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检索条件"主题词=Signal Processing Algorithms"

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An Intelligent Particle Filter Based Joint Probabilistic Data Association Algorithm for Long-Duration Multi-Target Tracking in Clutter Environment

An Intelligent Particle Filter Based Joint Probabilistic Dat...

引用

IEEE International Conference on Information Communication and signal processing (ICICSP)

作者： Xihao Wang Die Chen Yanru Zhao School of Mathematical Sciences University of Electronic Science and Technology of China Chengdu China

ISBN: (数字)9798350355895

ISBN: (纸本)9798350355901

The Joint Probabilistic Data Association Algorithm based on Particle Filter (JPDA-PF) is used in the tracking problem of nonlinear multi-target systems in dense clutter environments. However, during long time tracking, the tracking accuracy decreases with the increase of time. Meanwhile JPDA produces a combinatorial explosion in the number of associated events as the number of targets and clutter increases, greatly increasing the computational task. In this paper, a Joint Probabilistic Data Association Algorithm based on dolphin swarm particle filter (JPDA-DSPF) is proposed. Using the dolphin swarm optimization algorithm as a resampling method for particle filter can optimize the particle distribution to be closer to the region of high likelihood, and enhance the particle diversity to weaken the particle degradation phenomenon. We use an adaptive sequential approach to reduce computational costs by discarding correlated events with small probabilities. Finally, it is proved by simulation experiments that the method can effectively reduce the tracking error, inhibit the increasing trend of the error with time, effectively reduce the computation task and enhance the timeliness during multi-target encounters.

关键词： Target tracking signal processing algorithms signal processing Probabilistic logic Market research Information filters Particle filters Clutter Particle swarm optimization Dolphins

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End-to-End Human Pose Reconstruction from Radar signals Based on Deep Residual Networks

End-to-End Human Pose Reconstruction from Radar Signals Base...

引用

signal, Information and Data processing (ICSIDP), IEEE International Conference on

作者： Tao Wang Yongkun Song Tian Jin Yongpeng Dai Tianxing Yan Ke Zhang Computer and Communication Engineering Institute Changsha University of Science and Technology Changsha China College of Electronic Science and Technology National University of Defense Technology Changsha China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

The reconstruction of human pose using radar primarily focuses on effectively estimating the position and pose of a target based on radar echo signals. Compared to traditional optical methods, radar signals can operate under limited lighting conditions, in the presence of occlusions, or beyond the line of sight. This paper proposes an end-to-end human pose reconstruction method based on a deep residual network. This method bypasses traditional imaging and detection algorithms, directly extracting amplitude and phase features from radar echo signals, and outputs the target's azimuth, distance, and pose information. Experimental results verify the effectiveness of the algorithm.

关键词： Laser radar Target tracking Simulation signal processing algorithms Radar detection Radar imaging Reconstruction algorithms Feature extraction Image reconstruction Residual neural networks

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An Improved Lane Detection Algorithm Based on Hybrid Model

An Improved Lane Detection Algorithm Based on Hybrid Model

引用

signal processing and Intelligent Computing (SPIC), International Conference on

作者： Xiaoqi Zhang Shiqing Ren Fang Zhao School of Information Science and Engineering Shenyang Ligong University Shenyang China Guangzhou College of Technology and Business Guangzhou China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

To minimize the incidence of erroneous detections of lane identification algorithm under different road environment such as shadow occlusion of lane line and white phenomenon of road surface. This paper presents an improved hybrid deep neural network model for lane identification by utilizing a series of consecutive frames captured from driving scenes. Based on SegNet-ConvLSTM network, this paper introduced Focal loss function and added improved channel attention mechanism and Bp-ASPP pyramid, so that the enhanced network could further extract image features and aggregate context information of different regions on the basis of the original network. Experiments were carried out on TuSimple dataset, and the accuracy of the proposed method was significantly improved compared with the previous method, which verified the proficiency of the approach.

关键词： Attention mechanisms Accuracy Lane detection Computational modeling Roads signal processing algorithms Feature extraction Robustness Context modeling Testing

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Dynamic Environment Optimization Algorithm Based on Dynamic Lion Swarm Algorithm and Its Implementation

Dynamic Environment Optimization Algorithm Based on Dynamic ...

引用

International Conference on signal processing Proceedings (ICSP)

作者： Feng Wang Mingyan Jiang Keqin Jiang Xianxu An Ze Zhao Dept. School of Information Science and Engineering Shandong University QingDao China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

In the information age, many practical problems involve dynamic objective functions that change over time or due to other factors. Optimizing in such dynamic environments is crucial both theoretically and practically, as it addresses prominent challenges in optimization today. For instance, dynamic vehicle path planning requires adapting to changing vehicle and road conditions to determine the optimal route. In image processing, dynamic multi-objective segmentation can enhance recognition accuracy by addressing changing image data. In engineering, designing dynamic welded beams involves adjusting for changes in material properties over time. Traditional lion swarm optimization (LSO) algorithms often struggle with dynamic environments, tending to optimize based on outdated conditions and missing global optimal solutions. This paper improves LSO by incorporating dynamic particle swarm optimization mechanisms and the black-winged kite algorithm's hunting and elimination strategies to better track global optima in changing environments. The performance of the improved dynamic lion swarm algorithm is evaluated using the four-peak DF1 dynamic environment model, enhanced by the bimodal DFI model.

关键词： Adaptation models Heuristic algorithms Welding Roads signal processing algorithms signal processing Structural beams Vehicle dynamics Particle swarm optimization Optimization

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A method of EEG emotion recognition based on residual contraction network

A method of EEG emotion recognition based on residual contra...

引用

signal processing and Intelligent Computing (SPIC), International Conference on

作者： Ningxi Su Fang Liu School of Information Science and Engineering Shenyang Ligong University Shen Yang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

The emotional constantly affects human body, mind and behavior. The accurate recognition of emotion has a broad application prospect in the medical field. Although deep learning algorithms have made some achievements in emotion recognition research, there are still some problems such as insufficient extraction of EEG (Electroencephalogram) frequency features, spatial features and time features, noise interference and complex model parameters. In this thesis, the 4D-RSC-BiLSTM (Four-Dimensional Residual Shrinkage Convolutional Bidirectional Long Short Term Memory Neural Network) is constructed, and the convolutional layer is inserted into the deep separable convolutional network to complete the improvement of network structure and parameter adjustment. Then the residual shrinkage module is combined with the improved deep separable convolutional network to remove redundant features and reduce the network computing cost while solving the problem of model gradient disappearance. The experimental results show that the proposed model is feasible in emotion recognition.

关键词： Deep learning Emotion recognition Costs Convolution Computational modeling signal processing algorithms Feature extraction Brain modeling Electroencephalography Convolutional neural networks

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Multi-Gateway Hybrid Space-Ground Precoding for Multi-Beam Satellite Communications

Multi-Gateway Hybrid Space-Ground Precoding for Multi-Beam S...

引用

International Conference on Communications, signal processing, and their Applications (ICCSPA)

作者： Huda Ali Yuan He Xuan Feng Kang Liu Min Chen Qing Su Rui Chen State Key Lab. of ISN Xidian University Xian China Institute of Telecommunication and Navigation Satellites China Academy of Space Technology Innovation Center for Satellite Communication System Beijing China

ISBN: (数字)9798350384819

ISBN: (纸本)9798350384826

Multi-beam satellite communication system has received significant attention due to its potential in greatly enhancing ground-space transmission rate. For maximizing spectral efficiency (SE), multi-gateway multi-beam satellite systems with multiple feeds per beam (MFPB) has been developed recently, in which each gateway serves a cluster of beams. Due to full frequency reuse between multiple beams, precoding is essential to mitigate inter-user interference. In this paper, a hybrid spaceground precoding architecture, which consists of sub-connected MFPB analog beamforming on the satellite and baseband digital precoding on the ground, is proposed for balancing the SE and satellite complexity. The corresponding recursive hybrid spaceground precoding algorithm enables the architecture based on decomposing the sum rate maximization problem into sub-rate maximization problems for each user. Numerical simulations show that the proposed scheme achieves higher SE than the optimal on-ground precoding and traditional four-color reuse scheme.

关键词： Satellites Baseband Array signal processing Spectral efficiency Precoding signal processing algorithms Logic gates Numerical simulation Complexity theory Satellite communications

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Pedestrian Detection Algorithm Based on Improved YOLO v5

Pedestrian Detection Algorithm Based on Improved YOLO v5

引用

6th International Conference on Intelligent Computing and signal processing (ICSP)

作者： Haoyang Tang Zhe Li Xi'an University of Posts and Telecommunications College of Automation Xi'an Shaanxi China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Aiming at the problems such as large scale change and poor detection accuracy, an improved YOLOv5 pedestrian detection algorithm based on drone view was ***, a multi-scale bidirectional feature network structure was proposed to enhance the multi-scale feature detection ability of YOLOv5 network, so as to adapt to pedestrian change detection at different ***, a location attention detection layer is established to suppress background interference. Finally, the occlusion screening module is created to screen out the occluded pedestrians and improve the detection accuracy of the *** order to verify the performance of the algorithm, a comparison experiment is carried out with the existing pedestrian detection algorithm. The experimental results show that compared with the original YOLOv5 algorithm, the algorithm's accuracy is improved by 10.01%, and compared with the YOLOv5-tiny pedestrian detection algorithm, the algorithm's accuracy is improved by 5.74%, which can detect pedestrians well in the perspective of drones.

关键词： YOLO Pedestrians Accuracy Change detection algorithms signal processing algorithms Interference signal processing Feature extraction Detection algorithms Drones

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Automated Algorithm Design with Partial Reconstruction Heuristics for Solving Vehicle Routing Problems

Automated Algorithm Design with Partial Reconstruction Heuri...

引用

International Conference on signal processing Proceedings (ICSP)

作者： Liang Ma Xingxing Hao Ruibang Zhang Li Chen School of Information Science and Technology Northwest University Xi’an China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Automatic algorithm design has gained extensive research interest in addressing combinatorial optimization problems (COPs). Neural Combinatorial Optimization (NCO) is a novel application of automated algorithm design, which can be divided into two categories, i.e., the Constructive Neural Combinatorial Optimization (CNCO) that constructs solutions from scratch and Perturbative Neural Combinatorial Optimization (PNCO) that improves existing solutions via perturbing them. However, the proper integration of CNCO methods and PNCO methods into an automated algorithm design framework appears to be a research gap in the field of COPs. In this study, we propose an automatic algorithm design framework called Automatic Search Algorithm Framework (AutoSAF) that integrates CNCO into PNCO methods. AutoSAF can intelligently select the appropriate heuristics from the reconstruction pool, improvement pool, and perturbation pool, to improve the current solutions. The reconstruction pool consists of neural network heuristics. i.e., the CNCO methods, aiming at optimizing the solutions by reconstructing fragments of them. The heuristics in the improvement pool are to fine tune the solutions through minor perturbation. The heuristics in the perturbation pool are to modify the solutions significantly, preventing them from getting stuck in local optima. Additionally, we design an Adversarial Transformer Model (ATM) to provide rich learning-based meta-heuristics for the reconstruction pool. Specifically, employing a divide-and-conquer strategy, ATM iteratively reconstructs partial solutions to facilitate the optimization of the entire solution. The effectiveness of AutoSAF is validated on the Vehicle Routing Problems (VRPs).

关键词： Training Perturbation methods Heuristic algorithms Vehicle routing Neural networks Metaheuristics signal processing algorithms signal processing Transformers Optimization

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An Intelligent Auction-Based Framework for Electric Vehicles Charging Management Utilizing Reinforcement Learning and Blockchain

An Intelligent Auction-Based Framework for Electric Vehicles...

引用

International Conference on Communications, signal processing, and their Applications (ICCSPA)

作者： Alia Al Sadawi Eiman ElGhanam Mohamed S. Hassan Ahmed Osman Department of Industrial Engineering American University of Sharjah Sharjah UAE Department of Electrical Engineering American University of Sharjah Sharjah UAE

ISBN: (数字)9798350384819

ISBN: (纸本)9798350384826

Different electric vehicle (EV) charging coordination techniques are proposed in the literature to match the EV energy demand with the available supply through wired and wireless charging stations, and other nearby EVs. This station-to-EV and EV-to-EV energy exchange creates an energy trading market (ETM) that needs to be accurately managed to ensure efficient operation. Auctioning provides an ideal monetary mechanism to manage the EV ETM, since it incentives EVs to provide truthful information thus reaching optimal ETM state. Accordingly, this study presents a review of state-of-the-art literature on energy coordination within EV ETMs, and uses the findings of this review to propose an intelligent auction-based framework for EV energy trading using reinforcement learning (RL) algorithms and blockchain technologies. The proposed framework aims to satisfy the EV owners' charging needs at optimal response time and energy pricing. It also preserves EV owners' privacy, considers the remaining battery charge, maximizes the charging systems' profit, and balances the energy demand and supply. Hence, this work paves the way for future studies into implementing and validating the proposed framework for different case studies of EV ETMs.

关键词： Energy consumption Privacy Reviews Inductive charging signal processing algorithms Reinforcement learning Pricing signal processing Blockchains Time factors

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YOLO-Based Approach for Hostile Vehicle Identification Using Automated Dataset

YOLO-Based Approach for Hostile Vehicle Identification Using...

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International Conference on signal processing, Communication, Power and Embedded System (SCOPES)

作者： Avik Das Anushko Roy Choudhury Nabamita Ghosh Dibyarup Mukherjee Sarita Nanda Jaydeep Rath Electronics and Telecommunication Kalinga Institute of Industrial Technology Kolkata India Electronics and Telecommunication Kalinga Institute of Industrial Technology Bhubaneswar Odisha KIIT International School Bhubaneswar Odisha

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

Unmanned Aerial Vehicles (UAVs), commonly known as drones, have emerged as pivotal tools in various fields due to their ability to cover vast areas swiftly and efficiently. This research focuses on the utilization of UAVs in the domain of surveillance, particularly for reconnaissance missions in military applications. The primary advantage of UAVs lies in their capacity to gather strategic intelligence without exposing human lives to potential risks. In military scenarios, UAVs play a crucial role in collecting data on opponent activities, with a specific emphasis on hostile vehicle detection. One of the primary challenges in implementing object detection on drones is the acquisition of diverse and representative datasets. This paper introduces a novel algorithm designed to automate the process of dataset collection and subsequent machine learning model training. The proposed algorithm aims to enable Unmanned Aerial Vehicles to autonomously learn and adapt to variations in hostile vehicle appearances, enhancing the accuracy and robustness of the detection system.

关键词： Training Machine learning algorithms Surveillance Vehicle detection signal processing algorithms Object detection signal processing Autonomous aerial vehicles Robustness Drones

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