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Web-Based Interaction and Visualization of Spectral Reflectance Images: Application to Vegetation Inspection

SN computer science 2022年第1期3卷 12页

作者： Colantoni, Philippe Thomas, Jean-Baptiste Hébert, Mathieu Caissard, Jean-Claude Trémeau, Alain Université de Lyon Université Jean Monnet CNRS Institut d’Optique Graduate School Laboratoire Hubert Curien UMR 5516 42023 18 Rue Professeur Benoît Lauras Bâtiment Saint-Étienne 42000 France Department of Computer Science IDI NTNU - Norwegian University of Science and Technology The Norwegian color and Visual Computing Laboratory Teknologivegen 22 Gjøvik 2815 Norway Université de Lyon Université Jean Monnet Laboratoire Biotechnologies Végétales appliquées aux plantes aromatiques et médicinales FRE CNRS 3727 Faculté de Sciences et Techniques 23 Rue Dr Paul Michelon Saint-Etienne Cedex 2 42023 France

Visualization of spectral images and interaction with them is still a challenge. We demonstrate an edge-computing, web-technology based solution to handle spectral mage data and allow real-time interaction with it. The solution is flexible, efficient and applicable. It includes visualization strategies based on color science, image processing and statistics. An example of use is provided through a collaboration with a domain knowledge expert for an application related to vegetation inspection. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2021.

关键词： Data visualization Plant phenotyping Real-time spectral image processing Spectral imaging Web technologies

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Reducing system dimensionality with long-range collective dipole-dipole interactions

arXiv

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arXiv 2023年

作者： Boddeti, Ashwin K. Wang, Yi Juarez, Xitlali G. Boltasseva, Alexandra Odom, Teri W. Shalaev, Vladimir Alaeian, Hadiseh Jacob, Zubin Elmore Family School of Electrical and Computer Engineering Purdue University West LafayetteIN47907 United States Birck Nanotechnology Center Purdue University West LafayetteIN47907 United States Graduate Program in Applied Physics Northwestern University EvanstonIL60208 United States Department of Materials Science and Engineering Northwestern University EvanstonIL60208 United States Department of Chemistry Northwestern University EvanstonIL60208 United States School of Physics and Astronomy Purdue University West LafayetteIN47907 United States

Dimensionality plays a crucial role in long-range dipole-dipole interactions (DDIs). We demonstrate that a resonant nanophotonic structure modifies the apparent dimensionality in an interacting ensemble of emitters, as revealed by population decay dynamics. Our measurements on a dense ensemble of interacting quantum emitters in a resonant nanophotonic structure with long-range DDIs reveal an effective dimensionality reduction to d¯ = 2.20(12), despite the emitters being distributed in 3D. This contrasts the homogeneous environment, where the apparent dimension is d¯ = 3.00. Our work presents a promising avenue to manipulate dimensionality in an ensemble of interacting emitters. © 2023, CC0.

关键词： Dipole moment

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2019 Novel Coronavirus Disease (Covid-19): Toward a Novel Design for Disinfection Robot to Combat Coronavirus (Covid-19) Using IoT Based technology 12

2019 Novel Coronavirus Disease (Covid-19): Toward a Novel De...

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12th IEEE Control and System graduate Research Colloquium, ICSGRC 2021

作者： Mohammed, M.N. Hazairin, Nurul Aslamiah Arif, Arif Sameh Al-Zubaidi, S. Alkawaz, Mohammed Hazim Sairah, A.K. Jazlan, Ahmad Izzul Iman Md Yusuf, Eddy Management Science University Faculty of Information Sciences and Engineering Department of Engineering and Technology Shah Alam40100 Malaysia Management Science University School of Graduate Studies Shah Alam40100 Malaysia Middle Technical University Institute of Administration-Al Rusafa Department of Computer Systems Techniques Baghdad10047 Iraq University of Baghdad Al-Khwarizmi College of Engineering Department of Automated Manufacturing Engineering Baghdad10071 Iraq Jakarta Global University Faculty of Pharmacy Depok16412 Indonesia

ISBN: (纸本)9781665440110

Coronavirus Disease 19 or well known as Covid-19 is infectious disease that attack human respirator system. This virus is contagious due to its fast transmission from small droplets that come to people that sneezing, coughing, or even talking. This virus can stay in air for long time and it also can survive on the inanimate surface things. During this time, some places like hospital, mall, and station are places where crowds gathered. People in those places have possibility to spread the virus not only through close contact but also due to touching the infected surfaces. That is why device that able to clean the infected surfaces needed. The Internet of Things based robots may give big impact in combating the coronavirus that stay on inanimate surfaces. The proposed system is the robot that able to disinfect the surfaces of things using UV-C lights. The implementation of UV disinfectant robot will indeed help health authorities in reducing the transmission. © 2021 IEEE.

关键词： Coronavirus

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Prediction of antifungal peptides by deep learning with character embedding

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IPSJ Transactions on Bioinformatics 2020年 12卷 21-29页

作者： Fang, Chun Moriwaki, Yoshitaka Li, Caihong Shimizu, Kentaro School of Computer Science and Technology Shandong University of Technology Shandong255049 China Graduate School of Agricultural and Life Sciences University of Tokyo Bunkyo Tokyo113-8657 Japan

Conventional computerbased methods for identifying antifungal peptides (AFPs) are primarily based on large amounts of task-specific knowledge in feature extraction. This paper introduces a method, called AFPDeep, which only uses peptide sequences as input, without calculating any features by human intervention for the prediction of AFPs. First, an embedding layer is used to automatically code the sequence and to learn a dense vector representation for each kind of amino acid appearing in the training dataset. Second, a convolutional neural network (CNN) layer followed by a long short term memory (LSTM) layer is used to capture the local clues that are good indicators of AFPs, and to learn the long-term dependence and contextual information of the input data in a flexible way. Lastly, all the above layers are intertwined into hybrid neural network architecture for antifungal peptide prediction. Upon comparison with other methods on the same datasets, the AFPDeep yielded competitive results, achieving an AUC of on the Antifp Main dataset, on the Antifp DS1 dataset, and on the Antifp DS2 dataset. These results suggest that the hybrid architecture of CNN-LSTM combined with character embedding is an effective model for improving the prediction of AFPs. © 2019 Information Processing Society of Japan. All rights reserved.

关键词： Peptides

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Non-invasive biopsy diagnosis of diabetic kidney disease via deep learning applied to retinal images: a population-based study

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The Lancet Digital Health 2025年第5期7卷 100868页

作者： Meng, Ziyao Guan, Zhouyu Yu, Shujie Wu, Yilan Zhao, Yaoning Shen, Jie Lim, Cynthia Ciwei Chen, Tingli Yang, Dawei Ran, An Ran He, Feng Hamzah, Haslina Singh, Sarkaaj Abd Raof, Anis Syazwani Lee-Boey, Jian Wen Samuel Lim, Soo-Kun Sun, Xufang Ge, Shuwang Xu, Gang Su, Hua Cheng, Yang Lu, Feng Liao, Xiaofei Jin, Hai Deng, Chenxin Ruan, Lei Zhang, Cuntai Wu, Chan Dai, Rongping Jin, Yixiao Wang, Wenxiao Li, Tingyao Liu, Ruhan Li, Jiajia Shu, Jia Lu, Yuwei Wang, Xiangning Wu, Qiang Qin, Yiming Tang, Jin Sheng, Xiaohua Jiao, Qiong Yang, Xiaokang Guo, Minyi McKay, Gareth J Hogg, Ruth E Liew, Gerald Chee, Evelyn Yi Lyn Hsu, Wynne Lee, Mong Li Szeto, Simon Luk, Andrea O Y Chan, Juliana C N Cheung, Carol Y Tan, Gavin Siew Wei Tham, Yih-Chung Cheng, Ching-Yu Sabanayagam, Charumathi Lim, Lee-Ling Jia, Weiping Li, Huating Sheng, Bin Wong, Tien Yin Shanghai Belt and Road International Joint Laboratory of Intelligent Prevention and Treatment for Metabolic Diseases Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Institute for Proactive Healthcare Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Centre for Diabetes Shanghai Key Laboratory of Diabetes Mellitus Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Beijing Tsinghua Changgung Hospital Eye Center School of Clinical Medicine Tsinghua Medicine Tsinghua University Beijing China Medical Records and Statistics Office Shanghai Sixth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Renal Medicine Singapore General Hospital SingHealth-Duke Academic Medical Centre Singapore Singapore Department of Ophthalmology Shanghai Health and Medical Center Wuxi China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong Special Administrative Region China Singapore Eye Research Institute Singapore National Eye Centre Singapore Department of Medicine Faculty of Medicine Universiti Malaya Kuala Lumpur Malaysia Department of Ophthalmology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Hubei Wuhan China Department of Nephrology Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Nephrology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China Department of Ophthalmology Union Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Centre for Big Dat

Background: Improving the accessibility of screening diabetic kidney disease (DKD) and differentiating isolated diabetic nephropathy from non-diabetic kidney disease (NDKD) are two major challenges in the field of diabetes care. We aimed to develop and validate an artificial intelligence (AI) deep learning system to detect DKD and isolated diabetic nephropathy from retinal fundus images. Methods: In this population-based study, we developed a retinal image-based AI-deep learning system, DeepDKD, pretrained using 734 084 retinal fundus images. First, for DKD detection, we used 486 312 retinal images from 121 578 participants in the Shanghai Integrated Diabetes Prevention and Care System for development and internal validation, and ten multi-ethnic datasets from China, Singapore, Malaysia, Australia, and the UK (65 406 participants) for external validation. Second, to differentiate isolated diabetic nephropathy from NDKD, we used 1068 retinal images from 267 participants for development and internal validation, and three multi-ethnic datasets from China, Malaysia, and the UK (244 participants) for external validation. Finally, we conducted two proof-of-concept studies: a prospective real-world study with 3 months' follow-up to evaluate the effectiveness of DeepDKD in screening DKD;and a longitudinal analysis of the effectiveness of DeepDKD in differentiating isolated diabetic nephropathy from NDKD on renal function changes with 4·6 years' follow-up. Findings: For detecting DKD, DeepDKD achieved an area under the receiver operating characteristic curve (AUC) of 0·842 (95% CI 0·838–0·846) on the internal validation dataset and AUCs of 0·791–0·826 across external validation datasets. For differentiating isolated diabetic nephropathy from NDKD, DeepDKD achieved an AUC of 0·906 (0·825–0·966) on the internal validation dataset and AUCs of 0·733–0·844 across external validation datasets. In the prospective study, compared with the metadata model, DeepDKD could detect DKD wit

关键词： Deep learning

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Optimal Design for Level-Shifter-Less Approach Using Channel Length Modulation & Body Biasing

Optimal Design for Level-Shifter-Less Approach Using Channel...

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International Technical Conference o Circuits/Systems, computers and Communications (ITC-CSCC)

作者： Tatsuya Watanabe Kimiyoshi Usami Graduate School of Engineering and Science Shibaura Institute of Technology Tokyo Japan Department of Computer Science and Engineering Shibaura Institute of Technology Tokyo Japan

ISBN: (数字)9784885523281

ISBN: (纸本)9781728198484

A multi-V DD design realizes LSIs to be low power by allowing to use multiple different power supply voltages. In this design, conversion of the voltage amplitude of the signal is necessary. This is usually done by inserting a circuit called a level shifter, between voltage domains as an interface. However, insertion of level shifter has disadvantages in silicon footprint, power consumption, and delays. In this paper, we propose a level-shifter-less approach by increasing channel length. We also propose the optimal design using both channel length modulation and body biasing.

关键词： Delays Logic gates Receivers Inverters Short-circuit currents Energy consumption Power supplies

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NOTES ON UNIVERSALITY IN SHORT INTERVALS AND EXPONENTIAL SHIFTS

arXiv

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arXiv 2023年

作者： Andersson, Johan Garunkštis, Ramūnas Kačinskaite, Roma Nakai, Keita Pankowski, Lukasz Sourmelidis, Athanasios Steuding, Rasa Steuding, Jörn Wananiyakul, Saeree Division of Mathematics Institute of Science and Technology Örebro University Sweden Institute of Mathematics Faculty of Mathematics and Informatics Vilnius University Naugarduko 24 Vilnius03225 Lithuania Institute of Mathematics Faculty of Mathematics and Informatics Vilnius University Naugarduko str. 24 Vilnius03225 Lithuania Graduate school of Mathematics Nagoya University Chikusa-Ku Nagoya464-8602 Japan Faculty of Mathematics and Computer Science Adam Mick-iewicz University Uniwersytetu Poznańskiego 4 Poznań61-614 Poland Institute of Analysis and Number Theory Graz University of technology Steyrergasse 30 Graz8010 Austria Department of Mathematics Würzburg University Emil Fischer-Str. 40 Würzburg97 074 Germany Department of Mathematics and Computer Science Faculty of Science Chulalongkorn University Bangkok10330 Thailand

We improve a recent universality theorem for the Riemann zeta-function in short intervals due to Antanas Laurinčikas with respect to the length of these intervals. Moreover, we prove that the shifts can even have exponential growth. This research was initiated by two questions proposed by Laurinčikas in a problem session of a recent workshop on *** Codes 11M06 Copyright © 2023, The Authors. All rights reserved.

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Decision support system using data warehouse for retail system

Decision support system using data warehouse for retail syst...

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International Conference on Engineering, technology and Innovative Researches 2019, ICETIR 2019

作者： Girsang, Abba Suganda Arisandi, Geri Elysisa, Calista Michelle Saragih, Melva Hermayanty Computer Science Department BINUS Graduate Program Master of Computer Science Bina Nusantara University Jakarta Indonesia Management Department BINUS Business School Undergraduate Program Bina Nusantara University Jakarta Indonesia

CMK company is a retail company that sell and distributes miniature vehicle items to small stores. CMK has several branches in various countries that use for operational and storage warehouse. This company faces the problem for analysing sales to decide the useful strategy since this company only uses Online Transaction Processing (OLTP) database. This study implements Online Analysis Processing (OLAP) database and data warehouse to provide useful information for this company by using the nine-step method designed by Kimball & Ross. The data will be performed into dashboard to make easier for CMK Company to analyze data. Furthermore, many useful information can be provided in short and efficient time. © 2019 IOP Publishing Ltd. All rights reserved.

关键词： Data warehouses

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Emerging contaminants:A One Health perspective

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The Innovation 2024年第4期5卷 140-170,139页

作者： Fang Wang Leilei Xiang Kelvin Sze-Yin Leung Martin Elsner Ying Zhang Yuming Guo Bo Pan Hongwen Sun Taicheng An Guangguo Ying Bryan WBrooks Deyi Hou Damian EHelbling Jianqiang Sun Hao Qiu Timothy MVogel Wei Zhang Yanzheng Gao Myrna JSimpson Yi Luo Scott XChang Guanyong Su Bryan MWong Tzung-May Fu Dong Zhu Karl JJobst Chengjun Ge Frederic Coulon Jean Damascene Harindintwali Xiankui Zeng Haijun Wang Yuhao Fu Zhong Wei Rainer Lohmann Changer Chen Yang Song Concepcion Sanchez-Cid Yu Wang Ali El-Naggar Yiming Yao Yanran Huang Japhet Cheuk-Fung Law Chenggang Gu Huizhong Shen Yanpeng Gao Chao Qin Hao Li Tong Zhang Natàlia Corcoll Min Liu Daniel SAlessi Hui Li Kristian KBrandt Yolanda Pico Cheng Gu Jianhua Guo Jianqiang Su Philippe Corvini Mao Ye Teresa Rocha-Santos Huan He Yi Yang Meiping Tong Weina Zhang Fidèle Suanon Ferdi Brahushi Zhenyu Wang Syed AHashsham Marko Virta Qingbin Yuan Gaofei Jiang Louis A.Tremblay Qingwei Bu Jichun Wu Willie Peijnenburg Edward Topp Xinde Cao Xin Jiang Minghui Zheng Taolin Zhang Yongming Luo Lizhong Zhu Xiangdong Li DamiàBarceló Jianmin Chen Baoshan Xing Wulf Amelung Zongwei Cai Ravi Naidu Qirong Shen Janusz Pawliszyn Yong-guan Zhu Andreas Schaeffer Matthias C.Rillig Fengchang Wu Gang Yu James M.Tiedje State Key Laboratory of Soil and Sustainable Agriculture Institute of Soil ScienceChinese Academy of SciencesNanjing 210008China University of Chinese Academy of Sciences Beijing 100049China Department of Chemistry Hong Kong Baptist UniversityHong KongChina HKBU Institute of Research and Continuing Education Shenzhen Virtual University ParkShenzhenChina Technical University of Munich TUM School of Natural SciencesInstitute of Hydrochemistry85748 GarchingGermany School of Resources&Environment Northeast Agricultural UniversityHarbin 150030China Climate Air Quality Research UnitSchool of Public Health and Preventive MedicineMonash UniversityMelbourneVICAustralia Faculty of Environmental Science&Engineering Kunming University of Science&TechnologyKunming 650500China Ministry of Education Key Laboratory of Pollution Processes and Environmental Criteria College of Environmental Science and EngineeringNankai UniversityTianjin 300350China Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control School of Environmental Science and EngineeringInstitute of Environmental Health and Pollution ControlGuangdong University of TechnologyGuangzhou 510006China Ministry of Education Key Laboratory of Environmental Theoretical Chemistry South China Normal UniversityGuangzhouGuangdong 510006China Department of Environmental Science Baylor UniversityWacoTXUSA Center for Reservoir and Aquatic Systems Research(CRASR) Baylor UniversityWacoTXUSA School of Environment Tsinghua UniversityBeijing 100084China School of Civil and Environmental Engineering Cornell UniversityIthacaNY 14853USA Key Laboratory of Microbial Technology for Industrial Pollution Control of Zhejiang Province Zhejiang University of TechnologyHangzhou 310014China School of Environmental Science and Engineering Shanghai Jiao Tong UniversityShanghai 200240China Laboratoire d’Ecologie Microbienne Universite Claude Bernard Lyon 1UMR CNRS 5557UMR INRAE 1418VetAgro Sup69622 VilleurbanneFrance Department of Pla

Environmental pollution is escalating due to rapid global development that often prioritizes human needs over planetary *** global efforts to mitigate legacy pollutants,the continuous introduction of new substances remains a major threat to both people and the *** response,global initiatives are focusing on risk assessment and regulation of emerging contaminants,as demonstrated by the ongoing efforts to establish the UN’s Intergovernmental science-Policy Panel on Chemicals,Waste,and Pollution *** review identifies the sources and impacts of emerging contaminants on planetary health,emphasizing the importance of adopting a One Health *** for monitoring and addressing these pollutants are discussed,underscoring the need for robust and socially equitable environmental policies at both regional and international *** actions are needed to transition toward sustainable pollution management practices to safeguard our planet for future generations.

关键词： pollution planet contaminants

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Approximate Computing based on Latest-result Reuse for Image Edge Detection

Approximate Computing based on Latest-result Reuse for Image...

引用

International Technical Conference o Circuits/Systems, computers and Communications (ITC-CSCC)

作者： Kimiyoshi Usami Hajime Ochi Yoshinori Ono Department of Computer Science and Engineering Shibaura Institute of Technology Tokyo Japan Graduate School of Engineering and Science Shibaura Institute of Technology Tokyo Japan

ISBN: (数字)9784885523281

ISBN: (纸本)9781728198484

This paper describes a memo-table-free approximate computing technique for an image edge detector to reuse the computed result only in the closest past. Decision of reuse is made based on tolerance-level controllable similarity checking for two consecutive 3x3 input pixels. We designed and implemented the proposed approximate edge detector in an FPGA and evaluated with 6 benchmark images. Results have demonstrated that by controlling the tolerance level the execution time is reduced by 15% and the energy dissipation is saved by 3-12% compared with the precise computation at acceptable quality in edge detected images.

关键词： Image edge detection Detectors Registers Approximate computing Benchmark testing Degradation Field programmable gate arrays

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