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2024 international conference on Metaverse Computing, Networking, and Application

作者： Pan, Xiaonan Sun, Qilei Wang, Jia Lim, Eng Gee Xian Jiaotong Liverpool Univ Sch Adv Technol Suzhou Peoples R China

ISBN: (纸本)9798331516000;9798331515997

multi-view deep learning models have demonstrated significant promise in addressing pedestrian detection and tracking challenges, such as heavy occlusion in monocular cameras and their restricted field of view. However, these models demand a considerable volume of training data, the acquisition of which is time-consuming, labour-intensive, and further complicated by privacy and ethical concerns. The currently available public multi-view datasets are insufficient to support the extensive training required for these models. To solve the paucity of multi-view training data, this paper presents a novel multi-view synthetic dataset pipeline, named WildPerception, based on integrated techniques of Unity Perception, SyntheticHumans Package and multiviewX. WildPerception simulates pedestrians in a photo-realistic scene along with multiple overlapping views, allowing an instant generation of large-scale and labeled video training datasets in WILDTRACK format. Our pipeline is modular and can be easily tailored to the demands of diverse specific multi-view tasks. Experiments were carried out to validate the efficiency of this pipeline. Moreover, the models trained on these synthesized datasets also benefit the robust adaptability when deployed on datasets gathered from novel environments. The code for the pipeline is publicly available on GitHub at https://***/TsingLoo/*** to facilitate reproducibility and further research.

关键词： computer vision multi-view pedestrian detection and tracking computer graphics dataset synthesis

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A High-Gain DC-DC Boost Converter for Solar Microgrid Systems Using a Switching Inductor and a Capacitor 1

A High-Gain DC-DC Boost Converter for Solar Microgrid System...

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1st international conference on Advances in computer science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Prasanth, S. Priyadarsini, S. Lakshmi, R. Sneha, G. Ajitha, S.S. Saravanakumar, R. Narendra, Konnaiah Gari Unnamalai Institute of Technology Department of Computer Science and Engineering Tamil Nadu Kovilpatti India PSR Engineering College Department of Computer Science and Engineering Tamil Nadu Sivakasi India Siddharth Institute of Engineering and Technology Department of Elecrical and Electroncis Engineering Puttur India St. Joseph's Institute of Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Department of Electronics and Communication Engineering Tamil Nadu Chennai India Iconix Software Solution Tamil Nadu Tirunelveli India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electonics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798331518578

This paper outlines the design and implementation of a solar microgrid-specific high-gain DC-DC booster converter that makes use of a variable inductor and capacitors. To improve the suitability of photovoltaic (PV) panels for incorporation into microgrids, the proposed converter efficiently increases their low-voltage outputs. Conventional boost converters can't offer that kind of voltage increase because they lose too much performance and put too much strain on their internal components. The converter increases the voltage gain dramatically while maintaining a high degree of efficiency thanks to a switching inductor-capacitor network. The main breakthrough is an inventive multi-stage energy transfer and storage device that cleverly employs a switching inductor and capacitor. This configuration increases the output voltage while decreasing the voltage and current ripples, which in turn reduces the amount of electromagnetic interference (EMI) and increases the life of the components. Thorough assessments of the converter's working via theoretical modeling and simulation studies confirm its performance across several load and input situations common to solar microgrids. The prototype's testing findings show a significant voltage increase and a peak efficiency of over 95% when compared to traditional boost converters. Moreover, the converter's reliable performance over a wide range of voltage sources and workload scenarios proves its suitability for practical solar applications. As a result, the proposed high-gain boost converter will greatly improve the efficiency and reliability of solar microgrids, leading to a rise in the use of renewable energy sources in DGS. © 2025 IEEE.

关键词： Microgrids

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Reduced Switch Count Asymmetrical Seven-Level Inverter

Reduced Switch Count Asymmetrical Seven-Level Inverter

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2024 IEEE international Students' conference on Electrical, Electronics and computer science, SCEECS 2024

作者： Shukla, Tanmay Patidar, N.P. Kumawat, R.K. Jain, Gaurav Nandanwar, Sonali Maulana Azad National Institute of Technology Department of Electrical Engineering Bhopal India Poornima College of Engineering Department of Electrical Engineering Jaipur India Ies College of Technology Department of Electrical & Electronics Engineering Bhopal India

ISBN: (纸本)9798350348460

The idea of a multilayer inverter originates from the extension of inverters to over two layers in order to lessen distortion from the basic sinusoidal waveform. The drawback of using multiple level inverter is inclusion of extra switches that makes the system bulkier and costly and the reliability of the system reduces due to increment in count of components. In this art, a seven level inverter is presented with reduced number of switches as compared to symmetrical H-bridged seven level inverter. The idea is built on MATLAB platform and results are analysed to verify its utility. © 2024 IEEE.

关键词： multi Level Inverter (MLI) Pulse Width Modulation (PWM) Switches

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ICECOS 2019 - 3rd international conference on Electrical engineering and computer science, Proceeding

ICECOS 2019 - 3rd International Conference on Electrical Eng...

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3rd international conference on Electrical engineering and computer science, ICECOS 2019

ISBN: (纸本)9781728147147

The proceedings contain 84 papers. The topics discussed include: narrow bipolar events within reversal distance and associated UHF-VHF emissions;application of interleaved bidirectional converter on pond aerators with electricity sources from solar panels;design analysis on tele-operatic supernumerary robotic limb;simulation analysis of formation control design of leader-follower robot using fuzzy logic controller;stress detection application based on heart rate variability (HRV) and K-nearest neighbor (KNN);hydrogen gas production using water electrolyzer as hydrogen power;real-time monitoring of power quality for web based electrical power panel using LabVIEW;detecting sensor coordination in a calibrated lightning locating system;and low cost transient earth voltage probe for partial discharge detection.

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Crop Classification on Hyperspectral Images Using Residual Network and multi-Head Self-Attention Feature Extraction 2

Crop Classification on Hyperspectral Images Using Residual N...

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2nd IEEE international conference on Data science and Network Security, ICDSNS 2024

作者： Saranya, N. Naga Vivekanandhan, V. Al-Farouni, Mohammed Shreyas, A.V. Kumar, Gotte Ranjith Saveetha College of Liberal Arts & Science Simats Department of Computer Applications Chennai India Malla Reddy College of Engineering Department of Computer Science and Engineering Hyderabad Secunderabad India College of Technical Engineering The Islamic University Department of Computers Techniques Engineering Najaf Iraq Nitte Meenakshi Institute of Technology Department of Civil Engineering Bengaluru India School of Computer Science & Artificial Intelligence Sr University Warangal India

ISBN: (纸本)9798350373110

Nowadays, hyperspectral imaging has empowered classification and prediction in many fields such as agriculture, land usage, tourism, etc. due to advancements in deep learning. This research focuses on obtaining efficient crop classification using Residual Network, multi-Head Self- Attention, Convolutional Neural Network (ResNet-MHSA- CNN) approach. The Adaptive Wiener Filtering (AWF) is applied in preprocessing to eliminate noise, handles blurred parts of images effectively and decreases error rate in inverse filtering. The Residual Network (ResNet) method is employed by implement residual learning to eliminate degradation issues of deep neural networks. The high-resolution depth features of hyperspectral images are extracted by residual learning ResNet network. The multi-Head Self-Attention (MHSA) utilized to capture long-range dependencies and global context information and helps the model in learning diverse patterns. Finally, the extracted features are given to Convolutional Neural Network (CNN) with complex pattern learning ability that excels in image classification with multiple layers of filters. The proposed ResNet-MHSA-CNN approach is evaluated with Indian Pines dataset and achieved 99.74%, 99% accuracy and kappa value respectively. The proposed method outperformed state-of-art methods such as Cross-Mixing Residual Network of Convolutional Neural Network (CMR-CNN), Deep Stacked Denoising Autoencoder (DSDA). © 2024 IEEE.

关键词： Convolutional neural networks

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MDS-UNet: multi-Dimensional and multi-Scale UNet for Medical Image Segmentation 4

MDS-UNet: Multi-Dimensional and Multi-Scale UNet for Medical...

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4th international conference on Electronic Information engineering and computer Communication, EIECC 2024

作者： Zhu, Liheng Yang, Shuqun Shanghai University of Engineering Science School of Electronic and Electrical Engineering Shanghai China

ISBN: (纸本)9798331534622

Transformer and its variants, along with Mamba and its variants, have found extensive applications in medical image segmentation tasks. However, these hybrid models are often burdened with significant parameter counts and computational demands, which restrict their effectiveness in environments with limited resources. To address this issue, we propose a lightweight medical image segmentation network, MDS-UNet, which integrates the multi-Scale Receptive Field Fusion Module, multi-Axis Attention Module, and multi-Scale Masked Feature Aggregation Module. This design enables the extraction of both global and local features from multiple dimensions and multiple scales, effectively capturing contextual information. The model achieves superior segmentation performance as a lightweight network. The proposed model was validated using the ISIC 2018 skin lesion dataset, and experimental results indicate that MDS-UNet achieves superior performance while substantially reducing the number of parameters to 0.77M and the computational complexity to 1.52 GFLOPs. Specifically, the model attained a mean Intersection over Union of 81.73% and a Dice Similarity Coefficient of 90.32%. Compared to existing advanced models, MDS-UNet exhibits notable advantages in both lightweight design and segmentation accuracy. © 2024 IEEE.

关键词： Medical imaging

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Direction of arrival estimation for coherent signals based on convolutional neural network with attention mechanism 3

Direction of arrival estimation for coherent signals based o...

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3rd international conference on Electronic Information engineering, Big Data, and computer Technology, EIBDCT 2024

作者： Qi, Jiajie Cui, Yue Wang, Junfeng Kong, Aokun Yang, Feiyu Zhang, Xiaoyuan College of Computer and Information Engineering Tianjin Normal University Tianjin China

ISBN: (纸本)9781510680449

A direction of arrival (DOA) estimation method for coherent signals is proposed based on an improved convolutional neural network (CNN) with attention mechanism. The real, imaginary and phase components of the covariance matrix are employed as CNN multi-channel inputs. A regression model is adopted as CNN output which avoids the grid mismatch problem caused by multi-classification output. Moreover, to enhance the accuracy of the estimation, attention mechanism is added to the CNN, which could give more weight to the main features effectively. The simulation results demonstrate that the proposed method surpasses the performance of other approaches, which is suitable to resolve coherent signals in multipath environments. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Covariance matrix

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A Comparative Analysis of multi-Plant Disease Detection and Classification Using Neural Network Architectures

A Comparative Analysis of Multi-Plant Disease Detection and ...

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2024 IEEE international Women in engineering (WIE) conference on Electrical and computer engineering, WIECON-ECE 2024

作者： Majumder, Sweety Rani Islam, Akib Jayed Salehin, Sultanus Bhowmik, Subashis Roy Amrine, Taofica Nur, Shafayet Port City International University Computer Science and Engineering Chattogram Bangladesh Norwegian University of Science and Technology Computational Colour and Spectral Imaging Gjovik Norway The University of Alabama Electrical and Computer Engineering Tuscaloosa United States

ISBN: (纸本)9798331535476

Plant diseases severely impact agriculture and economies, causing significant c rop l osses. E arly a nd accurate detection is essential to minimize damage and spread. Traditional manual inspection methods are time-consuming and error-prone, highlighting the need for automated solutions. This research investigates the use of neural networks for multi-plant disease detection and classification t o p romote s ustainable agriculture. Data from bell peppers, potatoes, tomatoes, rice, and maize were preprocessed through scaling, grayscale conversion, and augmentation. multiple deep learning models, including CNN, MobileNetV2, VGG16, Hybrid CNN-LSTM, InceptionV3, and EfficientNetB3, w ere e valuated. A mong t hese, EfficientNetB3 consistently outperformed others, achieving notable accuracy rates using an 80-20% data split: 99.59% for bell peppers, 98.04% for potatoes, 92.52% for tomatoes, 97.52% for rice, and 96.25% for maize. For the 70-30% split, EfficientNetB3 achieved 94% for bell peppers and 84% for potatoes, while for the 90-10% split, it maintained competitive performance with 89% for bell peppers and 90% for potatoes. Video classification using EfficientNetB3 f urther d emonstrated i ts e ffectiveness in identifying diseases. These findings u nderscore t he p otential of EfficientNetB3 in enhancing plant disease detection accuracy and efficiency, c ontributing t o s ustainable a gricultural practices. © 2024 IEEE.

关键词： Fruits

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Developing a multi-user virtual reality training system for emergency medical technicians: Determinants of acceptance and adoption

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Educational Technology and Society 2025年第2期28卷 416-433页

作者： Chen, Shih-Yeh Lai, Ying-Hsun Lin, Pei-Hsuan Su, Yu-Sheng Department of Engineering Science National Cheng Kung University Taiwan Department of Computer Science and Information Engineering National Taitung University Taiwan Department of Information Management National Taichung University of Science and Technology Taiwan Department of Computer Science and Information Engineering National Chung Cheng University Taiwan Advanced Institute of Manufacturing with High-tech Innovations National Chung Cheng University Taiwan Department of Computer Science and Engineering National Taiwan Ocean University Taiwan

There is an increasing international focus on multi-user virtual reality (VR) training for all emergency medical technicians within the framework of their respective disaster medical preparedness initiatives. While multiuser VR is often deployed in a variety of digital educational settings, it has not been meaningfully explored in emergency medicine. This study was conducted to fill this research gap by developing a multi-user VR training system for the donning and doffing procedures associated with Level C protective gear. Furthermore, the current investigation also considered the determinants influencing whether emergency medical technicians are willing to adopt and accept multi-user VR training courses via conceptualizing the extended unified theory of acceptance and use of technology. An empirical survey was conducted involving 80 emergency medical technicians in the eastern region of Taiwan to evaluate their experiences with the multi-user VR system. The data were analyzed using Partial Least Squares Structural Equation Modeling, and the results showed that performance expectancy, social impact, facilitating conditions, price value, and habit significantly influenced emergency medical technicians’ readiness to adopt multi-user VR training courses. Moreover, performance expectancy, facilitating conditions, and behavioral intention emerged as pivotal determinants of course adoption. However, effort expectancy and hedonic motivation did not exert a significant influence on adoption behavior. The findings of the study have important theoretical and practical implications. © (2024), (international Forum of Educational Technology and Society). All Rights Reserved.

关键词： Behavioral intention Emergency medical preparedness Extended unified theory of acceptance and use of technology (UTAUT 2) multi-user virtual reality Virtual environment for emergency medical technicians

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Land Cover Segmentation for Agricultural Monitoring Using Synthetic Aperture Radar (SAR) Imagery with Convolutional Neural Networks and Attention-Based Transformer Models

Land Cover Segmentation for Agricultural Monitoring Using Sy...

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2025 international conference on multi-Agent Systems for Collaborative Intelligence, ICMSCI 2025

作者： Prakash, E.P. Sathiya, K.G. Manoranjitham, S. Sekar, G. Munaf, S. Santhia, R.K. Dr. N.G.P. Institute of Technology Department of Computer Science and Engineering Coimbatore641048 India Arunai Engineering College Department of Electronics and Communication Engineering Tiruvannamalai606604 India Agni college of Technology Department of Information Technology Chennai600130 India Vsb College Of Engineering Department of Electronics and Communication Engineering Technical Campus Coimbatore642109 India Sri Ramakrishna Institute of Technology Department of Electronics and Communication Engineering Coimbatore641010 India Pondicherry University Department of Computer Science and Engineering 605014 India

ISBN: (纸本)9798331509828

Automated classification of land cover types is one of the most crucial aspects within many remote sensing applications, including the monitoring of agricultural land and territories. This work proposes a novel approach for division of land covers using a hybrid of CNNs and Attention-based Transformer networks. CNNs are adopted to extract the spatial features from different levels of the SAR image, while Transformer Model Arch applies self-attention functions to explore the long-range dependency and contextual relation of the entire image. The model learning process is performed end-to-end from multi-temporal SAR data to differentiate different types of land covers, including crops, vegetation, water, and urban surfaces. In this case, we apply the proposed approach to different agricultural areas to show that it outperforms traditional methods, such as thresholding and other machine learning approaches. The results highlight the viability of this mixed form to solve the noise and changeable nature of SAR imagery, in terms of enhancing the use of the agricultural monitoring application. © 2025 IEEE.

关键词： Convolutional neural networks

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