This book gives senior undergraduate and beginning graduate students and researchers in computer vision, applied mathematics, computer graphics, and robotics a self-contained introduction to the geometry of 3D vision; that is the reconstruction of 3D models of objects from a collection of 2D images. Following a brief introduction, Part I provides background materials for the rest of the book. The two fundamental transformations, namely rigid body motion and perspective projection are introduced and image formation and feature extraction discussed. Part II covers the classic theory of two view geometry based on the so-called epipolar constraint. Part III shows that a more proper tool for studying the geometry of multiple views is the so- called rank considtion on the multiple view matrix. Part IV develops practical reconstruction algorithms step by step as well as discusses possible extensions of the theory. Exercises are provided at the end of each chapter. Software for examples and algorithms are available on the author's website.

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 Mathematical Introduction to Robotic Manipulation presents a mathematical formulation of the kinematics, dynamics, and control of robot manipulators. It uses an elegant set of mathematical tools that emphasizes the geometry of robot motion and allows a large class of robotic manipulation problems to be analyzed within a unified framework. The foundation of the book is a derivation of robot kinematics using the product of the exponentials formula. The authors explore the kinematics of open-chain manipulators and multifingered robot hands, present an analysis of the dynamics and control of robot systems, discuss the specification and control of internal forces and internal motions, and address the implications of the nonholonomic nature of rolling contact are addressed, as well. The wealth of information, numerous examples, and exercises make A Mathematical Introduction to Robotic Manipulation valuable as both a reference for robotics researchers and a text for students in advanced robotics courses.

The emergence of fuzzy logic and its applications has dramatically changed the face of industrial control engineering. Over the last two decades, fuzzy logic has allowed control engineers to meet and overcome the challenges of developing effective controllers for increasingly complex systems with poorly defined dynamics. Today's engineers need a working knowledge of the principles and techniques of fuzzy logic-Intelligent Control provides it.

The author first introduces the traditional control techniques and contrasts them with intelligent control. He then presents several methods of representing and processing knowledge and introduces fuzzy logic as one such method. He highlights the advantages of fuzzy logic over other techniques, indicates its limitations, and describes in detail a hierarchical control structure appropriate for use in intelligent control systems. He introduces a variety of applications, most in the areas of robotics and mechatronics but with others including air conditioning and process/production control. One appendix provides discussion of some advanced analytical concepts of fuzzy logic, another describes a commercially available software system for developing fuzzy logic application.

Intelligent Control is filled with worked examples, exercises, problems, and references. No prior knowledge of the subject nor advanced mathematics are needed to comprehend much of the book, making it well-suited as a senior undergraduate or first-year graduate text and a convenient reference tool for practicing professionals.

This book provides a comprehensive introduction to the latest advances in the mathematical theory and computational tools for modeling high-dimensional data drawn from one or multiple low-dimensional subspaces (or manifolds) and potentially corrupted by noise, gross errors, or outliers. This challenging task requires the development of new algebraic, geometric, statistical, and computational methods for efficient and robust estimation and segmentation of one or multiple subspaces. The book also presents interesting real-world applications of these new methods in image processing, image and video segmentation, face recognition and clustering, and hybrid system identification etc. This book is intended to serve as a textbook for graduate students and beginning researchers in data science, machine learning, computer vision, image and signal processing, and systems theory. It contains ample illustrations, examples, and exercises and is made largely self-contained with three Appendices which survey basic concepts and principles from statistics, optimization, and algebraic-geometry used in this book. Rene Vidal is a Professor of Biomedical Engineering and Director of the Vision Dynamics and Learning Lab at The Johns Hopkins University. Yi Ma is Executive Dean and Professor at the School of Information Science and Technology at ShanghaiTech University. S. Shankar Sastry is Dean of the College of Engineering, Professor of Electrical Engineering and Computer Science and Professor of Bioengineering at the University of California, Berkeley.

There has been much excitement over the emergence of new mathematical techniques for the analysis and control of nonlinear systems. In addition, great technological advances have bolstered the impact of analytic advances and produced many 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.

Hybrid systems are interacting networks of digital and continuous systems. - brid systems arise throughout business and industry in areas such as interactive distributed simulation, trac control, plant process control, military command and control, aircraft and robot design, and path planning. Three of the fun- mental problems that hybrid systems theory should address are: How to model physical and information systems as hybrid systems; how to verify that their - havior satis es program or performance specic ations; and how to extract from performancespeci cationsforanetworkofphysicalsystemsandtheirsimulation models digital control programs which will force the network to obey its perf- mance speci cation. This rapidly developing area is at the interface of control, engineeringandcomputer science. Methods under developmentareextensionsof thosefromdiverseareassuchasprogramveri cation, concurrentanddistributed processes, logic programming, logics of programs, discrete event simulation, c- culus of variations, optimization, di erential geometry, Lie algebras, automata theory, dynamical systems, etc. When the rst LNCS volume Hybrid Systems was published in 1993, the e ect was to focus the attention of researchers worldwide on developing theory andengineeringtoolsapplicabletohybridsystemsinwhichcontinuousprocesses interact with digital programs in real time. At the time of publication of this fth volume, there is general agreement that this is an important area in which mathematics, control engineering, and computer science can be fruitfully c- bined. There are now hybrid system sections in many engineering and computer scienceinternationalmeetings, hybridsystems researchgroupsin manyuniver- ties and industrial laboratories, and also other excellent series of hybrid systems conferenc

This book constitutes the refereed proceedings of the First International Workshop on Hybrid Systems: Computation and Control, held in Berkeley, California, USA, in April 1998.
The volume presents 27 revised full papers selected from a total of 55 submissions. The papers focus on mathematical methods for the rigorous and systematic design and analysis of hybrid systems. Hybrid systems consist of digital devices that interact with analog environments; they are particularly important in context with safety-critical systems and dependable computing. The present volume extends the line of hybrid systems research documented in volumes 736, 999, 1066, 1201, and 1273 of the LNCS series.

This book constitutes the thoroughly refereed post-conference documentation of the Fourth International Conference on Hybrid Systems held in Ithaca, NY, USA, in October 1996. The volume presents 19 carefully revised full papers selected from numerous submissions. Hybrid systems research focuses on modeling, design, and validation of interacting systems (plants) and computer programs (control automata). This volume is devoted to hybrid systems models, formal verification, computer simulation, goal reachability, algorithms for extracting hybrid control programs, and application models for avionics, highway traffic control, and air traffic control.

This book documents the scientific outcome of the Third International Workshop on Hybrid Systems, held in Ithaca, NY, USA, in October 1994. It presents a selection of carefully reviewed and revised full papers chosen from the workshop contribution and is the successor to LNCS 736, the seminal "Hybrid Systems" volume edited by Grossman, Nerode, Ravn, and Rischel.
Hybrid systems are models for networks of digital and continuous devices, in which digital control programs sense and supervise continuous and discrete plants governed by differential or difference equations. The investigation of hybrid systems is creating a new and fascinating discipline bridging mathematics, computer science, and control engineering.

A Mathematical Introduction to Robotic Manipulation presents a mathematical formulation of the kinematics, dynamics, and control of robot manipulators. It uses an elegant set of mathematical tools that emphasizes the geometry of robot motion and allows a large class of robotic manipulation problems to be analyzed within a unified framework.

The foundation of the book is a derivation of robot kinematics using the product of the exponentials formula. The authors explore the kinematics of open-chain manipulators and multifingered robot hands, present an analysis of the dynamics and control of robot systems, discuss the specification and control of internal forces and internal motions, and address the implications of the nonholonomic nature of rolling contact are addressed, as well.

The wealth of information, numerous examples, and exercises make A Mathematical Introduction to Robotic Manipulation valuable as both a reference for robotics researchers and a text for students in advanced robotics courses.

This book introduces the geometry of 3-D vision, that is, the reconstruction of 3-D models of objects from a collection of 2-D images. It details the classic theory of two view geometry and shows that a more proper tool for studying the geometry of multiple views is the so-called rank consideration of the multiple view matrix. It also develops practical reconstruction algorithms and discusses possible extensions of the theory.

With a focus on linear, continuous time, single-input, single-output systems, this volume surveys the major results and techniques of analysis in the field of adaptive control. The authors offer a clear, conceptual presentation of adaptive methods, enabling a critical evaluation of these techniques and suggesting avenues of further development.
A brief historical overview of adaptive control is followed by a review of mathematical preliminaries and the development of several adaptive identification algorithms. Succeeding chapters examine averaging techniques, the robustness of adaptive schemes, and advanced topics -- including the use of prior information and multivariable adaptive control -- followed by a concise introduction to the control of a class of nonlinear systems. The treatment is largely self-contained, assuming only some graduate-level background in basic control systems and in linear systems theory.