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检索条件"主题词=Signal Processing Algorithms"

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Passive radar multi-target detection experimental study at sea

Passive radar multi-target detection experimental study at s...

引用

Computer and Information processing Technology (ISCIPT), International Symposium on

作者： Zaiyuan Wu Jie Song Zhongxun Wang Qian Wang Shandong Data Open Innovation Application Laboratory of Smart Grid Advanced Technology Yantai University School of Physics and Electronic Information Yantai University Yantai China Naval Aviation University Yantai China

ISBN: (数字)9798350388404

ISBN: (纸本)9798350388411

The non-cooperative dual-base radar provides superior performance in terms of safety and robustness of target detection. This paper presents a series of experimental maritime target detections using a passive radar system and the collection of a large amount of data. The first is an introduction to the principle of passive radar and the key technologies involved, the next step is to describe the experimental scenario, finally, the experimental data were used for the plotting of a radar system diagram and the analysis of the resultant graphs. Experimental results showed that the system could succeed in detecting targets at sea.

关键词： Passive radar Radar cross-sections Radar detection signal processing algorithms Object detection Radar signal processing Stability analysis

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Design and Development of Search and Rescue Bot

Design and Development of Search and Rescue Bot

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IEEE International Conference on Intelligent Techniques in Control, Optimization and signal processing (INCOS)

作者： J Ponraj Krishna Raghavendran C R E Kaliappan Department of Electrical and Electronics Engineering Easwari Engineering College Chennai-89

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361193

Recent advancements in Unmanned Aerial Vehicles (UAVs) have transformed them into viable platforms for carrying sensors and computer systems capable of executing complex tasks. This research presents a method for implementing imaging and object recognition on standard hardware within an autonomous UAV to detect humans at a high frame rate in outdoor settings. By geo-locating these detected human positions, a map highlighting various points of interest can be generated. This saliency map can be invaluable in scenarios such as disaster relief operations, where it can assist in scheduling the delivery of medical supplies. The methodology has been successfully deployed and tested on the UAV Tech autonomous unmanned helicopter platform as part of a complete autonomous mission. This platform demonstrates stable flight characteristics and can effectively monitor and collect data in diverse regions. UAVs are increasingly utilized in hazardous environments, offering access to areas that are otherwise unreachable, facilitating tasks with remarkable ease. One of the primary applications of drones is in detecting human presence in areas affected by natural disasters, signaling nearby rescue teams via GSM technology. The outcomes of this study have enhanced the performance of this technique and have addressed its limitations

关键词： Ethics Face recognition Disasters signal processing algorithms Chatbots Autonomous aerial vehicles Stakeholders

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Enhanced Binary Classification of Gait Disorders Using a Machine Learning Majority Voting Approach

Enhanced Binary Classification of Gait Disorders Using a Mac...

引用

Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

作者： Ahmed Khalil Muhammad Saad Kareem Chaar Reza Tafreshi Shameel Abdulla MD Ferdous Wahid Department of Mechanical Engineering Texas A&M University at Qatar Doha Qatar

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

This study introduces a machine learning-based methodology for classifying healthy individuals and those with gait disorders, employing a merged data set from ‘GaitRec’ and ‘Gutenberg.’ Key gait features were extracted from the normalized ground reaction force data that had 2435 subjects, including maximum peaks, load rate, lowest troughs, and area under the curve. Numerous machine learning models, such as Support Vector Machine, Logistic Regression, Random Forest, Gradient Boosting, K-Nearest Neighbor, Bagging, Adaboost, and Neural Network, were developed, and their hyperparameters were optimized using a grid search approach. The model’s performance was evaluated using metrics such as accuracy, sensitivity, specificity, and F1 score. These assessment metrics are calculated through a repeated hold-out strategy, where 80% of the data is utilized for training the model, and the remaining 20% is reserved for testing purposes. Notably, the study highlights the superiority of an ensemble model that combines these algorithms through majority voting. This majority voting model, which was compared against individual models, achieved an accuracy of 96.63%, surpassing existing benchmarks in the field. This approach underscores the efficacy of ensemble techniques, particularly majority voting, in enhancing classification accuracy, thus contributing significantly to the field of biomedical signal processing by offering a scalable and cost-effective solution for accurate gait disorder identification.

关键词： Legged locomotion Support vector machines Training Accuracy Biological system modeling signal processing algorithms Benchmark testing Feature extraction Tuning Load modeling

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Analysis of the Factors Affecting the Decoding Success Rate of High rate Shear Valve Pulser

Analysis of the Factors Affecting the Decoding Success Rate ...

引用

Intelligent Control, Measurement and signal processing and Intelligent Oil Field (ICMSP), International Conference on

作者： Zhiming Wang Kun Chen Lei Xiao Dingyi Wen Haizhen Wu COSL Well-Tech Division China Key Laboratory of Deep Sea Deep Formation Energy Engineering of Hainan Province China School of Energy Resources China University of Geosciences(Beijing) Beijing China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

The "Xuanji" system is a revolutionary drilling technology independently developed by China Oilfield Services Limited(COSL). It is composed of two major technologies, namely rotary steering drilling "Welleader" and logging while drilling "Drilog", and has currently achieved large-scale application. While the High rate Shear Valve Pulser(HSVP), as a crucial component of the "Xuanji" system, can reach a maximum transmission rate of 20 bps under actual drilling conditions. The high-rate mud pulser utilizes mud pulses for signal transmission and can upload downhole logging data, wellbore trajectory, and engineering parameters to the surface system in real time. The surface data acquisition and demodulation system parses the received mud pulse signals to realize real-time communication between downhole instruments and the surface system. However, channel transmission characteristics such as acquisition noise and pump noise can interfere with the signal quality and affect the decoding success rate of surface signals. This paper analyzes the sources of noise, discusses the influences of channel characteristics, pump noise, acquisition noise, bottom hole assembly(BHA), drilling engineering parameters, mud parameters, and signal processing parameters on the decoding success rate, and adopts corresponding methods to handle the noise that affects the decoding success rate. This has greatly enhanced the decoding success rate of the signals and is of great significance for improving the real-time demodulation effect of high-speed mud pulse data transmission.

关键词： Drilling Noise Noise reduction signal processing algorithms Interference Valves Real-time systems Decoding Data communication Demodulation

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Photovoltaic Power Load Forecasting Method Based on DBO-DELM

Photovoltaic Power Load Forecasting Method Based on DBO-DELM

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

作者： Hongtao Hu Meng Yang School of Computer Science Xi'an Shiyou University Xi'an Shaanxi China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

Load forecasting is crucial to the stable operation of the power system, BP neural network and LSTM are commonly used for forecasting. However, BP neural network is prone to gradient disappearance or gradient explosion problems, while LSTM is more complex. In order to avoid the shortcomings of the above algorithms, this paper proposes to use the Dung Beetle Optimization (DBO) to optimize the parameters of the Deep Extreme Learning Machine (DELM) model. Firstly, the parameters of DELM are set, and the structural parameters of DELM are input into the dung beetle optimization algorithm in the form of a dictionary for iterative optimization to prevent DELM from falling into the local optimal solution; secondly, the obtained global optimal parameters are used to construct and train DELM load forecasting model; finally, the model is verified with real data from oil field photovoltaic power stations. Experimental results show that the DBO-DELM model performs better in terms of load forecast accuracy and algorithm complexity.

关键词： Photovoltaic systems Accuracy Load forecasting Computational modeling Oils signal processing algorithms Predictive models Data models Optimization Load modeling

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A Multi-Coverage Fusion Tracking Method for LEO Targets via Space-based Netted Radar Systems

A Multi-Coverage Fusion Tracking Method for LEO Targets via ...

引用

International Conference on signal processing Proceedings (ICSP)

作者： Qingwei Yang Libing Jiang Shuyu Zheng Yingjian Zhao Zhuang Wang College of Electronic Science and Technology National University of Defense Technology Changsha China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

The space-based surveillance netted radar is pivotal in complementing the existing space surveillance system, which consists of space-based optical (SBO) and ground-based radar (GBR) components. It enables continuous 24-hour observation of low-orbit space targets. Given the limitations imposed by the power-aperture product (PAP) on the performance of space-based radar (SBR), this paper initially deduces the PAP requirements for SBR guided by Two-Line Elements (TLE) data. Subsequently, a space-based netted radar walker constellation is proposed to effectively blanket the low-orbit space, facilitating cooperative relay observation of low-orbit space targets. The paper also employs the covariance intersection (CI) fusion algorithm to estimate the motion state of space targets, thereby enhancing the tracking accuracy of the space surveillance system for low-orbit targets. Experimental results indicate that the space-based surveillance netted radar achieves an observation coverage rate of 87.7%, significantly improving the surveillance system's observation frequency and cataloging performance for space targets.

关键词： Space vehicles Target tracking Laser radar Accuracy Spaceborne radar Surveillance signal processing algorithms Apertures Radar tracking Radar signal processing

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A Novel Machine Learning Approach for Medical Recommendation System

A Novel Machine Learning Approach for Medical Recommendation...

引用

International Conference on Communications and signal processing

作者： P Kumar P Yashini Department of CSE Rajalakshmi Engineering College Chennai India Department of CSE REC Chennai India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

Hospitals are using online recommender systems more and more frequently. Nowadays, the vast majority of consumers research potential prescriptions online before consulting their doctors for a variety of medical issues. When pandemics, floods, occur, the medical recommendation system may come in handy. Using fewer resources, recommender systems provide more precise, dependable, and accurate clinical predictions. The patient receives trustworthy information regarding the medication, dose, and potential side effects from the medication recommendation system. The patient's symptoms are taken into account while choosing the right medication, which is then delivered based on the user profile. This system uses K-means clustering algorithms to analyze patient data and provide personalized medical recommendations. These algorithms use characteristics like patient demographics, medical histories, and symptoms—all of which are gathered from a sizable dataset of medical records— to produce precise suggestions. This system's objective is to enhance patient outcomes by giving fast, precise recommendations that are tailored to each person's particular need.

关键词： Machine learning algorithms Pandemics Hospitals signal processing algorithms Clustering algorithms Machine learning signal processing

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Edge Computing Resource Allocation Method Based on Federated Reinforcement Learning

Edge Computing Resource Allocation Method Based on Federated...

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

作者： Yingchun Huang Tao Xie School of Information Science and Engineering Shenyang Ligong University Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

With the increase in the number of smart devices, the data generated is growing exponentially, leading to a large amount of data flowing to the cloud and causing congestion in core network communication. Edge computing can effectively reduce the burden of the core network, but there are still challenges in resource allocation between edge nodes and cloud centers. A method based on federated learning and reinforcement learning (FLRL) has been proposed to address this issue, and the server and edge hosts jointly formulate resource allocation strategies, which is beneficial for the global system. Experiments have shown that this method outperforms traditional deep reinforcement learning algorithms in terms of system average utility, average time step, and average resource utilization.

关键词： Performance evaluation Federated learning Simulation signal processing algorithms signal processing Deep reinforcement learning Resource management Servers Smart devices Edge computing

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Influence Level Assessment in Suppressing the Spread of Undesirable Information in Online Social Networks

Influence Level Assessment in Suppressing the Spread of Unde...

引用

International Congress on Image and signal processing, BioMedical Engineering and Informatics (CISP-BMEI)

作者： Shan Liu Yihan Jiang Wangzhu Chen Department of Intelligent Science Communication University of China

ISBN: (数字)9798331507398

ISBN: (纸本)9798331507404

Undesirable information can spread rapidly in social networks and cause great damage. This paper introduces an innovative Priority Recovery Control (PRC-SIRS) model aimed at curbing the dissemination of undesirable information. In this model, infected nodes are not left to heal spontaneously. Instead, network administrators strategically prioritize the blocking of infected nodes with the greatest influence. We then propose an innovative Influence Level Assessment (ILA) strategy that fully considers both the likelihood of nodes being influenced and their own influence during information dissemination. Experiments based on real-world social network datasets show that the ILA strategy proposed in this paper outperforms traditional heuristic algorithms.

关键词： Social networking (online) Heuristic algorithms signal processing algorithms signal processing Informatics Biomedical engineering

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Antenna Selection and Beamforming in ELAA-based ISAC System: A Distributed Approach

Antenna Selection and Beamforming in ELAA-based ISAC System:...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Qian Gao Ruikang Zhong Yuanwei Liu Queen Mary University of London London UK The University of Hong Kong Hong Kong

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

An extremely large-scale antenna array (ELAA) based integrated sensing and communication (ISAC) system is investigated. In contrast to existing near-field ISAC systems, we consider the complexity of beamforming design and antenna selection for ELAA under a user-roaming environment. The formulated problem is maximizing long-term sum data rate by jointly optimizing antenna selection and beamforming, subject to the constraints of transmitting power and sensing targets’ beam pattern gain. We propose a dimension reduction assisted multi-agent proximal policy optimization (DR-MAPPO) algorithm against the dynamic scenario: 1) an OrderConv layer is designed, which reduces the input dimension by removing the channel state information of unselected antenna while keeping the order information of selected ones, 2) multiple agents are employed to execute antenna selection and beamforming in a distributed manner. Numerical results demonstrate that the proposed DR-MAPPO algorithm improve the computational efficiency of joint antenna selection and beamforming optimization by reducing the size of the input dimension while obtaining comparable data rates and beampattern gain for sensing targets.

关键词： Wireless communication Array signal processing Heuristic algorithms signal processing algorithms Directive antennas Integrated sensing and communication Arrays Antennas Optimization Antenna arrays

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