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检索条件"主题词=Filtering algorithms"

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Performance analysis of adaptive filtering algorithms for denoising of ECG signals

Performance analysis of adaptive filtering algorithms for de...

引用

International Conference on Advances in Computing, Communications and Informatics (ICACCI)

作者： Nasreen Sultana Yedukondalu Kamatham Bhavani Kinnara Dept of ECE Bhoj Reddy Engineering College Hyderabad India Dept of EIE Vignan Institute of Technology and Science Hyderabad India

Electrocardiogram (ECG) can help to diagnose range of diseases including heart arrhythmias, heart enlargement, heart inflammation (pericarditis or myocarditis) and coronary heart disease. ECG consists of noise which is non stationary that affects the reliability of ECG waveform. In this paper an adaptive filter for denoising ECG signal based on Least Mean Squares (LMS), Normalized Least Mean Square (NLMS), Affine Projection LMS (APA-LMS) and Recursive least Squares algorithm (RLS) is presented with experimental results and the results are found to be encouraging. The performances of these algorithms are compared in terms of various parameters such as SNR, PSNR, MSE and SD. To validate the proposed methods, real time recorded data from the MIT-BIH database is used. RLS algorithm is found to exhibit lower MSE, and higher SNR compared to other algorithms. Therefore the results demonstrate superior performance of adaptive RLS filter for denoising of ECG signal.

关键词： Electrocardiography Adaptive filters Least squares approximations filtering algorithms Convergence Noise Finite impulse response filters

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Stopping and Restarting Adaptive Updates to Recursive Least-Squares Lattice Adaptive filtering algorithms

Stopping and Restarting Adaptive Updates to Recursive Least-...

引用

IEEE Mountain Workshop on Adaptive and Learning Systems

作者： Jake Gunther Wang Song Tamal Bose Department of Electrical & Computer Engineering Utah State University Logan UT USA Qualcomm San Diego CA USA

ISBN: (纸本)1424401666

This paper reports several observations about stopping and restarting adaptive updates to recursive least-squares lattice (LSL) adaptive filtering algorithms. When updates are stopped, the adaptive filter becomes a fixed filter. Simulation examples demonstrate that large output error results from abruptly stopping or restarting adaptive updates. A remedy to the problem is to transition the adaptive updates to an off or on state gradually by driving the unknown system and the adaptive filter simultaneously to the all zero state. This is accomplished by setting the input signal to zero. The length (in number of samples) of the transition period is equal to the length of the adaptive filter. Simulation examples are given to illustrate the problem and the effectiveness of the proposed remedy

关键词： Lattices Adaptive filters filtering algorithms Error correction Resonance light scattering Drives Electronic mail Computational modeling Reflection System identification

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Performance study of adaptive filtering algorithms for noise cancellation of ECG signal

Performance study of adaptive filtering algorithms for noise...

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International Conference on Information, Communications and Signal Processing

作者： Syed Zahurul Islam Syed Zahidul Islam Razali Jidin Mohd. Alauddin Mohd. Ali Universiti Tenaga Nasional (UNITEN) Malaysia Universiti Kebangsaan Malaysia Bangi Selangor MY

ISBN: (纸本)9781424446568

Removal of noises from ECG (Electrocardiogram) signal is a classical problem. Moreover, nullifying AC and DC noises using the two adaptive algorithms-the LMS and the RLS from the ECG is a new study in biomedical science. In this paper, the four types of AC and DC noises have been implemented according to their basic properties. After that, these noises have been mixed with ECG signal and nullify these noises using the LMS and the RLS algorithms. At the end of this paper, a performance study has been done between these algorithms based on their parameters and also discussed the effect of filter length and the corresponding correlation coefficient. Results indicate that the DC bias noises cannot be handled by the LMS filtering whereas the RLS can handle both types of noises. Also, it is true for both algorithms that the filter length is proportional to MSE (Mean Square Error) rate and it takes more time to converge for both algorithms. Furthermore, most of the cases the RLS has achieved best effective noise cancellation performance although its convergence time is slightly high. But eventually its error has always dipped down below that of the LMS algorithm.

关键词： Adaptive filters filtering algorithms Noise cancellation Electrocardiography Least squares approximation Resonance light scattering Frequency Power supplies Nonlinear filters Mean square error methods

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Modeling and Comparison of Trajectory filtering algorithms in MLAT Systems

Modeling and Comparison of Trajectory Filtering Algorithms i...

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Systems of Signals Generating and Processing in the Field of on Board Communications

作者： A. A. Chugunov R. S. Kulikov V. B. Pudlovskiy N. I. Petukhov N. V. Masalkova National Research University MPEI Moscow Russia

In this article, an algorithm for determining RRS coordinates on the basis of Extended Kalman Filter with the processing of Time of Arrival (ToA) type measurements is proposed. The feature of the proposed algorithm is to determine the radio radiation source (RRS) time of transmission (ToT) in the MLAT system time scale and its filtration using priori information about the period of transmission. It's shown that evaluation of ToT and its filtration at a known period allows you to get closer to the accuracy provided by Time Of Fligth (ToF) method, which can not be implemented directly for MLAT systems. This hypothesis was confirmed by modeling several algorithms: one-step TDoA, filtered TDoA, filtered ToA and one-step ToF. Modeling was performed for both the known constant period and the non constant period. In both cases, the efficiency of the proposed ToA-F algorithm was the same. The efficiency of various algorithms was compared, including the traditional ones based on the calculation of arrival time differences and the proposed one based on ToA observations and using information about transmission period. The most important result was that the ToT estimation and filtering allows to achieve the properties of the ToF method in the MLAT system.

关键词： Location awareness Coordinate measuring machines Filtration Estimation filtering algorithms Information filters Time measurement

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Non linear filtering algorithms for chaotic signals: A comparative study

Non linear filtering algorithms for chaotic signals: A compa...

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International Workshop on Nonlinear Dynamics and Synchronization, INDS

作者： V. Kontorovich Z. Lovtchikova Electrical Engineering Department Communications section CINVESTAV Mexicali Mexico Engineering and Advanced Technology Interdisciplinary Professional Unit UPIITA-IPN Mexicali Mexico

ISBN: (纸本)9781424438440;9781424422876

A comparative study related to the approximate methods of nonlinear filtering for chaotic signals is presented in this paper. In the following, we considered chaos models given as the output signals of three well known strange attractors: Lorenz, Chua and Rossler. From the implementation point of view for real time applications of the approximate nonlinear filtering algorithms, the extended Kalman filtering (EKF) could be the most prospective one.

关键词： Maximum likelihood detection filtering algorithms Chaos Chaotic communication Differential equations Interference Nonlinear filters Markov processes Electronic mail Stochastic resonance

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A new family of gradient-based adaptive filtering algorithms with variable step size

A new family of gradient-based adaptive filtering algorithms...

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IEEE Conference and Exhibition on Global Telecommunications (GLOBECOM)

作者： Zhimin Du Peng Wan Tingrui Pei Weiling Wu School of Information Engineering Beijing University of Posts and Telecommunications Beijing China

Under a uniform framework, this paper develops a new family of adaptive filtering algorithms, where the wellknown normalized LMS algorithm and normalized constant modulus algorithm (CMA) are included. They all update the weight according to the gradient descent method, but this time a variable and relatively optimal step size is used instead of a constant one. Some application examples are also given to show their efficiencies.

关键词： Adaptive filters filtering algorithms Least squares approximation Cost function Equations Resonance light scattering Convergence Stochastic processes Computational complexity Least squares methods

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Ratio-based collaborative filtering algorithms

Ratio-based collaborative filtering algorithms

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International Symposium on Systems and Control in Aerospace and Astronautics (ISSCAA)

作者： Yaqiu Liu Zhendi Wang Man Li Northeast Forestry University Harbin China Heilongjiang Institute of Science and Technology Harbin China

Collaborative filtering is the process of predicting how a user would rate a given item from other user ratings. we propose a new collaborative filtering algorithms, ratio-based collaborative filtering algorithms, by calculating the ratio between the ratings of one item and another for users who rated both to predict the ratings. Ratio-based collaborative filtering algorithms are easy to implement, and have reasonably accurate, by factoring in the weighted average methods and the preference parameter, we achieve results competitive with traditional memory-based algorithms over the Movielens data sets. The result is sufficient to support our claim.

关键词： Collaboration filtering algorithms Recommender systems Information filtering Information filters Predictive models Motion pictures Collaborative work Clustering algorithms Machine learning algorithms

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Stochastic gradient based adaptive filtering algorithms with general cost functions

Stochastic gradient based adaptive filtering algorithms with...

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Asilomar Conference on Signals, Systems & Computers

作者： R. Sharma W.A. Sethares J.A. Bucklew Envirnomental Research Institute of Michigan Ann Arbor MI USA

Analysis of stochastic gradient based adaptive algorithms with general cost functions is carried out. The analysis holds under mild assumptions on the inputs and the cost function. Previous analyses typically consider mean and mean square behavior, we consider almost sure behavior. The parameter estimates are shown to enter a small neighborhood about the optimum value and remain there for a finite length of time. The asymptotic distribution of the parameter estimates is shown to be Gaussian. Adaptive algorithms which fall under the framework of this paper are signed error LMS, dual sign LMS, quantized state LMS, least mean fourth, dead zone algorithms, and momentum algorithms. Some discussion is presented regarding stochastic gradient algorithms where the regressor is replaced with a general function of the regressor.

关键词： Stochastic processes Adaptive filters filtering algorithms Cost function Least squares approximation Parameter estimation Adaptive algorithm Algorithm design and analysis System identification Adaptive systems

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Using Temporal Information to Improve Predictive Accuracy of Collaborative filtering algorithms

Using Temporal Information to Improve Predictive Accuracy of...

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International Asia-Pacific Web Conference (APWEB)

作者： Jia Rongfei Jin Maozhong Liu Chao School of Computer Science and Technology Beijing Aeronautics and Astronautics University Beijing China

In recent years, collaborative filtering becomes one of the most successful recommender systems. Its key technique is to predict new ratings from the known ratings. Unfortunately, in the previous research, the temporal information was rarely applied. That is to say, the ratings at different time were considered the same. However, from our point of view, not only the mean values of ratings in different periods are different, but users' opinions toward items may change with the passage of time as well. We analyze the influence of the temporal information and introduce three methods to apply the temporal information. Firstly, by mixing user, item and time attributes, we present a regression-based method. Secondly, to guarantee that the ratings in different time can contribute different weights to the predicting rating, we adjust the prediction function by adding a parameter, which is a function of the time between the predicting rating and the known rating. Thirdly, we select different methods to predict ratings of different periods. Experiments on two real large datasets show that our methods are effective and can improve the accuracy.

关键词： Accuracy filtering algorithms Recommender systems International collaboration Information filtering Information filters Collaborative work Chaos Computer science Space technology

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Least Squares Criterion Adaptive filtering algorithms as Tools for Calibration of Arrays with Digital Beamforming

Least Squares Criterion Adaptive Filtering Algorithms as Too...

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2020 IEEE Ukrainian Microwave Week (UkrMW)

作者： Victor Djigan Vladislav Kurganov Institute for Design Problems in Microelectronics of RAS Moscow Russia

ISBN: (数字)9781728173139

ISBN: (纸本)9781728173146

This paper explores the application of the adaptive filtering algorithms, based on Least Squares (LS) criterion for the calibration of the receiving arrays with digital beamforming. The digital beamforming assumes the access to the signals in the array channels as a requirement, which has to be provided if the adaptive signal processing is used. In this case, the adaptive filtering algorithms, based on the minimization of the LS error between the desired and array output signals, are able to estimate the channel parameter spread, which values can be stored and used for correction of the array weights during the beam steering. If a set of steered beam positions is known, for example in scanning arrays, the proposed algorithm allows to create a codebook of the array weights, which ensures the random variation of channel's parameters compensation and correct beam steering. The architecture of the proposed solution and mathematical description of a Recursive Least Squares (RLS) adaptive filtering algorithm, on which the calibration is based on, are presented. The algorithm uses a dynamic regularization of the array input signal correlation matrix. No specific correlation properties are required for the algorithm training signal. The simulation results demonstrate the ability of the calibration algorithm to compensate the channel's parameters spread. If the array channel Signal-to-Noise Ratio (SNR) is 0 ... 30 dB, then the achieved spread of the calibrated channels gain errors is about -20 dB. The norm of the ideal and calibrated array difference is also about -20 dB. The results are obtained with the Phase Shift Keying (PSK) and Quadrature Amplitude Modulated (QAM) training signals. The ability of the proposed calibration algorithm to operate in interference conditions is also demonstrated.

关键词： Training Array signal processing Heuristic algorithms Signal processing algorithms Adaptive arrays filtering algorithms Calibration

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