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检索条件"主题词=filtering algorithms"

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Order Statistics Based algorithms in filtering of 2D-3D Images and Objects

Order Statistics Based Algorithms in Filtering of 2D-3D Imag...

引用

International Kharkov Symposium on Physics and Engineering of Microwaves, Millimeter, and Submillimeter Waves

作者： Volodymyr Ponomaryov ESIME-Culhuacan National Polytechnic Institute Mexicali Mexico

Novel vector rank M-type K-nearest neighbor filters are adapted for multichannel image processing. Ultrasound images are contaminated by impulsive noise and 3-D filtering algorithms are applied.

关键词： Statistics filtering algorithms Speckle PSNR Ultrasonic imaging Video sequences Additive noise Colored noise Adaptive filters Cities and towns

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Experimental Results on Item-Based algorithms for Independent Domain Collaborative filtering

Experimental Results on Item-Based Algorithms for Independen...

引用

International Conference on Automated Production of Cross Media Content for Multi-Channel Distribution (AXMEDIS)

作者： Maria Laura Clemente Research and Development in Sardinia CRS4 Center for Advanced Studies Pula Italy

A research analysis on item-based algorithms for collaborative filtering is presented. The aim of the presented activity was to find a configuration of an item-based algorithm capable of providing good results but also independent from the data set. Four data sets were used for the algorithm validation: Netflix, MovieLens, BookCrossing, and Jester. The experimentation involved the following aspects: similarity computation, size of the neighbourhood, prediction computation, minimum number of co-rated items. Results were evaluated in terms of root mean squared error (RMSE). The result of the activity is an independent domain configuration for an item-based algorithm which produced satisfactory results with most of the above mentioned data sets.

关键词： filtering algorithms Recommender systems Collaborative work Web services International collaboration Algorithm design and analysis History Research and development Testing Root mean square

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Least-squares algorithms for adaptive linear-phase filtering

Least-squares algorithms for adaptive linear-phase filtering

引用

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

作者： B. Friedlander M. Morf Systems Control Technology Inc. Palo Alto CA USA Department of Electrical Engineering University of Stanford Stanford CA USA

In many applications it is desirable to use filters with linear phase characteristics. This paper presents an approach for developing such filters for adaptive signal processing. Several least-squares algorithms are derived for adjusting the coefficients of an adaptive linear phase filter. It is shown that smoothing filters, rather than the commonly used prediction filters, are a more natural choice if linear phase characteristics are required.

关键词： filtering algorithms Adaptive filters Finite impulse response filter Nonlinear filters Equations Signal processing algorithms Frequency Digital filters Delay Algorithm design and analysis

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QR-decomposition based algorithms for adaptive Volterra filtering

QR-decomposition based algorithms for adaptive Volterra filt...

引用

IEEE International Symposium on Circuits and Systems (ISCAS)

作者： M.A. Syed V.J. Mathews Department of Electrical Engineering University of Utah Salt Lake UT USA Dept. of Electr. Eng. Utah Univ. Salt Lake City UT USA

The authors present an approach to the development of fast and numerically stable recursive least squares (RLS) algorithms for adaptive nonlinear filtering using QR-decomposition of the data matrix. They introduce a pair of QR-RLS adaptive algorithms for second-order Volterra filtering. Both the algorithms are based solely on Given's rotation. Hence both are numerically stable and highly amenable to parallel implementations using arrays. One of the algorithms is a block processing algorithm in the sense that it processes all the channels simultaneously. The other processes the channels sequentially. The sequential algorithm is computationally much more efficient than the block algorithm and is comparable to that of fast RLS Volterra filters. Another attractive feature of sequential processing is that knowledge of the single-channel algorithm can be applied to the multichannel case.< >

关键词： filtering algorithms Adaptive filters Nonlinear filters Transversal filters Resonance light scattering Vectors Computational complexity Polynomials Least squares methods Signal processing

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Kalman filtering in stochastic gradient algorithms: construction of a stopping rule

Kalman filtering in stochastic gradient algorithms: construc...

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

作者： B. Bittner L. Pronzato Laboratoire I3S Université de Nice-Sophia Antipolis-CNRS Sophia-Antipolis France

Stochastic gradient algorithms are widely used in signal processing. Whereas stopping rules for deterministic descent algorithms can easily be constructed, using for instance the norm of the gradient of the objective function, the situation is more complicated for stochastic methods since the gradient needs first to be estimated. We show how a simple Kalman filter can be used to estimate the gradient, with some associated confidence, and thus construct a stopping rule for the algorithm. The construction is illustrated by a simple example. The filter might also be used to estimate the Hessian, which would open the way to a possible acceleration of the algorithm. Such developments are briefly discussed.

关键词： Kalman filters Stochastic processes filtering algorithms Signal processing algorithms Equations Acceleration Gradient methods Statistics Symmetric matrices State estimation

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Analysis of conjugate gradient algorithms for adaptive filtering

Analysis of conjugate gradient algorithms for adaptive filte...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Pi Sheng Chang A.N. Willson Electrical Engineering Department University of California Los Angeles CA USA

We describe and analyze two approaches to the implementation of the Conjugate Gradient algorithm for adaptive filtering. In particular their convergence rate and misadjustment are compared. A new analysis approach in the z-domain is used in order to find the asymptotic performance, and stability bounds are established. The behavior of the algorithms in finite word-length computation are described and dynamic range considerations are discussed. It is shown that, close to steady-state, the algorithms' behaviors are similar to the Steepest Descent algorithm, where the stalling phenomenon is also observed. Using 16-bit fixed-point number representation, our simulations show that the algorithms are numerically stable.

关键词： Algorithm design and analysis filtering algorithms Adaptive filters Convergence Performance analysis Stability analysis Asymptotic stability Dynamic range Steady-state Computational modeling

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On some efficient algorithms for moving-average filtering

On some efficient algorithms for moving-average filtering

引用

Midwest Symposium on Circuits and Systems (MWSCAS)

作者： V. Lyandres S. Briskin Department of Electrical and Computer Engineering Ben-Gurion University of the Negev Beersheba Israel

A new approach to moving-average filtering is proposed based on a model of an input mixture as a finite sum of sinusoidal segments. The correspondent filter is particularly efficient when processing very short-duration mixtures. Among its advantages are high precision of useful signal recovery and relative simplicity of construction. algorithms and the results of computer simulations of the separation of signals as well as of the filtering of signals from interference including white noise are presented.< >

关键词： filtering algorithms Signal processing algorithms Interference Signal processing Sampling methods Computer simulation White noise Smoothing methods Digital filters Frequency

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Projection algorithms for two-dimensional adaptive filtering applications

Projection algorithms for two-dimensional adaptive filtering...

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Asilomar Conference on Signals, Systems & Computers

作者： R.A. Soni W.K. Jenkins Coordinated Science Laboratory Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign Urbana-Champaign IL USA University of Illinois at Urbana-Champaign Urbana IL US

In this paper we present a family of "new" two-dimensional adaptive filtering algorithms for image processing applications. These algorithms are multidimensional versions of the families of data-reusing and projection algorithms. These two classes of algorithms allow the adaptive filtering system designer to choose performance and computational complexity by changing parameters without actually changing algorithm structure. By changing parameters, the desired convergence rate can be achieved at the expense of additional computational complexity. Experiments show that significant improvement may be obtained by marginal increases in computational complexity over the traditional normalized LMS algorithm.

关键词： Projection algorithms Adaptive filters filtering algorithms Convergence Least squares approximation Application software Computational complexity Tin Algorithm design and analysis Autocorrelation

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A new family of approximate QR-LS algorithms for adaptive filtering

A new family of approximate QR-LS algorithms for adaptive fi...

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IEEE/SP Workshop on Statistical Signal Processing (SSP)

作者： Y. Zhou S.C. Chan Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong China

This paper proposes a new family of approximate QR-based least squares (LS) adaptive filtering algorithms called p-TA-QR-LS algorithms. It extends the TA-QR-LS algorithm by retaining different number of diagonal plus off-diagonals (denoted by an integer p) of the triangular factor of the augmented data matrix. For p=1 and N it reduces respectively to the TA-QR-LS and the QR-RLS algorithms. It not only provides a link between the QR-LMS-type and the QR-RLS algorithms through a well-structured family of algorithms, but also offers flexible complexity-performance tradeoffs in practical implementation. These results are verified by computer simulation and the mean convergence of the algorithms is also analyzed

关键词： filtering algorithms Adaptive filters Least squares approximation Arithmetic Convergence Covariance matrix Computer simulation Algorithm design and analysis Communication system control Application software

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Collaborative filtering for Multi-class Data Using Belief Nets algorithms

Collaborative Filtering for Multi-class Data Using Belief Ne...

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International Conference on Tools for Artificial Intelligence (ICTAI)

作者： Xiaoyuan Su Taghi M. Khoshgoftaar Computer Science and Engineering Florida Atlantic University Boca Raton FL USA

As one of the most successful recommender systems, collaborative filtering (CF) algorithms can deal with high sparsity and high requirement of scalability amongst other challenges. Bayesian belief nets (BNs), one of the most frequently used classifiers, can be used for CF tasks. Previous works of applying BNs to CF tasks were mainly focused on binary-class data, and used simple or basic Bayesian classifiers (Miyahara and Pazzani, 2002; Breese et al., 1998). In this work, we apply advanced BNs models to CF tasks instead of simple ones, and work on real-world multi-class CF data instead of synthetic binary-class data. Empirical results show that with their ability to deal with incomplete data, extended logistic regression on naive Bayes and tree augmented naive Bayes (NB-ELR and TAN-ELR) models (Greiner et al., 2005) consistently perform better than the state-of-the-art Pearson correlation-based CF algorithm. In addition, the ELR-optimized BNs CF models are robust in terms of the ability to make predictions, while the robustness of the Pearson correlation-based CF algorithm degrades as the sparseness of the data increases

关键词： Collaboration filtering algorithms Bayesian methods Robustness Recommender systems Collaborative work Scalability Logistics Regression tree analysis Predictive models

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