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IEEE Princeton Section Sarnoff Symposium

作者： Behtash Babadi Nicholas Kalouptsidis Vahid Tarokh School of Engineering and Applied Sciences Harvard University Cambridge MA USA Department of Informatics and Telecommunications National and Kapodistrian University of Athens Athens Greece

In this paper, we overview the low complexity recursive L 1 -Regularized Least Squares (SPARLS) algorithm proposed in [2], for the estimation of sparse signals in an adaptive filtering setting. The SPARLS algorithm is based on an Expectation-Maximization type algorithm adapted for online estimation. Simulation results for the estimation of multi-path wireless channels show that the SPARLS algorithm has significant improvement over the conventional widely-used Recursive Least Squares (RLS) algorithm, in terms of both mean squared error (MSE) and computational complexity.

关键词： Resonance light scattering filtering algorithms Adaptive filters Signal processing algorithms Least squares approximation Computational complexity Least squares methods State estimation Signal processing Convergence

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Transputer parallel implementation algorithms for multisensor Kalman filtering

Transputer parallel implementation algorithms for multisenso...

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IEEE Instrumentation and Measurement Technology Conference

作者： I. Dancea Department of Computer Science University of Quebec QUE Canada

The objective of this paper is to analyze some factors influencing the performance of different farmer parallel programs, developed for the hierarchical Kalman filtering. During our research we systematically modified a number of hardware and/or software parameters which characterize the farmer and we have shown that these modifications has a reduced influence to linear speed-up rate.

关键词： filtering algorithms Kalman filters Equations Signal processing algorithms Time measurement Noise measurement Covariance matrix Noise figure State estimation Parallel algorithms

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Stable simplified gradient algorithms for total least squares filtering

Stable simplified gradient algorithms for total least square...

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Asilomar Conference on Signals, Systems & Computers

作者： B.E. Dunne G.A. Williamson Tellabs Research Center Mishawaka IN USA Department of Elec. and Comp. Engr Illinois Institute of Technology Chicago IL USA

ISBN: (纸本)0780365143

An algorithm for total least squares filtering is developed based on a gradient descent approach. The total least squares approach can mitigate parameter estimation bias resulting from noise effects in the adaptive filter. The stability properties of the algorithm are analyzed and stable and convergent behavior is established The proposed algorithm is implemented without divisions or square roots, and does not require normalization. Despite recent interest in iterative total least squares methods, there are few algorithms that both avoid the computational complexities described above and have been shown to possess stable and convergent behavior.

关键词： Least squares methods Iterative algorithms filtering algorithms Least squares approximation Parameter estimation Adaptive filters Stability analysis Algorithm design and analysis Iterative methods Computational complexity

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Optimal algorithms for max/min filtering

Optimal algorithms for max/min filtering

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： D. Coltuc Research Institute for Electrical Engineering University Politehnica Bucharest Romania

This paper proposes an approach for the derivation of optimal algorithms for max/min filtering. The transfer matrix for the input-output description of max/min filters is introduced. In connection with the filter realization problem, the decomposition of transfer matrices is analyzed. The decomposition is based on two properties, namely chaining and weak superposition. Matrix decomposition is further used to derive flow-charts for max/min computation. Optimization criteria with respect to the computational complexity (comparisons/sample) of the derived algorithms are defined. Two examples are presented. For certain window sizes, the derived algorithms perform in less than log/sub 2/n comparisons per sample.

关键词： filtering algorithms Matrix decomposition Filters Equations Quantum computing Computational complexity

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Robust learning algorithms for nonlinear filtering

Robust learning algorithms for nonlinear filtering

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： D. Neumerkel R. Shorten A. Hambrecht Intelligent System Group Daimler-Benz Research Berlin Germany

A nonlinear adaptive filter for use in steel mill applications is described. This filter takes the form of a generalised regression network and is used to remove eccentricities from a steel mill force signal. Online adaptation is achieved by means of standard recursive parameter update algorithms suitable for linear regression type models. It is demonstrated that second order methods can lead to severe parameter biasing effects and a more serious effect known as bursting, whereby the parameter estimation becomes numerically unstable. This paper discusses the above effects form a theoretical and practical viewpoint, and considers the suitability of several learning algorithms for the eccentricity filtering application. This analysis leads to a numerically robust filtering structure, the efficacy of which is demonstrated by means of results from a real steel rolling mill.

关键词： Robustness filtering algorithms Adaptive filters Steel Milling machines Least squares methods Nonlinear filters Approximation algorithms Stochastic processes Least squares approximation

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A local filtering scheme for FMM/PWTD algorithms

A local filtering scheme for FMM/PWTD algorithms

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Antennas and Propagation Society International Symposium

作者： M. Lu E. Michielssen Center for Computational Eleenomognetics Depormenr of Electrical and Computer Engineering University of Illinois Urbana-Champaign Urbana-Champaign IL USA

ISBN: (纸本)0780383028

Accurate and efficient schemes for interpolating and filtering/anterpolating far field radiation patterns are key to the operation of the multilevel fast multipole method (FMM) (Song, J.M. et al., IEEE Trans. Antennas and Propag., vol.45, p.1488-93, 1997) and plane wave time domain (PWTD) (Shanker, B. et al., IEEE Trans. Antennas and Propag., vol.51, 2003) accelerated frequency and time domain integral equation solvers. Local interpolators and filters/anterpolators, like those used by Song et al., offer distinct advantages over their global counterparts (Shanker et al., 2003; Sarvas, J., 2002), especially in parallel implementations. We propose a simple local filtering scheme suitable for incorporation into FMM and PWTD field evaluation kernels. The filter relies on periodic approximate prolate spheroidal (APS) functions (Bucci, O.M. et al., 1991) to restrict coupling among radiation directions. A numerical example shows that the error due to the proposed local filtering scheme decays exponentially fast with respect to the oversampling and "time-bandwidth" factors that define the periodic APS function and can be made arbitrarily small.

关键词： filtering algorithms Kernel Acceleration Integral equations Filters Sampling methods

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filtering for the Interference Signal Caused by Grounding Potential Difference in High-Voltage Disconnector

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IEEE SENSORS JOURNAL 2021年第6期21卷 7768-7775页

作者： Bian Haihong Cao Minjian Zhao Kai Wang Lihui Nanjing Inst Technol Sch Elect Power Engn Nanjing 211167 Peoples R China Nanjing Inst Technol Jiangsu Collaborat Innovat Ctr Smart Distribut Ne Nanjing 211167 Peoples R China Southeast Univ Sch Elect Engn Nanjing 210096 Peoples R China Southeast Univ Sch Instrument Sci & Engn Nanjing 210096 Peoples R China

The disconnector operation in the high-voltage substations forms a very fast transient overvoltage (VFTO), which leads to a sudden rise in local grounding potential. Due to double grounding of the secondary cable, the current transformer signal is interfered by the grounding grid potential difference. To study the mechanism of the interference signal, a simulation model based on the electro-magnetic transient program is built and the coupling path of the grounding grid potential difference is analyzed. According to the characteristics of interference signal, an adaptive algorithm is put forward and it mainly consists of three parts, including starting element, decision algorithm, and filtering algorithm. By selecting the appropriate parameters of wavelet transform, the filtering algorithm can discompose and reconstruct signal to remove the interference signal. The simulation results demonstrate that the filtering algorithm can effectively eliminate the interference signal caused by the grounding grid potential difference, with a 91 reduction of the root mean square error and a 135 improvement of the signal-to-noise ratio. Moreover, the accuracy of the measured data is ensured and the reliability of the relay protection is improved.

关键词： Grounding Interference Transient analysis filtering algorithms Wires Substations Electric potential Disconnector operation VFTO grounding grid potential difference simulation model filtering algorithm

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Joint iterative detection and phase noise estimation algorithms using Kalman filtering

Joint iterative detection and phase noise estimation algorit...

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Canadian Workshop on Information Theory (CWIT)

作者： Tarik S. Shehata Mohamed El-Tanany Department of Systems and Computer Engineering Carleton University Ottawa ONT Canada

The problem of a slow varying phase noise (PN) estimation at a low signal to noise ratio (SNR) in a non-pilot aided receiver is considered. Two iterative algorithms using Kalman filtering are proposed to estimate the PN under the expectation-maximization (EM) framework. The first algorithm uses a soft decision directed extended Kalman smoother (EKS) to provide a suboptimal PN estimate. While the second algorithm overcomes the nonlinear structure of the EKS and improves the PN estimate by using a Kalman smoother (KS) which is applied assuming small PN values. An enhanced KS is proposed to maintain the KS performance in large PN values. Simulation results show that the performance of the proposed algorithms is close to the performance of the synchronized system with no PN effect.

关键词： Phase detection Phase noise Phase estimation Iterative algorithms filtering algorithms Kalman filters Iterative decoding Signal to noise ratio Maximum likelihood estimation Wiener filter

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On lattice-form algorithms for adaptive IIR filtering

On lattice-form algorithms for adaptive IIR filtering

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： J.J. Shynk Department of Electrical & Computer Engineering University of California Santa Barbara CA USA

Lattice-form algorithms are described for adaptive IIR (infinite-impulse-response) filtering that are based on the Gauss-Newton search method. Several approximations of the Hessian matrix that have different levels of computational complexity are considered, and the results are compared to a previous IIR lattice algorithm. Computer simulations in a system-identification configuration illustrate their convergence properties. algorithms for the two-multiplier lattice with distinct coefficients in each section, and the one-multiplier form, are outlined.< >

关键词： filtering algorithms Adaptive filters IIR filters Lattices Newton method Least squares methods Recursive estimation Search methods Computational complexity Computer simulation

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On the Performance of SVD-Based algorithms for Collaborative filtering

On the Performance of SVD-Based Algorithms for Collaborative...

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Balkan Conference in Informatics (BCI)

作者： Manolis Vozalis Angelos Markos Konstantinos Margaritis Department of Applied Informatics University of Macedonia Thessaloniki Greece

In this paper, we describe and compare three Collaborative filtering (CF) algorithms aiming at the low-rank approximation of the user-item ratings matrix. The algorithm implementations are based on three standard techniques for fitting a factor model to the data: Standard Singular Value Decomposition (sSVD), Principal Component Analysis (PCA) and Correspondence Analysis (CA). CA and PCA can be described as SVDs of appropriately transformed matrices,which is a key concept in this study. For each algorithm we implement two similar CF versions. The first one involves a direct rating prediction scheme based on the reduced user-item ratings matrix, while the second incorporates an additional neighborhood formation step. Next, we examine the impact of the aforementioned approaches on the quality of the generated predictions through a series of experiments. The experimental results showed that the approaches including the neighborhood formation step in most cases appear to be less accurate than the direct ones. Finally, CA-CF outperformed the SVD-CF and PCA-CF in terms of accuracy for small numbers of retained dimensions, but SVD-CF displayed the overall highest accuracy.

关键词： filtering algorithms Collaboration Principal component analysis Matrix decomposition Approximation algorithms Singular value decomposition Algorithm design and analysis Informatics Clustering algorithms Iterative algorithms

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