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检索条件"主题词=Signal Processing Algorithms"

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Evaluation of the Applicability of Three-Dimensional Woven Knee Pads Taping Based on Fuzzy algorithms

Evaluation of the Applicability of Three-Dimensional Woven K...

引用

IEEE International Symposium on Intelligent signal processing and Communication Systems (ISPACS)

作者： Chien-Yu Lu Yi-Chun Lin Ta-Ching Chou Wei-Zhen Su Te-Jen Su Depart. Industrial Education and Technology National Changhua University of EducationTaiwan Changhua R. O. C Depart. Industrial Education and Technology National Changhua University of Education National Changhua University of EducationTaiwan Changhua R. O. C Depart. Electronic Engineering National Kaohsiung University of Science and TechnologyTaiwan Kaohsiung R. O. C

ISBN: (数字)9798350389210

ISBN: (纸本)9798350389227

This study is based on fuzzy algorithm and aims to evaluate the applicability of using three-dimensional woven bracing knee for sports injury treatment. This research set three types of specific directional forces: shear elastic force, tensile elastic force, and downward pressure force. This study evaluates the effectiveness and practicality of three-dimensional woven bracing knee products through fuzzy algorithms, providing new ideas for sports injury research.

关键词： Force signal processing algorithms signal processing Weaving Protection Injuries Stress Shearing Research and development Sports

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SDAR-OMP: An Efficient Algorithm for Generalized Kronecker Compressive Sensing

SDAR-OMP: An Efficient Algorithm for Generalized Kronecker C...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Yu-Tai Chiew Yuan-Pei Lin Inst. of Electrical and Control Engr. National Yang Ming Chiao Tung Univ. Hsinchu Taiwan

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

In this paper, we consider multidimensional generalized Kronecker compressive sensing (G-KCS) problems, which find applications in channel estimation and various areas of multidimensional signal processing. Solving a G-KCS problem often involves heavy computations due to multiple dimensions. We show that a D-dimensional G-KCS problem can be solved using a successive recovery algorithm, one dimension at a time, based on orthogonal matching pursuit (OMP). In each one-dimensional recovery, the sensing matrix has a much smaller size, so the complexity is reduced greatly. Furthermore, earlier recovery results, called decisions, are exploited for subsequent recovery, i.e., successive decision-aided recovery-OMP (SDAR-OMP). The exploitation of earlier decisions allows us to have a more relaxed recovery condition and also further reduction in complexity. We apply SDAR-OMP to channel estimation for mmWave systems and demonstrate that the performance of SDAR-OMP is comparable to that of OMP but with a much lower complexity.

关键词： Performance evaluation Multidimensional signal processing Matching pursuit algorithms signal processing algorithms Channel estimation Complexity theory Sensors Global communication Millimeter wave communication Compressed sensing

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An Approximate Algorithm for Transient Near Field Scattering from a Rough Surface in 2-D Mode

An Approximate Algorithm for Transient Near Field Scattering...

引用

International Symposium on Antennas, Propagation and EM Theory (ISAPE)

作者： Wei Tian Xin-Bo He Yi-Di Shi School of physics and electronic information Yan'an University Yan'an china School of Physics Xidian University Xi'an china

ISBN: (数字)9798350353129

ISBN: (纸本)9798350353136

An approximate algorithm is proposed to improve the computational efficiency for analyzing the transient near-field electromagnetic (EM) scattering from a rough surface in two-dimensional (2-D) mode, which is a combination of the time domain physical optics (TDPO) with the time domain Huygens principle. In this scheme, the equivalent EM currents and charges on rough surface are computed by applying the TDPO method. The near-field radiation of these equivalent sources is solved in marching-on-in-time way based on the time domain Huygens principle, where the semi-analytical recursive convolution (SARC) scheme is utilized to reduce the computational cost of the convolution integration of equivalent source and time domain Green's function based on digital signal processing idea. The proposed hybrid algorithm is validated through the comparison with the finite difference time domain (FDTD) method, indicating its reliability and efficiency in the calculation of the transient near-field scattering from rough surface.

关键词： Convolution signal processing algorithms Physical optics Approximation algorithms Surface roughness Rough surfaces Time-domain analysis Transient analysis Surface treatment Finite difference methods

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Elimination of Sensor Error Sources with Particle Swarm Optimization Algorithm

Elimination of Sensor Error Sources with Particle Swarm Opti...

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IEEE signal processing and Communications Applications (SIU)

作者： İ. Ahmet Altaş C. Nurettin Yilmaz Seyrüsefer Sistemleri Birimi Anova Arge Teknolojileri A.Ş. Ankara Türkiye

ISBN: (数字)9798350388961

ISBN: (纸本)9798350388978

Sensors are one of the most important technological inventions used in many areas of daily life. Accelerometers, magnetometers, pressure sensors, temperature sensors, etc. Many sensors such as are frequently used in many areas of industry. However, these sensors contain many errors due to their production. Sensor calibration is required to eliminate these errors. In this paper, high accuracy sensor data was obtained by performing sensor calibration with the PSO (Particle Swarm Optimization) method. With this calibration process, distortions caused by sensor error sources are detected and used to correct the measurement results. In addition, the PSO algorithm defines certain parameters and enables us to improve the data collected from sensors by using data from a reference product.

关键词： Temperature sensors Pressure sensors Technological innovation Magnetic sensors signal processing algorithms Production Particle measurements

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Towards Efficient Gait Analysis Using Gyroscope: Preliminary Results and Insights

Towards Efficient Gait Analysis Using Gyroscope: Preliminary...

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International Conference on Smart Sensors and Application (ICSSA)

作者： Thenmoli Manogar Ahmad Shakaff Ali Yeon Mohd Nor Fakhzan Mohd Kazim Shuhaida Yahud Centre of Excellence for Advanced Sensor Technology University Malaysia Perlis (UniMAP) Arau Perlis Faculty of Electrical Engineering Technology University Malaysia Perlis (UniMAP) Arau Perlis Faculty of Electronic Engineering and Technology University Malaysia Perlis (UniMAP) Arau Perlis

ISBN: (数字)9798350354294

ISBN: (纸本)9798350354300

Wearable sensor technology, combined with smartphone capabilities, has become a powerful and costeffective solution for gait analysis, offering real-time monitoring of human movements. This study introduces a sensor-based gait event detection system using gyroscope data from the publicly available MotionSense dataset to analyze walking and jogging. The dataset, which includes six subjects of varying demographics, provides a broader understanding of gait patterns. Gyroscope data from the X-axis was used to detect key biomechanical events, such as heel strikes, toe-offs, and mid-swings, as these align with the sagittal plane and are critical in understanding walking dynamics. The gait event detection algorithm The detection algorithm, developed in Python, incorporates advanced signal processing techniques like the Savitzky-Golay filter, to reduce noise while preserving key data. By using a threshold-based and sliding window methods, the system identifies gait events accurately and calculates key gait parameters such as cadence, stance time, swing time, and phase distributions. The results show significant differences between walking and jogging, with jogging exhibiting a higher cadence (82.71-90.23 steps/min) and shorter stance times (0.37-0.46 seconds) compared to walking (52.63-60.15 steps/min, stance time 0.61-0.85 seconds). This research demonstrates that wearable sensor technology offers precise and scalable alternatives to expensive, complex traditional gait analysis systems. The findings show the system's potential for clinical monitoring, rehabilitation, sports science, and smart wearable device development.

关键词： Legged locomotion Event detection Heuristic algorithms signal processing algorithms Gain measurement Gyroscopes Wearable sensors Monitoring Intelligent sensors Sports

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Life Span Prediction of Lithium-Ion Battery Using Optimization Technique

Life Span Prediction of Lithium-Ion Battery Using Optimizati...

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

作者： Sabarinathan P Sujeethvishnu V Vijayabalan S Yesvanth B K. Sundararaju Indhupriya S Electrical and Electronics Engineering M. Kumarasamy College of Engineering Karur India

ISBN: (数字)9798350361186

ISBN: (纸本)9798350361193

The process to determine the unknown parameters of a battery model using experimental data is known as parameter identification of lithium-ion batteries utilizing optimization techniques. This challenge is solved by using optimization techniques to identify the set of parameters that minimizes the error between the experimental data and the model output. When a lithium-ion battery is being parameter identified utilizing the Grey Wolf Algorithm (GWO). Specific goals of a lithium-ion battery parameter identification using the Grey Wolf Optimization Method include figuring out the parameters of a model that can be used to estimate the battery's voltage, current, state of charge (SOC), state of temperature (SOT), state of health (SOH), and remaining useful life (RUL). Identify the features that define the battery's ability to produce electricity at different discharge rates. The performance and lifespan of the battery are determined by these factors.

关键词： Lithium-ion batteries Parameter estimation Temperature signal processing algorithms signal processing Prediction algorithms Data models

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MM-DCDR: A Benchmark of Device Configuration and Data Representation for mmWave-Based Human Sensing

MM-DCDR: A Benchmark of Device Configuration and Data Repres...

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International Conference on Wireless Communications and signal processing (WCSP)

作者： Yating Gao Ruixu Geng Dongheng Zhang Yang Hu Hui Lin Yan Chen School of Cyber Science and Technology University of Science and Technology of China Hefei China School of Information Science and Technology University of Science and Technology of China Hefei China

ISBN: (数字)9798350390643

ISBN: (纸本)9798350390650

Millimeter-wave radar-based human sensing has become increasingly popular due to its non-invasive and lightrobust characteristics. While existing studies focus on improving performance with specific setups, they fail to consider the impact of different device configurations and data representations, which determine hardware capabilities and contain distinct features, respectively. Moreover, exploring the impact of those two factors presents inherent trade-offs such as balancing the velocity resolution and angle resolution, as well as choosing between heatmap or point clouds. To address these issues, we introduce MM-DCDR, the first large-scale multi-radar dataset consisting of both a highvelocity resolution radar and a high-angle resolution radar. The dataset comprises 352K frames of time-aligned radar and camera frames, covering 8 activities performed by 11 subjects. Leveraging this dataset, we systematically evaluate the trade-offs between velocity and angle resolution in their impact on human sensing. We also conduct extensive experiments with various deep neural networks to investigate the performance gap between different data representations. We believe our large-scale dataset and comprehensive benchmark would provide valuable insights into exploring optimal device configurations and data representations for practical radar-based sensing systems. The dataset is publicly available at https://***/yating-gao/MM-DCDR.

关键词： Performance evaluation Wireless communication Point cloud compression Data visualization signal processing algorithms Millimeter wave radar Benchmark testing signal processing Sensors Millimeter wave communication

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Detection of Hyperbolic Reflections on Real GPR Data Using YOLO Model Trained with Synthetic Data

Detection of Hyperbolic Reflections on Real GPR Data Using Y...

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International Conference on Telecommunications and signal processing (TSP)

作者： Željko Bugarinović Milan Gavrilović Aleksandar Ristić Milka Šarkanović Bugarinović Aleksandra Radulović Miro Govedarica Department for computing and control engineering Faculty of Technical Sciences University of Novi Sad Novi Sad Serbia

ISBN: (数字)9798350365597

ISBN: (纸本)9798350365603

This paper analyzes the application of the YOLO algorithm to automatically recognize hyperbolic reflections on radargrams. A specific shape of hyperbolic reflection on radargrams occurs with objects of circular cross-section that are scanned perpendicular to their axis, such as underground pipes. Requirements for processing a large number of radargrams are increasingly being imposed in practice. Therefore, the development of algorithms for the automated finding of hyperbolic reflections on radargrams has been intensified in recent years. Conventional methods take a lot of time and are more susceptible to noise than algorithms based on deep learning. One of such algorithms is also applied in this paper. The model was trained with 2000 synthetic radargrams generated using gprMax software. The basic idea is reflected in the analysis of the successful detection of hyperbolic reflections on real radargrams using a model trained only with synthetic radargrams. Experimental results show a high level of success.

关键词： YOLO Training Software algorithms Noise signal processing algorithms Reflection Data models

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A Cryptographically Secure Image Steganography Scheme Based On Arduino Microcontrollers

A Cryptographically Secure Image Steganography Scheme Based ...

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International Conference on Communications and signal processing

作者： Aqib Khan P Dhavakumar School of Computer Science and Engineering Vellore Institute of Technology Chennai India

ISBN: (数字)9798350353068

ISBN: (纸本)9798350353075

In today’s digitally pervasive environment, safeguarding sensitive information concealed within images has become increasingly critical. Conventional steganographic techniques may prove inadequate against sophisticated detection algorithms and statistical analysis. To address this challenge, integrating a pseudo-random number generator with hardware based entropy sources into the channel selection process offers a dynamic and adaptive solution to enhance information hiding security in images. As machine learning techniques for steganography become more sophisticated, they often introduce significant computational overhead. These ML-based algorithms require extensive computational resources for training, inference, and analysis, leading to increased complexity in terms of both time and computational power. By capitalizing on the inherent randomness introduced by the watchdog timer’s RC oscillator in an arduino embedded microcontroller, we can amplify the unpredictability of channel selection during embedding. This paper proposes a novel hybrid model integrating Hardware Random Number Generators and Pseudo-Random Number Generators to augment entropy in the channel selection process within steganography. An examination of the performance of this hybrid model is presented.

关键词： Training Steganography Microcontrollers Statistical analysis signal processing algorithms signal processing Generators

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Facial Emotion Recognition using YOLO based Deep Learning Classifier

Facial Emotion Recognition using YOLO based Deep Learning Cl...

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

作者： Sri Vaibhav Mohan Dev Vanamoju Moturi Veda Vineetha Hitesh Tekchandani Pallavi Joshi Praveen Kumar Shukla Anita Khanna Electronics and Communication Koneru Lakshmaiah Education Foundation Hyderabad India Artificial Intelligence and Data Science Koneru Lakshmaiah Education Foundation Hyderabad India Department of Computer Science Amrita Vishwa Vidyapeetham Mysuru Mysuru India School of Computing and Intelligent Systems Manipal University Jaipur Jaipur India Electrical Engineering Guru Ghasidas Vishwavidyalaya Bilaspur Bilaspur India

ISBN: (数字)9798350364590

ISBN: (纸本)9798350375381

Understanding facial expressions is key to successful communication. It's like decoding a secret language that helps us know if someone is happy, sad, angry, surprised, or something else altogether. Harnessing facial emotion recognition (FER) in machines holds the potential to revolutionize human-computer interaction, improve customer service experiences and provide invaluable support in medical diagnosis. Machines can comprehend human's needs more effectively by reading the emotions from their facial expression and therefore this results in a more natural conversation between them and the machine. Various machine and deep learning methodologies have been implemented for FER. In this research work, the authors propose the state-of-the-art YOLO (You Only Look Once) based deep learning classifier for FER. The YOLO algorithm is a single-shot approach that efficiently classifies objects in a single pass. It achieves this by using a single neural network to predict bounding boxes and target class probabilities by utilizing the entire image as input. YOLOv8 is a recent iteration of the YOLO object detection algorithm is popular for its speed and accuracy which offers improvements over prior versions, making it a suitable choice for real-time intended task of FER. With the proposed classifier, the authors achieved test accuracy of 72.56% on the FER-2013 dataset in a seven-class FER task.

关键词： YOLO Deep learning Emotion recognition Accuracy Neural networks signal processing algorithms signal processing Prediction algorithms Real-time systems Classification algorithms

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