This book provides a general and comprehensible overview of imbalanced learning. It contains a formal description of a problem, and focuses on its main features, and the most relevant proposed solutions. Additionally, it considers the different scenarios in Data Science for which the imbalanced classification can create a real challenge. This book stresses the gap with standard classification tasks by reviewing the case studies and ad-hoc performance metrics that are applied in this area. It also covers the different approaches that have been traditionally applied to address the binary skewed class distribution. Specifically, it reviews cost-sensitive learning, data-level preprocessing methods and algorithm-level solutions, taking also into account those ensemble-learning solutions that embed any of the former alternatives. Furthermore, it focuses on the extension of the problem for multi-class problems, where the former classical methods are no longer to be applied in a straightforward way. This book also focuses on the data intrinsic characteristics that are the main causes which, added to the uneven class distribution, truly hinders the performance of classification algorithms in this scenario. Then, some notes on data reduction are provided in order to understand the advantages related to the use of this type of approaches. Finally this book introduces some novel areas of study that are gathering a deeper attention on the imbalanced data issue. Specifically, it considers the classification of data streams, non-classical classification problems, and the scalability related to Big Data. Examples of software libraries and modules to address imbalanced classification are provided. This book is highly suitable for technical professionals, senior undergraduate and graduate students in the areas of data science, computer science and engineering. It will also be useful for scientists and researchers to gain insight on the current developments in this area of study, as well as future research directions.

In almost all depictions of the future, one technology plays a prominent role - the robot. Science fiction movies and stories tell us that, in the not so distant future, robots - from Robbie the Robot to C3PO to the Terminator - will be everywhere, taking care of every human need. Those are projections of the future. But robots have a history as well, and The Robot: A Life Story of a Technology tells their story. During the Englightenment, "automata" were mechnical toys that could simulate human actions. Robots became essential parts of factories, performing tasks perfectly that would be either too dangerous or boring for humans. And recent advances in artificial intelligence have made robots that can behave with intelligence. The Robot covers all aspects of the history of robotics: BLThe mechanical toys in the Enlightenment, from mechanical timepieces and early computational devices, to music boxes and automated human-like figures designed to amuse royalty. BLThe use of automation in the Industrial Revolution, such as the Jacquard punch card loom and the famous "difference engine" of Charles Babbage and Ada Lovelace, and the use of robotic arms in automation. How by the mid-20th century robots became the icon for the future, and became an important feature of the popular culture. BLCurrent trends in robotics, such as research into artificial intelligence and the creation of "friendly robots" that could make an impact in the marketplace. The volume includes a timeline of important events, a glossary of terms, and a bibliography of important primary and secondary sources that are useful for further research.

An increasing complexity of models used to predict real-world systems leads to the need for algorithms to replace complex models with far simpler ones, while preserving the accuracy of the predictions. This two-volume handbook covers methods as well as applications. This first volume focuses on real-time control theory, data assimilation, real-time visualization, high-dimensional state spaces and interaction of different reduction techniques.

This book examines some of the underlying processes behind different forms of information management, including how we store information in our brains, the impact of new technologies such as computers and robots on our efficiency in storing information, and how information is stored in families and in society. The editors brought together experts from a variety of disciplines. While it is generally agreed that information reduces uncertainties and that the ability to store it safely is of vital importance, these authors are open to different meanings of "information": computer science considers the bit as the information block; neuroscience emphasizes the importance of information as sensory inputs that are processed and transformed in the brain; theories in psychology focus more on individual learning and on the acquisition of knowledge; and finally sociology looks at how interpersonal processes within groups or society itself come to the fore. The book will be of value to researchers and students in the areas of information theory, artificial intelligence, and computational neuroscience.

This book focuses on small flying drones and their applications in conducting geographic surveys. Scholars and professionals will discover the potential of this tool, and hopefully develop a conceptual and methodological framework for doing the following things: a) Translate their data acquisition needs into specifications. (b) Use the developed specifications to choose the best accessible configuration for their drones, and (c) Design and organize effective and low-cost field deployment and flight operations by integrating technical aspects with regulatory and research requirements. Readers can apply this knowledge to work in cartography, environmental monitoring and analysis, land-use studies and landscape archaeology. Particular attention is also given to the reasons why a drone can dramatically boost a geographer's capability to understand geographic phenomena both from hard-science and humanities-oriented approach.

Mechanical laws of motion were applied very early for better understanding anthropomorphic action as suggested in advance by Newton "For from hence are easily deduced the forces of machines, which are compounded of wheels, pullies, levers, cords, and weights, ascending directly or obliquely, and other mechanical powers; as also the force of the tendons to move the bones of animals". In the 19th century E.J. Marey and E. Muybridge introduced chronophotography to scientifically investigate animal and human movements. They opened the field of motion analysis by being the first scientists to correlate ground reaction forces with kinetics. Despite of the apparent simplicity of a given skilled movement, the organization of the underlying neuro-musculo-skeletal system remains unknown. A reason is the redundancy of the motor system: a given action can be realized by different muscle and joint activity patterns, and the same underlying activity may give rise to several movements. After the pioneering work of N. Bernstein in the 60's on the existence of motor synergies, numerous researchers "walking on the border" of their disciplines tend to discover laws and principles underlying the human motions and how the brain reduces the redundancy of the system. These synergies represent the fundamental building blocks composing complex movements. In robotics, researchers face the same redundancy and complexity challenges as the researchers in life sciences. This book gathers works of roboticists and researchers in biomechanics in order to promote an interdisciplinary research on anthropomorphic systems at large and on humanoid robotics in particular.

This monograph investigates the existence of higher order sliding mode in discrete-time systems and propounds a new concept of discrete-time higher order sliding mode. The authors propose a definition of discrete-time higher order sliding mode and a control law is designed by means of a concept for an uncertain linear-time invariant system, as well as the behavior of the closed-loop system is analyzed. Moreover, the book includes a thorough treatment of the probabilistic and non-deterministic case, i.e. stochastic discrete-time higher order sliding mode. The target audience primarily comprises research experts in control theory but the book may also be beneficial for graduate students alike.

The integration of electronic engineering, mechanical engineering, control and computer engineering - Mechatronics - lies at the heart of the innumerable gadgets, processes and technology without which modern life would seem impossible. From auto-focus cameras to car engine management systems, and from state-of-the-art robots to the humble washing machine, Mechatronics has a hand in them all.

This book describes a unified framework for networked teleoperation systems involving multiple research fields: networked control systems for linear and nonlinear forms, bilateral teleoperation, trilateral teleoperation, multilateral teleoperation and cooperative teleoperation. It closely examines networked control as a field at the intersection of systems & control and robotics and presents a number of experimental case studies on testbeds for robotic systems, including networked haptic devices, robotic network systems and sensor network systems. The concepts and results outlined are easy to understand, even for readers fairly new to the subject. As such, the book offers a valuable reference work for researchers and engineers in the fields of systems & control and robotics.

This book focuses on the latest applications of nonlinear approaches in engineering and addresses a range of scientific problems. Examples focus on issues in automotive technology, including automotive dynamics, control for electric and hybrid vehicles, and autodriver algorithm for autonomous vehicles. Also included are discussions on renewable energy plants, data modeling, driver-aid methods, and low-frequency vibration. Chapters are based on invited contributions from world-class experts who advance the future of engineering by discussing the development of more optimal, accurate, efficient, cost, and energy effective systems. This book is appropriate for researchers, students, and practising engineers who are interested in the applications of nonlinear approaches to solving engineering and science problems. Presents a broad range of practical topics and approaches; Explains approaches to better, safer, and cheaper systems; Emphasises automotive applications, physical meaning, and methodologies.

Optimization of Manufacturing Systems Using the Internet of Things extends the IoT (Internet of Things) into the manufacturing field to develop an IoMT (Internet of Manufacturing Things) architecture with real-time traceability, visibility, and interoperability in production planning, execution, and control. This book is essential reading for anyone interested in the optimization and control of an intelligent manufacturing system. As modern manufacturing shop-floors can create bottlenecks in the capturing and collection of real-time field information, and because paper-based manual systems are time-consuming and prone to errors, this book helps readers understand how to alleviate these issues, assisting them in their decision-making on shop-floors.

This volume comprises the latest developments in both fundamental science and patient-specific applications, discussing topics such as: cellular mechanics; injury biomechanics; biomechanics of heart and vascular system; medical image analysis; and both patient-specific fluid dynamics and solid mechanics simulations. With contributions from researchers world-wide, the Computational Biomechanics for Medicine series of titles provides an opportunity for specialists in computational biomechanics to present their latest methodologies and advancements.

A restricted source of power supply is analysed in this book - namely the switching mode power supplies (SMPS), which utilise the Pulse Width Modulation (PWM) technique to operate. Even though restricted, such a class of power supply is vital to circuits as most of modern electronic equipment is dependant on this form of technology to feed electronic boards. Its main advantages are greater efficiency, and its minimum physical size and weight.

Interdisciplinary knowledge such as circuit theory, semiconductor devices theory and technology, magnetics theory and technology, linear and nonlinear control theory is involved in the design of an SMPS.

This volume provides designers with an in-depth overview on the latest interdisciplinary methods involved in the design of switching mode power supplies, providing the reader with a tool whereby the learning time can be reduced and information about switching mode power supplies design processes can be easily accessed. The book adopts a systemic approach, subdividing the information into functional blocks whose properties and relationships are evaluated and compared with each other. A comprehensive list of references to the literature is given thus enabling the reader to follow up on more detail.

This book develops a set of reference methods capable of modeling uncertainties existing in membership functions, and analyzing and synthesizing the interval type-2 fuzzy systems with desired performances. It also provides numerous simulation results for various examples, which fill certain gaps in this area of research and may serve as benchmark solutions for the readers. Interval type-2 T-S fuzzy models provide a convenient and flexible method for analysis and synthesis of complex nonlinear systems with uncertainties.

This book presents an Introduction and 11 independent chapters, which are devoted to various new approaches of intelligent image processing and analysis. The book also presents new methods, algorithms and applied systems for intelligent image processing, on the following basic topics: Methods for Hierarchical Image Decomposition; Intelligent Digital Signal Processing and Feature Extraction; Data Clustering and Visualization via Echo State Networks; Clustering of Natural Images in Automatic Image Annotation Systems; Control System for Remote Sensing Image Processing; Tissue Segmentation of MR Brain Images Sequence; Kidney Cysts Segmentation in CT Images; Audio Visual Attention Models in Mobile Robots Navigation; Local Adaptive Image Processing; Learning Techniques for Intelligent Access Control; Resolution Improvement in Acoustic Maps. Each chapter is self-contained with its own references. Some of the chapters are devoted to the theoretical aspects while the others are presenting the practical aspects and the analysis of the modeling of the developed algorithms in different application areas.

This book contributes to making urban rail transport fast, punctual and energy-efficient -significant factors in the importance of public transportation systems to economic, environmental and social requirements at both municipal and national levels. It proposes new methods for shortening passenger travel times and for reducing energy consumption, addressing two major topics: (1) train trajectory planning: the authors derive a nonlinear model for the operation of trains and present several approaches for calculating optimal and energy-efficient trajectories within a given schedule; and (2) train scheduling: the authors develop a train scheduling model for urban rail systems and optimization approaches with which to balance total passenger travel time with energy efficiency and other costs to the operator. Mixed-integer linear programming and pseudospectral methods are among the new methods proposed for single- and multi-train systems for the solution of the nonlinear trajectory planning problem which involves constraints such as varying speed restrictions and maximum traction/braking force. Signaling systems and their effects are also accounted for in the trajectory planning model.Origin-destination passenger demand is included in the model formulation for train scheduling. Iterative convex programming and efficient bi-level approaches are utilized in the solution of the train-scheduling problem. In addition, the splitting rates and route choices of passengers are also optimized from the system point of view. The problems and solutions described in Optimal Trajectory Planning and Train Scheduling for Urban Rail Transit Systems will interest researchers studying public transport systems and logistics whether from an academic or practitioner background as well as providing a real application for anybody studying optimization theory and predictive control.

This book compiles some of the latest research in cooperation between robots and sensor networks. Structured in twelve chapters, this book addresses fundamental, theoretical, implementation and experimentation issues. The chapters are organized into four parts namely multi-robots systems, data fusion and localization, security and dependability, and mobility.

In industrial engineering and manufacturing, control of individual processes and systems is crucial to developing a quality final product. Rapid developments in technology are pioneering new techniques of research in control and automation with multi-disciplinary applications in electrical, electronic, chemical, mechanical, aerospace, and instrumentation engineering. The Handbook of Research on Advanced Intelligent Control Engineering and Automation presents the latest research into intelligent control technologies with the goal of advancing knowledge and applications in various domains. This text will serve as a reference book for scientists, engineers, and researchers, as it features many applications of new computational and mathematical tools for solving complicated problems of mathematical modeling, simulation, and control.

This book explains the fundamentals of control theory for Internet of Things (IoT) systems and smart grids and its applications. It discusses the challenges imposed by large-scale systems, and describes the current and future trends and challenges in decision-making for IoT in detail, showing the ongoing industrial and academic research in the field of smart grid domain applications. It presents step-by-step design guidelines for the modeling, design, customisation and calibration of IoT systems applied to smart grids, in which the challenges increase with each system's increasing complexity. It also provides solutions and detailed examples to demonstrate how to use the techniques to overcome these challenges, as well as other problems related to decision-making for successful implementation. Further, it anaylses the features of decision-making, such as low-complexity and fault-tolerance, and uses open-source and publicly available software tools to show readers how they can design, implement and customise their own system control instantiations. This book is a valuable resource for power engineers and researchers, as it addresses the analysis and design of flexible decision-making mechanisms for smart grids. It is also of interest to students on courses related to control of large-scale systems, since it covers the use of state-of-the-art technology with examples and solutions in every chapter. And last but not least, it offers practical advice for professionals working with smart grids.

This book focuses on the characteristics of cooperative control problems for general linear multi-agent systems, including formation control, air traffic control, rendezvous, foraging, role assignment, and cooperative search. On this basis and combined with linear system theory, it introduces readers to the cooperative tracking problem for identical continuous-time multi-agent systems under state-coupled dynamics; the cooperative output regulation for heterogeneous multi-agent systems; and the optimal output regulation for model-free multi-agent systems. In closing, the results are extended to multiple leaders, and cooperative containment control for uncertain multi-agent systems is addressed. Given its scope, the book offers an essential reference guide for researchers and designers of multi-agent systems, as well as a valuable resource for upper-level undergraduate and graduate students.

This book explores novel aspects of social robotics, spoken dialogue systems, human-robot interaction, spoken language understanding, multimodal communication, and system evaluation. It offers a variety of perspectives on and solutions to the most important questions about advanced techniques for social robots and chat systems. Chapters by leading researchers address key research and development topics in the field of spoken dialogue systems, focusing in particular on three special themes: dialogue state tracking, evaluation of human-robot dialogue in social robotics, and socio-cognitive language processing. The book offers a valuable resource for researchers and practitioners in both academia and industry whose work involves advanced interaction technology and who are seeking an up-to-date overview of the key topics. It also provides supplementary educational material for courses on state-of-the-art dialogue system technologies, social robotics, and related research fields.

This monograph introduces novel responses to the different problems that arise when multiple robots need to execute a task in cooperation, each robot in the team having a monocular camera as its primary input sensor. Its central proposition is that a consistent perception of the world is crucial for the good development of any multi-robot application. The text focuses on the high-level problem of cooperative perception by a multi-robot system: the idea that, depending on what each robot sees and its current situation, it will need to communicate these things to its fellows whenever possible to share what it has found and keep updated by them in its turn. However, in any realistic scenario, distributed solutions to this problem are not trivial and need to be addressed from as many angles as possible. Distributed Consensus with Visual Perception in Multi-Robot Systems covers a variety of related topics such as: * distributed consensus algorithms; * data association and robustness problems; * convergence speed; and * cooperative mapping. The book first puts forward algorithmic solutions to these problems and then supports them with empirical validations working with real images. It provides the reader with a deeper understanding of the problems associated to the perception of the world by a team of cooperating robots with onboard cameras. Academic researchers and graduate students working with multi-robot systems, or investigating problems of distributed control or computer vision and cooperative perception will find this book of material assistance with their studies.