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检索条件"主题词=Signal Processing Algorithms"

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Collaborative Learning for Incremental Classification of EMG signals

Collaborative Learning for Incremental Classification of EMG...

引用

IEEE Workshop on Machine Learning for signal processing

作者： Karl Moore Shamahil Ibunu Stephen R. Alty Clive Cheong Took Department of Electronic Engineering Royal Holloway University Egham Hill Egham

ISBN: (数字)9798350372250

ISBN: (纸本)9798350372267

In this work, we address the problem of online learning for electromyogram (EM G) classification. The main challenge of EMG is its streaming nature, which implies that not all data are available at once. As such, offline and batch methods such as deep learning are generally not appropriate for such small data. To address this problem, we propose an incre-mental learning technique whereby the algorithm enhances its knowledge about the data progressively, as it receives new data. Moreover, we propose to employ multiple learners (in-stead of a single learner); each learning and focusing on a specific class. This federated learning strategy in incremental learning shows superior performance over both existing incre-mental learning and deep learning methods in our simulation studies on small data.

关键词： Deep learning Incremental learning Machine learning algorithms Federated learning signal processing algorithms Focusing signal processing Electromyography Data models Classification algorithms

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Enhanced Mobile Robot Obstacle Avoidance: A Reinforcement Learning Approach with State-Dependent Action Set

Enhanced Mobile Robot Obstacle Avoidance: A Reinforcement Le...

引用

IEEE International Conference on signal processing, Informatics, Communication and Energy Systems (SPICES)

作者： Sachin John Thomas Imthias Ahamed T. P. Syed Muhammed Fahd Pritvi J Centre for Artificial Intelligence T.K.M. College of Engineering Kerala India Department of Mechanical Engineering T.K.M. College of Engineering Kerala India Dept. of Electrical Engineering T.K.M. College of Engineering Kerala India

ISBN: (数字)9798350376135

ISBN: (纸本)9798350376142

In this paper, we address the challenge of enabling a mobile robot to navigate through environment such as a a hospital corridor while avoiding collisions with obstacles. The main objective of the agent is to navigate through these corridor without colliding with any of the static or dynamic obstacles in its way. The robot or the agent is equipped with three proximity sensors which determines the proximity of the obstacles in it’s respective direction. The learning is achieved through Q-learning algorithm which is a value based reinforcement learning technique. In order to achieve much lesser computational time and cost we implement state dependant actions such that we can decrement the number of iterations for learning. For testing and visualization Python’s Pygame library is involved. The agent undergoes training with a grid-based environment.

关键词： Training Visualization Q-learning Machine learning algorithms Navigation Hospitals Heuristic algorithms signal processing algorithms Mobile robots Collision avoidance

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Intelligent EEG Artifact Removal in Motor ImageryBCI: Synergizing FCIF,FCFBCSP, and Modified DNN with SNR, PSD, and Spectral Coherence Evaluation

Intelligent EEG Artifact Removal in Motor ImageryBCI: Synerg...

引用

Circuit, Systems and Communication (ICCSC), International Conference on

作者： Srinath Akuthota K. Rajkumar Ravichander Janapati Dept. of ECE SR University Warangal Telangana India Govt Polytechnic for Women Medak Telangana India

ISBN: (数字)9798350365306

ISBN: (纸本)9798350365313

Motor Imagery Brain-Computer Interfaces (MI BCIs) face challenges due to artifacts in electroencephalogram (EEG) signals, hindering accurate decoding of imagined movements. This study introduces an innovative method for intelligent EEG artifact removal in MI BCIs. By synergizing Four Class Iterative Filtering (FCIF), Four Class FBCSP (FCFBCSP), and a Modified Deep Neural Network (DNN) with signal-to-Noise Ratio (SNR), Power Spectral Density (PSD), and Spectral Coherence evaluation, our approach enhances feature extraction specificity and extends the Filter Bank Common Spatial Pattern (FBCSP) algorithm for four MI classes. The primary objective is to improve MI BCI reliability through refined artifact removal, thus advancing EEG signal processing for MI applications.

关键词： signal processing algorithms Artificial neural networks Coherence signal processing Motors Electroencephalography Iterative algorithms

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Decoding Engagement: The Role of Closeness Cues in Human-Robot Interactions

Decoding Engagement: The Role of Closeness Cues in Human-Rob...

引用

IEEE International Workshop on Robot and Human Communication (ROMAN)

作者： Kira Loos Mara Brandt Anna-Lisa Vollmer Medical School OWL Bielefeld University Bielefeld Germany

ISBN: (数字)9798350375022

ISBN: (纸本)9798350375039

This work examines the critical role of nonverbal cues in initiating successful human-robot interactions (HRI), focusing on the development of an algorithm capable of decoding subtle cues that indicate a human’s intention to interact with a robot. The foundation for the analysis is the concept of closeness which relates to the individual feeling of intimacy and comfort, and indicates the engagement potential in diverse settings and interactions. This paper introduces a novel algorithm designed to quantify a person’s willingness to interact based on analyzing the displayed closeness on a series of images. The algorithm’s efficacy is evaluated through an empirical study involving 20 participants, generating a substantial dataset of 130 video sequences. These sequences were analyzed to assess the algorithm’s ability to predict communication initiation intentions, with findings suggesting a significant distinction between participants engaged in interaction with a robot and those instructed to ignore it. The results underscore the algorithm’s potential in facilitating more nuanced and socially sustainable HRI, laying the groundwork for future advancements in the field. This research contributes to the growing body of work on social robotics, emphasizing the importance of integrating nonverbal cue analysis for enhancing robots’ interactive capabilities and fostering more meaningful human-robot connections.

关键词： Video sequences signal processing algorithms Human-robot interaction Focusing Prediction algorithms Decoding Behavioral sciences Robots

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Algorithm for Identifying Critical Nodes in Networks Based on Spectral Clustering to Construct Hypergraphs

Algorithm for Identifying Critical Nodes in Networks Based o...

引用

International Conference on Wireless Communications and signal processing (WCSP)

作者： Xujie Li Fei Shao Haotian Li College of Computer Science and Software Engineering Hohai University Nanjing China College of Information Science and Engineering Hohai University Nanjing China

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

Currently, the basis for critical nodes definition and identification lies in the representation learning of the network and the extraction of local and global features of the nodes. The effectiveness of the algorithm is closely related to network modeling and network structural characteristics. However, with the increasing complexity of real-world systems, utilizing the continuity and integrity of traditional bipartite graph representation faces significant challenges. Different from traditional graph modeling, the hyperedges of a hypergraph can connect multiple nodes simultaneously, thus forming richer structural features and interaction information between nodes for the analysis and judgment of critical nodes. To more accurately identify critical nodes in complex networks, we propose an algorithm that establishes a hypergraph representation for traditional graph data to extract higher-order features of nodes for neural network training, in order to identify critical nodes in complex networks. The effectiveness of this method is proven by comparing it with various critical node identification algorithms under different infection rates.

关键词： Training Representation learning Wireless communication Neural networks signal processing algorithms Clustering algorithms Complex networks Feature extraction Vectors Topology

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A Multi-Criteria Evaluation Approach for Ensemble Machine Learning algorithms in Precision Brain Cancer Detection

A Multi-Criteria Evaluation Approach for Ensemble Machine Le...

引用

Artificial Intelligence and signal processing (AISP), INDIA, International Conference on

作者： Gurugubelli Visalakshi Laavanya Mohan Vignan's Foundation for Science Technology and Research Guntur Andhra Pradesh India

ISBN: (数字)9798350350654

ISBN: (纸本)9798350350661

A brain tumour is a lump or collection of abnormal brain cells that has the potential to spread to nearby tissues and a precise diagnosis is necessary for effective treatment planning. The development of an algorithm for multi-criteria ensemble learning to identify brain cancer with decrease the time needed to process the classifier, which will improve accuracy. After using SMOTE and data augmentation, the model pre-processes the images. After that, a convolution neural network is used to analyze the data to extract features with Squared Hinge Loss function. The images that have been optimized are used as input for ensemble learning algorithms that are based on the gradient-boost and Ada-boost decision tree methods. The algorithm was selected with the intention of reducing processing complexity and temporal complexity without sacrificing CNN's accuracy. The system's accuracy for cancer diagnosis is discovered to be 98.45%, which is a superior value than the current approach.

关键词： Accuracy Machine learning algorithms signal processing algorithms Brain cancer Feature extraction Classification algorithms Complexity theory Planning Ensemble learning Tumors

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Investigation of Water Cycle Algorithm in Renewable Based Microgrid

Investigation of Water Cycle Algorithm in Renewable Based Mi...

引用

Advances in signal processing, Power, Communication, and Computing (ASPCC), IEEE International Conference on

作者： Prasantini Samal Niranjan Nayak Sujit Kumar Bhuyan Department of EE ITER SOA Deemed to be University Bhubaneswar Resource Assessment & Asset Analysis Manikaran Analytics Limited New Delhi India

ISBN: (数字)9798350355093

ISBN: (纸本)9798350355109

This study explores load frequency control (LFC) in a two-area power system, focusing on three controllers: a standard PID, a PID tuned with Particle Swarm Optimization (PSO-PID), and a PID optimized using the Whale Optimization Algorithm (WCA-PID). By comparing these controllers, the study evaluates their effectiveness in maintaining stability and minimizing frequency and tie-line power deviations under varied load conditions. While the basic PID controller is robust, PSO and WCA optimizations enhance its performance. Simulation results show that both PSO-PID and WCA-PID offer improvements in settling time, overshoot, and steady-state error, with WCA-PID achieving the best overall performance. The study underscores the value of optimization techniques in advancing LFC capabilities, offering insights for future power system control strategies.

关键词： Simulation signal processing algorithms Power system stability Whale optimization algorithms Stability analysis Steady-state Frequency control Optimization Standards Water cycle

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Context-aware Codebook Design for 6G Extreme MIMO Systems

Context-aware Codebook Design for 6G Extreme MIMO Systems

引用

IEEE Workshop on signal processing Advances in Wireless Communications (SPAWC)

作者： Ryan M. Dreifuerst Ibrahim Kilinc Robert W. Heath Dept. of Electrical and Computer Engineering North Carolina State University Dept. of Electrical and Computer Engineering University of California San Diego

ISBN: (数字)9798350393187

ISBN: (纸本)9798350393194

Beam management is the defacto approach for configuring the antennas in 5G MIMO communication systems. Extending the beam management framework to larger arrays— also known as extreme MIMO systems—is challenging as the overheads grow with the array dimensions. One solution is to make use of the wealth of sensor data that is becoming available in integrated sensing and communication (ISAC) systems. In this paper, we propose a neural architecture for codebook design using environmental context derived from sensor data. In particular, we combine beamspace transformations with local occupancy grids obtained through network sensing to maximize the achievable rate in vehicular operations. Our results show significant performance gains over traditional codebooks while requiring less overhead than standard 5G beam management.

关键词： 6G mobile communication Wireless communication 5G mobile communication Heuristic algorithms Discrete Fourier transforms signal processing algorithms Integrated sensing and communication Arrays Vehicle dynamics MIMO communication

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Ensemble Learning for Diabetes Detection: A Voting-Based Classifier Approach

Ensemble Learning for Diabetes Detection: A Voting-Based Cla...

引用

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

作者： Debendra Muduli Santosh Kumar Sharma Suryakanta Mahapatra Debasish Pradhan Nihar Ranjan Panda Sourav Parija Dept. of Computer Science & Engineering C.V Raman Global University Bhubaneswar Odisha Dept. of Computer Science & IT NIIS Institute of Business Administration

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

Disasters often lead to diseases affecting both adults and children, causing physical and mental illness. Diabetes, a rapidly rising chronic illness, is difficult to diagnose early without regular doctor visits. The International Diabetes Federation reports that diabetes affects 1 out of 11 individuals worldwide, with a higher prevalence in middle to low-income nations. The increasing rate of diagnosis underscores the importance of early detection. In this study, classifiers like SVM, XGB, LR, RF, and LDA were tested for accurate prediction, with a voting classifier achieving 90.10% accuracy using the PIMA Indian dataset.

关键词： Support vector machines Accuracy Supervised learning signal processing algorithms Medical services signal processing Predictive models Diabetes Standards Classification tree analysis

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Anomaly Detection in Hyperspectral Images with High Dimensional Model Representation

Anomaly Detection in Hyperspectral Images with High Dimensio...

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IEEE signal processing and Communications Applications (SIU)

作者： Derya Bodur Evrim Korkmaz Özay Burcu Tunga Yazılım Mühendisliði Matematik Mühendisliði Bahçeşehir Üniversitesi İstanbul Teknik Üniversitesi İstanbul Türkiye

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

Hyperspectral imaging (HSI) is an important imaging technology that enables the high sensitivity and accuracy analysis of wide areas through its multispectral band structure. Anomaly detection in hyperspectral images can be defined as the detection of pixels that do not belong to the background and whose spectral properties are unknown. In this study, a method based on High Dimensional Model Representation (HDMR) is proposed for detecting anomalies in hyperspectral images. The effectiveness of the proposed method has been tested on a sample image and compared with commonly used anomaly detection methods. The HDMR-based anomaly detection algorithm has shown to be more effective in suppressing the background compared to other methods by making anomaly pixels more visible.

关键词： Band structures Sensitivity Accuracy signal processing algorithms Imaging signal processing Anomaly detection

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