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检索条件"主题词=filtering algorithms"

共 10342 条记录，以下是441-450 订阅

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Research on filtering Denoising Algorithm in Trace Image Enhancement Technology

Research on Filtering Denoising Algorithm in Trace Image Enh...

引用

Smart Grid and Artificial Intelligence (SGAI), International Conference on

作者： Han-Xin Zhang College of Forensic Science Criminal Investigation Police University of China Shenyang Liaoning P.R. China

ISBN: (数字)9798331518806

ISBN: (纸本)9798331518813

With the rapid development of technology, trace image enhancement techniques are playing an increasingly important role in modern criminal investigation work. This article focuses on the rapid and accurate identification of trace images at crime scenes and studies the performance of various filters in noise removal during image smoothing processing. Through a comparative analysis of a series of experimental dates, it is found that the Wiener filter demonstrates the best technical effect in enhancing the clarity of trace images, laying a solid theoretical foundation for subsequent edge detection and feature segmentation of trace images.

关键词： Image quality Wiener filters Accuracy Filters Image edge detection Noise filtering algorithms Solids Feature extraction Image enhancement

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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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Azimuth Pre-filtering Implemented in FPGA Based on Multi-phase Decomposition

Azimuth Pre-filtering Implemented in FPGA Based on Multi-pha...

引用

IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

作者： Xiaoyu Huang Yang Li Mengjie Jiang Shuo Sun Gang Xu The State Key Laboratory of Millimeter Waves School of Information Science and Engineering Southeast University Nanjing P.R. China Xi’an Microelectronic Technology Institute Xian P.R. China

ISBN: (数字)9798350351019

ISBN: (纸本)9798350351026

Azimuth pre-filtering is usually used to address the redundancy issue in Synthetic Aperture Radar (SAR) raw echo data, and reduce data size while improving the Signal-to-Noise Ratio (SNR). An azimuth pre-filtering method based on multi-phase decomposition for FPGA implementation is proposed in this paper. Firstly, a multi-phase decomposition-based Finite Impulse Response (FIR) decimation filter is derived for filtering and extraction through dividing the filter into multiple sub-filters. Then, the implementation process of the above-mentioned filter is designed for FPGA, and the module framework is provided. Finally, experimental results are given to demonstrate the correctness and effectiveness of the proposed method.

关键词： Finite impulse response filters Azimuth filtering Redundancy filtering algorithms Antennas and propagation Real-time systems Synthetic aperture radar Field programmable gate arrays Signal to noise ratio

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A Composite of Design of Collaborative filtering Models for Stay Recommendation

A Composite of Design of Collaborative Filtering Models for ...

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Advance Computing and Innovative Technologies in Engineering (ICACITE), International Conference on

作者： R Sivapriyan D. Santhakumar Pramod Kumar Naik E. Afreen Banu Waleed Sadeq Jaaywel Hassn Safi Department of EEE R & D Centre Sir MVIT Bengaluru department of computer science engineering saveetha school of engineering saveetha institute of medical and technical sciences chennai department of cse dayananda sagar university bengaluru department of cse saveetha school of engineering saveetha institute of medical and technical sciences (simats) chennai college of technical engineering national university of science and technology dhi qar iraq Al-Farahidi University Baghdad Iraq

ISBN: (数字)9798350360165

ISBN: (纸本)9798350360172

Analytics and filtering are becoming more and more important in the online retail space to improve consumer experience and guide corporate strategy. The abundance of faked reviews, known as spam, poses a danger to the accuracy of this data as it attempts to sway consumers’ opinions about rival brands and goods. Numerous researches have looked at effective ways to identify spam reviews in order to address this problem; these methods frequently make use of text analysis and artificial intelligence systems. In this paper, we offer a unique approach for filtering of personalized reviews related to hotel industry. Our method shows encouraging results across several performance parameters after thorough testing on a variety of review datasets. We do more than just remove spam from search results; We also provide customers with accurate product information, improving the overall hotel stay experience.

关键词： Industries Accuracy Text analysis Machine learning algorithms Reviews Computational modeling filtering algorithms

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Design of Recursive filtering Scheme via An Improved Weighted Try-Once Discard Protocol

Design of Recursive Filtering Scheme via An Improved Weighte...

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International Conference on Industrial Artificial Intelligence (IAI)

作者： Kaiwen Zheng Jun Hu Junhua Du Hui Yu Department of Applied Mathematics Harbin University of Science and Technology Harbin China Heilongjiang Provincial Key Laboratory of Optimization Control and Intelligent Analysis for Complex Systems Harbin University of Science and Technology Harbin China School of Automation Harbin University of Science and Technology Harbin China

ISBN: (数字)9798350356618

ISBN: (纸本)9798350356625

This paper investigates the recursive filtering (RF) problem for stochastic multi-rate (MR) systems, where the information transmission is regulated by an improved weighted try-once-discard protocol (IWTODP). In order to reduce communication overhead and mitigate network congestion, the IWTODP is firstly proposed and designed to schedule the order of data transmission from the sensors. The main objective of this paper is to design a new RF scheme to minimize the upper bound (UB) on the filtering error (FE) covariance in each iteration step in the presence of IWTODP and MR sampling. In particular, an iterative way is given to provide the expression form of the gain of RF. Finally, the effectiveness of the proposed RF method is illustrated through simulation example.

关键词： Radio frequency Schedules Protocols Upper bound filtering filtering algorithms Iron Sensor systems Data communication State estimation

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A Data-Reuse Regularized Recursive Least-Squares Adaptive filtering Algorithm

A Data-Reuse Regularized Recursive Least-Squares Adaptive Fi...

引用

International Symposium on Electronics and Telecommunications (ISETC)

作者： Radu Otopeleanu Laura-Maria Dogariu Cristian-Lucian Stanciu Constantin Paleologu Jacob Benesty Silviu Ciochină Technology POLITEHNICA Bucharest National University of Science Bucharest Romania Academy of Romanian Scientists Ilfov 3 Bucharest Romania INRS-EMT University of Quebec Montreal QC H5A 1K6 Canada

ISBN: (数字)9798350390865

ISBN: (纸本)9798350390872

The recursive least-squares (RLS) algorithm is very popular for adaptive filtering applications, mainly due to its fast convergence rate. The regularized version of this algorithm owns improved robustness features, especially in noisy environments. On the other hand, this robust behavior could influence the tracking capability of the algorithm and its overall convergence performance. To reach a proper balance between these performance criteria, a data-reuse regularized RLS algorithm is proposed in this paper. The specific data-reuse parameter of the resulting algorithm can be used as an additional control mechanism, thus reducing the influence of tuning the main convergence parameter of the RLS filter, which is the forgetting factor. Simulation results obtained in the framework of echo cancellation sustain the performance characteristics.

关键词： Hands Echo cancellers filtering Simulation filtering algorithms Robustness Telecommunications Noise measurement Tuning Convergence

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Research on Collaborative filtering Recommendation Algorithm Optimization in Study Tour Route Recommendation System

Research on Collaborative Filtering Recommendation Algorithm...

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Information and Communication Engineering (JCICE), International Joint Conference on

作者： Xinyi Lai Wenlong Li Chenke Yan KeJian Shen LingXuan Wu Xiaohua Zhang Information Engineering School Wenzhou Business College Wenzhou China

ISBN: (数字)9798350388725

ISBN: (纸本)9798350388732

This paper addresses two primary challenges in personalized Tour Route recommendation systems using collaborative filtering algorithms: data sparsity and scalability. To resolve data sparsity, it introduces the Slope One algorithm for filling missing values in the initial rating matrix and then applies a K-means clustering-based collaborative filtering algorithm for rating predictions. For scalability issues, the paper proposes an enhanced K-means algorithm with centroid agglomeration parameters. Comparative experiments using the MovieLens dataset show that these improvements significantly enhance recommendation accuracy, effectively addressing the problems of data sparsity and scalability. The study's findings offer valuable insights into collaborative filtering recommendation algorithms and provide a theoretical framework for refining the accuracy of recommendation systems.

关键词： Accuracy Collaborative filtering Scalability Refining Clustering algorithms filtering algorithms Prediction algorithms Information filters Recommender systems Optimization

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Research on Automotive LiDAR Performance Based on Simulated filtering Algorithm

Research on Automotive LiDAR Performance Based on Simulated ...

引用

Electronic Technology, Communication and Information (ICETCI), IEEE International Conference on

作者： Jiaoyang Liu Dandan Wu Rupeng Dou China Automotive Technology and Research Center Co. Ltd Tianjin China

ISBN: (数字)9798350361643

ISBN: (纸本)9798350361650

As an important part of the perception level of autonomous driving, point cloud data of automotive LiDAR is the key to performance testing. Due to the fact that the angle of obstacles and light factors change a lot during the driving, it is easy to have problems such as interstitial points and ghosts that affect the driving safety of the vehicle. In this paper, a point cloud performance test system is proposed to simulate the interference phenomenon in vehicle driving and test the performance of LiDAR. After testing and verification, the proposed method can be effectively tested and provide guarantee for the safe driving of vehicles. Since the noise of the point cloud will affect the performance of LiDAR and the driving safety of the vehicle, this paper proposes to filter the LiDAR point cloud through a simulated filtering algorithm to avoid the impact of noise on the performance.

关键词： Point cloud compression Laser radar Accuracy Vehicle driving Noise Interference filtering algorithms

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PPP Parameter Estimation Algorithm Based on Hybrid filtering

PPP Parameter Estimation Algorithm Based on Hybrid Filtering

引用

IEEE Information Technology, Networking, Electronic and Automation Control Conference

作者： Shihui Wang Xiyan Sun Yuanfa Ji Guangxi Key Laboratory of Precision Navigation Technology and Application Guilin University of Electronic Technology Guilin China Information and Communicaiton Schnool Guilin University of Electronic Technology Guilin China National & Local Joint Engineering Research Center of Satellite Navigation Positioning and Location Service Guilin China GUET-Nanning E-Tech Research Institute Co. Ltd. Nanning China

ISBN: (数字)9798350370805

ISBN: (纸本)9798350370812

Precise Point Positioning (PPP) is a technology that utilizes the Global Navigation Satellite System (GNSS) for high accuracy positioning. Starting from the quality control of PPP, this paper focuses on the convergence speed and positioning error of PPP, and the article proposes an improved optimization algorithm for parameter estimation in the data processing stage. A hybrid filtering algorithm is proposed for the re-convergence caused by data interruptions during data processing and the dispersion of localization results caused by abnormal observations. The algorithm combines Previous Data-Constrained Kalman Filter (PD-CKF) and Robust Adaptive Kalman Filter (RAKF), and takes the actual localization results of JFNG station as an example to confirm the new algorithm's. The simulation results show that the improved algorithm can effectively avoid the phenomenon of re-convergence caused by data interruption, and can dynamically adapt to the changes of the observation data by adaptively adjusting the filter parameters, so as to improve the system's effect of anti-differential.

关键词： Location awareness Global navigation satellite system Parameter estimation Heuristic algorithms Noise Quality control filtering algorithms Data processing Kalman filters Standards

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Research on Point Cloud filtering Algorithm Based on Kmeans++ Clustering and WLOP

Research on Point Cloud Filtering Algorithm Based on Kmeans+...

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Frontiers Technology of Information and Computer (ICFTIC), IEEE International Conference on

作者： Yingwei Liu Xiaoxiong Zhou Guangming Zhang Shuohuang Railway Development Co. Ltd of CHN ENERGY Beijing China College of Electrical Engineering and Control Science Nanjing Tech University Nanjing China

ISBN: (数字)9798331541750

ISBN: (纸本)9798331541767

In computer vision and 3D data processing, effective point cloud filtering is crucial for high-quality model reconstruction and analysis. Traditional methods like the weighted local optimal projection (WLOP) can reduce noise but often lead to over-smoothing, especially in complex or dense point clouds. This study introduces a novel point cloud filtering technique that integrates Kmeans++ clustering with an improved WLOA methodology. By pre-classifying the point cloud data with Kmeans++, the method enhances filtering efficiency and preserves more structural details. Experimental results demonstrate that this approach significantly outperforms traditional WLOP in terms of filtering accuracy and edge detail retention, particularly in large-scale or noisy environments. Additionally, this research assesses the impact of different clustering techniques on filtering performance, highlighting the effectiveness of Kmeans++ clustering within this framework. These findings advance point cloud processing technology and offer new insights and tools for further research and practical applications in the field.

关键词： Point cloud compression Solid modeling Three-dimensional displays filtering Noise reduction Noise filtering algorithms Data processing Data models Noise measurement

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