Abstract not found

Recent developments in video-tracking allow the outlines of moving, natural objects in a video-camera input stream to be tracked live, at full video-rate. Previous systems have been available to do this for specially illuminated objects or for naturally illuminated but polyhedral objects. Other systems have been able to track nonpolyhedral objects in motion, in some cases from live video, but following only centroids or key-points rather than tracking whole curves. The system described here can track accurately the curved silhouettes of moving non-polyhedral objects at frame-rate, for example hands, lips, legs, vehicles, fruit, and without any special hardware beyond a desktop workstation and a video-camera and framestore. The new algorithms are a synthesis of methods in deformable models, B-spline curve representation and control theory. This paper shows how such a facility can be used to turn parts of the body --- for instance, hands and lips --- into input devices. Rigid motion of a...

Ensuring performance isolation and differentiation among workloads that share a storage infrastructure is a basic requirement in consolidated data centers. Existing management tools rely on resource provisioning to meet performance goals; they require detailed knowledge of the system characteristics and the workloads. Provisioning is inherently slow to react to system and workload dynamics, and in the general case, it is impossible to provision for the worst case.

This dissertation is about building learning control architectures for agents embedded in finite, stationary, and Markovian environments. Such architectures give embedded agents the ability to improve autonomously the efficiency with which they can achieve goals. Machine learning researchers have developed reinforcement learning (RL) algorithms based on dynamic programming (DP) that use the agent's experience in its environment to improve its decision policy incrementally. This is achieved by adapting an evaluation function in such a way that the decision policy that is "greedy" with respect to it improves with experience. This dissertation focuses on finite, stationary and Markovian environments for two reasons: it allows the develop...

1 This chapter provides a basic introduction to various of the computational issues that arise in the study of motor control and motor learning. A broad set of topics is discussed, including feedback control, feedforward control, the problem of delay, observers, learning algorithms, motor learning, and reference models. The goal of the chapter is to provide a unified discussion of these topics, emphasizing the complementary roles that they play in complex control systems. The choice of topics is motivated by their relevance to problems in motor control and motor learning; however, the chapter is not intended to be a review of specific models. Rather we emphasize basic theoretical issues with broad applicability. Many of the ideas described here are developed more fully in standard textbooks in modern systems theory, particularly textbooks on discrete-time systems (˚Aström & Wittenmark, 1984), adaptive signal processing (Widrow & Stearns, 1985), and adaptive control systems (Goodwin & Sin, 1984; ˚Aström & Wittenmark, 1989). These texts assume a substantial background in control

Multiple Clock Domain (MCD) processors are a promising future alternative to today’s fully synchronous designs. Dynamic Voltage and Frequency Scaling (DVFS) in an MCD processor has the extra flexibility to adjust the voltage and frequency in each domain independently. Most existing DVFS approaches are profile-based offline schemes which are mainly suitable for applications whose execution characteristics are constrained and repeatable. While some work has been published about online DVFS schemes, the prior approaches are typically heuristic-based. In this paper, we present an effective online DVFS scheme for an MCD processor which takes a formal analytic approach, is driven by dynamic workloads, and is suitable for all applications. In our approach, we model an MCD processor as a queue-domain network and the online DVFS as a feedback control problem with issue queue occupancies as feedback signals. A dynamic stochastic queuing model is first proposed and linearized through an accurate linearization technique. A controller is then designed and verified by stability analysis. Finally we evaluate our DVFS scheme through a cycle-accurate simulation with a broad set of applications selected from MediaBench and SPEC2000 benchmark suites. Compared to the best-known prior approach, which is heuristicbased, the proposed online DVFS scheme is substantially more effective due to its automatic regulation ability. For example, we have achieved a 2-3 fold increase in efficiency in terms of energy-delay product improvement. In addition, our control theoretic technique is more resilient, requires less tuning effort, and has better scalability as compared to prior online DVFS schemes. We believe that the techniques and methodology described in this paper can be generalized for energy control in processors other than MCD, such as tiled stream processors.

Rate-based feedback congestion control has been proposed as a form of traffic management for available bit rate traffic in ATM networks. This paper discusses applying linear control theory to these algorithms. A congestion control scheme for simple networks is designed and analyzed using the tools of classical control theory. This allows insight into the trade-offs in such schemes and suggests approaches to larger networks. I.

this article will expedite this mapping, letting software engineers exploit the vast amounts of knowledge and experience accumulated in control theory.

In this paper we present two strategies to design a hybrid controller for a system described by several nonlinear vector fields. Besides the overall goal to find a controller that stabilizes the closed-loop hybrid system, the selection will also be made in such a way that an exponentially stable closed-loop system is obtained. The design strategies are based on stated stability and exponential stability theorems for hybrid systems. The first approach results in regions where it is possible to change vector fields guaranteeing (exponential) stability of the closed-loop hybrid system. The second design strategy utilizes the fact that a system is (exponentially) stable if it is always possible to choose a vector field that points in a direction such that the trajectory approaches the equilibrium point. These conditions can be verified by solving a linear matrix inequality (LMI) problem. The presented methods are illustrated by examples. Keywords: Hybrid systems, Hybrid controller, Stabili...

An important class of linear time-varying systems consists of plants where the state-space matrices are fixed functions of some time-varying physical parameters `. Small Gain techniques can be applied to such systems to derive robust time-invariant controllers. Yet, this approach is often overly conservative when the parameters ` undergo large variations during system operation. In general, much higher performance can be achieved by control laws that incorporate available measurements of ` and therefore "adjust" to the current plant dynamics. This paper discusses extensions of H∞ synthesis techniques to allow for controller dependence on time-varying but measured parameters. When this dependence is linear fractional, the existence of such gain-scheduled H1 controllers is fully characterized in terms of linear matrix inequalities (LMIs). The underlying synthesis problem is therefore a convex program for which efficient optimization techniques are available. The formalism and...