Pub Date : 2024-01-01DOI: 10.2352/j.imagingsci.technol.2024.68.1.010504
Takamasa Hase, Takumi Ishikura, Shinichi Kuramoto, Koichi Kato, K. Fushinobu
{"title":"Development of Paper Temperature Prediction Method in Electrophotographic Processes by Using Machine Learning and Thermal Network Model","authors":"Takamasa Hase, Takumi Ishikura, Shinichi Kuramoto, Koichi Kato, K. Fushinobu","doi":"10.2352/j.imagingsci.technol.2024.68.1.010504","DOIUrl":"https://doi.org/10.2352/j.imagingsci.technol.2024.68.1.010504","url":null,"abstract":"","PeriodicalId":15924,"journal":{"name":"Journal of Imaging Science and Technology","volume":"12 6","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139127901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-01DOI: 10.2352/j.imagingsci.technol.2024.68.1.010505
Jianheng Huang, Jiacheng Zeng, Minghui Zhu, Chengming Feng, Y. Lei, Xin Liu, Ji Li
{"title":"Detection Performance of X-ray Cascaded Talbot–Lau Interferometers Using W-absorption Gratings","authors":"Jianheng Huang, Jiacheng Zeng, Minghui Zhu, Chengming Feng, Y. Lei, Xin Liu, Ji Li","doi":"10.2352/j.imagingsci.technol.2024.68.1.010505","DOIUrl":"https://doi.org/10.2352/j.imagingsci.technol.2024.68.1.010505","url":null,"abstract":"","PeriodicalId":15924,"journal":{"name":"Journal of Imaging Science and Technology","volume":"23 23","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139127182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-01-01DOI: 10.2352/j.imagingsci.technol.2024.68.1.010502
Lihe Hu, Yi Zhang, Yang Wang
{"title":"Salient Semantic-SIFT for Robot Visual SLAM Closed-loop Detection","authors":"Lihe Hu, Yi Zhang, Yang Wang","doi":"10.2352/j.imagingsci.technol.2024.68.1.010502","DOIUrl":"https://doi.org/10.2352/j.imagingsci.technol.2024.68.1.010502","url":null,"abstract":"","PeriodicalId":15924,"journal":{"name":"Journal of Imaging Science and Technology","volume":"25 3","pages":""},"PeriodicalIF":1.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139127174","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.2352/j.imagingsci.technol.2023.67.6.060403
Seok Lee
{"title":"Camera Motion Estimation Method using Depth-Normalized Criterion","authors":"Seok Lee","doi":"10.2352/j.imagingsci.technol.2023.67.6.060403","DOIUrl":"https://doi.org/10.2352/j.imagingsci.technol.2023.67.6.060403","url":null,"abstract":"","PeriodicalId":15924,"journal":{"name":"Journal of Imaging Science and Technology","volume":"3 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139293399","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.2352/j.imagingsci.technol.2023.67.6.060502
Y. Su
{"title":"Color Image Stitching Elimination Method based on Co-occurrence Matrix","authors":"Y. Su","doi":"10.2352/j.imagingsci.technol.2023.67.6.060502","DOIUrl":"https://doi.org/10.2352/j.imagingsci.technol.2023.67.6.060502","url":null,"abstract":"","PeriodicalId":15924,"journal":{"name":"Journal of Imaging Science and Technology","volume":" ","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46085981","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.2352/j.imagingsci.technol.2023.67.6.060402
Kexin Wang, Jack Miller, Philippe Meister, Michael C. Dorneich, Lori Brown, Geoff Whitehurst, E. Winer
. In 2021, there were 1,157 general aviation (GA) accidents, 210 of them fatal, making GA the deadliest civil aviation category. Research shows that accidents are partially caused by ineffective weather theory training. Current weather training in classrooms relies on 2D materials that students often find difficult to map into a real 3D environment. To address these issues, Augmented Reality (AR) was utilized to provide 3D immersive content while running on commodity devices. However, mobile devices have limitations in rendering, camera tracking, and screen size. These limitations make the implementation of mobile device based AR especially challenging for complex visualization of weather phenomena. This paper presents research on how to address the technical challenges of developing and implementing a complex thunderstorm visualization in a marker-based mobile AR application. The development of the system and a technological evaluation of the application’s rendering and tracking performance across different devices is presented.
.2021 年，共发生 1,157 起通用航空（GA）事故，其中 210 起为致命事故，使通用航空成为死亡人数最多的民用航空类别。研究表明，事故的部分原因是气象理论培训效果不佳。目前课堂上的气象培训依赖于二维材料，学生往往难以将其映射到真实的三维环境中。为了解决这些问题，我们利用增强现实技术（AR）在商品设备上运行，提供身临其境的三维内容。然而，移动设备在渲染、摄像头跟踪和屏幕尺寸方面存在限制。这些限制使得基于移动设备的增强现实技术的实施对于复杂的天气现象可视化尤其具有挑战性。本文介绍了如何解决在基于标记的移动 AR 应用程序中开发和实施复杂雷暴可视化所面临的技术挑战。本文介绍了该系统的开发过程，以及对该应用在不同设备上的渲染和跟踪性能进行的技术评估。
{"title":"Development and Implementation of an Augmented Reality Thunderstorm Simulation for General Aviation Weather Theory Training","authors":"Kexin Wang, Jack Miller, Philippe Meister, Michael C. Dorneich, Lori Brown, Geoff Whitehurst, E. Winer","doi":"10.2352/j.imagingsci.technol.2023.67.6.060402","DOIUrl":"https://doi.org/10.2352/j.imagingsci.technol.2023.67.6.060402","url":null,"abstract":". In 2021, there were 1,157 general aviation (GA) accidents, 210 of them fatal, making GA the deadliest civil aviation category. Research shows that accidents are partially caused by ineffective weather theory training. Current weather training in classrooms relies on 2D materials that students often find difficult to map into a real 3D environment. To address these issues, Augmented Reality (AR) was utilized to provide 3D immersive content while running on commodity devices. However, mobile devices have limitations in rendering, camera tracking, and screen size. These limitations make the implementation of mobile device based AR especially challenging for complex visualization of weather phenomena. This paper presents research on how to address the technical challenges of developing and implementing a complex thunderstorm visualization in a marker-based mobile AR application. The development of the system and a technological evaluation of the application’s rendering and tracking performance across different devices is presented.","PeriodicalId":15924,"journal":{"name":"Journal of Imaging Science and Technology","volume":"13 1","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139305111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-11-01DOI: 10.2352/j.imagingsci.technol.2023.67.6.060404
Quan Liu, Hanyu Zheng, Brandon T Swartz, Ho Hin Lee, Zuhayr Asad, Ivan Kravchenko, Jason G Valentine, Yuankai Huo
Deep neural networks (DNNs) utilized recently are physically deployed with computational units (e.g., CPUs and GPUs). Such a design might lead to a heavy computational burden, significant latency, and intensive power consumption, which are critical limitations in applications such as the Internet of Things (IoT), edge computing, and the usage of drones. Recent advances in optical computational units (e.g., metamaterial) have shed light on energy-free and light-speed neural networks. However, the digital design of the metamaterial neural network (MNN) is fundamentally limited by its physical limitations, such as precision, noise, and bandwidth during fabrication. Moreover, the unique advantages of MNN's (e.g., light-speed computation) are not fully explored via standard 3×3 convolution kernels. In this paper, we propose a novel large kernel metamaterial neural network (LMNN) that maximizes the digital capacity of the state-of-the-art (SOTA) MNN with model re-parametrization and network compression, while also considering the optical limitation explicitly. The new digital learning scheme can maximize the learning capacity of MNN while modeling the physical restrictions of meta-optic. With the proposed LMNN, the computation cost of the convolutional front-end can be offloaded into fabricated optical hardware. The experimental results on two publicly available datasets demonstrate that the optimized hybrid design improved classification accuracy while reducing computational latency. The development of the proposed LMNN is a promising step towards the ultimate goal of energy-free and light-speed AI.
{"title":"Digital Modeling on Large Kernel Metamaterial Neural Network.","authors":"Quan Liu, Hanyu Zheng, Brandon T Swartz, Ho Hin Lee, Zuhayr Asad, Ivan Kravchenko, Jason G Valentine, Yuankai Huo","doi":"10.2352/j.imagingsci.technol.2023.67.6.060404","DOIUrl":"10.2352/j.imagingsci.technol.2023.67.6.060404","url":null,"abstract":"<p><p>Deep neural networks (DNNs) utilized recently are physically deployed with computational units (e.g., CPUs and GPUs). Such a design might lead to a heavy computational burden, significant latency, and intensive power consumption, which are critical limitations in applications such as the Internet of Things (IoT), edge computing, and the usage of drones. Recent advances in optical computational units (e.g., metamaterial) have shed light on energy-free and light-speed neural networks. However, the digital design of the metamaterial neural network (MNN) is fundamentally limited by its physical limitations, such as precision, noise, and bandwidth during fabrication. Moreover, the unique advantages of MNN's (e.g., light-speed computation) are not fully explored via standard 3×3 convolution kernels. In this paper, we propose a novel large kernel metamaterial neural network (LMNN) that maximizes the digital capacity of the state-of-the-art (SOTA) MNN with model re-parametrization and network compression, while also considering the optical limitation explicitly. The new digital learning scheme can maximize the learning capacity of MNN while modeling the physical restrictions of meta-optic. With the proposed LMNN, the computation cost of the convolutional front-end can be offloaded into fabricated optical hardware. The experimental results on two publicly available datasets demonstrate that the optimized hybrid design improved classification accuracy while reducing computational latency. The development of the proposed LMNN is a promising step towards the ultimate goal of energy-free and light-speed AI.</p>","PeriodicalId":15924,"journal":{"name":"Journal of Imaging Science and Technology","volume":"67 6","pages":""},"PeriodicalIF":1.0,"publicationDate":"2023-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10970463/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140305875","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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