and experimental validation of a haptic steering interface for

Abstract not found

Many control systems contain control inputs which are constrained to be non--negative. Unfortunately, the unilateral nature of such inputs makes them similar to a drift term, and correspondingly complicates any attempt to determine controllability. We present a controllability test in which Lie

multi-input, multi-output feed-back control design for linear systems using the paradigms, theory, and tools of robust con-trol that have arisen during the past two decades. The book is aimed at graduate students and practicing engineers who have a basic knowledge of classical con-trol design

in product and input markets) to the control of a majority of seats on a corporation’s board of directors. We define corporate control as the rights to determine the management of

A two-process theory of human information processing is proposed and applied to detection, search, and attention phenomena. Automatic processing is activa-tion of a learned sequence of elements in long-term memory that is initiated by appropriate inputs and then proceeds automatically

This paper presents the first randomized approach to kinodynamic planning (also known as trajectory planning or trajectory design). The task is to determine control inputs to drive a robot from an initial configuration and velocity to a goal configuration and velocity while obeying physically based

channel and a priori inputs, and the extrinsic value. The extrinsic value is used as an a priori value for the next iteration. Decoding algorithms in the log-likelihood domain are given not only for convolutional codes hut also for any linear binary systematic block code. The iteration is controlled by a

Suppose you are given some dataset drawn from an underlying probability distribution P and you want to estimate a "simple" subset S of input space such that the probability that a test point drawn from P lies outside of S is bounded by some a priori specified between 0 and 1. We

Abstract. Active learning differs from "learning from examples " in that the learning algorithm assumes at least some control over what part of the input domain it receives information about. In some situations, active learning is provably more powerful than learning from examples