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PARALLEL IMPLEMENTATION OF EFFICIENT LS algorithms FOR filtering AND PREDICTION

IEEE TRANSACTIONS ON ACOUSTICS SPEECH AND SIGNAL PROCESSING 1987年第11期35卷 1565-1569页

作者： KALOUPTSIDIS, N THEODORIDIS, S UNIV PATRAS DEPT COMP ENGNPATRASGREECE

A highly concurrent algorithm for LS FIR filtering and prediction is derived in this paper. The resulting reflection coefficients associated with the solution of Toeplitz and near to Toeplitz systems of equations are computed in an order recursive manner. This results in a highly parallel algorithm requiring O(p) computing time and O(p) processors, p being the order of the filter.

关键词： filtering algorithms Finite impulse response filter Statistics Signal processing algorithms Equations Concurrent computing Symmetric matrices Reflection Parallel algorithms Nonlinear filters

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Research on intelligent fault diagnosis of equipment parts based on improved wavelet domain Perona-Malik filtering algorithm

Research on intelligent fault diagnosis of equipment parts b...

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IEEE Information Technology and Mechatronics Engineering Conference (ITOEC)

作者： Pan Zhang Jing Zhang Hao Li Officers College of PAP Chengdu China

ISBN: (数字)9798331529482

ISBN: (纸本)9798331529499

An improved Perona-Malik filtering algorithm in wavelet domain was put forward for rotating mechanical vibration signals in multiplicative noise by integrating wavelet transform and Perona-Malik nonlinear anisotropy diffusion filtering model. At first, partial differential equation theories were introduced and Perona-Malik model, as one of important nonlinear anisotropy diffusion filtering algorithms, was brought in. From their applications in image denoising, it can be analyzed and inferred that both wavelet threshold denoising algorithm and Perona-Malik filtering model have their respective disadvantages. However there were a few interrelations and complementarities between them. Thus by integrating wavelet transform and Perona-Malik filtering model, an improved Perona-Malik filtering algorithm in wavelet domain was put forward for multiplicative signal denoising. Furthermore its algorithm principle was elaborated with flow chart. At last, by comparison with wavelet threshold denoising algorithm and Perona-Malik filtering model on bearing vibration signals, it can be indicated that this new improved Perona-Malik filtering algorithm is very appropriate for mechanical vibration signals in multiplicative noise. Above all, this new improved Perona-Malik filtering algorithm can not only realize signal denoising but also reserve signal features without signal distortions. It is a perfect synthesis of wavelet transform and Perona-Malik filtering model with better denoising performance and better anti-noise robustness.

关键词： Wavelet transforms Vibrations Wavelet domain filtering Noise reduction Noise filtering algorithms Wavelet analysis Mathematical models Signal denoising

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Analysis of the “Extended Kalman Filter – Adaptive Digital Filter” System Operation in filtering of Non-Stationary Signals

Analysis of the “Extended Kalman Filter – Adaptive Digital...

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International Russian Smart Industry Conference (SmartIndustryCon)

作者： Petr Bezmen Department of mechanics mechatronics and robotics Southwest State University Kursk Russian Federation

ISBN: (数字)9798331511241

ISBN: (纸本)9798331511258

Processing of various origin non-stationary signals in control systems for robotic devices is an urgent problem. The use of the extended Kalman filter (EKF) for working with non-stationary signals, including signal filtering, is limited by the precise settings of the filter — the observed system model and the covariance matrices of the EKF algorithm, which requires the adaptability of the filter algorithm under conditions of an input rapidly changing noised non-stationary signal. In this regard, the paper proposes to use the system “extended Kalman filter — adaptive digital filter” (EKF-ADF system) for filtering of non-stationary signals, which is the EKF augmented with an adaptive digital filter with the NLMS (normalized least mean squares) adaptation algorithm in order to compensate for the error in the operation of the EKF when non-stationary signal filtering is performing. The study of the operation of the EKF-ADF system as a filter for non-stationary signals shows that under conditions of a significant noise applied on a non-stationary signal, the algorithm of the EKF-ADF system, in comparison with the EKF algorithm, which has settings similar to the EKF settings in the EKF-ADF system, possesses the highest values of the “signal-to-noise ratio” evaluation of filtering results. The EKF-ADF system can be used as a state observer of robotic devices, which is capable of operating under conditions of non-stationary processes.

关键词： Adaptation models Adaptive systems filtering Service robots Systems operation filtering algorithms Kalman filters Digital filters Convergence Signal to noise ratio

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LEAST-SQUARES algorithms FOR ADAPTIVE LINEAR-PHASE filtering

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IEEE TRANSACTIONS ON ACOUSTICS SPEECH AND SIGNAL PROCESSING 1982年第3期30卷 381-390页

作者： FRIEDLANDER, B MORF, M STANFORD UNIV DEPT ELECT ENGNINFORMAT SYST LABSTANFORDCA 94305

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： Least squares methods filtering algorithms Adaptive filters Finite impulse response filter Nonlinear filters Signal processing algorithms Adaptive signal processing Smoothing methods Digital filters Frequency

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DIGITAL filtering AND EDGE-DETECTION algorithms FOR NUCLEAR-MEDICINE IMAGES

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IEEE TRANSACTIONS ON NUCLEAR SCIENCE 1980年第1期27卷 537-540页

作者： CAHILL, PT HO, SL KNOWLES, RJR CORNELL UNIV MED CTRNEW YORK HOSPNEW YORKNY 10021 LONG ISL COLL HOSP BROOKLYNNY 11201

Several edge detection algorithms were compared. The nearest neighbor algorithm (NNA) was shown to be superior to the other methods. The NNA was then used to evaluate the effects of nine common digital filters. It was shown that high pass filters improve relative constrast at the expense of edge definition. Furthermore, low pass filters only improve edge detection in the limit of low signal to noise ratios. Finally, all filters tested significantly distorted area relationships and perimeter contours.

关键词： Digital filters Image edge detection filtering algorithms Nuclear medicine Low pass filters Imaging phantoms Cameras Magnetic separation Nearest neighbor searches Signal to noise ratio

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Ham or Spam? A comparative study for some Content-based Classification algorithms for Email filtering

Ham or Spam? A comparative study for some Content-based Clas...

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17th IEEE Mediterranean Electrotechnical Conference (MELECON)

作者： Saab, Salwa Adriana Mitri, Nicholas Awad, Mariette Amer Univ Beirut Fac Elect & Comp Engn Beirut Lebanon

ISBN: (纸本)9781479923373

Spam emails are widely spreading to constitute a significant share of everyone's daily inbox. Being a source of financial loss and inconvenience for the recipients, spam emails have to be filtered and separated from legitimate ones. This paper presents a survey of some popular filtering algorithms that rely on text classification to decide whether an email is unsolicited or not. A comparison among them is performed on the SpamBase dataset to identify the best classification algorithm in terms of accuracy, computational time, and precision/recall rates.

关键词： e-mail filters electronic mail Unsolicited electronic mail Sorting algorithm Ham Text categorization filtering algorithms Recipients

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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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International Conference on Image Processing

作者： Liu, P Chong, MN Goh, WB NANYANG TECHNOL UNIV SCH APPL SCISINGAPORE 2263SINGAPORE

ISBN: (纸本)0780331222

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： Motion compensation Parallel algorithms Load balancing load forecasting unbalanced load filtering algorithms Filtration dynamic loading Theoretical analysis Median

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Advanced Adaptive Median Filter for Reducing Salt-and-Pepper Noise in GPR Data

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2025年 22卷

作者： Wang, Wentian Du, Wei Li, Yabin Jia, Zhuo Inst Disaster Prevent Hebei Key Lab Earthquake Dynam Sanhe 065201 Peoples R China Yunnan Univ Sch Earth Sci Chenggong Campus Kunming 650106 Yunnan Peoples R China Jilin Univ Coll Geoexplorat Sci & Technol Changchun 130012 Jilin Peoples R China Changsha Univ Sci & Technol Sch Civil Engn Changsha 410114 Hunan Peoples R China

Due to the influence of both the observation environment and the instruments themselves, ground-penetrating radar (GPR) data are often contaminated by random noise, which degrades data quality. Salt-and-pepper noise is a common type of such noise. Adaptive median filtering is an effective technique for removing this noise. However, it has the drawback of replacing original values that are not affected by noise with the median, which can lead to a degradation in image quality. In this letter, we propose an improved adaptive median filtering method. First, we assess whether the original value is contaminated by salt-and-pepper noise. If the value is affected, filtering is applied. The window size is adaptively increased, and the window is subdivided into smaller sections. Multiple median calculations are then performed on the segmented windows to ensure the validity of the median. When the noise density is high, the median of the nonnoise points in the largest window is selected as the output, thereby minimizing the negative impact of noise on the median calculation. Both synthetic and real-world data validations demonstrate that the improved method significantly outperforms traditional adaptive median filtering, conventional median filtering, and other filtering methods, particularly in high-noise scenarios, thus confirming the superiority of the proposed algorithm.

关键词： Noise filtering Wavelet transforms Reflection Noise measurement Ground penetrating radar filtering algorithms Synthetic data Surface roughness Rough surfaces Adaptive median filtering ground-penetrating radar (GPR) random noise suppression signal processing

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Ant Colony Optimized Extended Kalman Filter for State of Charge Estimation of Lithium-Ion Batteries

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IEEE TRANSACTIONS ON INSTRUMENTATION AND MEASUREMENT 2025年 74卷

作者： Kannan, M. Sundareswaran, K. Srinivasa Rao Nayak, P. Simon, Sishaj P. Mithun, T. LBS Coll Engn Dept Elect & Elect Engn Kasaragod 671542 Kerala India Natl Inst Technol Dept Elect & Elect Engn Tiruchirappalli 620015 India Expert Global Solut Pune 431210 India

This work reports the practical implementation of ant colony optimized (ACO) extended Kalman filter (EKF) for state of charge (SoC) estimation of lithium-ion batteries (LiBs). The scheme is realized by employing a low-priced microcontroller. Two prominent parameters of EKF, namely, process (Q) and measurement (R) error covariances, are fine-tuned using the ACO algorithm. The problem of finding optimum values of Q and R together with SoC estimation is reformulated as an optimization problem. The proposed method has several advantages, such as computational simplicity, derivative-free operation, and near-optimal convergence with random initial guess. The performance, effectiveness, and robustness of the new SoC estimation are demonstrated for four different operating points of the tested battery. Measured mean square, mean absolute, and mean absolute percentage errors of 0.0165%, 1.2%, and 2%, respectively, with 99.34% R-squared value indicates that the proposed method is a promising candidate for the estimation of SoC of LiBs. Furthermore, the effectiveness of the ACO-tuned EKF approach is experimentally validated in comparison to the conventional EKF method.

关键词： Batteries Estimation Battery charge measurement Mathematical models Kalman filters Voltage measurement Temperature measurement Noise measurement Noise filtering algorithms Ant colony optimization (ACO) code composer studio (CCS) extended Kalman filter (EKF) lithium-ion battery (LiB) microcontroller state of charge (SoC)

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An Information Flow Switching-Based Despeckling Network Under Real Dual-Polarization SAR Conditions

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IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 2025年 63卷

作者： Lin, Liupeng Shen, Huanfeng Li, Jie Wu, Jingan Shi, Shaowei Yuan, Qiangqiang Wuhan Univ Sch Resource & Environm Sci Wuhan 430079 Peoples R China Wuhan Univ Sch Geodesy & Geomat Wuhan 430079 Peoples R China Sun Yat Sen Univ Sch Geospatial Engn & Sci Zhuhai 519082 Peoples R China

Polarimetric synthetic aperture radar (SAR) can capture rich polarization information of targets, but it is inherently affected by speckle. Learning-based methods have demonstrated superior speckle suppression potential. Most existing methods use optical images to simulate SAR noise for model training. Because of the significant differences in the imaging mechanisms between optical and SAR images, the data characteristics of these two types differ significantly, resulting in poor generalization performance. To this end, an Information Flow Switching-based Despeckling Network (IFSDN) is proposed for dual-polarization SAR image. By using the long time series data, the first dual-polarization SAR real dataset is constructed. The hybrid feature extraction module (HFEM) is constructed to independently extract and integrate features from both the diagonal and nondiagonal elements of the covariance matrix. Additionally, the multihierarchical residual attention despeckling (MRAD) module performs despeckling on feature maps from low to high levels. On this basis, the information flow switching mechanism facilitates the interaction of dominant features before and after despeckling, injecting spatial details into the despeckled results, reducing speckle noise, and preserving polarization information. By considering temporal changes, an adaptive joint loss function is, furthermore, constructed to guide the network training process, achieving high-fidelity despeckling while maintaining spatial-polarization information. Experiments show that IFSDN outperforms existing state-of-the-art methods in the speckle removal task for real dual-polarization SAR images, which can effectively preserve spatial and polarization information while suppressing speckles. Besides, generalization experiments demonstrate that the proposed model can be effectively applied to diverse datasets across various climate zones, showcasing its strong robustness.

关键词： Radar polarimetry Covariance matrices Speckle Feature extraction Training Optical filters Noise reduction Noise measurement filtering algorithms Microwave filters Deep learning dual-polarization SAR (Dual-SAR) image despeckling information flow switching mechanism real synthetic aperture radar (SAR) data remote sensing

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