MIT Press eBooks Page 3

This history of computing focuses not on chronology (what came first and who deserves credit for it) but on the actual architectures of the first machines that made electronic computing a practical reality. The book covers computers built in the United States, Germany, England, and Japan. It makes clear that similar concepts were often pursued simultaneously and that the early researchers explored many architectures beyond the von Neumann architecture that eventually became canonical. The contributors include not only historians but also engineers and computer pioneers. An introductory chapter describes the elements of computer architecture and explains why being first is even less interesting for computers than for other areas of technology. The es...

Mobile robots range from the teleoperated Sojourner on the Mars Pathfinder mission to cleaning robots in the Paris Metro. Introduction to Autonomous Mobile Robots offers students and other interested readers an overview of the technology of mobility -- the mechanisms that allow a mobile robot to move through a real world environment to perform its tasks -- including locomotion, sensing, localization, and motion planning. It discusses all facets of mobile robotics, including hardware design, wheel design, kinematics analysis, sensors and perception, localization, mapping, and robot control architectures.The design of any successful robot involves the integration of many different disciplines, among them kinematics, signal analysis, information theory,...

An Introduction to Distributed Algorithms takes up some of the main concepts and algorithms, ranging from basic to advanced techniques and applications, that underlie the programming of distributed-memory systems such as computer networks, networks of workstations, and multiprocessors. Written from the broad perspective of distributed-memory systems in general it includes topics such as algorithms for maximum flow, program debugging, and simulation that do not appear in more orthodox texts on distributed algorithms. Moving from fundamentals to advances and applications, ten chapters-with exercises and bibliographic notes-cover a variety of topics. These include models of distributed computation, information propagation, leader election, distributed ...

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behavior, recognize faces or spoken speech, optimize robot behavior so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to Machine Learning is a comprehensive textbook on the subject, covering a broad array of topics not usually included in introductory machine learning texts. It discusses many methods based in different fields, including statistics, pattern recognition, neural networks, artificial intelligence, signal processing, control, a...