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检索条件"主题词=Signal Processing Algorithms"

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Improvement of Euler Angle Algorithm Based on Extended Kalman Filtering

Improvement of Euler Angle Algorithm Based on Extended Kalma...

引用

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

作者： Yitao Zhu Yu Wang Department of Mechanical Nanjing University of Science and Technology Nanjing China

ISBN: (数字)9798331516550

ISBN: (纸本)9798331516567

Euler angles are essential parameters to describe the attitude of a carrier, and the Euler angles computed by the Inertial Measurement Unit (IMU) are affected by the performance of the components. Traditional Kalman filtering algorithms struggle to effectively suppress nonlinear Gaussian noise in engineering applications. Therefore, this paper employs an improved Extended Kalman Filtering algorithm(EKF), which linearizes the nonlinear system using the Jacobian matrix. It utilizes quaternions and gyroscope errors as state variables, while accelerometers serve as measurement inputs to update the quaternions, thereby obtaining the Euler angles. Building on the updated Euler angles, a magnetometer is incorporated to further refine the calculation, mitigating the impact of gyroscope z-axis drift on the heading angle and eliminating external magnetic interference. Experimental results demonstrate that, compared to the Madgwick algorithm and complementary filtering, the improved algorithm significantly reduces the influence of component errors on Euler angle calculations, minimizes bias drift, and better reflects the actual operational conditions of the carrier, effectively achieving filtering results.

关键词： Magnetometers Filtering Magnetic separation Quaternions signal processing algorithms Process control Interference Filtering algorithms Gyroscopes Kalman filters

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Research on Concrete Structure Crack Classification Based on Improved FasterNet Network

Research on Concrete Structure Crack Classification Based on...

引用

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

作者： Shishi Liu School of Computer Science Hubei University of Technology Wuhan China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

The economic losses caused by the damage of concrete structures are huge, so regular inspection and monitoring are essential. Deep learning technology provides a new way for crack detection. But the CNN perception field is limited, while the VIT computation is large. Therefore, a fusion model of CNN and transformer based on haar discrete wavelet transform is proposed in this paper, which combines the advantages of both to realize high-precision crack identification. With only 1.89M parameters and 0.28G FLOPs, the model achieved a high accuracy of 92.78% on the SDNET2018 dataset.

关键词： Deep learning Accuracy Image edge detection Computational modeling signal processing algorithms signal processing Feature extraction Transformers Discrete wavelet transforms Data mining

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Investigating the Effect of Intra-Subject Variability in Seismocardiogram Analysis

Investigating the Effect of Intra-Subject Variability in Sei...

引用

European signal processing Conference (EUSIPCO)

作者： Eda Demirsoy Beren Semiz Physics Bogazici University Istanbul Turkey Electrical and Electronics Engineering Koc University Istanbul Turkey

ISBN: (数字)9789464593617

ISBN: (纸本)9798331519773

Seismocardiogram (SCG) signal corresponds to the local chest vibrations originating from the contraction of the heart and has been leveraged to assess cardiovascular parameters and pathologies, especially in studies focusing on wearable system design. Two common challenges encountered in the analysis of SCG are the presence of inter-subject and intra-subject variability, affecting the generalizability and performance of the models. In this work, we focus on the intra-subject variability to show how the local body compositions and surrounding systems affect the morphology of the SCG signals. More specifically, we investigated the effects of sensor location and respiration phases on the SCG signals. Analysis of tri-axial SCG signals from 16 different locations on torso was treated as a 16-class classification problem, whereas the effect of inhalation and exhalation was formulated as a binary classification task. Using features from 5 different groups, our models achieved a distinction rate of %92 and %90 for the sensor location and respiration effect, respectively. Additionally, we showed that the effect of intra-subject variability is not the same for different SCG axes or feature groups. Overall, intra-subject variability has a great impact on the SCG characteristics, thus the inaccuracies stemming from sensor location or surrounding physiological systems should be minimized during analyses to have more generalizable models.

关键词： Vibrations Torso Analytical models Pipelines Morphology signal processing algorithms signal processing Feature extraction System analysis and design Remote monitoring

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Federated Deep Learning Multiple Households Energy Consumption Forecasting Considering Data Privacy-Preserving

Federated Deep Learning Multiple Households Energy Consumpti...

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

作者： Xiang He Hongliang Liu Jiaquan Zhang College of Automation and Electronic Information Xiangtan University Xiangtan China

ISBN: (数字)9798331507398

ISBN: (纸本)9798331507404

Energy consumption forecasting is vital for the development of smart grids. Currently, much of the research in this field concentrates on forecasting energy consumption at the individual household level. Few methods focus on forecasting energy consumption for multiple households, and those that do exist suffer from poor accuracy, ignore user privacy, and require high communication resources. This paper proposes a method for forecasting energy consumption across multiple households, which combines federated learning(FL), bidirectional long short-term memory(BiLSTM) network, and attention mechanism(AM). The bidirectional context of the input sequence is captured using forward and backward long short-term memory, while the attention mechanism is employed to weigh inputs and emphasize key information, thereby improving prediction accuracy. The federated learning framework safeguards against privacy breaches of household energy consumption data during the training process. Experimental results show that the proposed method can implement simultaneous prediction of multiple households energy consumption with a consistent model via a transmission control protocol, with reduced mean absolute error(MAE), mean absolute percentage error(MAPE), and root mean square error(RMSE) compared with traditional household energy consumption forecasting methods. Additionally, it has improved prediction accuracy, reduced computing time, and enhanced generalization capability.

关键词： Training Energy consumption Data privacy Accuracy signal processing algorithms Computer architecture signal processing Privacy breach Smart grids Forecasting

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Development of a Haptic Exo-Suit with Tactile Feedback in Hand Rehabilitation

Development of a Haptic Exo-Suit with Tactile Feedback in Ha...

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Intelligent signal processing and Effective Communication Technologies (INSPECT), IEEE International Conference on

作者： Parikshith Chavakula Soinik Ghosh Neeraj Sharma Deepesh Kumar School of Biomedical Engineering Indian Institute of Technology (BHU) Varanasi India

ISBN: (数字)9798350379525

ISBN: (纸本)9798350379532

Stroke survivors with hemiplegia or paralysis often struggle with daily tasks due to impaired hand function and timely physiotherapy is essential for motor function recovery. This study presents a haptic forearm exo-suit designed to assist stroke rehabilitation by mimicking a physiotherapist's tactile feedback. The exo-suit integrates force-myography (FMG) and flexible tactile sensors to monitor muscle activity, finger movements, arm rigidity and tactile force in real-time. An AI-driven algorithm processes this sensor data to control the exo-suit's actuation system, which mimics natural finger movements and provides targeted therapeutic exercises. Initial testing demonstrated the exo-suit's effectiveness in simulating a physiothera-pist's precise and gradual actuation, improving the rehabilitation process. Evaluations on both healthy participants and those under simulated paralysis confirmed the hardware's capability and the reliability of the signal processing algorithms. Among the machine learning algorithms applied, k-nearest neighbors (KNN) achieved the highest accuracy at 86 %, with 89% precision and 84% recall. The proposed exo-suit offers an affordable and adaptive solution for individuals with partial arm paralysis, with real-time therapy adjustments that may significantly enhance recovery and improve the quality of life for stroke patients.

关键词： MIMICs Tactile sensors signal processing algorithms Medical treatment Stroke (medical condition) Nearest neighbor methods Real-time systems Paralysis Haptic interfaces Monitoring

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Enhanced Privacy in Collaborative Data Collection with Sparse Healthcare Data

Enhanced Privacy in Collaborative Data Collection with Spars...

引用

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

作者： Blessa Binolin Pepsi M Abinaya S M Rajshre M Siva Darshini M Mepco Schlenk Engineering College Sivakasi Tamilnadu India

ISBN: (数字)9798331506452

ISBN: (纸本)9798331506469

Privacy-preserving techniques are essential for analyzing sensitive information from the Internet of Things and healthcare, but missing values often reduce analysis accuracy. This paper introduces a method that addresses missing values while ensuring data privacy. It anonymizes patient data and employs Harris hawk optimization combined with particle swarm optimization to select indices, enhancing accuracy and reducing computation. A generative model and contingency table are built on the server using expectation-maximization and Gaussian copula techniques. The client-server architecture improves performance and security. Experiments on synthetic healthcare data, including COVID-19 data, show 80-90% accuracy, outperforming existing methods that do not handle missing values.

关键词： COVID-19 Accuracy signal processing algorithms Medical services signal processing Robustness Reproducibility of results Servers Security Particle swarm optimization

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Modified Incremental learning (ensemble) System with Weight Updating Rule

Modified Incremental learning (ensemble) System with Weight ...

引用

Innovations in High Speed Communication and signal processing (IHCSP), International Conference on

作者： Garima Priya Manmohan Singh Sonika Thapak Sugandh Singh Computer Science and Engineering IES College of Technology Bhopal M.P India

ISBN: (数字)9798350368949

ISBN: (纸本)9798350368956

Discrimination is a method of finding models that define and compare mathematical classes or principles. To use the model to predict the beauty of objects of unknown size. Using an ensemble system is one of the best-known methods of clustering. Acceleration is one of the many classification techniques. It was originally intended to increase the accuracy of weaker subjects by selecting study sets for weaker classifiers based on the individual's overall memory ***++ uses the relative representation power of a set of classifiers to learn additional facts from new data. Learn++ can learn new lessons without forgetting existing knowledge. Even if previously used data is no longer available. Mathematical substitution rules based on all functions of common sense. For choosing a course for the next weak classifier. As the optimal probability of remembering increases k', new learning may occur. An individual-based distribution rotation rule guarantees stability and prevents deterioration in overall efficiency. While partition rotation rules based on shared logic allow for green incremental control capabilities. I have a new algorithm where the right combination of people and ideas can lead to better performance.

关键词： Training Technological innovation Incremental learning signal processing algorithms signal processing Predictive models Stability analysis Robustness Classification algorithms Partitioning algorithms

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Research on Intelligent System of Key Technology of Long Distance Guided Wave Radar in Level Measurement of Nuclear Power Unit

Research on Intelligent System of Key Technology of Long Dis...

引用

IEEE International Conference on Power, Intelligent Computing and Systems (ICPICS)

作者： Hongwei Yu Jian Wang CGN Digital Technology Co. Ltd. Beijing China

ISBN: (数字)9798350374315

ISBN: (纸本)9798350374322

In this paper, a key technology intelligent system based on long distance guided wave radar is proposed to meet the special requirements of liquid level measurement in nuclear power units. By integrating advanced signal processing algorithm model, the system effectively overcomes the problem of insufficient accuracy and poor stability of traditional liquid level measurement method in complex nuclear power environment. An efficient signal processing algorithm is developed, which can accurately identify and analyze the guided wave radar echo signal, so as to achieve accurate measurement of liquid level. In addition, in order to verify the effectiveness of the algorithm and the reliability of the system, simulation modeling is also carried out in this paper. The liquid level variation under various practical conditions is simulated, and the response speed and measurement error of the system are evaluated strictly. The experimental results show that the proposed intelligent system can run stably in the complex nuclear power environment, its measurement accuracy is significantly higher than the state of the art, and it has good anti-interference ability and fast response characteristics.

关键词： Accuracy Power measurement Liquids Radar measurements signal processing algorithms Radar Stability analysis Level measurement Intelligent systems Nuclear measurements

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Multi-Step Reiterative Super Resolution: Robust Doppler-Angle Spectrum Estimation of Accelerated Targets with Antenna Position Errors

Multi-Step Reiterative Super Resolution: Robust Doppler-Angl...

引用

IEEE International Symposium on Phased Array Systems and Technology

作者： Takayuki Kitamura Tadashi Oshima Yoshitsugu Sawa Information Technology R&D Center Mitsubishi Electric Corporation Kanagawa Japan

ISBN: (数字)9798350392142

ISBN: (纸本)9798350392159

Recently, ReIterative Super Resolution (RISR) has gained attention in radar signal processing. RISR inherently provides a high-resolution angular spectrum. By expanding its error model, accurate spectrum calculations can be achieved, even with antenna position errors. However, this error model extension has previously been limited to antenna position errors alone. In this paper, we introduce a multi-step RISR (MS-RISR) algorithm designed for accurate estimation of a two-dimensional Doppler-angle spectrum in scenarios involving accelerated targets observed by array antennas with antenna position errors. The proposed algorithm redefines the acceleration components of the targets as velocity observation errors, thereby incorporating both antenna position and velocity observation errors into its system model to achieve an accurate Doppler-angle spectrum. Simulations demonstrate that the MS-RISR algorithm, consisting of multiple processing steps, can estimate the Doppler-angle spectrum with high resolution.

关键词： Phased arrays Accuracy Superresolution signal processing algorithms Estimation Parallel processing Robustness Radar signal processing Spectral analysis Convergence

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Hand Movement Classification with Four Channel EMG signals for Underactuated Hand Prosthesis Test Platform

Hand Movement Classification with Four Channel EMG Signals f...

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IEEE signal processing and Communications Applications (SIU)

作者： Atila Yılmaz Barış Büyükyılmaz Hasan Can Sert Oğulcan Uğuroğlu Ayber Eray Algüner Elektrik ve Elektronik Mühendisliği Bölümü Hacettepe Üniversitesi Ankara Türkiye

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

People who undergo upper extremity amputation for various reasons face a dramatic decrease in their quality of life. Prostheses designed to remedy this situation, at least partially, are becoming more successful day by day. In this study, the “Nvidia Jetson“ card-supported experiment platform, designed especially for the development of artificial intelligence procedures in under-actuated myoelectric hand prostheses, is presented. The system, in which electromyography (EMG) signals are processed with artificial intelligence procedures and converted into control signals, is designed modularly so that different methods and equipment can be tested. The platform, where all the subunits that form the whole system are combined and their interactions are ensured, was tested with a support vector machine (SVM) based motion classification procedure selected as an example and the results were examined. In the design output, it was seen that hand control was achieved with 92.5% accuracy in five hand movements. Using this platform, different algorithms will be tested, and performance will be increased with the hardware and training used

关键词： Support vector machines Training signal processing algorithms Process control signal processing Electromyography Vectors

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