Many professionals and students in engineering, science, business, and other application fields need to develop Windows-based and web-enabled information systems to store and use data for decision support, without help from professional programmers. However, few books are available to train professionals and students who are not professional programmers to develop these information systems. Developing Windows-Based and Web-Enabled Information Systems fills this gap, providing a self-contained, easy-to-understand, and well-illustrated text that explores current concepts, methods, and software tools for developing Windows-based and web-enabled information systems. Written in an easily accessible style, the book details current concepts, methods, and software tools for Windows-based and web-enabled information systems that store and use data. It is self-contained with easy-to-understand small examples to walk through concepts and implementation details along with large-scale case studies. The book describes data modeling methods including entity-relationship modeling, relational modeling and normalization, and object-oriented data modeling, to develop data models of a database. The author covers how to use software tools in the Microsoft application development environment, including Microsoft Access, MySQL, SQL, Visual Studio, Visual Basic, VBA, HTML, and XML, to implement databases and develop Windows-based and web-enabled applications with the database, graphical user interface, and program components. The book takes you through the entire process of developing a computer and network application for an information system, highlighting concepts and operation details. In each chapter, small data examples are used to manually walk through concepts and operational details. These features and more give you the conceptual understanding and practical skill required, even if you don't have a computer science background, to develop Windows-based or web-enabled applications for your specialized information system.

Highlights the contributions of different optimization techniques, decision analytics (predictive, prescriptive, and descriptive), multi-criteria decision making "Helps develop intelligent machines to provide solutions to real-world problems, which are not modelled or are too difficult to model mathematically in hospital management systems " Discusses machine learning-based analytics such as GAN networks, autoencoders, computational imaging, quantum computing will be rigorously applied to smart cloud computing Explores evolutionary algorithms that demonstrate their ability as robust approaches to cope with the fundamental steps of image processing, image analysis, and computer vision pipeline (e.g., restoration, segmentation, registration, classification, reconstruction, or tracking), Creates a bridge between Industrial Engineering concepts and Computational Intelligence for designing complex and convoluted hospital management problems

This book focuses on multi-model systems, describing how to apply intelligent technologies to model complex multi-model systems by combining stochastic jumping system, neural network and fuzzy models. It focuses on robust filtering, including finite-time robust filtering, finite-frequency robust filtering and higher order moment robust filtering schemes, as well as fault detection problems for multi-model jump systems, such as observer-based robust fault detection, filtering-based robust fault detection and neural network-based robust fault detection methods. The book also demonstrates the validity and practicability of the theoretical results using simulation and practical examples, like circuit systems, robot systems and power systems. Further, it introduces readers to methods such as finite-time filtering, finite-frequency robust filtering, as well as higher order moment and neural network-based fault detection methods for multi-model jumping systems, allowing them to grasp the modeling, analysis and design of the multi-model systems presented and implement filtering and fault detection analysis for various systems, including circuit, network and mechanical systems.

This book addresses privacy in dynamical systems, with applications to smart metering, traffic estimation, and building management. In the first part, the book explores statistical methods for privacy preservation from the areas of differential privacy and information-theoretic privacy (e.g., using privacy metrics motivated by mutual information, relative entropy, and Fisher information) with provable guarantees. In the second part, it investigates the use of homomorphic encryption for the implementation of control laws over encrypted numbers to support the development of fully secure remote estimation and control. Chiefly intended for graduate students and researchers, the book provides an essential overview of the latest developments in privacy-aware design for dynamical systems.

The book is a multidisciplinary space and serves as a platform to share and learn about the frontier knowledge between different areas related to "Recent trends in sustainable engineering." Sustainable engineering promotes the responsible use of resources and materials involved in the different manufacturing processes or the execution stages of a service. An interdisciplinary approach is required in all aspects of engineering. In this sense, engineers, researchers, and the academic community will play a fundamental role in developing new technologies that respect the environment, still, at the same time, that considers social and economic factors.

This book develops and substantiates methods for structural mathematical modeling in the context of protecting machines and equipment from vibration effects. It analyzes problems concerning the dynamic interactions of elements in mechanical oscillatory systems, constructing suitable mathematical models, estimating their dynamic properties, and adapting structural mathematical models to the equivalent forms. In turn, it develops a methodological basis for identifying the lever linkages and taking into account the peculiarities of their influence on the dynamic properties of systems. Given its scope, the book offers a valuable resource for specialists in the fields of dynamics and strength of machines, vibration protection systems for equipment, and maintenance of the dynamic quality of vibrating machines, as well as students in related degree programs.

The goal of this book is to familiarize readers with the latest research on, and recent advances in, the field of Informatics in Control, Automation and Robotics. It gathers a selection of papers highlighting the state-of-the-art in Intelligent Control Systems, Optimization, Robotics and Automation, Signal Processing, Sensors, Systems Modelling and Control. Combining theoretical aspects with practical applications, the book offers a well-balanced overview of the latest achievements, and will provide researchers, engineers and PhD students with both a vital update and new inspirations for their own research.

This book highlights the latest innovations and applications in robotics, as presented by leading international researchers and engineers at the ROMANSY 2020, the 23rd CISM IFToMM Symposium on Theory and Practice of Robots and Manipulators. The ROMANSY symposium is the first established conference that focuses on robotics theory and research, rather than industrial aspects. Bringing together researchers from a broad range of countries, the symposium is held bi-annually and plays a vital role in the development of the theory and practice of robotics, as well as the mechanical sciences. ROMANSY 2020 marks the 23rd installment in a series that began in 1973. The event was also the first topic-specific conference of the IFToMM, though not exclusively intended for the IFToMM community.

This book describes an original approach to solving tasks of individual and collective choice: classification, ranking, and selection of multi-attribute objects. Object representation with multisets allows considering simultaneously numerical and symbolic variables. In group verbal decision analysis, judgments of all participants are taken into account without a compromise between contradictory. Natural language is used to describe problems and objects, formalize knowledge of experts and preferences of decision makers, and explain results. Verbal methods and technologies are more transparent, less laborious for a person, and weakly sensitive to measurement errors. The book also includes examples of applying new tools in real ill-structured high-dimensional choice tasks. It is intended for researchers, managers, consultants, analysts, and developers as well as for teachers and students of applied mathematics, computer science, information processing, engineering, economics, and management.

Iterative Learning Control (ILC) differs from most existing control methods in the sense that, it exploits every possibility to incorporate past control informa tion, such as tracking errors and control input signals, into the construction of the present control action. There are two phases in Iterative Learning Control: first the long term memory components are used to store past control infor mation, then the stored control information is fused in a certain manner so as to ensure that the system meets control specifications such as convergence, robustness, etc. It is worth pointing out that, those control specifications may not be easily satisfied by other control methods as they require more prior knowledge of the process in the stage of the controller design. ILC requires much less information of the system variations to yield the desired dynamic be haviors. Due to its simplicity and effectiveness, ILC has received considerable attention and applications in many areas for the past one and half decades. Most contributions have been focused on developing new ILC algorithms with property analysis. Since 1992, the research in ILC has progressed by leaps and bounds. On one hand, substantial work has been conducted and reported in the core area of developing and analyzing new ILC algorithms. On the other hand, researchers have realized that integration of ILC with other control techniques may give rise to better controllers that exhibit desired performance which is impossible by any individual approach."

Discover New Methods for Dealing with High-Dimensional Data A sparse statistical model has only a small number of nonzero parameters or weights; therefore, it is much easier to estimate and interpret than a dense model. Statistical Learning with Sparsity: The Lasso and Generalizations presents methods that exploit sparsity to help recover the underlying signal in a set of data. Top experts in this rapidly evolving field, the authors describe the lasso for linear regression and a simple coordinate descent algorithm for its computation. They discuss the application of 1 penalties to generalized linear models and support vector machines, cover generalized penalties such as the elastic net and group lasso, and review numerical methods for optimization. They also present statistical inference methods for fitted (lasso) models, including the bootstrap, Bayesian methods, and recently developed approaches. In addition, the book examines matrix decomposition, sparse multivariate analysis, graphical models, and compressed sensing. It concludes with a survey of theoretical results for the lasso. In this age of big data, the number of features measured on a person or object can be large and might be larger than the number of observations. This book shows how the sparsity assumption allows us to tackle these problems and extract useful and reproducible patterns from big datasets. Data analysts, computer scientists, and theorists will appreciate this thorough and up-to-date treatment of sparse statistical modeling.

This book presents the latest achievements of Russian scientists in the field of theory and practice of decision-making in SEMS, taking into account the information received from the sensors of its central nervous system (CNS). Recently, in the field of theory and practice of intelligent robotics systems management, the solution to the problem of SEMS type urgent task of making decisions about their expedient behavior is based on the integration of the processes of obtaining, processing and storing information, computing, control and monitoring. This enables the efficiency, reliability and safety of operation of SEMS in real time. Decision-making methods are described, both in the autonomous behavior of SEMS and in their group interaction, based on the principles of bionics, adaptability, intelligence and parallelism in information processing and computation. This book is intended for students, scientists and engineers specializing in the field of smart electromechanical systems and robotics.

This book presents the development of SLAM-based mobile robot control systems as an integrated approach that combines the localization, mapping and motion control fields, and reviews several techniques that represent the basics of the mathematical description of wheeled robots, their navigation and path planning approaches, localization and map creating techniques. It examines SLAM paradigms and Bayesian recursive state and map estimation techniques, which include Kalman and particle filtering, and enable the development of a SLAM-based integrated system for the inspection task performed. The system's development is divided into two phases: a single-robot approach and multirobot inspection system. The book describes an original approach to 2D SLAM in multi-floor buildings that covers each 2D level map, as well as continuous 3D pose tracking, and views the multirobot inspection system as a group of homogeneous mobile robots. The last part of the book is dedicated to multirobot map creation and the development of path planning solutions, which allow the robots' homogeneous behavior and configuration to be used to develop a multirobot system without theoretical limitations on the number of robots used.

In this concise yet comprehensive Open Access textbook, future inventors are introduced to the key concepts of Cyber-Physical Systems (CPS). Using modeling as a way to develop deeper understanding of the computational and physical components of these systems, one can express new designs in a way that facilitates their simulation, visualization, and analysis. Concepts are introduced in a cross-disciplinary way. Leveraging hybrid (continuous/discrete) systems as a unifying framework and Acumen as a modeling environment, the book bridges the conceptual gap in modeling skills needed for physical systems on the one hand and computational systems on the other. In doing so, the book gives the reader the modeling and design skills they need to build smart, IT-enabled products. Starting with a look at various examples and characteristics of Cyber-Physical Systems, the book progresses to explain how the area brings together several previously distinct ones such as Embedded Systems, Control Theory, and Mechatronics. Featuring a simulation-based project that focuses on a robotics problem (how to design a robot that can play ping-pong) as a useful example of a CPS domain, Cyber-Physical Systems: A Model-Based Approach demonstrates the intimate coupling between cyber and physical components, and how designing robots reveals several non-trivial control problems, significant embedded and real-time computation requirements, and a need to consider issues of communication and preconceptions.

Nonlinear Control Systems and Power System Dynamics presents a comprehensive description of nonlinear control of electric power systems using nonlinear control theory, which is developed by the differential geometric approach and nonlinear robust control method. This book explains in detail the concepts, theorems and algorithms in nonlinear control theory, illustrated by step-by-step examples. In addition, all the mathematical formulation involved in deriving the nonlinear control laws of power systems are sufficiently presented. Considerations and cautions involved in applying nonlinear control theory to practical engineering control designs are discussed and special attention is given to the implementation of nonlinear control laws using microprocessors. Nonlinear Control Systems and Power System Dynamics serves as a text for advanced level courses and is an excellent reference for engineers and researchers who are interested in the application of modern nonlinear control theory to practical engineering control designs.

There has been a great deal of excitement over the last few years concerning the emergence of new mathematical techniques for the analysis and control of nonlinear systems: witness the emergence of a set of simplified tools for the analysis of bifurcations, chaos and other simplified tools for the analysis of bifurcations, chaos and other complicated dynamical behaviour and the development of a comprehensive theory of nonlinear control. Coupled with this set of analytic advances has been the vast increase in computational power available both for the simulation of nonlinear systems as well as for the implementation in real time of sophisticated, real-time nonlinear control laws. Thus, technological advances have bolstered the impact of analytic advances and produced a tremendous variety of new problems and applications which are nonlinear in an essential way. This book lays out in a concise mathematical framework the tools and methods of analysis which underlie this diversity of applications. The material presented in this book is culled from different 1st year graduate courses that the author has taught at MIT and at Berkeley.

A Tour of Data Science: Learn R and Python in Parallel covers the fundamentals of data science, including programming, statistics, optimization, and machine learning in a single short book. It does not cover everything, but rather, teaches the key concepts and topics in Data Science. It also covers two of the most popular programming languages used in Data Science, R and Python, in one source. Key features: Allows you to learn R and Python in parallel Cover statistics, programming, optimization and predictive modelling, and the popular data manipulation tools - data.table and pandas Provides a concise and accessible presentation Includes machine learning algorithms implemented from scratch, linear regression, lasso, ridge, logistic regression, gradient boosting trees, etc. Appealing to data scientists, statisticians, quantitative analysts, and others who want to learn programming with R and Python from a data science perspective.

The main objectives of the book are to introduce the design method of boundary control strategies for the axially moving structures to reduce their vibration. This book provides the reader with a thorough grounding in the boundary controller design. Our goal is to provide advanced boundary controller design methods and their stability analysis methods and offer simulation examples and MATLAB programs for each boundary control algorithm. For each chapter, several engineering application examples are given and the contents of each chapter in this book are independent, so that readers can just read their own needs. In this book, all the control algorithms and their programs are described separately and classified by the chapter name, which can be run successfully in MATLAB. The book can benefit researchers, engineers, and graduate students in the fields of PDE modeling and boundary vibration control of flexible structures.

Introduces different optimization algorithms together to solve complex combinatorial optimization problems related to hospital management system or healthcare Applies machine learning-based analytics such as GAN networks, autoencoders, computational imaging, and quantum computing to authentic hospital management problems Discusses metaheuristic algorithms such as evolutionary algorithms to cope with the fundamental steps of image processing, image analysis, and computer vision pipeline (e.g., restoration, segmentation, registration, classification, reconstruction, or tracking) Creates a bridge between Computational Intelligence and Industrial Engineering towards designing complex and convoluted hospital management problems

This book describes new theories and applications of artificial neural networks, with a special focus on answering questions in neuroscience, biology and biophysics and cognitive research. It covers a wide range of methods and technologies, including deep neural networks, large scale neural models, brain computer interface, signal processing methods, as well as models of perception, studies on emotion recognition, self-organization and many more. The book includes both selected and invited papers presented at the XXII International Conference on Neuroinformatics, held on October 12-16, 2020, Moscow, Russia.

This book presents iterative learning control (ILC) to address practical issues of flexible structures. It is divided into four parts: Part I provides a general introduction to ILC and flexible structures, while Part II proposes various types of ILC for simple flexible structures to address issues such as vibration, input saturation, input dead-zone, input backlash, external disturbances, and trajectory tracking. It also includes simple partial differential equations to deal with the common problems of flexible structures. Part III discusses the design of ILC for flexible micro aerial vehicles and two-link manipulators, and lastly, Part IV offers a summary of the topics covered. Unlike most of the literature on ILC, which focuses on ordinary differential equation systems, this book explores distributed parameter systems, which are comparatively less stabilized through ILC.Including a comprehensive introduction to ILC of flexible structures, it also examines novel approaches used in ILC to address input constraints and disturbance rejection. This book is intended for researchers, graduate students and engineers in various fields, such as flexible structures, external disturbances, nonlinear inputs and tracking control.

First-hand technical expertise – torch has been developed in/by the author’s team at RStudio. Presupposes only a minimal background in mathematics. Presupposes no background in optimization and/or deep learning (but some general ideas about machine learning). Entertaining and fun to read (at least that’s the intention!). Concept-first approach

Despite major advances in healthcare over the past century, the successful treatment of cancer has remained a significant challenge, and cancers are the second leading cause of death worldwide behind cardiovascular disease. Early detection and survival are important issues to control cancer. The development of quantitative methods and computer technology has facilitated the formation of new models in medical and biological sciences. The application of mathematical modelling in solving many real-world problems in medicine and biology has yielded fruitful results. In spite of advancements in instrumentations technology and biomedical equipment, it is not always possible to perform experiments in medicine and biology for various reasons. Thus, mathematical modelling and simulation are viewed as viable alternatives in such situations, and are discussed in this book. The conventional diagnostic techniques of cancer are not always effective as they rely on the physical and morphological appearance of the tumour. Early stage prediction and diagnosis is very difficult with conventional techniques. It is well known that cancers are involved in genome level changes. As of now, the prognosis of various types of cancer depends upon findings related to the data generated through different experiments. Several machine learning techniques exist in analysing the data of expressed genes; however, the recent results related with deep learning algorithms are more accurate and accommodative, as they are effective in selecting and classifying informative genes. This book explores the probabilistic computational deep learning model for cancer classification and prediction.

This book presents the interdisciplinary and international "Virtual and Remote Tower" research and development work. It has been carried out since nearly twenty years with the goal of replacing the conventional aerodrome control tower by a new "Remote Tower Operation" (RTO) work environment for enhancing work efficiency and safety and reducing cost. The revolutionary human-system interface replaces the out-of-windows view by an augmented vision video panorama that allows for remote aerodrome traffic control without a physical tower building. It enables the establishment of a (multiple) remote control center (MRTO, RTC) that may serve several airports from a central location. The first (2016) edition of this book covered all aspects from preconditions over basic research and prototype development to initial validation experiments with field testing. Co-edited and -authored by DLR RTO-team members Dr. Anne Papenfuss and Joern Jakobi, this second extended edition with nearly doubled number of chapters includes further important aspects of the international follow-up work towards the RTO-deployment. Focus of the extension with new contributions from ENRI/Japan and IAA/Dublin with Cranfield University, is on MRTO, workload, implementation, and standardization. Specifically, the two revised and nine new Chapters put the focus on inclusion of augmented vision and virtual reality technologies, human-in-the-loop simulation for quantifying workload and deriving minimum (technical) requirements according to standards of the European Organization for Civil Aviation Equipment (EUROCAE), and MRTO implementation and certification. Basics of optical / video design, workload measures, and advanced psychophysical data analysis are presented in four appendices.

This edited book covers recent advances of techniques, methods and tools treating the problem of learning from data streams generated by evolving non-stationary processes. The goal is to discuss and overview the advanced techniques, methods and tools that are dedicated to manage, exploit and interpret data streams in non-stationary environments. The book includes the required notions, definitions, and background to understand the problem of learning from data streams in non-stationary environments and synthesizes the state-of-the-art in the domain, discussing advanced aspects and concepts and presenting open problems and future challenges in this field. Provides multiple examples to facilitate the understanding data streams in non-stationary environments; Presents several application cases to show how the methods solve different real world problems; Discusses the links between methods to help stimulate new research and application directions.