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IEEE International Caracas Conference on Devices, Circuits and Systems

作者： G. Brando A. Del Pizzo E. Faccenda Dept. of Electrical Engineering University “Federico II” Napoli Italy

ISBN: (纸本)0780373804

This paper deals with parallel active filtering by means of IGBT-Voltage Source Inverters. The control algorithm is based on the evaluation of active and reactive power by detecting line voltages and load currents. Then these powers are split in their average and alternative components using proper low-pass filters. The features linked to some different techniques of current control are investigated with reference to time behaviour and frequency spectrum of the resultant line current. Simulation results are discussed and compared with reference to a specific case-study.

关键词： filtering algorithms Active filters Power harmonic filters Reactive power Power system harmonics Inverters Low pass filters Voltage control Reactive power control Thyristors

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Conditional central algorithms for worst-case estimation and filtering

Conditional central algorithms for worst-case estimation and...

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IEEE Conference on Decision and Control

作者： A. Garulli A. Vicino G. Zappa Dipartimento di Ingegneria dellE28099Informazione Università di Siena Siena Italy Dipartimento di Sistemi e Informatica Università di Firenze Florence Italy

ISBN: (纸本)0780341872

This paper deals with conditional central algorithms in a worst-case setting. The role and importance of these algorithms in identification and filtering is illustrated by showing that problems like /spl Hscr//sub 2/ optimal identification and state filtering, in contexts where disturbances are described through norm bounds, are reducible to the computation of conditional central algorithms. The solution of the conditional Chebichev center problem is completely characterized for the case when energy norm bounded disturbances are considered. A closed form solution is obtained in terms of finding the unique real root of a polynomial equation.

关键词： filtering algorithms Vectors Electronic mail Ellipsoids Closed-form solution Polynomials Equations Upper bound Energy measurement Noise measurement

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IIR notch filtering-comparisons of four adaptive algorithms for frequency estimation

IIR notch filtering-comparisons of four adaptive algorithms ...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： T.S. Ng J.F. Chicharo Department of EEE University of Hong Kong Hong Kong China Department of EC&E University of Wollongong NSW Australia

This paper compares the parameter estimation accuracy of four adaptive algorithms for frequency estimation when applied to an IIR digital notch filter. All four algorithms were subjected to the same experimental conditions and the variance of parameter estimates are compared to the Cramer Rao Lower Bound. Results show that the RML yielded the most accurate parameter estimates although its computational burden is quite high. The AML produced good parameter estimates and it has the advantages of proven convergence properties as well as lower computational burden over the RML. For applications where the signal to noise ratio is moderate it is shown that the AGB algorithm may be suitable, particularly where minimal computational burden is desired.

关键词： Adaptive filters IIR filters Adaptive algorithm Frequency estimation Parameter estimation Maximum likelihood estimation Yield estimation Convergence filtering algorithms Error correction

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Averaging sign algorithms for adaptive filtering

Averaging sign algorithms for adaptive filtering

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Asilomar Conference on Signals, Systems & Computers

作者： G. Yin V. Krishnamurthy C. Ion Department of Mathematics Wayne State University Detroit MI USA Department of Electrical and Computer Engineering University of British Columbia Vancouver Canada

This paper is motivated by the recent developments on iterate averaging of recursive stochastic approximation algorithms and asymptotic analysis of sign-error algorithms for adaptive filtering. We develop averaging algorithms for adaptive filtering. The proposed algorithms are based on constructions of a sequence of estimates using large step sizes followed by iterate averaging and averaging on both iterates and observations. We demonstrate that the performance of the algorithms are improved via the use of averaging. The proof is based on establishing asymptotic normality of a suitably scaled sequence of the estimation errors. The asymptotic covariance is calculated and shown to be the smallest possible leading to asymptotic efficiency. We also propose and investigate the variants of the algorithm including sign-regressor procedures and constant-step algorithms. As applications, we demonstrate how averaging algorithms can be used for blind multiuser detection in DS/CDMA systems.

关键词： filtering algorithms Adaptive filters Signal processing algorithms Approximation algorithms Convergence Stochastic processes Least squares approximation Smoothing methods Gaussian approximation Signal processing

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Extended Relief algorithms in Instance-Based Feature filtering

Extended Relief Algorithms in Instance-Based Feature Filteri...

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International Conference on Advanced Language Processing and Web Information Technology (ALPIT)

作者： Heum Park Hyuk-Chul Kwon AI Laboratory Department of Computer Science Pusan National University Busan South Korea

ISBN: (纸本)9780769529301;0769529305

This paper presents extended Relief algorithms and their use in instance-based feature filtering for document feature selection. The Relief algorithms are general and successful feature estimators that detect conditional dependencies of features between instances, and are applied in the preprocessing step for document classification and regression. Since the introduction the Relief algorithm, many kinds of extended Relief algorithms have been suggested as solutions to problems of redundancy, irrelevant and noisy features as well as Relief algorithm's limitations in two-class and multi-class datasets. In this paper, we introduce additional problems including the negative influence of computation similarities and weights caused by the small number of features in an instance, the absence of nearest Hits or nearest Misses for some instances using Relief algorithms, and other of problems. We suggest new extended Relief algorithms to solve those problems, having in the course of our research, and experimented on the estimation of the quality of features from instances, and classified datasets, and having compared the results of the new extended Relief algorithms. Indeed in the experimental results, the new extended Relief algorithms showed better performances for all of the datasets than did the Relief algorithms

关键词： filtering algorithms Iterative algorithms Information technology Information filtering Information filters Artificial intelligence Computer science Electronic mail Computer vision Statistics

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Efficient and Fast Optimization algorithms for Quantum State filtering and Estimation

Efficient and Fast Optimization Algorithms for Quantum State...

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International Conference on Intelligent Control and Information Processing (ICICIP)

作者： Kun Zhang Shuang Cong Jiao Ding Jiaojiao Zhang Kezhi Li University of Science and Technology of China Hefei China The Chinese University of Hong Kong Shatin Hong Kong N.T. Imperial College London London England UK

ISBN: (数字)9781728100159

ISBN: (纸本)9781728100166

In this paper, based on Alternating Direction Multiplier Method (ADMM) and Compressed Sensing (CS), we develop three types of novel convex optimization algorithms for the quantum state estimation and filtering. Considering sparse state disturbance and measurement noise simultaneously, we propose a quantum state filtering algorithm. At the same time, the quantum state estimation algorithms for either sparse state disturbance or measurement noise are proposed, respectively. Contrast with other algorithms in literature, simulation experiments verify that all three algorithms have low computational complexity, fast convergence speed and high estimation accuracy at lower measurement rates.

关键词： Convex functions Sparse matrices Noise measurement filtering State estimation filtering algorithms Convergence

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Parallel algorithms for 3D multi-level median filtering with motion compensation

Parallel algorithms for 3D multi-level median filtering with...

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IEEE International Conference on Image Processing

作者： P. Liu M.N. Chong W.B. Goh School of Applied Science Nanyang Technological University Singapore Singapore

This paper aims to develop effective parallel processing techniques for 3-dimensional (3D) multi-level median filtering (3DMMF) with motion compensation on video sequences. Due to the nature of unbalanced load of the filtering algorithm and the objective for the parallel approach, two methods of dynamic load balancing have been proposed and compared. They are sender-initiated-load-balancing (SILB) and receiver-initiated-load-balancing (RILB) algorithms. We propose a SILB algorithm which utilises the spatial-temporal characteristics of the processed sequences for load prediction to achieve dynamic load balancing. Both theoretical analysis and experimental results on the IBM SP2 computing surface have been presented in this paper.

关键词： Parallel algorithms Motion compensation Load management Image restoration Concurrent computing Parallel processing filtering algorithms Motion estimation Workstations Motion pictures

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Split-Levinson-type algorithms For LS Linear-phase filtering And Fbpm Prediction/sup 1/

Split-Levinson-type Algorithms For LS Linear-phase Filtering...

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Asilomar Conference on Signals, Systems & Computers

作者： D.T.M. Slock T. Kailath Information Systems Laboratory University of Stanford Stanford CA USA

In this paper, the correspondence between the efficient Split-Levinson algorithm and linear-phase filtering is exploited. Least-squares estimation subject to a linear-phase constraint leads to a persynnnetric sample covariance matrix in the normal equations. We introduce a new orthogonal- ization scheme for the data vectors. This scheme is appropriate for problems with the structure of a liticar-phase constraint. The symmetric linear-phase prediction polynomials effecting this orthogonaligmtion procedure obey a thrcetemn recursion, as in the Split-Levinson algorithin, with the addition of an extra term involving the Kalman gain. As a byproduct, the predictor polynoinial of the Forward-Backward Prediction Method (FBPM) can be recovered from the linear-pliase polynomial, paralleling a similar relationship in the Split-Levinson algorithm. Finally, we also describe how the batch-processing fast LS algorithm can be converted into a time-recursive solution. This yields a fast lattice algorithin for solving the corresponding RLS problems. The multichannel prewindowing formulation is considered.

关键词： filtering algorithms Signal processing algorithms Covariance matrix Polynomials Algorithm design and analysis Transversal filters Resonance light scattering Nonlinear filters Lattices Digital filters

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filtering schemes for dispersion-optimized FDTD algorithms

Filtering schemes for dispersion-optimized FDTD algorithms

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Antennas and Propagation Society International Symposium

作者： S. Wang F.L. Teixeira R. Lee J. Lee ElectroScience Laboratory and Department of Electrical Engineering Ohio State Uinversity Columbus OH USA

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FURTHER RESULTS ON ADAPTIVE filtering WITH EMBEDDED CFAR

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IEEE TRANSACTIONS ON AEROSPACE AND ELECTRONIC SYSTEMS 1994年第4期30卷 1009-1020页

作者： CAI, LJ WANG, H SYRACUSE UNIV DEPT ELECT & COMP ENGNSYRACUSENY 13244

Among the few known adaptive filtering algorithms which have an embedded (integrated) constant false alarm rate (CFAR) performance feature, the generalized likelihood ratio (GLR) test algorithm has been found to be robust in non-Gaussian clutter. This paper examines the detection performance of the GLR algorithm in nonhomogeneous/nonstationary clutter environments which lead to nonidentical distribution of secondary (training) data. For two common types of nonhomogeneity, i.e., the so-called ''signal contamination'' and ''clutter edge'', the asymptotic detection performance is derived and compared with simulations. These asymptotic results are relatively simple to use and they predict the GLR performance in nonhomogeneous environments quite well. The GLR performance loss due to the nonhomogeneity is also evaluated. It is found that the ''generalized angle'' between the desired and contaminating signal plays an important role in the study of the effects of signal contamination. It is also found that the performance degradation due to the clutter edge depends largely on the width of the clutter spectrum and target-clutter Doppler separation.

关键词： Adaptive filters Signal processing algorithms filtering algorithms Clutter Robustness Testing Covariance matrix Computer aided instruction Performance loss Contamination

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