2023 7th International Conference on Machine Vision and Information Technology (CMVIT)最新文献_第3页

An attention grading of students’ attention in online learning under different light environments 不同光环境下学生在线学习注意力的注意力分级

2023 7th International Conference on Machine Vision and Information Technology (CMVIT) Pub Date : 2023-03-01 DOI: 10.1109/cmvit57620.2023.00036

Yalong Yang, Chang Yang, Rui Zhang, Yufu Liu, Cheng Wang, Lin Hu, Xulai Zhu

{"title":"An attention grading of students’ attention in online learning under different light environments","authors":"Yalong Yang, Chang Yang, Rui Zhang, Yufu Liu, Cheng Wang, Lin Hu, Xulai Zhu","doi":"10.1109/cmvit57620.2023.00036","DOIUrl":"https://doi.org/10.1109/cmvit57620.2023.00036","url":null,"abstract":"With the rapid development of Internet technology and the influence of irresistible factors, online learning plays an increasingly prominent role in the field of education. For this study, students were recruited to participate two stages of online learning experiments. Twelve college students underwent EEG continuous recording by a portable device during a 6-hour experiment when the indoor lighting environment was set 300 lx, 4100 K (Stage 1) and when the indoor lighting environment was under five lighting setups (300 lx, 3000 K; 300 lx, 4000 K; 300 lx, 6500 K; 500 lx, 4000 K; 1000 lx, 4000 K; Stage 2). The EEG collected in the first stage was used to develop the attention grading model (AGM). In the second stage, EEGs were collected under different lighting environments and classified according to the model to analyze the students’ attention. The results show that the AGM can accurately classify students’ EEG signals into three levels, and the classification accuracy was up to 93.17%. Under the selected lighting conditions, the most suitable combination of lighting environments for online learning is 500 lx and 4100 K, which can promote concentration in a relatively short time and the concentration state lasts for a long time.","PeriodicalId":191655,"journal":{"name":"2023 7th International Conference on Machine Vision and Information Technology (CMVIT)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130314274","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Improve long-range source localization in the South China Sea by suppressing frequency-difference autoproduct cross-term 通过抑制频差汽车产品交叉项改善南海远程源定位

2023 7th International Conference on Machine Vision and Information Technology (CMVIT) Pub Date : 2023-03-01 DOI: 10.1109/CMVIT57620.2023.00013

Chenxiang Zhao, Hangfang Zhao

{"title":"Improve long-range source localization in the South China Sea by suppressing frequency-difference autoproduct cross-term","authors":"Chenxiang Zhao, Hangfang Zhao","doi":"10.1109/CMVIT57620.2023.00013","DOIUrl":"https://doi.org/10.1109/CMVIT57620.2023.00013","url":null,"abstract":"Match field processing (MFP), combining with underwater acoustics and physics, is a signal processing technology and is popular in passive source localization. Unfortunately, in many situations, especially when long-range sources are involved, incomplete understanding of the actual propagation environment hinders accurate propagation modeling, leading to the failure of source localization via MFP. Recently, low-frequency MFP using the frequency-difference autoproduct achieved some long-range source localization success fully. While this method has been proved more robust than conventional methods, many of the metrics, such as Peak-to-Background Ratio (PBR) and ambiguity surface peak values, are lower than commonly observed levels. This performance degradation is related to the cross-term of frequency-difference autoproduct. In this paper, we combine low rank matrix representation (LRR) to suppress the influence of cross-term. This method is used to improve source localization metrics in the South China Sea (deep ocean) environment. The experimental results show that the PBR and ambiguity surface peak values are improved by about 2.5dB and 4.5dB respectively.","PeriodicalId":191655,"journal":{"name":"2023 7th International Conference on Machine Vision and Information Technology (CMVIT)","volume":"87 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131056089","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Trajectory Smoothing Algorithm Based on Kalman Filter 基于卡尔曼滤波的轨迹平滑算法

2023 7th International Conference on Machine Vision and Information Technology (CMVIT) Pub Date : 2023-03-01 DOI: 10.1109/CMVIT57620.2023.00019

Yingjie Liu, Zhiying Yang

引用次数: 0

Past, Present, and Future of the Augmented Reality (AR)-Enhanced Interactive Techniques: A Survey 增强现实(AR)增强交互技术的过去、现在和未来:一项调查

2023 7th International Conference on Machine Vision and Information Technology (CMVIT) Pub Date : 2023-03-01 DOI: 10.1109/CMVIT57620.2023.00035

Vighnesh Bharat Gholap, Wanwan Li

引用次数: 1

Payload Scheme Research Based on Flexible Access Strategy of Satellite Constellation 基于卫星星座柔性接入策略的有效载荷方案研究

2023 7th International Conference on Machine Vision and Information Technology (CMVIT) Pub Date : 2023-03-01 DOI: 10.1109/cmvit57620.2023.00033

Jie He, Wei Qiao, Jingyang Zhang, Lixiang Huang, Lu Peng

{"title":"Payload Scheme Research Based on Flexible Access Strategy of Satellite Constellation","authors":"Jie He, Wei Qiao, Jingyang Zhang, Lixiang Huang, Lu Peng","doi":"10.1109/cmvit57620.2023.00033","DOIUrl":"https://doi.org/10.1109/cmvit57620.2023.00033","url":null,"abstract":"In recent years, SpaceX’s Starlink program is rapidly staking their claims in this market, and China is not shy to vigorously develop low-orbit Internet satellite constellations. Both low-orbit Internet constellations and other satellite constellations aim to achieve high global coverage with real-time data transmission and low latency, which are incomparable to terrestrial network systems in all dimensions. It not only has important strategic significance, but also meets rich commercial needs. The topology of the satellite constellation is time-varying and cannot form a stable transmission model like the terrestrial Internet of Things. Therefore, the flexible access technology of satellite payloads directly affects the operation of the constellation and user experience. This paper combines the current development status of satellite payload technology, according to different business needs and different application scenarios, studies the pay scheme, involves microwave, laser and multiple-access technologies, and proposes a rapid inter-satellite access idea. It is hoped that through research, ideas can be inspired to promote the research and development of satellite payload fast access strategy.","PeriodicalId":191655,"journal":{"name":"2023 7th International Conference on Machine Vision and Information Technology (CMVIT)","volume":"162 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134159584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Real-time Machine Learning Framework for Smart Home-based Yoga Teaching System 智能家庭瑜伽教学系统的实时机器学习框架

2023 7th International Conference on Machine Vision and Information Technology (CMVIT) Pub Date : 2023-03-01 DOI: 10.1109/CMVIT57620.2023.00029

Jothika Sunney, Musfira Jilani, Pramod Pathak, Paul Stynes

{"title":"A Real-time Machine Learning Framework for Smart Home-based Yoga Teaching System","authors":"Jothika Sunney, Musfira Jilani, Pramod Pathak, Paul Stynes","doi":"10.1109/CMVIT57620.2023.00029","DOIUrl":"https://doi.org/10.1109/CMVIT57620.2023.00029","url":null,"abstract":"Practicing yoga poses in a home-based environment has increased due to Covid19. Yoga poses without a trainer can be challenging, and incorrect yoga poses can cause muscle damage. Smart home-based yoga teaching systems may aid in performing accurate yoga poses. However, the challenge with such systems is the computational time required to detect yoga poses. This research proposes a real-time machine learning framework for teaching accurate yoga poses. It combines a pose estimation model, a pose classification model, and a real-time feedback mechanism. The dataset consists of five popular yoga poses namely the downdog pose, the tree pose, the goddess pose, the plank pose, and the warrior pose. The BlazePose model was used for yoga pose estimation which transforms the image data into 3D landmark points. The output of the pose estimation model was then passed to the pose classification model for yoga pose detection. Four machine learning classifiers namely, Random Forest, Support Vector Machine, XGBoost, Decision Tree, and two neural network classifiers LSTM and CNN were evaluated based on accuracy, latency and size. Results demonstrate that XGBoost outperforms other models with an accuracy of 95.14 percentage, latency of 8 ms, and size of 513 KB. The output of the XGBoost Classifier was then used to correct yoga poses by displaying real-time feedback to the user. This novel framework has the potential to be integrated into mobile applications which can be used by people for the unsupervised practice of yoga at home.","PeriodicalId":191655,"journal":{"name":"2023 7th International Conference on Machine Vision and Information Technology (CMVIT)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132986425","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Research on ship detection technology based on improved YOLOv5 基于改进型YOLOv5的舰船检测技术研究

2023 7th International Conference on Machine Vision and Information Technology (CMVIT) Pub Date : 2023-03-01 DOI: 10.1109/CMVIT57620.2023.00023

Yutai Huan, Lin Chen, Bin Liu, Wenjie Wang

引用次数: 0

Learning-based 4D Millimeter Wave Automotive Radar Sensor Model Simulation for Autonomous Driving Scenarios 自动驾驶场景下基于学习的4D毫米波汽车雷达传感器模型仿真

2023 7th International Conference on Machine Vision and Information Technology (CMVIT) Pub Date : 2023-03-01 DOI: 10.1109/CMVIT57620.2023.00031

Bin Tan, Lianqing Zheng, Zhixiong Ma, Jie Bai, Xichan Zhu, Libo Huang

{"title":"Learning-based 4D Millimeter Wave Automotive Radar Sensor Model Simulation for Autonomous Driving Scenarios","authors":"Bin Tan, Lianqing Zheng, Zhixiong Ma, Jie Bai, Xichan Zhu, Libo Huang","doi":"10.1109/CMVIT57620.2023.00031","DOIUrl":"https://doi.org/10.1109/CMVIT57620.2023.00031","url":null,"abstract":"With the development of autonomous driving technology, scenario simulation is considered an important part of autonomous driving development and testing. Due to its ability to work in complex weather, millimeter wave radar is one of the critical sensors in autonomous driving. Therefore, the simulation of millimeter wave radar sensors is also an essential part of the autonomous driving scenario simulation. Unlike conventional millimeter wave radar, 4D automotive millimeter-wave radar improves the angular resolution by cascading antennas and is able to measure the height of the target. In this paper, we present a method for modeling 4D millimeter-wave radar. First, the spatial distribution models of different types of targets are modeled by different Gaussian mixture models. Then, the spatial distribution model of the point cloud is then used to generate the spatial distribution of scattered points for the radar target model. Next, the millimeter wave radar waveform generation model, target echo model, and intermediate frequency (IF) signal generation model is established to simulate the radar radio frequency. Furthermore, the radar signal processing model is established to process the IF signal and obtain the point cloud of the target. Finally, the simulation experiments show the point cloud effects generated by the millimeter wave radar simulation model under different traffic scenarios.","PeriodicalId":191655,"journal":{"name":"2023 7th International Conference on Machine Vision and Information Technology (CMVIT)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132732966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Research on Test Method of Gain Flatness of On-board Waveguide Amplifier under Wideband Noise Condition 宽带噪声条件下机载波导放大器增益平坦度测试方法研究

2023 7th International Conference on Machine Vision and Information Technology (CMVIT) Pub Date : 2023-03-01 DOI: 10.1109/cmvit57620.2023.00034

Zhen Qin, Hong Wang, Xiao Min Yang, Xin Lei Li, Peng Li, Xinwei Xu, Peng Tao Fang

引用次数: 0

VINS-FEN: Monocular Visual-Inertial SLAM Based on Feature Extraction Network vin - fen:基于特征提取网络的单目视觉惯性SLAM

2023 7th International Conference on Machine Vision and Information Technology (CMVIT) Pub Date : 2023-03-01 DOI: 10.1109/CMVIT57620.2023.00025

Ke Wang, Cheng Zhang, Di Su, Kai Sun, Tian Zhan

{"title":"VINS-FEN: Monocular Visual-Inertial SLAM Based on Feature Extraction Network","authors":"Ke Wang, Cheng Zhang, Di Su, Kai Sun, Tian Zhan","doi":"10.1109/CMVIT57620.2023.00025","DOIUrl":"https://doi.org/10.1109/CMVIT57620.2023.00025","url":null,"abstract":"Monocular visual-inertial simultaneous localization and mapping (SLAM) technology is able to be widely used to provide pose for unmanned aerial vehicles. It usually uses artificially designed feature points and descriptors as the feature and basis for image matching. However, it is easy to cause the problem of difficult feature extraction and feature matching error under uneven illumination and weak texture environment. In order to solve the above problems, this paper adopts the deep convolutional neural network (CNN) instead of traditional artificial design features to replace the traditional front end of visual-inertial system (VINS). My main work includes designing deep convolutional neural Network–Feature Extraction Network (FEN), for feature extraction, proposing a two-stage matching strategy, and porting the above improvements to the front end of VINS to form a complete system. Finally, verification is conducted on HPatches dataset and EuRoc dataset. The experimental results show that FEN is 3%~23% higher than the traditional method in repeatability and accuracy of extracting feature points. The VINS with FEN as the front end has stronger robustness and improves localization accuracy by 17.3% under uneven illumination and weak texture conditions.","PeriodicalId":191655,"journal":{"name":"2023 7th International Conference on Machine Vision and Information Technology (CMVIT)","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128754983","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0