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检索条件"主题词=filtering algorithms"

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Numba-Based Non-Local Means Filter for SAR Image Denoising

Numba-Based Non-Local Means Filter for SAR Image Denoising

引用

International Conference on Computer and Automation Engineering, ICCAE

作者： G Devendhar Pratibha Singh Rakesh Sharma Dept. of Electronics and Communication Engg. National Institute of Technology Hamirpur Himachal Pradesh India

ISBN: (数字)9798350370058

ISBN: (纸本)9798350370164

Synthetic Aperture Radar (SAR) images are an essential tool for earth observation and remote sensing, but speckle can degrade the quality of such images. Image processing techniques such as filtering and averaging can effectively reduce the impact of noise on images. This paper evaluates the performance of the hybrid nonlocal means (NLM) filter in denoising Sentinel-1 SAR images. The proposed approach combines adaptive exponential kernel-based pre-processing with an NLM filter. The NLM algorithm is computationally intensive and time-consuming due to its pixel-by-pixel denoising based on extended neighborhood similarities. To address this challenge, this paper explores parallelization of the NLM algorithm using NUMBA, a Python compiler, to accelerate its performance. It is found that the proposed approach shows a significant improvement in terms of Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and Mean Squared Error (MSE) and performs better than conventional NLM filters and even provides the greatest speedup when compared to earlier parallel NLM techniques.

关键词： PSNR filtering Noise reduction filtering algorithms Speckle Radar polarimetry Indexes

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Extended Target Tracking with 3D-INSEG and its Benefits in Dense Scenarios

Extended Target Tracking with 3D-INSEG and its Benefits in D...

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International Conference on Information Fusion

作者： Nicolás Fierro Martin Adams Leonardo Cament Department of Electrical Engineering Universidad de Chile Santiago Chile

ISBN: (数字)9781737749769

ISBN: (纸本)9798350371420

In Multiple Extended Object Tracking (MEOT), it is assumed that a solitary target can produce multiple measurements. The quality of these measurements is paramount for obtaining accurate estimates of tracks over time. To test state-of-the-art MEOT algorithms, both simulated and real laser data, recorded in open spaces, have been used. MEOT algorithms work well in these scenarios, but when applied in more cluttered or restricted spaces, they often fail to produce good results, because close proximity target measurements are considered as measurements with the same origin. To address these cases, this article applies the 3D INstance SEGmentation (3D-INSEG) algorithm to MEOT to process stereo image sequences, extracting 3D information corresponding to each detected target using cameras. The algorithm selects pixels from each detected target and calculates the disparity map from stereo pairs, projecting them into 3D space using this disparity map. Subsequently, these measurements undergo processing by an extended target Poisson multi-Bernoulli mixture (PMBM) filter with a gamma Gaussian inverse-Wishart (GGIW) implementation. The advantages of MEOT with the 3D-INSEG-generated data are demonstrated in this article via a comparison with MEOT based on Velodyne LiDAR data points recorded from the same scenario processed by the same MEOT algorithm.

关键词： Instance segmentation Target tracking Three-dimensional displays Laser radar Measurement by laser beam Object detection filtering algorithms Time measurement Object tracking Laser fusion

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Diagnosis of Dyslexia using 2D EEG Feature Map and Convolutional Neural Network

Diagnosis of Dyslexia using 2D EEG Feature Map and Convoluti...

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Medical Technologies National Conference (TIPTEKNO)

作者： Ozlem Karabiber Cura Fatma Gunseli Yasar Ciklacandir Günet Eroğlu Dept. of Biomedical Engineering Izmir Katip Celebi University Izmir TURKEY Dept. of Computer Engineering Izmir Katip Celebi University Izmir TURKEY Dept. of Computer Engineering Bahçeşehir University Istanbul Turkey

ISBN: (数字)9798331529819

ISBN: (纸本)9798331529826

Neuroscience has been significantly impacted by developments in computational techniques and machine learning algorithms, especially in the early diagnosis and classification of neurological illnesses. This study has been conducted to classify EEG data obtained from theta, alpha, beta1, beta2, and gamma bands to diagnose dyslexic and normal children. In addition to examining the individual classification performances of the data taken from these five bands, the classification success of the set created by combining the data from all bands has also been examined. The well-known models such as AlexNet, GoogleNet, and ResNet18 are compared with the revised version of ResNet18 developed in the study and the classification of handcrafted features with support vector machine (SVM). When the models’ performances are compared, it is seen that Revised ResNet18 gives results with the highest accuracy value in all bands except the Gamma band. Additionally, it has been observed that the classification success of handcrafted features is lower than the success of CNN-based models.

关键词： Support vector machines Pediatrics Sensitivity Accuracy filtering algorithms Brain modeling Dyslexia Electroencephalography Classification algorithms Overfitting

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An Improved Dehazing Algorithm Combining Dark Channel Prior and Artificial Bee Colony Algorithm

An Improved Dehazing Algorithm Combining Dark Channel Prior ...

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International Applied Computational Electromagnetics Society Symposium (ACES)

作者： ZhenGuo Li Lisheng Ma Yibin Hou JiaXin Li Tao Tao School of Computer Science and Technology Anhui University of Technology P.R. China School of Computer and Information Engineering Chuzhou University P.R. China

ISBN: (数字)9798350355581

ISBN: (纸本)9798350355598

Aiming to address significant issues like severe halo effects and excessive noise present in images processed by the traditional dark channel prior-based dehazing algorithm using fixed values, a proposed enhancement method leverages the characteristics of the artificial bee colony algorithm for parameter optimization. It seeks the optimal atmospheric light transmission factor values within different average image brightness ranges and integrates these values into the dark channel prior-based dehazing algorithm. Simultaneously, in estimating the atmospheric light, a median filtering algorithm replaces the original twostep minimum value filtering process. This improvement exhibits notable effectiveness in reducing halo effects and noise compared to the traditional dark channel prior-based dehazing algorithm.

关键词： Image quality PSNR filtering Noise Brightness filtering algorithms Artificial bee colony algorithm Indexes Information entropy Optimization

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Method of grounding grid detection signal filtering

Method of grounding grid detection signal filtering

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Signal Processing, Communications and Engineering Management Conference (ISPCEM), International

作者： Yankai Xie Xinhong Fan Honggang Chen Xiaojuan Li Yiyao Tang Lanzhou Longneng Electric Power Science and Technology CO. Ltd. Lanzhou China State Grid Gansu Electric Power Research Institute Lanzhou China

This paper studies a filtering method for grounding grid detection signals, which includes: burying three-electrode sensors near the grounding grid; establishing a wavelet transform filtering model, substituting the collected data into the model, and performing wavelet multi-scale analysis on the detection data, determine the type of electromagnetic interference existing in the detection data; select the appropriate threshold value and substitute it into the wavelet transform filtering model, reconstruct the detection data, and obtain the detection data after filtering the electromagnetic interference; establish a Kalman filter model, and process the The detected data is substituted into the Kalman filter model for analysis; the type of Gaussian white noise existing in the detected data is judged; the appropriate adaptive factor is selected to further construct an adaptive Kalman filter model, and the Gaussian white noise existing in the detected data will be filtering is performed to finally obtain detection data with completely filtered interference, which solves the problem of low accuracy of detection data in the prior art.

关键词： Wavelet transforms Analytical models Adaptation models filtering Grounding White noise filtering algorithms

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Engineering Methods for Calculating the Process of Filtration of Vegetable Oils under Ultrasonic Cavitation Conditions

Engineering Methods for Calculating the Process of Filtratio...

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Energy Efficiency and Agricultural Engineering (EE&AE), International Conference on

作者： Petr I. Osadchuk Valentyna N. Bandura Yuriy I. Enakiev Blagoj P. Elenov Dmitry P. Domuschi Department of Electromechanics and Mechatronics Odesa National Technological University Odesa Ukraine Department of National University of Life and Environmental Sciences of Ukraine Kyiv Ukraine Department of Agricultural Mechanization and Hydromelorative Systems Protection “Nikola Pushkarov” Agricultural Academy Institute of Soil Science Agro-technology and Plant Sofia Bulgaria Department of Agroengineering Odesa State Agrarian University Odesa Ukraine

ISBN: (数字)9798350369380

ISBN: (纸本)9798350369397

The process of filtration of vegetable oils under conditions of ultrasonic cavitation is considered. The efficiency of use has been analyzed, and the prospects of using ultrasonic cavitation have been presented. The algorithm for processing the data obtained from experimental research during filtration under ultrasonic cavitation conditions is presented. Positive expectations have been confirmed by the results of theoretical research.

关键词： Vegetable oils Accuracy Filtration filtering algorithms Lubricating oils Acoustics Mathematical models

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Data-Driven Optimal Control for Continuous- Time Linear Nonzero-Sum Games

Data-Driven Optimal Control for Continuous- Time Linear Nonz...

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International Conference on Control, Automation, Robotics and Vision (ICARCV)

作者： Hongyang Li Qinglai Wei Ruizhuo Song State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China

ISBN: (数字)9798331518493

ISBN: (纸本)9798331518509

In this paper, the data-driven optimal control problem is studied for continuous-time linear nonzero-sum games. Two kinds of reinforcement learning algorithms, i.e., reinforcement learning algorithm with data-storage based least-square method and reinforcement learning algorithm with filter based least-square method, are presented to obtain the Nash equilibrium solution. The properties of the presented rein-forcement learning algorithms are analyzed. Simulation results show the efficiency of the presented reinforcement learning algorithms.

关键词： Automation Simulation Optimal control Reinforcement learning Games filtering algorithms Nash equilibrium Robots

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Node-Variant Graph Filter Based Multi-Agent Deep Reinforcement Learning for Aligned Game and Wireless Environments

Node-Variant Graph Filter Based Multi-Agent Deep Reinforceme...

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IEEE Conference on Industrial Electronics and Applications (ICIEA)

作者： Ajay Nagendra Nama Baltasar Beferull-Lozano Leila Ben Saad Jing Zhou Department of ICT WISENET Center University of Agder Norway SIGIPRO Department Simula Metropolitan Center for Digital Engineering Norway Department of Electrical Engineering IT and Cybernetics University of South-Eastern Norway Department of Engineering Sciences University of Agder Norway

ISBN: (数字)9798350360868

ISBN: (纸本)9798350360875

In Multi-Agent Reinforcement Learning, realistic communication model is required to evaluate the learning performance. Hence, the game and wireless environments are aligned by augmentation of actions and observations in both environments to make the end-to-end policy training differentiable. Communication environment consists of 1-hop broadcast Mobile Ad-hoc network and p-persistent Carrier Sense Multiple Access protocol. The transmitted signal strength may be affected by path loss, multi-path fading, attenuation and interference. We implement local q-values mixer using graph filter (GF) based graph neural networks (GNNs) with attention (ATT) coefficients. The nonlinear activation function in graph neural network architecture is replaced by a node variant graph filter (NVGF). We have compared the performance of proposed architectures over state-of-the-art for the Lumberjack environment. The results show that the proposed architectures outperform the existing state-of-art architectures.

关键词： Wireless communication Training Wireless sensor networks Recurrent neural networks Games filtering algorithms Propagation losses

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An Improved Probability Hypothesis Density Filter Based on Variational Bayes for Closely Spaced Multi-Target Tracking

An Improved Probability Hypothesis Density Filter Based on V...

引用

IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC)

作者： Xingchen Jiang Jingjie Gao Fei Hua Yuhai Gao Xinqi Du Wenqian Xu School of Information Engineering Chang'an University Xi'an China Xi'an Aeronautics Computing Technique Research Institute Xi'an China

ISBN: (数字)9798350366556

ISBN: (纸本)9798350366563

An improved probability hypothesis density filter based on variational bayes is proposed in closely spaced multi-target tracking scenario with unknown measurement noise in complex environment. The proposed algorithm combines adjacent Gaussian Inverse Wishart components representing the same target as much as possible, so as to minimize the number of components in the subsequent filtering iteration process, ensure the efficient operation of the multi-target tracking algorithm, and better adapt to the tracking scenarios of neighboring targets. The simulation results show that the improved algorithm can not only avoid the wrong merging of different target components, but also effectively improve the precision of target state and number estimation when the measurement noise is unknown and the target is in close proximity.

关键词： Target tracking Density measurement Simulation Noise Merging Signal processing algorithms Estimation filtering algorithms Bayes methods Noise measurement

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A Bias-Compensated Robust Affine Projection Algorithm Using Variable Matrix-type Step Size

A Bias-Compensated Robust Affine Projection Algorithm Using ...

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European Signal Processing Conference (EUSIPCO)

作者： Chan Park Seung Hyun Ryu PooGyeon Park Division of Electrical Engineering POSTECH Pohang Korea

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

We propose a novel system identification algorithm using adpative filter, which demonstrates superior performance compared to existing robust Affine Projection algorithms (APAs). Through systematic simulations, we validate the effectiveness of our algorithm in handling impulsive noise and input noise, crucial issues in practical applications of adaptive filters. Our approach incorporates variable matrix-type step sizes, derived through meticulous analysis of Mean Squared Deviation (MSD) curves, to enhance adaptability to diverse input vector characteristics. Furthermore, we introduce a compensation vector to mitigate bias induced by input noise, ensuring accurate estimation even in noisy environments. The algorithm proposed in this study demonstrates fast convergence and low steady-state error. This indicates the effectiveness of the proposed algorithm in performing system estimation.

关键词： Accuracy Noise Signal processing algorithms Adaptive filters Estimation filtering algorithms Vectors System identification Noise measurement Projection algorithms

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