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检索条件"主题词=Signal Processing Algorithms"

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Neighbor Selecting Cooperation Strategies for Distributed Learning

Neighbor Selecting Cooperation Strategies for Distributed Le...

引用

European signal processing Conference (EUSIPCO)

作者： Evangelos Georgatos Christos Mavrokefalidis Kostas Berberidis Computer Engineering and Informatics Department University of Patras Greece

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

In distributed learning, a network of agents co-operate for solving a common task, like training a particular neural network. The devices usually adopt an iterative procedure with two steps, namely, they, first, perform local optimization, using, e.g., stochastic gradient descent, and, then, they exchange information among them in order to achieve consensus on the final solution. In current literature, the proposed distributed algorithms achieve consensus using averaging rules over the received information at each agent. Here, the paper departs from this paradigm and focuses on “single agent” cooperation strategies in which each agent selects a particular neighbor at each iteration and uses only that information during the local optimization step. Three selection rules are designed and it is shown that they inherit the convergence properties of commonly used averaging rules. Moreover, their effectiveness is demonstrated experimentally in classification tasks over other algorithms using well-known datasets for a wide range of scenarios, capturing factors like non-IID datasets, network size, and AI model size.

关键词： Training Performance evaluation Computer aided instruction Distance learning signal processing algorithms Stochastic processes signal processing Classification algorithms Partitioning algorithms Optimization

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Optimizing VNF Service Provisioning in Mobile Edge Computing Networks Through PSO-RL

Optimizing VNF Service Provisioning in Mobile Edge Computing...

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

作者： K. Abinaya S. Dhanasekaran V. Vasudevan Department of Computer Science and Engineering Kalasalingam Academy of Research and Education Srivilliputtur Tamilnadu India Department of Information Technology Kalasalingam Academy of Research and Education Srivilliputtur Tamilnadu India

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361193

This study deals with setting up Virtual Network Function (VNF) services in Mobile Edge Computing (MEC) networks. It aims to balance different user needs in the everchanging MEC environment. Leveraging Particle Swarm Optimization-Reinforcement Learning algorithms (PSO-RL), our proposed system offers a dynamic provisioning approach adept at accommodating user mobility patterns and service delay prerequisites. The system's sequential flow comprises distinct stages designed for optimal functionality. The Firefly Algorithm efficiently maximizes utility by simplifying intricate issues and approximating optimal solutions. Additionally, our system implements Stochastic Gradient Descent and Simulated Annealing techniques for real-time throughput optimization, ensuring adaptability to ever-changing network conditions. Experimental simulations form a critical component of our study, providing a robust assessment of our proposed methodologies. Results obtained demonstrate substantial enhancements across critical metrics such as Throughput, Resource Utilization Efficiency, and End-to-End Delay. Varied scenarios encompassing different quantities of IoT users and mobile edge servers were simulated, reaffirming the efficacy of the PSO-RL method. These simulations underscore the potential of our proposed approach in significantly bolstering the performance and responsiveness of MEC networks. Ultimately, this study showcases promising prospects in addressing the intricate challenges associated with provisioning VNF services to mobile users in dynamic MEC environments.

关键词： Measurement Multi-access edge computing Scalability Heuristic algorithms signal processing algorithms Throughput Delays

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Automatic Categorization of Emails into Folders Based on the Content of the Messages

Automatic Categorization of Emails into Folders Based on the...

引用

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

作者： Anuradha T. Jasphin Jeni Sharmila P. Kanimozhiraman K. Kalaiselvi C. K. Shruthi Jose Anand A. Department of Electrical and Electronics Engineering KCG College of Technology Chennai Tamil Nadu India Department of CSE Chennai Institute of Technology Kancheepuram Tamil Nadu India Department of Mathematics KCG College of Technology Chennai Tamil Nadu India Department of Data Analytics Saveetha College of Liberal Arts and Sciences SIMATS Chennai Tamil Nadu India Department of IT Veltech High-tech Dr Rangarajan Dr.sakunthala Engineering College Avadi Tiruvallur Tamil Nadu Department of ECE KCG College of Technology Chennai

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361193

This task is related to the automatic classification of e-mail messages into folders based on the subject of the messages. Basically, it's about automating the process that sorts messages into different mailboxes. Appropriate message representation techniques, considering their particularities, and machine learning techniques for automatic classification will be studied. This work will be based on the available Enron Message which contains the entire inbox of one company's employees, 500,000 messages from 150 employees.

关键词： Support vector machines signal processing algorithms Feature extraction Vectors Bayes methods Frequency measurement Electronic mail

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Subspace Projection Algorithm based on Code Agile Waveform for Folded Clutter Suppression

Subspace Projection Algorithm based on Code Agile Waveform f...

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

作者： Zihao Liu Huayu Fan Zhaopeng Cao Lixiang Ren Quanhua Liu Erke Mao Radar Research Lab Beijing Institute of Technology Beijing China Key Laboratory of Electronic and Information Technology in Satellite Navigation Beijing Institute of Technology Beijing China Yangtze Delta Region Academy of Beijing Institute of Technology Beijing China Chongqing Innovation Center of Beijing Institute of Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

The Internal Clutter Motion (ICM) results in an expansion of the clutter Doppler spectrum. With conventional waveforms, the presence of range ambiguity causes short-range strong clutter with ICM to be folded into the long-range interval. The folded clutter, along with Doppler expansion, submerges low-speed small targets, significantly affecting the target detection performance of ground-based early warning radar. Agile waveforms improve the distinguishability of echoes from different range intervals by varying the waveform parameters between pulses, enhancing the degree of signal processing. Therefore, this paper proposes a subspace projection algorithm based on code agile waveform to suppress the folded clutter with ICM. First, the clutter subspace is constructed using the echo model of the code agile waveform and the distribution characteristics of clutter in the range-Doppler domain. Next, subspace projection is applied to each range interval, with iterative processing between intervals to achieve folded clutter suppression. Finally, the joint mismatch filtering and Pulse Doppler (PD) processing is performed to realize coherent accumulation in each range interval. The simulations and processing results of real measured data demonstrate the effectiveness of the proposed algorithm in suppressing folded clutter and improving the detection performance of small targets at long-ranges and low-speeds.

关键词： Codes Filtering Radar clutter signal processing algorithms Radar detection signal processing Data models Doppler effect Clutter Projection algorithms

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Research on Competitive Evaluation Based on Genetic Algorithm

Research on Competitive Evaluation Based on Genetic Algorith...

引用

signal processing, Communications and Engineering Management Conference (ISPCEM), International

作者： Diandong Liu Lianshan Sun Yongyi Han Hao Tian Shaanxi University of Science and Technology Xi’an China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

Genetic algorithms are machine learning algorithms inspired by the theory of natural evolution. By mimicking the process of natural selection and reproduction, genetic algorithms can provide high-quality solutions to a wide range of problems involving search, optimization, and learning. It can overcome some of the obstacles encountered by traditional search and optimization algorithms, especially for problems with many parameters and complex mathematical representations. To extend its application, this paper applies it to more subjective judging sessions. Firstly, after in-depth analysis, collation, and classification of judging data from several innovation competitions, genetic algorithms are subsequently used to optimize the allocation of works and judging experts, and the current standard score judging scheme is improved by non-linear correction of parameters. The results of the final data experiment show that the model greatly improves the concentration of scores and judging power and provides a reference for similar subjective scenario modelling optimization.

关键词： Technological innovation Machine learning algorithms signal processing algorithms Search problems Data models Mathematical models Classification algorithms Optimization Standards Genetic algorithms

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Abnormal Driver Behavior Detection Using Deep Learning

Abnormal Driver Behavior Detection Using Deep Learning

引用

International Conference on signal processing and Information Security (ICSPIS)

作者： Fatma Mohammed Al Ali Mouza Jamal Alnuaimi Sara Ali Alawadhi Saeed Abdallah Department of Electrical Engineering University of Sharjah Sharjah UAE

ISBN: (数字)9798350368673

ISBN: (纸本)9798350368680

Many road traffic accidents occur due to drivers’ reckless behaviors, leading to inattentiveness and unawareness of vehicles or pedestrians on the road. Such unsafe driving can result in legal, financial, emotional, and physical consequences. To prevent these accidents and injuries, drivers should follow traffic rules and avoid distractions. This project aims to reduce traffic accidents by detecting common abnormal driver behaviors and hazards, such as not wearing a seatbelt, using a mobile phone, smoking, eating, and fatigue. The proposed system employs a deep learning-based approach to ensure accuracy, reliability, and efficiency. Utilizing You Only Look Once (YOLO), a real-time object detection algorithm, the system captures and analyzes real-time footage of the driver through a camera connected to a Raspberry Pi. The detection accuracy is measured using intersection over union (IoU), which compares the predicted bounding box with the ground truth. When unsafe behavior is detected, the system alerts the driver and provides verbal guidance for correction. The project will present a physical prototype that captures real-time images of the driver and compares them with a trained dataset.

关键词： YOLO Accuracy Software design Prototypes signal processing algorithms Real-time systems Driver behavior Vehicles Accidents Testing

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Research on Complex Street View Detection Algorithm Based on YOLOv8

Research on Complex Street View Detection Algorithm Based on...

引用

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

作者： Haiyang Du Jun Liu School of Automation and Electrical Engineering Shenyang Ligong University Shenyang China

ISBN: (数字)9798350368888

ISBN: (纸本)9798350368895

The current street scene is becoming more and more complex, with high pedestrian and vehicular traffic and a large number of targets. Aiming at the problems that the accuracy of the current complex street scene-oriented detection algorithms is not satisfactory and the leakage rate of small targets is unstable, the intelligent detection algorithm YOLOv8s- for complex street scenes is proposed, aiming at the intelligent detection of multi-targets in daily street scenes. This study is based on the target detection model of YOLOv8s, proposing a C2f-DCNv2 module that can sample features flexibly; adding a small target detection layer in the head; and using the Shape IoU loss function. The improved algorithm improves the precision, recall, and average accuracy by 2.2%, 3.6%, and 5.0%, respectively, compared with the initial algorithm, and these experimental data validate that this study can have a good performance when targeting the detection task of complex street scenes.

关键词： Performance evaluation Accuracy Pedestrians Shape Urban areas signal processing algorithms Object detection Real-time systems Mobile handsets Detection algorithms

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Azimuth Spatially-Varying Phase Compensation for ISAR Based on Non-Uniform Fast Fourier Transform

Azimuth Spatially-Varying Phase Compensation for ISAR Based ...

引用

International Conference on Vision, Image and signal processing (ICVISP)

作者： Jiabo Fan Shuai Shao National Key Laboratory of Radar Signal Processing Xidian University Xi'an China

ISBN: (数字)9798331541460

ISBN: (纸本)9798331541477

Traditional Inverse Synthetic Aperture Radar (ISAR) imaging algorithms overlook the impact of higher-order rotational phase of target echoes, resulting in poor azimuth focusing and challenges in imaging maneuvering targets. To address this issue, this paper proposes a method for compensating azimuth spatially-varying phase errors based on Non-Uniform Fast Fourier Transform (NUFFT). This method establishes a model for the spatially-varying phase errors of maneuvering targets and utilizes the NUFFT technique to accelerate the rapid compensation of azimuth spatially-varying errors (AVSE) in two-dimensional ISAR images, effectively reducing computational complexity and achieving fast focusing of the images. Experimental results demonstrate that this method is effective and robust for ISAR imaging of maneuvering targets.

关键词： Azimuth Fast Fourier transforms Computational modeling signal processing algorithms Focusing Radar imaging signal processing Robustness Computational complexity Inverse synthetic aperture radar

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COVID-19 Detection from Cough Sound signal using Random Forest Learning of Deep Spectral Features

COVID-19 Detection from Cough Sound Signal using Random Fore...

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IEEE International Conference on signal processing, Information, Communication & Systems (SPICSCON)

作者： Waishari Das Puja Shafrin Sultana A. B. M. Aowlad Hossain Department of Electronics and Communication Engineering Khulna University of Engineering & Technology Khulna Bangladesh

ISBN: (数字)9798331510213

ISBN: (纸本)9798331510220

The COVID-19 epidemic and its subsequent rise have brought attention to the need for quick, precise, and reasonably priced testing techniques. Existing assays such as RT-PCR are slow and conditionally sensitive, which presents difficulties, particularly in low-resource settings. Researchers have shown that COVID-19 can be accurately detected by audio-based AI algorithms using cough sounds. In this work, a cough sound-based COVID-19 detection framework is presented. It utilizes a 1D Convolutional Neural Network (CNN) deep feature extractor from handcrafted spectral features and Random Forest classifier for classifying the healthy and Covid-19 subjects from cough sounds. The research considered the cough sound from two datasets named Coswara and Virufy emphasizing the generalization capability of the proposed framework. With the help of feature engineering, spectral features like Short-Term Fourier Transform (STFT), Mel Spectrogram, Mel Frequency Cepstrum Coefficient (MFCC), RMS Energy, Spectral Bandwidth, Zero Crossing Rate (ZCR), Spectral Centroid, and Spectral roll-off have been extracted which are further fed into a CNN based algorithm for extracting deep features. The deep features are more crucial for complex pattern analysis compared to the simple frequency domain analysis. Finally, the Random Forest model is used for classification tasks, which shows a predictive accuracy of 93% with robustness against data diversity. The proposed framework might be useful for smartphone-based COVID-19 detection applications.

关键词： COVID-19 Accuracy signal processing algorithms Feature extraction Robustness Classification algorithms Convolutional neural networks Random forests Spectrogram Testing

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Demonstrating the Potential of Adaptive LMS Filtering on FPGA-Based Qubit Control Platforms for Improved Qubit Readout in 2D and 3D Quantum processing Units

Demonstrating the Potential of Adaptive LMS Filtering on FPG...

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Quantum Computing and Engineering (QCE), IEEE International Conference on

作者： Hans Johnson Nicholas Bornman Taeyoon Kim David Van Zanten Silvia Zorzetti Jafar Saniie Department of Electrical and Computer Engineering Embedded Computing and Signal Processing (ECASP) Research Laboratory Illinois Institute of Technology Chicago IL U.S.A. Fermi National Accelerator Laboratory Superconducting Quantum Materials and Systems (SQMS) Center Batavia IL U.S.A. Department of Physics and Astronomy Center for Applied Physics and Superconducting Technologies (CAPST) Northwestern University Evanston IL U.S.A.

ISBN: (数字)9798331541378

ISBN: (纸本)9798331541385

Advancements in quantum computing underscore the critical need for sophisticated qubit readout techniques to accurately discern quantum states. This abstract presents our research intended for optimizing readout pulse fidelity for 2D and 3D Quantum processing Units (QPUs), the latter coupled with Superconducting Radio Frequency (SRF) cavities. Focusing specifically on the application of the Least Mean Squares (LMS) adaptive filtering algorithm, we explore its integration into the FPGA-based control systems to enhance the accuracy and efficiency of qubit state detection by improving signal-to-Noise Ratio (SNR). Implementing the LMS algorithm on the Zynq UltraScale+ RFSoC Gen 3 devices (RFSoC 4x2 FPGA and ZCU216 FPGA) using the Quantum Instrumentation Control Kit (QICK) open-source platform, we aim to dynamically test and adjust the filtering parameters in real-time to characterize and adapt to the noise profile presented in quantum computing readout signals. Our preliminary results demonstrate the LMS filter's capability to maintain high readout accuracy while efficiently managing FPGA resources. These findings are expected to contribute to developing more reliable and scalable quantum computing architectures, highlighting the pivotal role of adaptive signal processing in quantum technology advancements.

关键词： Three-dimensional displays Accuracy Filtering Qubit signal processing algorithms Superconducting filters Real-time systems Reliability Field programmable gate arrays signal to noise ratio

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