21408324
A Stability Property of T. Chan's Preconditioner
In this short note, we prove that T. Chan's preconditioner proposed in {TChan} [SIAM J. Sci. Statist. Comput., 9 (1988), pp. 766--771] is stable for matrices that are normal and stable.
7039845
An Algorithm for Solving Scaled Total Least Squares Problems
In this paper, we present a rankrevealing two-sided orthogonal decomposition method for solving the STLS problem. An error analysis of the algorithm is given. Our numerical experiments show that this algorithm computes the STLS solution as accurate as the SVD method with less computation.
1787368
Subdivision Tree Representation of Arbitrary Triangle Meshes
We investigate a new way to represent arbitrary triangle meshes. We prove that a large class of triangle meshes, called normal triangle meshes, can be represented by a subdivision tree, where each subdivision is one of four elementary subdivision types. We also show how to partition an arbitrary triangle mesh into a small set of normal meshes. The subdivision tree representation can be used to encode mesh connectivity information. Our theoretical analysis shows that such a coding scheme is very promising.
9323568
Robust relative pose estimation with integrated cheirality constraint
The cheirality constraint, which requires that reconstructed point correspondences lie in front of the cameras, has not typically been integrated into traditional RANSAC-based pose estimators. We have developed a new RANSAC-based relative pose estimator which incorporates the cheirality constraint not only to preempt invalid epipolar geometry hypotheses, but also as a criterion in identifying inliers for image feature correspondences. Because the application of the cheirality constraint is tightly related to the estimation of epipolar geometry, integrating them inside RANSAC can prevent inliers being falsely identified as cheirality outliers. The result is a more consistent and stable estimation which leaves denser feature correspondences for subsequent processing. Experimental comparison between the usual RANSAC-based approach and the proposed approach is performed.
46077798
A Component-Based Analytical Performance Model of IP-Based SAN
In this paper we propose a component-based analytical performance model for IP-based SAN. This model evaluates and analyzes the behavior of IP network, cache, RAID and disks under multiple synchronous I/O workloads by using closed queueing networks. The theoretical results are compared with the results from a real IP-based SAN using a variety of synthetic workloads. These experiments prove that the component-based method is an appropriate way to build performance model for a complex storage system. Using the model, we can find out that the bottleneck of SAN performance is network or storage node under different workloads or applications. We also identify areas for future research in the performance analysis of real IP-based SAN.
28282459
The design and characteristics of piezomotors using flexure-hinged displacement amplifiers
Two prototype piezomotors are described in terms of their operational principles, design, construction and some operating characteristics. Both are driven by mechanically amplified piezoelectric stack type actuators. One is a harmonic piezomotor in which the mechanically amplified displacements are used as the wave generator of a harmonic gear drive. The other uses similar mechanical amplifiers to drive a roller clutch motion generator. The requirements for effective mechanical amplifiers are discussed and the performance advantages of piezomotors based on them outlined. It is concluded that such motors can offer useful possibilities for specialist mechatronic applications.
7346557
Predictive Control for Dynamic Resource Allocation in Enterprise Data Centers
It is challenging to reduce resource over-provisioning for enterprise applications while maintaining service level objectives (SLOs) due to their time-varying and stochastic workloads. In this paper, we study the effect of prediction on dynamic resource allocation to virtualized servers running enterprise applications. We present predictive controllers using three different prediction algorithms based on a standard auto-regressive (AR) model, a combined ANOVA-AR model, as well as a multi-pulse (MP) model. We compare the properties of the predictive controllers with an adaptive integral (I) controller designed in our earlier work on controlling relative utilization of resource containers. The controllers are evaluated in a hypothetical virtual server environment where we use the CPU utilization traces collected on 36 servers in an enterprise data center. Since these traces were collected in an open-loop environment, we use a simple queuing algorithm to simulate the closed-loop CPU usage under dynamic control of CPU allocation. We also study the controllers by emulating the utilization traces on a test bed where a Web server was hosted inside a Xen virtual machine. We compare the results of these controllers from all the servers and find that the MP-based predictive controller performed slightly better statistically than the other two predictive controllers. The ANOVA-AR-based approach is highly sensitive to the existence of periodic patterns in the trace, while the other three methods are not. In addition, all the three predictive schemes performed significantly better when the prediction error was accounted for using a feedback mechanism. The MP-based method also demonstrated an interesting self-learning behavior