive backgrounds for new predictive methods that social science and business students can follow. The book also adds many other modern statistical tools complementary to predictive methods that cannot be easily found in econometrics textbooks: nonparametric methods, data exploration with predictive models, penalized regressions, model selection with sparsity, dimension reduction methods, nonparametric time-series predictions, graphical network analysis, algorithmic optimization methods, classification with imbalanced data, and many others. This book is targTable of Contents1. How We Define Machine Learning 2. Preliminaries Part 1. Formal Look at Prediction 3. Bias-Variance Tradeoff 4. Overfitting Part 2. Nonparametric Estimations 5. Parametric Estimations 6. Nonparametric Estimations - Basics 7. Smoothing 8. Nonparametric Classifier - kNN Part 3. Self-learning 9. Hyperparameter Tuning 10. Tuning in Classification 11. Classification Example Part 4. Tree-based Models 12. CART 13. Ensemble Learning 14. Ensemble Applications Part 5. SVM & Neural Networks 15. Support Vector Machines 16. Artificial Neural Networks Part 6. Penalized Regressions 17. Ridge 18. Lasso 19. Adaptive Lasso 20. Sparsity Part 7. Time Series Forecasting 21. ARIMA models 22. Grid Search for Arima 23. Time Series Embedding 24. Random Forest with Times Series 25. Recurrent Neural Networks Part 8. Dimension Reduction Methods 26. Eigenvectors and eigenvalues 27. Singular Value Decomposition 28. Rank r approximations 29. Moore-Penrose Inverse 30. Principle Component Analysis 31. Factor Analysis Part 9. Network Analysis 32. Fundamentals 33. Regularized Covariance Matrix Part 10. R Labs 34. R Lab 1 Basics 35. R Lab 2 Basics II 36. Simulations in R 37. Algorithmic Optimization 38. Imbalanced Data

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Book SynopsisThis textbook provides an excellent focus on the advanced topics of the power system protection philosophy and gives exciting analysis methods and a cover of the important applications in the power systems relaying. Each chapter opens with a historical profile or career talk, followed by an introduction that states the chapter objectives and links the chapter to the previous ones, and then the introduction for each chapter. All principles are presented in a lucid, logical, step-by-step approach. As much as possible, the authors avoid wordiness and detail overload that could hide concepts and impede understanding. In each chapter, the authors present some of the solved examples and applications using a computer program.Toward the end of each chapter, the authors discuss some application aspects of the concepts covered in the chapter using a computer program.In recognition of requirements by the Accreditation Board for Engineering and Technology (ABET) on integrating comTable of Contents1. Introduction to Power Protection Systems. 2. Protective Relays. 3. Protection Systems with SCADA Technology 4. Faults Analysis. 5. Fuses and Circuit Breakers. 6. Overcurrent Relay. 7. Transmission Line Protection. 8. Transformer Protection. 9. Generator, Motor, and Busbar Protection. 10. High-Impedance Faults. 11. Grounding of Power System.

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Book Synopsismlr3 is an award-winning ecosystem of R packages that have been developed to enable state-of-the-art machine learning capabilities in R. Applied Machine Learning Using mlr3 in R gives an overview of flexible and robust machine learning methods, with an emphasis on how to implement them using mlr3 in R. It covers various key topics, including basic machine learning tasks, such as building and evaluating a predictive model; hyperparameter tuning of machine learning approaches to obtain peak performance; building machine learning pipelines that perform complex operations such as pre-processing followed by modelling followed by aggregation of predictions; and extending the mlr3 ecosystem with custom learners, measures, or pipeline components.Features: In-depth coverage of the mlr3 ecosystem for users and developers Explanation and illustration of basic and advanced machine learning concepts Ready to use code samples that can be adapted by the useTable of Contents1. Introduction and Overview. 2. Data and Basic Modeling. 3. Evaluation and Benchmarking. 4. Hyperparameter Optimization. 5. Advanced Tuning Methods and Black Box Optimization. 6. Feature Selection. 7. Sequential Pipelines. 8. Non-sequential Pipelines and Tuning. 9. Preprocessing. 10. Advanced Technical Aspects of mlr3 .11. Model Interpretation and Explanation. 12. Model Interpretation. 13. Beyond Regression and Classification. 14. Algorithmic Fairness.

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Deep Learning for Engineers introduces the fundamental principles of deep learning along with an explanation of the basic elements required for understanding and applying deep learning models.As a comprehensive guideline for applying deep learning models in practical settings, this book features an easy-to-understand coding structure using Python and PyTorch with an in-depth explanation of four typical deep learning case studies on image classification, object detection, semantic segmentation, and image captioning. The fundamentals of convolutional neural network (CNN) and recurrent neural network (RNN) architectures and their practical implementations in science and engineering are also discussed.This book includes exercise problems for all case studies focusing on various fine-tuning approaches in deep learning. Science and engineering students at both undergraduate and graduate levels, academic researchers, and industry professionals will find the contents

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Book SynopsisThe breadth of problems that can be solved with data science is astonishing, and this book provides the required tools and skills for a broad audience. The reader takes a journey into the forms, uses, and abuses of data and models, and learns how to critically examine each step. Python coding and data analysis skills are built from the ground up, with no prior coding experience assumed. The necessary background in computer science, mathematics, and statistics is provided in an approachable manner.Each step of the machine learning lifecycle is discussed, from business objective planning to monitoring a model in production. This end-to-end approach supplies the broad view necessary to sidestep many of the pitfalls that can sink a data science project. Detailed examples are provided from a wide range of applications and fields, from fraud detection in banking to breast cancer classification in healthcare. The reader will learn the techniques to accomplish tasks that include predicting outcomes, explaining observations, and detecting patterns. Improper use of data and models can introduce unwanted effects and dangers to society. A chapter on model risk provides a framework for comprehensively challenging a model and mitigating weaknesses. When data is collected, stored, and used, it may misrepresent reality and introduce bias. Strategies for addressing bias are discussed. From Concepts to Code: Introduction to Data Science leverages content developed by the author for a full-year data science course suitable for advanced high school or early undergraduate students. This course is freely available and it includes weekly lesson plans.

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Book SynopsisThe Applied Genomic Epidemiology Handbook: A Practical Guide to Leveraging Pathogen Genomic Data in Public Health provides rationale, theory, and implementation guidance to help public health practitioners incorporate pathogen genomic data analysis into their investigations. During the SARS-CoV-2 pandemic, viral whole genome sequences were generated, analyzed, and shared at an unprecedented scale. This wealth of data posed both tremendous opportunities and challenges; the data could be used to support varied parts of the public health response but could be hard for much of the public health workforce to analyze and interpret, given a historical lack of experience working with pathogen genomic data.This book addresses that gap. Structured into eight wide-ranging chapters, this book describes how the overlapping timescales of pathogen evolution and infection transmission enable exploration of epidemiologic dynamics from pathogen sequence data. Different approaches to s

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Book SynopsisThis groundbreaking book explores the power of collaborative AI in amplifying human creativity and expertise. Written by two seasoned experts in data analytics, AI, and machine learning, the book offers a comprehensive overview of the creative process behind AI-powered content generation. It takes the reader through a unique collaborative process between human authors and various AI-based topic experts, created, prompted, and fine-tuned by the authors.This book features a comprehensive list of prompts that readers can use to create their own ChatGPT-powered topic experts. By following these expertly crafted prompts, individuals and businesses alike can harness the power of AI, tailoring it to their specific needs and fostering a fruitful collaboration between humans and machines. With real-world use cases and deep insights into the foundations of generative AI, the book showcases how humans and machines can work together to achieve better business outcomes and tackle complex

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Book SynopsisMachine learning is a dynamic and rapidly expanding field focused on creating algorithms that empower computers to recognize patterns, make predictions and continually enhance performance. It enables computers to learn from data and experiences, making decisions without explicit programming. For learners, mastering the fundamentals of machine learning opens doors to a world of possibilities to build robust and accurate models. In the ever-evolving landscape of machine learning, datasets play a pivotal role in shaping its future. The field has been revolutionized with the introduction of oneAPI, which provides a unified programming model across different architectures, including CPUs, GPUs, FPGAs and accelerators, fostering an efficient and portable programming environment. Embracing this unified model empowers practitioners to build efficient and scalable machine learning solutions, marking a significant stride in cross-architecture development. Dive into this fascinating field to m

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Book SynopsisResponsible use of AI in public sector applications requires engagement with various technical and non-technical areas such as human rights, inclusion, diversity, innovation, and economic growth. The book covers topics spanning the technological socio-economic spectrum including potential of AI/ML technologies to address social and political inequities, privacy enhancing technologies for datasets, friction less data sharing and data stewardship models, regional/geographical inequities in extraction and so forth.Features: Focuses on technical aspects of responsible AI in the public sector. Covers a wide range of topics spanning the technological socio-economic spectrum. Presents viewpoints from the public sector agencies as well as from practitioners. Discusses privacy enhancing technologies for collecting, processing and storing datasets, and friction. Reviews frameworks to identify and address biased AI outcomes in the design, development and use of AI. This book is aimed at professionals, researchers and students in artificial intelligence, computer science and engineering, policy makers, social scientists, economists, and lawyers.

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Book SynopsisComputational methods are necessary for the proper execution of the applications for the benefit of society and technological development. Technological development makes life easier by constructing powerful systems with the help of computational methods. The nature of computational methods changes from time to time and retains only efficient applicable theories. Researchers take the idea of existing computational systems and advanced them as per the possible future needs. Efficient computational methods also solve complex problems and help to make the system more intelligent. Automation process requires decision-making computational systems. A more intelligent system contains an efficient computational method, which is described by powerful algorithm development. The aim of this book is to identify the technological development for future computational systems, which ultimately reflects the more intelligent system. Automation is the need of todayâs world, and the computational systems need to be upgraded to that level to perform the required tasks. The most efficient computational algorithm acts like a human being and offers a full sense of intelligent automation.This book: Presents the latest research trends for the upcoming computational intelligent systems in a comprehensive manner. Focuses on the integration of multi-purpose and multi-dimension natural language into intelligent systems. Elaborates on nature-inspired and intelligent behaviour-based computational methods to deal with the observation of nature. Illustrates applications of quantum cellular energy-efficient computing methods for automation and applications of genetic algorithms in multi-disciplinary fields. Discusses aspects of intelligent automation like technology-based, architecture-based, logic implementation-based, and the different algorithms-based concepts. It is primarily written for senior undergraduates, graduate students, and academic researchers in the fields of electrical engineering, electronics and communications engineering, computer science and engineering, and information technology.

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Book SynopsisIs a computer simulation of a brain sufficient to make it intelligent? Do you need consciousness to have intelligence? Do you need to be alive to have consciousness? This book has a dual purpose. First, it provides a multi-disciplinary research survey across all branches of neuroscience and AI research that relate to this book's mission of bringing AI research closer to building a human-level AI (HLAI) system. It provides an encapsulation of key ideas and concepts, and provides all the references for the reader to delve deeper; much of the survey coverage is of recent pioneering research. Second, the final part of this book brings together key concepts from the survey and makes suggestions for building HLAI. This book provides accessible explanations of numerous key concepts from neuroscience and artificial intelligence research, including: The focus on visual processing and thinking and the possible role of brain lateralization toward visual thinking and intelligence.

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Book SynopsisAfter many years during which it languished in relative obscurity - in remote classrooms of computer science departments and in small prototype projects for tech companies - AI is now a searingly hot topic across the media. Yet much of the public discussion is so feverish that an understanding of the basic scientific and engineering elements of the field is easily lost, often resulting in exaggerated claims, as well as dangerously neglected threats.This concise and sober book presents a brief history of AI, explaining in clear language the central engineering innovations that have produced the current revolution, and distinguishing between imagined dangers and the very real problems that AI is creating. Spread across seven short and accessible chapters, the book explains the developments behind deep learning and the applications of deep neural networks (DNNs), addresses both the imagined and actual risks posed by the AI revolution, before outlining rational public policy on AI, covering topics like tech monopolies, disinformation, bias, hate speech, intellectual property rights, and inequality. Suitable for the general reader, Understanding the Artificial Intelligence Revolution: Between Catastrophe and Utopia is the ideal book for anyone seeking a clear and informed introduction to AI.

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Book SynopsisThis engineering textbook is designed to introduce advanced control systems for vehicles, including advanced automotive concepts and the next generation of vehicles for ITS. For each automotive control problem considered, the authors emphasise the physics and underlying principles behind the control system concept and design.Table of ContentsPreface; Part I. Introduction and Background: 1. Introduction; 2. Automotive control system design process; 3. Review of engine modeling; 4. Review of vehicle dynamics; 5. Human factors and driver modeling; Part II. Powertrain Control Systems: 6. Air-to-fuel ratio control; 7. Control of spark timing; 8. Idle speed control; 9. Transmission control; 10. Control of hybrid vehicles; 11. Modeling and control of fuel cells for vehicles; Part III. Vehicle Control Systems: 12. Cruise and headway control; 13. Antilock brake systems and traction control; 14. Vehicle stability control; 15. Four wheel steering; 16. Active suspensions; Part IV. Intelligent Transportation Systems (ITS): 17. Overview of ITS; 18. Preventing collisions; 19. Automated highway systems (AHS) and platooning; 20. Lateral active safety systems and automated steering; Appendix A. Review of control theory fundamentals; Appendix B. Two-mass three DOF vehicle lateral/yaw/roll model.

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Book SynopsisThis mathematically rigorous guide to convex optimization for power systems engineering includes convex models for a variety of real-world applications, and a selection of problems and practical examples. An invaluable resource for students and researchers from industry and academia in power systems, optimization, and control.Table of Contents1. Introduction; 2. Background; 3. Optimal power flow; 4. System operation; 5. Infrastructure planning; 6. Economics; 7. Future directions.

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Book SynopsisDesign optimization is a standard concept in engineering design, and in other disciplines which utilize mathematical decision-making methods. This textbook focuses on the close relationship between a design problem's mathematical model and the solution-driven methods which optimize it. Along with extensive material on modeling problems, this book also features useful techniques for checking whether a model is suitable for computational treatment. Throughout, key concepts are discussed in the context of why and when a particular algorithm may be successful, and a large number of examples demonstrate the theory or method right after it is presented. This book also contains step-by-step instructions for executing a design optimization project - from building the problem statement to interpreting the computer results. All chapters contain exercises from which instructors can easily build quizzes, and a chapter on 'principles and practice' offers the reader tips and guidance based on the auTrade Review'Principles of Optimal Design, third edition, offers an excellent combination of depth and breadth of fundamentals of mathematical modeling of systems design. Students and practitioners will find the textbook a great starting point to learn about the systems design methods and optimization theories from the fundamentals to the advanced numerical methods. The recent addition of the decomposition-based optimization method and analytical target cascading is a nice expansion to the traditional optimization methods. I use this textbook to teach graduate and advanced undergraduate students who have basic understanding of numerical analysis. Students appreciate the spectrum of contents and they become ready to apply what they learn from the textbook to complex systems design cases. I highly recommend the textbook.' Harrison Hyung, University of Illinois, Urbana-Champaign'Principles of Optimal Design has always been a well-structured textbook that introduces students to the fundamentals of optimal design while remaining accessible and enjoyable to read. The latest edition adds many brief but exciting glimpses of more advanced topics in optimization. These additions have transformed the book from a 'foundation' on which students can firmly stand to a 'catapult' that can propel them to exciting, new, and advanced topics in the broad discipline of optimal design.' Hosam Fathy, Penn State College of Engineering'This third edition brings to the reader an impressive array of new and useful topics in optimal design. For example, and among others, new chapters on non-gradient based methods and decomposition-based optimization (or multi-disciplinary optimization, MDO) have been added. The book can be used both as a textbook for a graduate level course in all engineering fields, but also as a must have reference material. I highly recommend it!' Shapour Azarm, University of Maryland'The Principles of Optimal Design, third edition, is an excellent first text for undergraduates and graduate students alike interested in gaining a firm grasp of practical design optimization methods. It blends the latest modeling techniques with a rigorous treatment of the mathematical analysis, allowing one to adeptly navigate the varied landscapes of modern design problems. From machine learning, automotive systems, financial portfolios, to even the modeling of human purchasing behavior, I have used this text to teach my students how to systematically apply the design process to a broad range of engineering problems.' George J. Delagrammatikas, The Cooper Union for the Advancement of Science and Art, New York'This book, almost thirty years after its first edition, remains the only comprehensive text on engineering design optimization. In our 'one-click' software era, it provides theory fundamentals that tend to be neglected, while complementing them with rigorous modeling and computation techniques. I cannot think of a better textbook for engineering optimization courses, including a plethora of excellent examples and exercises. The third edition is enhanced with new and extremely useful material on recent developments in derivative-free optimization and optimal system design.' Michael Kokkolaras, McGill University, Canada'I've found Principles of Optimal Design to be an excellent, comprehensive explanation of design optimization methods, grounded in rigorous mathematics, yet still accessible. The addition of a gradient-free optimization chapter is a welcome addition to the book.' John Whitefoot, University of Pittsburgh'I've recommended this book to several students. It's a great resource for students who need to use optimization for practical purposes, such as a senior project or an assignment at their co-op job. The book has a good balance between the underlying theory and the application of that theory to actual problems.' Diane Peters, University of MichiganTable of ContentsPreface; Notation; 1. Optimization models; 2. Model construction; 3. Model boundedness; 4. Interior optima; 5. Boundary optima; 6. Local computation; 7. Nongradient search; 8. Systems design; 9. Principles and practice; Notes; References; Author index; Subject index.

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Book SynopsisIn this comprehensive textbook about robot grasping, readers will discover an integrated look at the major concepts and technical results in robot grasp mechanics. A large body of prior research, including key theories, graphical techniques, and insights on robot hand designs, is organized into a systematic review, using common notation and a common analytical framework. With introductory and advanced chapters that support senior undergraduate and graduate level robotics courses, this book provides a full introduction to robot grasping principles that are needed to model and analyze multi-finger robot grasps, and serves as a valuable reference for robotics students, researchers, and practicing robot engineers. Each chapter contains many worked-out examples, exercises with full solutions, and figures that highlight new concepts and help the reader master the use of the theories and equations presented.Trade Review'The Mechanics of Robot Grasping, by two of the world's leading experts, fills an important gap in the literature by providing the first comprehensive survey of the mathematical tools needed to model the physics of grasping. The book uses configuration space to consistently characterize equilibrium, immobilizing, and caging grasps, and clearly conveys important points such as the distinction between first-order and second-order form closure. The book also contains new material on the effects of gravity, compliance, and hand mechanism design. Grasping remains a Grand Challenge for robots and this book provides the solid foundation for progress for students and researchers in the years ahead.' Ken Goldberg, University of California, Berkeley'This is a book on robotic hand grasping from new view points. Different from other books on grasping, this book concretely explains the equilibrium grasp, the immobilizing grasp and the caging grasp. In addition, I have never seen a book discussing the equilibrium stance of legged robots in relation to the equilibrium grasp. Classical topics on grasping mechanics are also covered in this book.' Kensuke Harada, Osaka University, JapanTable of Contents1. Introduction and overview; Part I. Basic Geometry of the Grasping Process: 2. Rigid-body configuration space; 3. Configuration space tangent and cotangent vectors; 4. Rigid body equilibrium grasps; 5. A catalog of equilibrium grasps; Part II. Frictionless Rigid Body Grasps and Stances: 6. Introduction to secure grasps; 7. First-order immobilizing grasps; 8. Second-order immobilizing grasps; 9. Minimal immobilizing grasps; 10. Multi-finger caging grasps; 11. Frictionless hand supported stances under gravity; Part III. Frictional Rigid-Body Grasps, Fixtures, and Stances: 12. Wrench resistant grasps; 13. Grasp quality functions; 14. Hand supported stances under gravity – Part I; 15. Hand supported stances under gravity – Part II; Part IV. Grasping Mechanisms: 16. The kinematics and mechanics of grasping mechanisms; 17. Grasp manipulability; 18. Hand mechanism compliance; Appendices; Index.

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Book SynopsisThis book addresses the reader to use synergistically the concepts of membranes and sensors materials. It contains insightful contributions from leading scientists working in both the fields. The focus is on the fabrication of smart membranes from sensor materials and related impact on many technologically sophisticated areas such as telemedicine, microfluidics, drug delivery targeting, (bio)separation, labs-on-a-chip, textiles, power storage and release, environment monitoring, agro-food safety, cosmetics, architecture, automotive and so on. This book covers various topics, including the choice of materials and techniques for assembling responsive membranes with ability to transport mass, energy and signals on demand; the reader will find through the book an extensive description of the best techniques used to monitor molecular scale events, which are regarded as responsible for the smartness of multifunctional objects and for the conversion of chemical signals into optical,Table of ContentsPreface Part 1: Sensing Materials for Smart Membranes 1 1 Interfaces Based on Carbon Nanotubes, Ionic Liquids and Polymer Matrices for Sensing and Membrane Separation Applications 3 María Belén Serrano-Santos, Ana Corres Ortega, and Thomas Schäfer 1.1 Introduction 3 1.2 Ionic Liquid-Carbon Nanotubes Composites for Sensing Interfaces 5 1.3 Ionic Liquid Interfaces for Detection and Separation of Gases and Solvents 11 1.4 Ionic Liquid-Polymer Interfaces for Membrane Separation Processes 16 1.5 Conclusions 18 Acknowledgement 19 References 19 2 Photo-Responsive Hydrogels for Adaptive Membranes 21 David Díaz Díaz and Jeremiah A. Johnson 2.1 Introduction 21 2.2 Photo-Responsive Hydrogel Membranes 23 2.3 Photo-Thermally Responsive Hydrogel Membranes 44 2.4 Summary 46 2.5 Acknowledgements 48 Abbreviations 48 References 49 3 Smart Vesicles: Synthesis, Characterization and Applications 53 Jung-Keun Kim, Chang-Soo Lee, and Eunji Lee 3.1 Introduction 53 3.2 Synthesis of Soft Vesicles 54 3.3 Synthesis of Hard Vesicles 64 3.4 Characterization of Vesicular Structures 68 3.5 Stimuli-Responsive Behaviors of Vesicular Structures 72 3.6 Application of Vesicles 78 3.7 Conclusions 91 Acknowledgment 92 References 92 Part 2: Stimuli-Responsive Interfaces 105 4 Computational Modeling of Sensing Membranes and Supramolecular Interactions 107 Giacomo Saielli 4.1 Introduction 107 4.2 Non-covalent Interactions: A Physical and a Chemical View 109 4.3 Physical Interactions 109 4.4 Chemical Interactions 114 4.5 Computational Methods for Supramolecular Interactions 117 4.6 Classical Force Fields 127 4.7 Conclusions 139 References 140 5 Sensing Techniques Involving Thin Films for Studying Biomolecular Interactions and Membrane Fouling Phenomena 145 Gabriela Diaconu and Thomas Schäfer 5.1 Introduction 145 5.2 Quartz Crystal Microbalance with Dissipation Monitoring (QCM-D) 146 5.3 Surface Plasmon Resonance (SPR) 148 5.4 Applications of SPR and QCM-D 151 5.5 Conclusions 159 Acknowledgements 160 References 160 6 Smart Membrane Surfaces: Wettability Amplification and Self-Healing 161 Annarosa Gugliuzza 6.1 Introduction 161 6.2 Basics of surface wettability 162 6.3 Amplified Wettability 164 6.4 Actuation Mechanisms 165 6.5 Self-Powered Liquid Motion 170 6.6 Self-Cleaning Mechanisms 172 6.7 Self-Healing Concepts And Strategies 175 6.8 Repairable Surface Properties 177 6.9 Conclusions and Perspectives 179 References 180 7 Model Bio-Membranes Investigated by AFM and AFS: A Suitable Tool to Unravel Lipid Organization and their Interaction with Proteins 185 Andrea Alessandrini and Paolo Facci 7.1 Introduction 186 7.2 Supported Lipid Bilayers 189 7.3 Atomic Force Microscopy (AFM) and Phase Behavior of Slbs 199 7.4 Atomic Force Spectroscopy (AFS) of Supported Lipid Bilayers 205 7.5 Lipid/Protein Interactions 213 7.6 Conclusions 218 References 218 Part 3: Directed Molecular Separation 227 8 Self-Assembled Nanoporous Membranes for Controlled Drug Release and Bioseparation 229 Dominique Scalarone, Pierangiola Bracco, and Francesco Trotta 8.1 Introduction 229 8.2 General Aspects of Block Copolymer Self-Assembly 231 8.3 Block Copolymer Based Membranes 233 8.4 Fabrication of Nanoporous Membranes Derived from Block Copolymers 234 8.5 Tunability of Surface Properties 242 8.6 Application of Block Copolymer Derived Membranes to Bioseparation and Controlled Drug Release 244 8.7 Conclusion 250 References 250 Abbreviations 253 9 Hybrid Mesoporous Silica for Drug Targeting 255 Luigi Pasqua, Piluso Rosangela, Ilenia Pelaggi, and Catia Morelli 9.1 Introduction 256 9.2 Synthesis and Characterization of Bifunctional Hybrid Mesoporous Silica Nanoparticles Potentially Useful for Drug Targeting 257 9.3 Drug-Loaded Folic-Acid-Grafted Msns Specifically Target FR Expressing Tumour Cells [16] 260 9.4 Conclusion 266 References 268 10 Molecular Recognition-driven Membrane Processes 269 Laura Donato, Rosalinda Mazzei, Catia Algieri, Emma Piacentini, Teresa Poerio, and Lidietta Giorno 10.1 Molecular Imprinting Technique 270 10.2 Affinity Membranes 275 10.3 Cyclodextrins As Molecular Recognition Elements 281 10.4 Zeolite Membranes as Molecular Recognition Devices: Preparation and Characterization 283 10.5 Functionalized Particles-loaded Membranes For Selective Separation Based On Molecular Recognition 287 10.6 Biphasic Enzyme Membrane Systems with Enantioselective Recognition Properties ror Kinetic Resolution 291 10.7 Membrane Surface Modification 292 References 296 Part 4: Membrane Sensors and Challenged Applications 301 11 Electrospun Membranes for Sensors Applications 303 Pierangiola Bracco, Dominique Scalarone, and Francesco Trotta 11.1 Introduction 303 11.2 Basic Principles of Electrospinning 304 11.3 Control of the Electrospinning Process 306 11.4 Application of Electrospun Materials to Ultrasensitive Sensors 311 11.5 Conclusions 329 Abbreviations 330 References 330 12 Smart Sensing Scaffolds 337 Carmelo De Maria, Yudan Whulanza, Giovanni Vozzi, and Arti Ahluwalia 12.1 Introduction 337 12.2 Composite Sensing Biomaterial Preparation 339 12.3 Composite Sensing Biomaterial Characterisation 340 12.4 SWNTs-Based Composite Films Structural Properties 341 12.5 Tensile Properties of SWNTs-Based Composite Films 343 12.6 Electrical Properties of SWNTs-Based Composites Films 348 12.7 Electromechanical Characterisation and Strain-Dependence Measurement 350 12.8 Cell Sensing Scaffolds 352 12.9 Processing of CNT Composite: Microfabrication of Sensing Scaffold 360 12.10 Conclusions 361 References 362 13 Nanostructured Sensing Emulsion Droplets and Particles: Properties and Formulation by Membrane Emulsification 367 Emma Piacentini, Alessandra Imbrogno, and Lidietta Giorno 13.1 Introduction 367 13.2 Emulsions and Emulsification Methods 370 13.3 Senging Particles Produced by Membrane-Based Process 389 13.4 Conclusions 397 References 398 14 Membranes for Ultra-Smart Textiles 401 Annarosa Gugliuzza and Enrico Drioli 14.1 Introduction 401 14.2 Membranes and Comfort 403 14.3 Adaptive Membranes for Smart Textiles 407 14.4 Barrier Functions of Membranes 411 14.5 Membrane Materials for Self-cleaning Function 413 14.6 Interactive Membranes for Wearable Electronics 414 14.7 Conclusions and Prospects 415 References 416

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