作者：
SALEH, RAWHITE, JKMIT
DEPT ELECT ENGN & COMP SCI CAMBRIDGE MA 02139 USA MIT
ELECTR RES LAB CAMBRIDGE MA 02139 USA
A new relaxation algorithm for circuit simulation that combines the advantages of iterated timing analysis (ITA) and waveform relaxation (WR) is described. The method is based on using an iterative stepsize refinement...
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A new relaxation algorithm for circuit simulation that combines the advantages of iterated timing analysis (ITA) and waveform relaxation (WR) is described. The method is based on using an iterative stepsize refinement strategy with a waveform-relaxation-newton (WRN) algorithm. All three relaxation techniques, ITA, WR, and WRN, are compared, and experimental results that indicate the strengths and weaknesses of the methods are presented. In addition, a new convergence proof for the waveform-Newton (WN) method for systems with nonlinear capacitors is provided. Finally, it is shown that the step-refined WRN algorithm can be implemented on a parallel processor in such a way that different subsystems can be processed in parallel and the solution at different timepoints of the same subsystem can also be computed in parallel.< >
A self-clocked fair queueing (SCFQ) scheme has been recently proposed [6], [5] as an easily implementable version of fair queueing. In this paper, the worst case network delay performance of a class of fair queueing a...
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A self-clocked fair queueing (SCFQ) scheme has been recently proposed [6], [5] as an easily implementable version of fair queueing. In this paper, the worst case network delay performance of a class of fair queueing algorithms, including the SCFQ scheme, is studied. We build upon and generalize the methodology developed by Parekh and Gallager [10], [11] to study this class of algorithms based on the leaky-bucket characterization of traffic. Under modest resource allocation conditions, the end-to-end session delays and backlogs corresponding to this class of algorithms are shown to be bounded. For the SCFQ scheme, these bounds are larger, but practically as good as the corresponding bounds for the PGPS scheme [11]. It is shown that the SCFQ scheme can provide adequate performance guarantees for the delay-sensitive traffic in ATM.
The paper presents a systematic approach for the numerical kinematic analysis of general parallel robotic manipulators. This approach consists of two parts. The first part deals with the structural analysis. Based on ...
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The paper presents a systematic approach for the numerical kinematic analysis of general parallel robotic manipulators. This approach consists of two parts. The first part deals with the structural analysis. Based on graph theory and the Depth First Search algorithm, a method for identifying and orientating the independent kinematic loops of the robot is developed. This method not only facilitates the assignment of the local coordinate systems attached to the robot, but also arranges them in the correct order to allow efficient recursive coordinate transformation. The second part of this approach deals with the displacement analysis. A set of recursion formulae is first developed for computing the forward coordinate transformations, and these formulae am then adopted in a two-phase computational algorithm to obtain the numerical solutions to the direct and the inverse kinematics problems. The two-phase algorithm developed in this paper is not only insensitive to the initial approximation of the solution vector, but also gives a rapid convergence rate. Furthermore, it is also useful for finding multiple solutions for the robot as well as for continuous trajectory planning, as shown by the numerical examples presented in this paper.
In this paper, a generalized inflation method which can adaptively and robustly converge to the noise-subspace is proposed to improve the performances of subspace algorithms used for tracking nonstationary sources. Th...
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In this paper, a generalized inflation method which can adaptively and robustly converge to the noise-subspace is proposed to improve the performances of subspace algorithms used for tracking nonstationary sources. This generalized inflation method, which includes an inflation factor developed in the view point of orthogonal projection, preserves the parallel structure for realizations and achieves better performances of convergence and initialization behavior than the inflation method, adaptive PHR algorithms, and other adaptive eigensubspace algorithms when the number of sources is not known. A bound of the inflation factor is also suggested to secure the noise-subspace-only adaptation. The general inflation method in use of weighted-subspace can further improve the tracking performances. Simulations for analyzing the tracking performances of the algorithms are also included.
Glottal inverse filtering is a technique by which the flow past the time varying glottal constriction is estimated by a filtering operation on the acoustic signal in human speech. The filtering operation removes the e...
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Glottal inverse filtering is a technique by which the flow past the time varying glottal constriction is estimated by a filtering operation on the acoustic signal in human speech. The filtering operation removes the effects of the vocal tract resonances to reveal the underlying voice source signal. A linear model of a glottal pulse waveform is described along with a procedure for jointly determining an AR model of the vocal tract response together with the parameters of the glottal pulse model. This technique is applied to inverse filtering in human speech and the results are compared to covariance method LPC analysis.
Two algorithms are presented for optimum timing recovery in digitally implemented equalizers. The first one is a polarity-type algorithm based on the conventional minimum mean-square error criterion. A theoretical ana...
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Two algorithms are presented for optimum timing recovery in digitally implemented equalizers. The first one is a polarity-type algorithm based on the conventional minimum mean-square error criterion. A theoretical analysis is made to characterize the algorithm phase detector and evaluate its steady-state phase jitter variance. Influence of various channel and system design parameters on the algorithm performance is illustrated using phase jitter probability densities obtained by means of computer simulations. Interaction of the algorithm with decision-directed carrier recovery is also examined. It is shown that interaction with carrier recovery may considerably degrade the timing acquisition performance, and a second algorithm is then presented which eliminates this interaction. The second algorithm is based on the minimization of a modified mean-square error criterion which provides a measure of the intersymbol interference, independently of the carrier phase. Decision-directed timing and carrier recoveries are thus decoupled and the system startup period is considerably reduced. Phase detector characteristic and steady-state jitter performance of the second algorithm are evaluated by analytical means and computer simulations, as in the first algorithm.
The key driving force behind any capture-the-flag competition is the scoring algorithm;the Cyber Grand Challenge (CGC) was no different. In this article, we describe design considerations for the CGC events, how these...
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The key driving force behind any capture-the-flag competition is the scoring algorithm;the Cyber Grand Challenge (CGC) was no different. In this article, we describe design considerations for the CGC events, how these algorithms intended to incentivize competitors, and effects these decisions had on the resulting gameplay.
The fusion of hesitant fuzzy set (HFS) and fuzzy-rough set (FRS) is explored and applied into the task of classification due to its capability of conveying hesitant and uncertainty information. In this paper, on the b...
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The fusion of hesitant fuzzy set (HFS) and fuzzy-rough set (FRS) is explored and applied into the task of classification due to its capability of conveying hesitant and uncertainty information. In this paper, on the basis of studying the equivalence relations between hesitant fuzzy elements and HFS operation updating, the target instances are classified by employing the lower and upper approximations in hesitant FRS theory. Extensive performance analysis has been conducted including classification accuracy results, execution time, and the impact of k parameter to evaluate the proposed hesitant fuzzy-rough nearest-neighbor (HFRNN) algorithm. The experimental analysis has shown that the proposed HFRNN algorithm significantly outperforms current leading algorithms in terms of fuzzy-rough nearest-neighbor, vaguely quantified rough sets, similarity nearest-neighbor, and aggregated-similarity nearest-neighbor.
An algorithm is presented which computes optimal weights for arbitrary linear arrays. The application of this algorithm to in situ optimal reshading of arrays with failed elements is discussed. It is shown that optima...
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An algorithm is presented which computes optimal weights for arbitrary linear arrays. The application of this algorithm to in situ optimal reshading of arrays with failed elements is discussed. It is shown that optimal reshading can often regain the original sidelobe level by slightly increasing the mainlobe beamwidth. Three examples are presented to illustrate the algorithm's effectiveness. Hardware and software issues are discussed. Execution time for a 25-element array is typically between 1 and 2 min on an HP9836C microcomputer.
Despite the importance of sparsity signal models and the increasing prevalence of high-dimensional streaming data, there are relatively few algorithms for dynamic filtering of time-varying sparse signals. Of the exist...
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Despite the importance of sparsity signal models and the increasing prevalence of high-dimensional streaming data, there are relatively few algorithms for dynamic filtering of time-varying sparse signals. Of the existing algorithms, fewer still provide strong performance guarantees. This paper examines two algorithms for dynamic filtering of sparse signals that are based on efficient l(1) optimization methods. We first present an analysis for one simple algorithm (BPDN-DF) that works well when the system dynamics are known exactly. We then introduce a novel second algorithm (RWL1-DF) that is more computationally complex than BPDN-DF but performs better in practice, especially in the case where the system dynamics model is inaccurate. Robustness to model inaccuracy is achieved by using a hierarchical probabilistic data model and propagating higher-order statistics from the previous estimate (akin to Kalman filtering) in the sparse inference process. We demonstrate the properties of these algorithms on both simulated data as well as natural video sequences. Taken together, the algorithms presented in this paper represent the first strong performance analysis of dynamic filtering algorithms for time-varying sparse signals as well as state-of-the-art performance in this emerging application.
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