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检索条件"主题词=filtering algorithms"

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A Wearable Multi-Sensor System for Flexible Wearing and Robust Monitoring of Muscle Activity

A Wearable Multi-Sensor System for Flexible Wearing and Robu...

引用

International Conference on Communications, Circuits and Systems (ICCCAS)

作者： Yu Gu Jiaxing Li Zixuan Huang Haozhou Zeng Xiangzhi Liu Chenhao Liu Ning Zhang Xiufeng Zhang Tao Liu School of Mechanical Engineering Zhejiang University Hangzhou China the National Research Center for Rehabilitation Technical Aids Beijing China

ISBN: (数字)9798350386271

ISBN: (纸本)9798350386288

Muscle activity monitoring plays a vital role in diverse fields, including clinical examination, rehabilitation assessment, and human-robotics interaction. However, the quality of acquired physiological signals often suffers due to limitations in sensor flexibility, robustness, and resistance to interference, which hampers the realization of long-term muscle activity monitoring. In this study, we introduced a novel wearable, flexible monitoring system, comprising a Magnetic-Inertial Measurement Unit (MIMU) and surface electromyography (sEMG) sensor. The designed flexible electrodes exhibited superior performance with low contact impedance and high signal-to-noise ratio. Additionally, we proposed an adaptive filtering algorithm based on a finite-state machine framework. This algorithm effectively compensated for orientation information by utilizing an electromagnetic interference observer and dynamically filtered electromyographic information through automated notch filter adjustments, enhancing the system's robustness against electromagnetic disturbances. Furthermore, validation experiments were conducted on five subjects across different muscle groups. The results, obtained from 12 hours of continuous monitoring with measurements taken every two hours, showcased consistent low contact impedance (60.32±12.15kΩ) and high signal-to-noise ratio (17.64±0.53 dB) of the designed flexible sensors. Notably, the system successfully improved posture-related information and dynamically filtered sEMG signals in environments with random magnetic field interference. In summary, our proposed flexible wearable monitoring system demonstrates promising capabilities in acquiring high-quality sEMG-orientation data, offering reliable and superiorquality information for applications in human-robot interaction.

关键词： Magnetic separation Surface resistance Muscles filtering algorithms Information filters Robustness Impedance

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A Lightweight Integrity-Driven Federated Learning Approach to Mitigate Poisoning Attacks in IoT

A Lightweight Integrity-Driven Federated Learning Approach t...

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IEEE Future Networks World Forum (FNWF)

作者： Shahid Latif Djamel Djenouri Jose L. Hernandez-Ramos Antonio Skarmeta Jawad Ahmad Univesity of the West of England Bristol United Kingdom University of Murcia Murcia Spain Prince Mohammad Bin Fahd University Alkhobar Saudi Arabia

ISBN: (数字)9798350379495

ISBN: (纸本)9798350379501

Despite its distributed nature and being privacy-preserving by nature, Federated Learning (FL) is vulnerable to poisoning attacks in which malicious actors can inject fake model parameters or false data to compromise the learning process. This article introduces a lightweight and efficient integrity verification scheme to mitigate these attacks on FL platforms in Internet of Things (IoT) networks. The core design of the proposed scheme is based on a customized feed-forward neural network (FFNN) integrated with a fast, secure, and efficient Keccak512 hashing algorithm. This combination balances security, speed, efficiency, and suitability for resource-constrained IoT devices. The proposed model was trained and evaluated using the real-time IDSIoT2024 dataset, and the results demonstrated a higher classification accuracy of 98.29% with a lower memory footprint of 87.58KB. Furthermore, the lower computation and communication overheads, low CPU and GPU memory utilization confirm the resource and time efficiency of the proposed scheme to effectively mitigate the poisoning attacks in FL architectures.

关键词： Privacy Accuracy Federated learning Computational modeling Memory management Graphics processing units filtering algorithms Real-time systems Classification algorithms Internet of Things

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Smart Basket: An E-Commerce Recommendation System

Smart Basket: An E-Commerce Recommendation System

引用

International Conference for Convergence of Technology (I2CT)

作者： Samket V Pujari Tarun R Adeesh Jain Neeraj S Prajwala T R Computer Science Department PES University Bangalore India

ISBN: (数字)9798350394474

ISBN: (纸本)9798350394481

A novel platform addressing the challenges prevalent in grocery shopping, especially amidst the expanding market choices and pandemic-induced complexities. The platform offers a comprehensive solution by enabling the comparison of groceries from various online sources, accompanied by personalized recommendations, with a focus on improving accessibility, convenience, and cost-effectiveness for users. Key features include an Image Detection system for efficient item search, user-specific coupon manager, and a tailored recommendation engine. By amalgamating these functionalities, the platform aims to revolutionize the grocery shopping experience, providing users with a diverse range of options while simplifying the product discovery process. User profiles further enhance penalization by storing preferences, while a recommendation engine suggests products based on individual behavior. This platform presents a practical solution to streamline grocery shopping, catering to users' diverse needs through its array of user-friendly features.

关键词： Navigation Transforms filtering algorithms Feature extraction Complexity theory Electronic commerce Engines

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State-of-Health Estimation for Lithium-ion Batteries Based on An Improved Gated Dual Attention Unit Neural Networks

State-of-Health Estimation for Lithium-ion Batteries Based o...

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International Conference on Power System Technology (POWERCON)

作者： Zhenglu Shi Jiazhu Xu Linjun Zeng Yang He College of Electrical and Information Engineering Hunan University ChangSha China School of Energy and Powe Engineering Changsha University of Science and Technology ChangSha China

ISBN: (数字)9798350351286

ISBN: (纸本)9798350351293

State of health (SoH) as a crucial parameter plays a vital role in battery management system, which can assess the degree of aging in lithium-ion batteries. However, the SoH values cannot be directly obtained through sensors. To solve the issue and obtain precision SoH values, an improved gated dual attention unit (IGDAU) neural network is proposed. Firstly, the moving average filter is applied to smooth the differential thermal voltammetry curve and health indicators with six are derived. Subsequently, the pearson correlation coefficient is utilized to analysis the relationship between health features and SoH. Then, the whale optimization algorithm method is employed to optimize the hyperparameters of the gated dual attention unit model. Finally, the proposed methods are validated by using open sources dataset provided by the University of Oxford. The results indicate that the IGDAU model can precisely estimate the SoH values. In comparison to the SVM, GPR and IGRU methods, the values of the four evaluation indexes are reduced, showing good estimation ability.

关键词： Lithium-ion batteries Support vector machines Neural networks Estimation Logic gates filtering algorithms Whale optimization algorithms Sensors Power systems Indexes

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RH-SQL: Refined Schema and Hardness Prompt for Text-to-SQL

RH-SQL: Refined Schema and Hardness Prompt for Text-to-SQL

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International Conference on Electronic Engineering and Informatics (EEI)

作者： Jiawen Yi Guo Chen Zixiang Shen Department of Automation Central South University Changsha China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

Text-to-SQL is a technology that converts natural language queries into the structured query language SQL. A novel research approach that has recently gained attention focuses on methods based on the complexity of SQL queries, achieving notable performance improvements. However, existing methods entail significant storage and training costs, which hampers their practical application. To address this issue, this paper introduces a method for Text-to-SQL based on Refined Schema and Hardness Prompt. By filtering out low-relevance schema information with a refined schema and identifying query hardness through a Language Model (LM) to form prompts, this method reduces storage and training costs while maintaining performance. It's worth mentioning that this method is applicable to any sequence-to-sequence (seq2seq) LM. Our experiments on the Spider dataset, specifically with large-scale LMs, achieved an exceptional Execution accuracy (EX) of 82.6%, demonstrating the effectiveness and greater suitability of our method for real-world applications.

关键词： Training Structured Query Language Costs Natural languages filtering algorithms Information filters Generators Complexity theory Informatics Lenses

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AutoEDA: Iterative Data Focusing and Exploratory Analysis Based on Attribute Frequency

AutoEDA: Iterative Data Focusing and Exploratory Analysis Ba...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Tong Wu Song Wang Xin Peng Department of Computer Science School of Computer Science and Technology Southwest University of Science and Technology Mianyang China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

The paper proposes an automated data exploration and analysis method based on Attribute Frequency Statistical Feature Ratio (AFSFR). It integrates AutoVis and Data Preprocessing Methods to design and develop AutoEDA-Segment. Addressing the Concentrate on Field Sequences (CFS) problem in data exploration and analysis, this study employs various classification models and combines AFSFR with field type and the Elbow Inflection Point (EIP) of index features to design a field type identification and field value assessment method. For evaluating the effectiveness of focus analysis, the approach provides clustering visualization effects and an analysis scheme based on Field Type Search Tree (FST) and cluster comparison profiles, using a custom CFS approach. Additionally, to enhance the value of focused subset analysis data, the approach introduces a Parallel Coordinates-Based Data Filter (PCF), forming an EDA feedback loop to achieve Iterative Exploratory Data Analysis for User-inferred Cognition (IEDA-UC). Finally, we engaged graduate students with varying levels of experience in visualization research for collaboration and discussion, validating the effectiveness and feasibility of the approach using structured data from Kaggle.

关键词： Feedback loop Design methodology Data preprocessing Data visualization Focusing filtering algorithms Iterative algorithms Data models Indexes Elbow

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Group Targets Separation Method with Segmented Hypothesis Tracking

Group Targets Separation Method with Segmented Hypothesis Tr...

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IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP)

作者： Yue Hu Ruixin Lai Shaoran Wang Qi Shi Mengmeng Li Dazhi Ding School of Microelectronics (School of Integrated Circuits) Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798350351019

ISBN: (纸本)9798350351026

This paper proposes a method for separating group targets based on segmented hypothesis tracking. In the case where multiple targets are identified by the same radar beam within a radar observation period, deviations and missing data may occur among the targets due to noise, crossovers, and occlusion effects. The segmented hypothesis tracking algorithm is utilized to correct the erroneous matching of scatter points between targets, further achieving target separation. Subsequently, the Kalman filtering algorithm is employed to correct the parameters of the target scatter center. This method provides an effective and feasible solution to the problem of group targets separation.

关键词： Correlation coefficient Target tracking Noise Radar filtering algorithms Radar tracking Antennas and propagation Kalman filters

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A Projection Filter Algorithm for Stochastic Systems with Correlated Noise and State-Dependent Measurement Covariance

A Projection Filter Algorithm for Stochastic Systems with Co...

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American Control Conference (ACC)

作者： Muhammad F. Emzir Lahouari Cheded Control and Instrumentation Engineering Department and Interdisciplinary Center of Smart Mobility and Logistics King Fahd University of Petroleum and Minerals (KFUPM) Dhahran Saudi Arabia KFUPM and is currently an independent researcher and consultant in UK

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

The solution of the optimal filtering problem for nonlinear stochastic systems can be efficiently approximated by using a projection filter. A recent work by Emzir et al. [1] proposed a novel framework that employs automatic differentiation to implement the projection filter for exponential family manifolds. This approach can effectively overcome the curse of dimensionality in the optimal filtering problem by exploiting the sparse-grid scheme. In this paper, we extend this framework to a class of stochastic differential equations where both state and measurement noises are correlated and the measurement processes have state-dependent covariances. We derive the projection filter equation for this class of systems on exponential family manifolds and present a numerical example to demonstrate its performance in solving a challenging nonlinear filtering problem.

关键词： Manifolds Monte Carlo methods filtering Stochastic systems Noise Differential equations filtering algorithms

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Smart Tutoring: Subject Assessor with Targeted Tutor Matching and Real-Time Emotion Engagement Analysis

Smart Tutoring: Subject Assessor with Targeted Tutor Matchin...

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International Conference on Computer and Applications (ICCA)

作者： Neil A. Flores Miloudanne G. Legaste James O. Tiatcho Vivien Agustin Criselle J. Centeno Mary Grace Nulud Richard C. Regala Management Pamantasan ng Lungsod ng Maynila College of Information Systems and Technology Manila Philippines

ISBN: (数字)9798350367560

ISBN: (纸本)9798350367577

This research examines the evolution and deployment of ‘smart tutoring’, which is an intelligent education and learning system introducing real-time emotion recognition and tutor matching to improve learning organization. This research has far-reaching consequences offering a workable and scalable strategy that could deal with varying issues of learners in different educational settings. The focus on the instruction methods as well as student motivation within the context of this study further illustrates the current trends in educational systems towards individual learning where personalization is sought after. It stresses that smart tutoring system systems can eliminate shortcomings of the traditional education systems by providing learning environments which are more flexible and complete even attaching emotion to enhanced student performance as well as overall wellness. The system's main features include scheduling and automated enrollment, virtual tutoring with real-time emotion recognition, and personalized tutor recommendations. To do this, researchers employed Convolutional Neural Networks (CNN) for emotion recognition, a content-based filtering algorithm, and prescriptive analytics for personalized tutor matching; the system recognizes students' emotional states and analyzes unique learning needs. The emotion recognition method analyzes pupils' facial movements in various emotional states to adapt or reinforce the mode of education. The content-based filtering algorithm, on the other hand, pairs students with tutors whose knowledge matches both the subject matter and the student's preferred learning technique. The system was evaluated using ISO 25010 criteria, which prioritized functional suitability, performance efficiency, usability, dependability, and security. Participants' responses (n=100) demonstrated primarily positive outcomes in these categories, with mean scores ranging from 3.82 to 4.33 on a 5-point Likert scale. Although the system achieved its

关键词： Emotion recognition Accuracy Education filtering algorithms Real-time systems Mathematical models Security Convolutional neural networks Reliability Usability

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Synergizing Support Vector Machines and Image Processing for Superior Skin Cancer Detection : A Crammer-Singer Approach

Synergizing Support Vector Machines and Image Processing for...

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International Conference on Communication and Electronics Systems (ICCES)

作者： S. Divya S. Thilagavathi Department of Computer Sciene Sri Krishna Adithya College of Arts and Science Coimbatore India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

Skin cancer, which primarily impacts skin exposed to ultraviolet (UV) rays against the sun, represents dangerous to the most significant organs in the human body, the skin. Usually, a spot, lump, or mole that appears on the skin is the primary suspicion of skin cancer. However, each of these can undergo changes in coloring or shape as time passes. Recovery for skin cancer is mostly possible if the disease is discovered early. Numerous medical diagnostic methods, such as Dermoscopy, biopsy, and ocular examination of the affected area, are useful in helping anticipate the development of skin cancer. However, these approaches have the disadvantage of delivering erroneous results because it is extremely difficult to distinguish between normal and malignant skin. Therefore, the drawback of these diagnostic procedures is that machine learning algorithms are currently used together with image processing techniques to examine the images for the purpose of precisely identifying skin cancer. The current research employs the ISIC dataset to develop a novel model for skin cancer classification that combines image processing techniques with advanced machine learning methods, including Crammer-Singer Support vector machine learning algorithms. The categorization of skin cancer begins with preprocessing the input image, which includes hair removal using a morphological filter and image enhancement using a median filter to minimize noise and increase image clarity. The ABCD approach is used to segment lesion images by evaluating them for asymmetry, border irregularity, color variability, and diameter. The crammer-Singer SVM algorithm is then used with these images to classify skin lesions into various types such as melanoma (MEL), melanocytic nevus (NV), basal cell carcinoma (BCC), actinic keratosis (AK), benign keratosis (BKL), dermatofibroma (DF), vascular lesion (VASC), and squamous cell carcinoma (SCC), leveraging its robust multi-class handling capabilities. The system achieve

关键词： Support vector machines Image segmentation Machine learning algorithms Accuracy filtering algorithms Skin Classification algorithms Lesions Image enhancement Skin cancer

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