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检索条件"主题词=Signal Processing Algorithms"

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Client Selection for Federated Learning Against Poisoning Attacks on Non-IID Data

Client Selection for Federated Learning Against Poisoning At...

引用

International Conference on Wireless Communications and signal processing (WCSP)

作者： Chen Wang Hongguang Sun Xuebo Wang Yi He Hongming Zhang College of Information Engineering Northwest A&F University Yangling Shaanxi China Key Laboratory of Agricultural Internet of Things Ministry of Agriculture and Rural Affairs Yangling Shaanxi China

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

Federated Learning (FL) trains a global model using local models uploaded by clients rather than raw data, leading to the protection on client privacy. However, FL is vulnerable to malicious attacks, such as poisoning attacks, which can degrade the performance of the global model. Especially in non-Independent and Identically Distributed (non-IID) scenarios, traditional malicious client detection methods are largely ineffective. To address these challenges, we propose an algorithm termed Federated Learning with Client Selection Based on Contribution and Credibility (FL-CSBC 2 ). Specifically, we design a client contribution model based on marginal contribution and a client credibility model utilizing the Rank-Sum Ratio (RSR) in the FL training process. By adopting the concept of evolutionary clustering, we utilize the current round's contribution and the previous round's credibility to quantify the credibility of each client by computing a client score, based on which we select clients that will participate in aggregation phase. At the same time, we incorporate the root dataset on the server side and utilize the server model trained by the root dataset as the initial aggregation model for each round. We evaluate the performance of our algorithm on two public datasets with different proportions of malicious clients under non-IID scenario. Extensive experimental results demonstrate the superiority of FL-CSBC 2 over a variety of baseline algorithms in terms of test accuracy.

关键词： Wireless communication Training Accuracy Federated learning Computational modeling signal processing algorithms signal processing Data models Robustness Servers

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A Small-sample Trajectory Classification Algorithm via Bayesian Program Learning

A Small-sample Trajectory Classification Algorithm via Bayes...

引用

International Conference on signal processing Proceedings (ICSP)

作者： Yanshan Li Ruoqiang Yao Institute of Intelligent Information Processing Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China Institute of Intelligent Information Processing Shenzhen University Shenzhen China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen China

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Trajectory classification algorithms are widely used in fields such as behavior recognition, anomaly detection and monitoring, and video content analysis. To overcome the issues of traditional trajectory classification algorithms that require large supporting data and continuous model updates to ensure effectiveness, we take inspiration from the concept learning approach proposed by Brenden et al. We simulate how humans learn rich concepts from a single hand-drawn sample and propose a Bayesian program learning-based trajectory classification algorithm. This algorithm first builds concepts from a set of hand-drawn trajectories and then classifies trajectory data based on these constructed concepts. Experimental results show that our algorithm outperforms traditional single-sample learning algorithms in trajectory classification tasks and also shows superior performance over traditional single-sample learning algorithms when the dataset includes noisy data.

关键词： Content management Noise signal processing algorithms Programming Data models Trajectory Classification algorithms Bayes methods Noise measurement Monitoring

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Intelligent URL Analysis for Phishing Threads

Intelligent URL Analysis for Phishing Threads

引用

International Conference on Communications and signal processing

作者： Serin V Simpson Bhanu Prakash Gatti Sandeep Setty Anusha Papepalli Balakrishna Sompalle Department of computer Science & Technology Madanapalle Institute of Technology & Science Angallu Andhra pradesh India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

This work explores the use of advanced Natural Language processing (NLP) techniques with machine learning algorithms to enhance the detection of phishing URLs. The intricacy and progression of phishing attempts have rendered old tactics more unsuitable. The work creates a dependable system that can dependably distinguish between unsafe and legitimate URLs by thoroughly examining a big dataset and applying sophisticated text analysis algorithms. The research, which emphasizes the proactive use of NLP, becomes an essential step in strengthening cybersecurity defences and reducing risks in the modern digital environment. By anticipating and adjusting to impending threats in addition to identifying current phishing attempts, the work presents a paradigm shift in URL phishing detection techniques. This work significantly contributes to the proactive reinforcement of cybersecurity defenses against the pervasive and dynamic nature of phishing attempts.

关键词： Uniform resource locators Logistic regression Machine learning algorithms Text analysis Phishing Instruction sets signal processing algorithms

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A small sample conventional circuit breaker fault diagnosis method based on SWT-STFT and double flow CNN-SVM

A small sample conventional circuit breaker fault diagnosis ...

引用

IEEE Conference and Expo Transportation Electrification Asia-Pacific (ITEC Asia-Pacific)

作者： Yujie Wang Dongming Lai School of Mechatronics and Vehicle Engineering Chongqing Jiaotong University Chongqing

ISBN: (数字)9798331529277

ISBN: (纸本)9798331529307

A small sample conventional circuit breaker fault diagnosis method based on SWT-STFT and double flow CNN-SVM is proposed to address the low accuracy of model fault diagnosis caused by the complex operating conditions, insufficient vibration signal samples, strong individual sample differences, and difficulty in extracting features of conventional circuit breakers. Firstly, feature parameters are extracted during the closing period of the conventional circuit breaker using SWT-STFT to form a high-dimensional feature dataset; Use CNN to adaptively extract features from the feature dataset, and then use SVM to classify and recognize the output of CNN's fully connected layers. Use Hippo Optimization (HO) Algorithm to optimize the parameters of SVM and improve its classification performance. The results show that the SWT-STFT and double flow CNN-SVM models have higher diagnostic accuracy for small sample conventional circuit breakers compared to traditional SVM CNN methods have shown good accuracy improvement. In the testing experiment, this model can accurately diagnose mechanical faults of small sample conventional circuit breakers, providing strong support for the stable operation of the power system.

关键词： Support vector machines Fault diagnosis Accuracy Circuit breakers signal processing algorithms Feature extraction Classification algorithms Convolutional neural networks Integrated circuit modeling Optimization

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An Enhanced Car Parking Detection Using YOLOv8 Deep Learning Capabilities

An Enhanced Car Parking Detection Using YOLOv8 Deep Learning...

引用

Electronics, Communication and signal processing (ICECSP), International Conference on

作者： Vishnu Shankar Vibhav Singh Vibhash Choudhary Sachin Agrawal Bhaskar Awadhiya Electronics & Communication Engineering National Institute of Technology Delhi

ISBN: (数字)9798350364590

ISBN: (纸本)9798350375381

This research paper introduces a novel approach for car parking slot detection using YOLOv8, an advanced object detection algorithm renowned for its state-of-the-art performance. The objective of this study is to address the increasing demand for efficient parking management in urban areas, where optimizing parking space utilization is essential to alleviate traffic congestion. The proposed model efficiently uses the power of YOLOv8's single-shot detection. It uses anchor boxes to automatically identify and classify vacant and occupied parking slots in real time. By utilizing YOLOv8's deep learning capabilities, the proposed method achieved impressive accuracy and efficiency in car parking slot detection. The simulation results showcase the system's effectiveness, providing valuable insights for urban mobility planning and management.

关键词： Deep learning Accuracy Simulation Urban areas signal processing algorithms Object detection signal processing Car parking Real-time systems Traffic congestion

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A3L-FEC-FSFB: Age-Aware Application Layer Forward Error Correction with Fixed Sampling Rate and Fixed Block-length

A3L-FEC-FSFB: Age-Aware Application Layer Forward Error Corr...

引用

IEEE signal processing and Communications Applications (SIU)

作者： Sajjad Baghaee Elif Uysal Department of Electrical and Electronics Engineering Middle East Technical University Ankara Turkey

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

The concept of Age of Information (AoI) has emerged as a measure of data freshness in status-update-oriented applications. Enhancing AoI often involves unconventional networking strategies that adjust update transmission rates based on network conditions and resource variability. However, most prior research on AoI has been theoretical, assuming ideal network models and known delay/service time distributions. In real-world networks, obtaining these statistics and optimizing for them is challenging. In this study, we introduce Age-Aware Application Layer Forward Error Correction ($\mathbf{A}^{3} L$-FEC), a data flow controller at the application layer of the OSI model. $\mathrm{A}^{3} \mathrm{~L}$ FEC aims to enhance data freshness by preventing age violations, achieving this through reliable transmission of fresh data using forward error correction and the user datagram protocol.

关键词： Protocols Data integrity signal processing algorithms TCPIP Open systems Forward error correction signal processing

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Research on Background Electromagnetic Interference Suppression Method Based on MUSIC-LCMV Algorithm

Research on Background Electromagnetic Interference Suppress...

引用

Progress in Electromagnetic Research Symposium (PIERS)

作者： Quan Deng Qiang-Ming Cai Xin Cao Longjian Zhou Haoran Li Zhen-Yong Du Yixiang Li Yuyu Zhu Yu-Teng Zheng Bo Pu Jun Fan School of Information Engineering Southwest University of Science and Technology Mianyang China Robot Technology Used for Special Environment Key Laboratory of Sichuan Province Mianyang China Tianfu Institute of Research and Innovation Southwest University of Science and Technology (SWUST-TIRI) China Chengdu Juji Millimeter Wave Technology Co. Ltd Chengdu China DeTooLIC Technology Co. Ltd Ningbo China

ISBN: (数字)9798350375909

ISBN: (纸本)9798350375916

Accurately extracting the electromagnetic radiation signal of the equipment to be tested from the environmental electromagnetic signal is one of the difficult problems that urgently need to be solved when conducting electromagnetic compatibility on-site testing. Virtual darkroom technology can accurately receive radiation information from the tested equipment in a noisy environment. However, there may be significant errors in the test results when there are strong interference signals. Based on the principle of spatial cancellation, this paper proposes a new method for suppressing background electromagnetic interference. This method combines the idea of simultaneously cancelling background noise in a virtual darkroom with the introduction of array signal processing, and uses beamforming as the operating mode to filter out background noise in on-site testing. This algorithm can overcome the shortcomings of traditional methods, that is, it does not need to assume that background noise is not related to the radiation signal of the tested equipment, the spatial distribution of background noise is uniform, and the signal-to-noise ratio is large. By providing less prior information or reference information of the on-site environment, good results can be achieved. Its core is high-precision direction of arrival estimation through multiple signal classification algorithm (MUSIC). After obtaining the direction of the signal, the Linear Constrained Least Variance (LCMV) algorithm is used to weight the signal received by each antenna. The weighted sum of each antenna suppresses background noise and preserves the radiation signal of the tested equipment intact. Finally, the correctness of this method was verified through an example.

关键词： Smoothing methods Graphical models Electromagnetic interference signal processing algorithms Filtering algorithms Electromagnetic compatibility Classification algorithms

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Adaptive Modulation and Coding Techniques Based on Reinforcement Learning

Adaptive Modulation and Coding Techniques Based on Reinforce...

引用

International Conference on Wireless Communications and signal processing (WCSP)

作者： Haozhe Gu Ningyan Guo Tianyu Xue Qixun Zhang School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

Traditional Adaptive Modulation and Coding (AMC) techniques rely on real-time and accurate signal-to-Noise Ratio (SNR) estimates, but often resulting in suboptimal data transmission performance in scenarios with significant link transmission delays and rapid channel changes. This paper introduces a reinforcement learning (RL) algorithm to enhance traditional AMC techniques. By applying Q-learning principles to define the RL framework, including state space, action space, and reward, this study proposes a Q-learning-based AMC technique. Leveraging RL's robust perception and decision-making capabilities in uncertain environments, this method mitigates the shortcomings of traditional AMC techniques, such as coarse channel state partitioning and mismatched state lookup tables, especially in dynamic channel conditions, thereby reducing resource inefficiencies and enhancing system throughput. Evaluation of spectral efficiency and bit error rate under conditions of rapid channel changes and SNR estimation errors due to feedback delays reveals that the Q-learning-based AMC technique achieves superior performance compared to threshold-based AMC methods.

关键词： Spectral efficiency Stability criteria Channel estimation signal processing algorithms Modulation Throughput Real-time systems Delays Thermal stability signal to noise ratio

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Enhanced SMC2: Leveraging Gradient Information from Differentiable Particle Filters Within Langevin Proposals

Enhanced SMC2: Leveraging Gradient Information from Differen...

引用

International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)

作者： Conor Rosato Joshua Murphy Alessandro Varsi Paul Horridge Simon Maskell Department of Pharmacology and Therapeutics University of Liverpool United Kingdom Department of Electrical Engineering and Electronics University of Liverpool United Kingdom

ISBN: (数字)9798350368031

ISBN: (纸本)9798350368048

Sequential Monte Carlo Squared $(\mathbf{SMC}^{2})$ is a Bayesian method which can infer the states and parameters of non-linear, non-Gaussian state-space models. The standard random-walk proposal in SMC 2 faces challenges, particularly with high-dimensional parameter spaces. This study outlines a novel approach by harnessing first-order gradients derived from a Common Random Numbers - Particle Filter (CRN-PF) using PyTorch. The resulting gradients can be leveraged within a Langevin proposal without accept/reject. Including Langevin dynamics within the proposal can result in a higher effective sample size and more accurate parameter estimates when compared with the random-walk. The resulting algorithm is parallelized on distributed memory using Message Passing Interface (MPI) and runs in $\mathcal{O}(\log_{2}N)$ time complexity. Utilizing 64 computational cores we obtain a 51x speed-up when compared to a single core. A GitHub link is given which provides access to the code.

关键词： Monte Carlo methods Message passing signal processing algorithms Particle filters Bayes methods State-space methods Proposals Time complexity Standards Software development management

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Wireless Tracking Algorithm with Direct signal in Strong Doppler Environment

Wireless Tracking Algorithm with Direct Signal in Strong Dop...

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IEEE International Conference on signal processing, Communications and Computing (ICSPCC)

作者： Yuhai Gao Jingjie Gao Fei Hua Chun Zhang Xingchen Jiang Peizhe Liu 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

With the development of wireless communication technology, orthogonal frequency division multiplexing (OFDM) technology faces challenges in high mobility and high frequency communication scenarios. To deal with this problem, orthogonal time frequency space (OTFS) modulation technology arises at the historic moment. It can perform signal processing in the delay-Doppler (DD) domain, convert time-varying channel into slow fading channels, and enhance the robustness of the system. In this study, a direct wireless signal in Doppler environment tracking (DWSD-Tra) method based on OTFS signal is adopted, which makes use of its stability against high speed in DD domain, reduce the intermediate steps and improve the positioning accuracy. The results show that the algorithm can reduce the tracking error in the strong Doppler environment, has excellent performance and strong robustness.

关键词： Wireless communication Time-frequency analysis Target tracking OFDM Space technology signal processing algorithms Modulation Time-varying channels Robustness Stability analysis

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