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Vehicle Position Prediction Using Particle filtering Based on 3D CNN-LSTM Model

IEEE TRANSACTIONS ON MOBILE COMPUTING 2024年第4期23卷 2992-3004页

作者： Wang, Jiaqin Liu, Kai Gong, Yi Beihang Univ Sch Elect & Informat Engn Beijing 100191 Peoples R China Beihang Univ Hangzhou Innovat Inst Hangzhou 310051 Peoples R China Beijing Informat Sci & Technol Univ Sch Informat & Commun Engn Minist Informat Ind Beijing 100876 Peoples R China Beijing Informat Sci & Technol Univ Key Lab Informat & Commun Syst Minist Informat Ind Beijing 100876 Peoples R China Beijing Informat Sci & Technol Univ Key Lab Modern Measurement & Control Technol Minist Informat Ind Beijing 100876 Peoples R China

Vehicle position prediction (VPP) is of great significance for navigation planning and traffic safety of intelligent vehicles. In general, particle filtering (PF) uses global navigation satellite system (GNSS) to implement VPP. However, it does not consider geographic layer information (GLI) and its particle weight is not combined with the real-world geographic position information, which leads to insufficient prediction preparation. To resolve this problem, we propose a novel PF-based VPP method by using three-dimensional convolutional neural network and long short-term memory (3D CNN-LSTM) network model. First, for data preprocessing, we extract kinematic information features from GNSS, and evenly divide the area around each GNSS point into multiple grids and calculate the probability of grids center belonging to each GLI type. In addition, in order to better reflect the relationship between two consecutive positions due to the factors such as the conversion angle, we construct tilted cells to represent possible positions of each vehicle at any time. Second, a novel 3D CNN-LSTM model is designed to calculate the vehicle occurrence probability (VOP) in each tilted cell by processing the GLI and GNSS data, which can optimize the PF weight of each particle, and then improve PF to make more precise position prediction. Finally, the experimental results demonstrate that the proposed VPP method can improve the cell prediction accuracy, and then significantly improve the position prediction precision.

关键词： Feature extraction Global navigation satellite system Prediction algorithms Three-dimensional displays Convolutional neural networks Predictive models filtering algorithms 3D CNN geographic layer information LSTM particle filtering vehicle position prediction

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Research on Personalized Recommendation algorithms Based on Collaborative filtering

Research on Personalized Recommendation Algorithms Based on ...

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Computer Engineering and Intelligent Control (ICCEIC) International Conference on

作者： Hangrui Cao Zhan Shi Hao Yang School of Computer Engineering Nanjing Institute of technology Nanjing China

ISBN: (数字)9798331507992

ISBN: (纸本)9798331508005

Traditional recommendation algorithms often suffer from issues such as data sparsity, lack of memory association, and prediction accuracy. This paper proposes a collaborative filtering recommendation algorithm that integrates memory and balance factors. When analyzing user interests and preferences, there are optimal design from multiple aspects such as memory factors and novelty of objects. This paper introduces a memory weight function to reflect the temporal characteristics of user interests, and incorporates a balance factor into user similarity calculation to reduce the impact of popular items on user similarity calculation, thus improving the accuracy of user similarity calculation. Experimental validation is conducted on the MovieLens dataset using MAE as the evaluation metric. The results demonstrate significant improvements in the experimental metrics, indicating enhanced recommendation accuracy and improved resilience to data sparsity.

关键词： Measurement Accuracy Collaborative filtering Clustering algorithms filtering algorithms Prediction algorithms Indexes Recommender systems Resilience Intelligent control

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Efficient Memory Layout for Pre-Alignment filtering of Long DNA Reads Using Racetrack Memory

IEEE COMPUTER ARCHITECTURE LETTERS

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IEEE COMPUTER ARCHITECTURE LETTERS 2024年第1期23卷 129-132页

作者： Khan, Asif Ali Hameed, Fazal Shahroodi, Taha Jones, Alex K. Castrillon, Jeronimo Tech Univ Dresden D-01069 Dresden Germany Amer Univ Sharjah AUS Sharjah U Arab Emirates Univ Pittsburgh Pittsburgh PA 15261 USA Delft Univ Technol NL-2628 Delft Netherlands

DNA sequence alignment is a fundamental and computationally expensive operation in bioinformatics. Researchers have developed pre-alignment filters that effectively reduce the amount of data consumed by the alignment process by discarding locations that result in a poor match. However, the filtering operation itself is memory-intensive for which the conventional Von-Neumann architectures perform poorly. Therefore, recent designs advocate compute near memory (CNM) accelerators based on stacked DRAM and more exotic memory technologies such as racetrack memories (RTM). However, these designs only support small DNA reads of circa 100 nucleotides, referred to as short reads. This letter proposes a CNM system for handling both long and short reads. It introduces a novel data-placement solution that significantly increases parallelism and reduces overhead. Evaluation results show substantial reductions in execution time ($1.32\times$1.32x) and energy consumption (50%), compared to the state-of-the-art.

关键词： Bioinformatics Genomics filtering filtering algorithms Layout DNA Sequential analysis Domain wall memory near memory computing racetrack memory sequence alignment

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Hybrid Geometric Algebra Correntropy: Definition and Application to Robust Adaptive filtering

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS II-EXPRESS BRIEFS 2024年第2期71卷 952-956页

作者： Long, Xiaoqiang Zhao, Haiquan Hou, Xinyan Southwest Jiaotong Univ Sch Elect Engn Chengdu 610031 Peoples R China Southwest Jiaotong Univ Key Lab Magnet Suspens Technol & Maglev Vehicle Minist Educ Chengdu 610031 Peoples R China

In order to address the performance degradation of the geometric algebra LMS algorithms (GA-LMS) in the face of non-Gaussian disturbances, this brief proposes a robust adaptive filtering algorithm based on hybrid maximum geometric algebra correntropy (HMGACC) perspective. Specially, we define the hybrid geometric algebra correntropy (HGAC) cost function, which can increase the flexibility of the correntropy. Secondly, we introduce a proportionality parameter to make the HGAC algorithm more general, which can be changed into a traditional single kernel GAC algorithm by changing the proportionality parameter. Furthermore, the addition of the proportionality parameter also allows for a non-fixed form of step-size, i.e., the step-size of the proposed algorithm in this brief is a hybrid step-size, which will be more useful for us to improve the performance of the algorithm. Finally, in the experimental simulations, the proposed algorithm can achieve better convergence and steady-state performance in comparison with other competing algorithms under different non-Gaussian disturbances, which confirms the feasibility of the proposed method.

关键词： Algebra Signal processing algorithms Optimized production technology Manganese filtering algorithms Steady-state Signal processing Geometric algebra hybrid maximum correntropy proportionality parameter hybrid step-size

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An adaptive iterative reweighted filtering methodology for urban MLS dataset

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JOURNAL OF SPATIAL SCIENCE 2024年第4期69卷 1075-1095页

作者： Suleymanoglu, Baris Soycan, Arzu Soycan, Metin Yildiz Tech Univ Dept Geomat Engn Istanbul Turkiye

This study presents a novel filtering methodology for Mobile Laser Scanning (MLS) data using robust iterative reweighting. Initially, 3D point clouds are projected onto a 2D grid to create surfaces from the lowest points. Weights are assigned based on the Height Above Ground (HAG) of these points. Ground points are distinguished by applying a surface function to the dataset via iterative reweighting. Among the tested four robust weight functions, the Denmark and Beaton-Tukey functions outperformed others, achieving total error values of 2.30 and 2.32 across three test areas, respectively. This method efficiently filters MLS data, irrespective of ground point proportions.

关键词： Mobile laser scanning systems point clouds filtering algorithms robust weighting

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Performance analysis of adaptive filtering algorithms for denoising of ECG signals

Performance analysis of adaptive filtering algorithms for de...

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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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Hybrid Nonsingleton Fuzzy Strong Tracking Kalman filtering for High Precision Photoelectric Tracking System

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IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2023年第3期19卷 2395-2408页

作者： Zhao, Tao Tong, Wei Mao, Yao Sichuan Univ Coll Elect Engn Chengdu 610065 Peoples R China Chinese Acad Sci Inst Opt & Elect Chengdu 610209 Peoples R China Chinese Acad Sci Key Lab Opt Engn Chengdu 610209 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

This article presents a hybrid nonsingleton fuzzy strong tracking Kalman filter (H-NFSTKF) for the high-precision photoelectric tracking system (PTS) to improve state estimation performance in the complex case of measurement noise change and velocity state mutation. The proposed H-NFSTKF is composed of three parts: A strong tracking Kalman filter (STKF) and two fuzzy logic systems (FLSs) including a singleton FLS (SFLS) and a nonsingleton FLS (NFLS). To compare different nonsingleton firing strength approaches, standard method (Sta-NS), centroid-based method (Cen-NS), similarity-based method (Sim-NS), and subsethood-based method (Sub-NS) are discussed. In addition, two traditional control strategies, i.e, dual closed-loop and feed-forward control, as well as four filtering methods including Kalman filter (KF), STKF, fuzzy STKF (FSTKF), and single nonsingleton FSTKF (S-NFSTKF) are also designed to show the superiority of the proposed H-NFSTKF. Finally, by means of comparative simulation analyses and experimental results, the excellence of the proposed H-NFSTKF is verified.

关键词： Noise measurement Fuzzy logic Fading channels filtering Estimation Kalman filters filtering algorithms Fuzzy logic systems (FLSs) photoelectric tracking system (PTS) Kalman filter (KF)

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Improvement of the Gerchberg-Saxton Algorithm Convergence in Phaseless Antenna Measurements via Spherical-Wave filtering

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2023年第5期71卷 4540-4545页

作者： Mezieres, Nicolas Le Coq, Laurent CNRS IETR UMR F-35700 Rennes France

The phaseless characterization of antennas enables configurations with reduced measurement efforts as the phase acquisition is highly sensitive to numerous error sources. However, both the magnitude and phase information are necessary to reconstruct the 3-D radiation pattern of the tested radiating system. The two scans technique allows for accurate phase retrievals from magnitude-only measurements. In this communication, the two concentric spheres setup is considered with a Gerchberg-Saxton (GS) algorithm as a phase retrieval procedure. These methods are known for their convergence problems, the price to pay for the easier, magnitude-only, measurements. A filtering approach is studied to mitigate these effects and a new, more efficient, filter is proposed. A loop of GS runs coupled with filters is shown to improve the radiation pattern reconstruction without much considerations. Validations are led by simulations and experimental data acquired using a commercial system.

关键词： Antenna measurements filtering algorithms Phase measurement Noise measurement Convergence Low-pass filters Indexes near field (NF) phase retrieval phaseless measurement spherical waves (SWs)

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Research on Background Learning Correlation filtering Algorithm With Multi-Feature Fusion

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IEEE ACCESS 2023年 11卷 32895-32906页

作者： Ren, Hongge Xing, Leigang Shi, Tao Tianjin Chengjian Univ Sch Control & Mech Engn Tianjin 300384 Peoples R China North China Univ Sci & Technol Coll Elect Engn Tangshan 063210 Peoples R China Tianjin Univ Technol Sch Elect Engn & Automat Tianjin 300384 Peoples R China

Aiming at the problems of occlusion, drift, and background change in visual image tracking, a background learning correlation filtering algorithm based on multi-feature fusion is proposed. In the framework of correlation filtering, multi-feature fusion, multi-template update, and background learning regularization are used to improve the performance of the filter in the problem of template contamination and object occlusion. The fast directional gradient histogram (FHOG), color feature (CN), and texture feature (ULBP) were extracted, and the feature channels were connected in series. Then the depth features of Conv4-4 and Conv5-4 layers were extracted through the VGG-19 network, and the appearance model of the target was constructed. To reduce the sensitivity of the filter to the sudden change of background, a background learning filter is constructed, and the alternate direction multiplier method (ADMM) is used to speed up the calculation of the filter. In the model update stage, aiming at the problem of pollution of the original template caused by target occlusion, a high-confidence multi-template fusion update strategy is proposed by fusing the template with the highest confidence in the current frame, the previous frame, and the history frame. Finally, the proposed algorithm is tested on OTB50, OTB100, UAV123, and TC128 experimental data sets, and some classical and latest algorithms. The experimental results show that the tracking accuracy and robustness of the correlation filtering algorithm are improved.

关键词： Target tracking filtering algorithms Feature extraction Correlation Information filters Solid modeling Predictive models machine vision correlation filtering background learning more templates

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Efficient 2D Tikhonov smoothness regularization with recursive filtering

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PATTERN RECOGNITION LETTERS 2023年 175卷 95-103页

作者： Ferreira, Hermes H. Gastal, Eduardo S. L. Univ Fed Rio Grande do Sul Inst Informat Porto Alegre RS Brazil

In this work, we describe an implementation of the 2D Tikhonov regularization filter which scales lin-early with the input signal's size. In the homogeneous case, we propose a novel algorithm to decompose the filter's 2D kernel as a sum of axis-aligned Gaussians. Our algorithm uses symmetries of the kernel to provide a fast computation of the Gaussian decomposition in the frequency domain, where the 2D Tikhonov kernel has a closed-form expression. The convolution with each Gaussian is then computed using linear-time separable recursive filtering. In the non-homogeneous case, we also decompose the 2D problem as a series of iterated linear-time separable recursive filters, which can be combined with the Bi-conjugate Gradient Stabilized method for fast convergence. In this way, a fast solution to the 2D Tikhonov regularization problem is obtained. (c) 2023 Elsevier B.V. All rights reserved.

关键词： Image processing filtering Convolution Frequency -domain analysis filtering algorithms

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