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32nd International Conference on Electrical Engineering (ICEE)

作者： Fard, Masoud T. Amiri, Mojtaba Olfat, Ali Univ Tehran Signal Proc & Commun Syst Lab Tehran Iran

ISBN: (纸本)9798350376357;9798350376340

Massive multiple-input multiple-output (MIMO) systems are a critical technology in modern wireless communications, allowing increased data rates and link reliability. Owing to the increasing number of receivers and transmitters, finding a reliable, low-complexity, and accurate algorithm for data detection is a major issue. In this paper, an innovative method that melds the traditional minimum mean square error (MMSE) detector with an adaptive filter-based reconstruction algorithm is introduced. It is noteworthy that the MMSE detector error often exhibits sparsity, wherein only a few elements in the error vector are non-zero, representing incorrectly detected symbols. This inherent sparsity enables the utilization of efficient sparse recovery techniques for accurate error reconstruction. Numerical results demonstrate that the proposed method enhances performance without a substantial increase in computational overhead.

关键词： Massive MIMO system MMSE detector sparse error recovery adaptive filter algorithms

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adaptive Channel Equalization for Frequency Response Correction of Instrument Transformers 20

Adaptive Channel Equalization for Frequency Response Correct...

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20th International Conference on Harmonics and Quality of Power (ICHQP) - Power Quality in the Energy Transition

作者： Resende, Denise F. Duque, Carlos A. Nepomuceno, Erivelton G. Lima, Marcelo A. A. Silva, Leandro R. M. Univ Fed Juiz de Fora Dept Elect Engn BR-36036900 Juiz De Fora Brazil Univ Fed Sao Joao del Rei BR-36307352 Sao Joao Del Rei Brazil

ISBN: (纸本)9781665416399

Power quality (PQ) assessment has proven to be important in recent years for the power grid. The influence of the instrumentation transformer frequency response on some PQ metrics can be critical. For example, the measurement of harmonics can exhibit great inaccuracy if the measurement transformers have a frequency response that is not constant in the frequency range of interest. This problem is very similar to that found in communication systems where a non-ideal communication channel distorts the transmitted information. Thus the channel equalization approach can be used to adaptively compensate for the distortions caused by the current and voltage transducers. In the equalization approach, adaptive filtering algorithms such as RLS (Recursive Least Square) and LMS (Least Mean Squares) are commonly used to find the equalization filter coefficients. In this work, the novelty is adaptive channel equalization methodology applied to compensate for the frequency response of instrumentation transformers. Simulated results show that the methodology can reduce significantly the TVE (Total Vector Error) caused by voltage and current transformers.

关键词： power quality instrument transformer adaptive channel equalization adaptive filter algorithms

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Performance Comparison of various adaptive filter algorithms for ECG Signal Enhancement and Baseline Wander Removal

Performance Comparison of various Adaptive Filter Algorithms...

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3rd International Conference on Computing Communication and Networking Technologies (ICCCNT)

作者： Rehman, Syed Ateequr Kumar, Ranjith Rao, Madhusudhana VTU Karnataka AMC Engn Coll Belagavi Karnataka India UG Consultants Bangalore Karnataka India

ISBN: (纸本)9781467356930

In this paper various adaptive filter based algorithms that can be applied to ECG signal in order to remove various artifacts from them are presented. The goal of the paper is to show the comparison based on signal to noise ratio of all adaptive filter algorithms used for the analysis of ECG signals with Baseline wander noise. Simulation studies shows that the proposed novel algorithms like NLMS, SRLMS and DNLMS based on adaptive systems present better performances compared to existing realizations LMS, DLMS and NSRLMS based procedures in terms of signal to noise ratio.

关键词： ECG adaptive filter algorithms BWN LMS NLMS DLMS DNLMS SRLMS NSRLMS

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Improved Hybrid Variable and Fixed Step Size Least Mean Square adaptive filter Algorithm with Application to Time Varying System Identification 10

Improved Hybrid Variable and Fixed Step Size Least Mean Squa...

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10th System of Systems Engineering Conference (SoSE)

作者： Farhan, Farqad Y. Ameen, Siddeeq Y. Koya Univ Software Engn Dept Koya Iraq Univ Mosul Dept Comp Engn Mosul Iraq

ISBN: (纸本)9781479976119

In this paper a new simplified adaptive filter algorithm is introduced which is based on the hybrid operation of variable step-size and fixed step-size least mean square adaptive algorithm. In this proposed algorithm the variable step-size is used in the first stage, the algorithm adopts the fixed step size least mean square (LMS) whenever an acceptable mean square error threshold is reached that ensures the required steady state error and stability. The simulation results obtained show that the new algorithm outperforms the standard least mean square (LMS) in the desired transient-response, and outperforms the normalized least mean square (NLMS) algorithm in the desired transient and the steady-state response. It is shown that this new algorithm is able to track time-varying systems with better performance response. Also, the computational-complexity for this algorithm is reduced as compared with the ordinary least mean square (LMS).

关键词： adaptive filter algorithms LMS VSSLMS NLMS time varying system identification

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Denoising of measured lightning electric field signals using adaptive filters in the fractional Fourier domain

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MEASUREMENT 2014年 55卷 616-626页

作者： Rojas, Herbert E. Cortes, Camilo A. Univ Disrital Francisco Jose de Caldas Electromagnet Compatibil & Interference Res Grp G Bogota Colombia Univ Nacl Colombia Electromagnet Compatibil Res Grp EMC UNC Bogota Colombia

In this paper, adaptive filters are applied (in the fractional Fourier transform domain - FRFd) for denoising lightning electric-field signals, both in high and low signal-to-noise-ratio (SNR) environments. These filters are based on the concentration energy property of the fractional Fourier transform (FRFT). The proposed method integrates the advantages of leakage least mean square (LLMS) and normalized least mean square (NLMS) algorithms, including a leakage factor gamma and a normalized step-size mu, in order to reduce the memory effect when tracking a non-stationary signal and also to reduce the effect of the input signal power on the algorithm performance, respectively. Parameter estimation of adaptive filters is analyzed in several case studies for various lightning-generated electric field signals. The adaptive algorithm is shown to provide better performance in low SNR environments. Finally, some analyses (in terms of temporal parameters of lightning electric-field signals) are included to demonstrate the validity of the method. (c) 2014 Elsevier Ltd. All rights reserved.

关键词： Lightning measurements Electric field adaptive filter algorithms Least mean square (LMS) Fractional Fourier transform Fractional Fourier domain

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Exact convergence analysis of adaptive filter algorithms without the persistently exciting condition

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 2007年第5期55卷 2077-2083页

作者： Sakai, Hideaki Yang, Jun-Mei Oka, Tetsuya Kyoto Univ Grad Sch Informat Dept Syst Sci Kyoto 6068501 Japan

Exact convergence analysis of the recursive least square and least mean square (LMS) algorithms in adaptive filtering is presented for the case of sinusoidal signal cancellation without the persistently exciting condition. This situation occurs when the number of tap coefficients of the adaptive filter exceeds that of the complex sinusoids in the input signal. The convergent point of both algorithms is shown to be the one determined by the pseudo inverse of the deterministic covariance matrix. The convergence proof for the LMS algorithm is based on the Lyapunov function method. Finally, the validity of the obtained results is supported by simulation results.

关键词： adaptive filter algorithms exact convergence analysis persistently exciting condition sinusoidal noise cancellation

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Car tracking by quantised input LMS, QX-LMS algorithm in traffic scenes

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IEE PROCEEDINGS-VISION IMAGE AND SIGNAL PROCESSING 2006年第1期153卷 37-45页

作者： Yazdi, HS Lotfizad, M Fathy, M Tarbiat Moallem Univ Sabzevar Fac Engn Sabzevar Iran Tarbiat Modares Univ Dept Elect Engn Tehran Iran Iran Univ Sci & Technol Fac Comp Engn Tehran Iran

The tracking algorithm is an important tool for motion analysis in computer vision. A new car tracking algorithm is proposed which is based on a new clipping technique in the field of adaptive filter algorithms. The uncertainty and occlusion of vehicles increase the noise in vehicle tracking in a traffic scene, so the new clipping technique can control noise in prediction of vehicle positions. The authors present a new quantised version of the LMS, namely the QX-LMS algorithm, which has a better tracking capability in comparison with the clipped LMS (CLMS) and the LMS and also involves less computation. The threshold parameter of the QX-LMS algorithm causes controllability and the increase of tracking and convergence properties, whereas the CLMS and LMS algorithms do not have these capabilities. The QX-LMS algorithm is used for estimation of a noisy chirp signal, for system identification and in car tracking applications. Simulation results for noisy chirp signal detection show that this algorithm yields a considerable error reduction in comparison to the LMS and CLMS algorithms. The proposed algorithm, in tracking some 77 vehicles in different traffic scenes, shows a reduction of the tracking error relative to the LMS and CLMS algorithms.

关键词： adaptive filters least mean squares methods quantised input LMS system identification computer vision image denoising image motion analysis traffic scenes Computer vision and image processing techniques motion analysis filtering methods in signal processing quantisation (signal) adaptive filter algorithms Interpolation and function approximation (numerical analysis) car tracking algorithm Optical, image and video signal processing noisy chirp signal estimation clipping technique noisy chirp signal detection road traffic vehicle tracking automobiles

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