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Exploring Novel Ai-Based Approaches for Plant Features Extraction in Image Datasets with Small Size: The Case Study of Nitrogen Estimation in Sorghum Using Uav-Based Rgb Sensing

SSRN

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SSRN 2023年

作者： Hammouch, Hajar Patil, Suchitra El-Yacoubi, Mounim A.A. Masner, Jan Kholová, Jana Choudhary, Sunita Anbazhagan, Krithika Vaněk, Jiří Qin, Huafeng Stočes, Michal Berbia, Hassan Jagarlapudi, Adinarayana Chandramouli, Magesh Mamidi, Srinivas Prasad, K.V.S.V. Baddam, Rekha SAMOVAR Telecom SudParis Institut Polytechnique de Paris Palaiseau91120 France Mohamed V University Rabat Morocco Centre of Studies in Resources Engineering Indian Institute of Technology Bombay Powai Maharashtra Mumbai400076 India Telangana Patancheru502 324 India Department of Information Technology K. J. Somaiya College of Engineering Vidyavihar Maharashtra Mumbai400 077 India Department of Information Technologies Czech University of Life Sciences Prague Prague165 00 Czech Republic Chongqing University China Computer Graphics Technology Purdue University NW HammondIN46323 United States Marut Dronetech Private Limited Gachibowli Telangana Hyderabad500032 India Patancheru Hyderabad502 324 India

This study was primarily triggered by the requirements of crop breeding programs to rapidly, accurately and cost-effectively select crop genotypes with enhanced biomass Nitrogen (N) content (an essential indicator of stover quality as feed) and, at the same time, possibly speed up the agriculture transition to the more sustainable and precise application of fertilizers (crop health status). This proof of concept study explores whether and how precisely it is possible to predict the N content in sorghum crop biomass using a minimum unmanned aerial vehicle (UAV)-carried imaging technology (Red Green Blue (RGB) camera) and a small dataset. To this end, we raised a post-rainy (rabi) 2021 and 2022 season sorghum crop in the field with standard and sub-optimal management practices (N and water stress) at the International Crop Research institute for Semi-arid Tropics (ICRISAT), India. RGB images were collected during different crop growth stages (rabi 2021, 2022) using a low-cost RGB camera DJI Zenmuse X5S mounted UAV DJI Matrice 210 *** collected three ground-truth points with corresponding RGB images to build the N-prediction model (rabi 2021) and then six ground-truth points with corresponding RGB to evaluate the model with a second independent test set (rabi 2022). We have assessed statistical and AI-based models for N prediction, namely multiple linear regression (MLR), multi-layer perceptron (MLP) and various convolutional neural network (CNN) architectures, as well as their combinations. The first two take as input five handcrafted features computed using RGB spectrum vegetation indices (VIs) while the CNN automatically extracts discriminant features from the raw images. These schemes have already been proposed in state-of-the-art and the novelty of our study lies in the assessment of several fusion strategies in order to leverage both the deep features learned automatically by the CNN and the human-expertise-based handcrafted features, harnessing, thereb

关键词： Crops

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Apple Vision Pro for Healthcare: "The Ultimate Display"? Entering the Wonderland of Precision Medicine

arXiv

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arXiv 2023年

作者： Egger, Jan Gsaxner, Christina Chen, Xiaojun Bian, Jiang Kleesiek, Jens Puladi, Behrus Girardetstraße 2 Essen45131 Germany Hufelandstraße 55 Essen45147 Germany Hufelandstraße 55 Essen45147 Germany Department of Oral and Maxillofacial Surgery University Hospital RWTH Aachen Pauwelsstraße 30 Aachen52074 Germany Institute of Computer Graphics and Vision Graz University of Technology Inffeldgasse 16/II Graz8010 Austria Institute of Biomedical Manufacturing and Life Quality Engineering State Key Laboratory of Mechanical System and Vibration School of Mechanical Engineering Shanghai Jiao Tong University Dongchuan Road 800 Minhang District Shanghai200240 China Institute of Medical Robotics Shanghai Jiao Tong University Dongchuan Road 800 Minhang District Shanghai200240 China Health Outcomes & Biomedical Informatics College of Medicine University of Florida GainesvilleFL32610 United States Partner Site Essen Hufelandstraße 55 Essen45147 Germany Department of Physics TU Dortmund University August-Schmidt-Str. 4 Dortmund44227 Germany Institute of Medical Informatics University Hospital RWTH Aachen Pauwelsstraße 30 Aachen52074 Germany

At the Worldwide Developers Conference (WWDC) in June 2023, Apple introduced the Vision Pro. The Vision Pro is a Mixed Reality (MR) headset, more specifically it is a Virtual Reality (VR) device with an additional Video See-Through (VST) capability. The VST capability turns the Vision Pro also into an Augmented Reality (AR) device. The AR feature is enabled by streaming the real world via cameras to the (VR) screens in front of the user’s eyes. This is of course not unique and similar to other devices, like the Varjo XR-3. Nevertheless, the Vision Pro has some interesting features, like an inside-out screen that can show the headset wearers’ eyes to "outsiders" or a button on the top, called "Digital Crown", that allows you to seamlessly blend digital content with your physical space by turning it. In addition, it is untethered, except for the cable to the battery, which makes the headset more agile, compared to the Varjo XR-3. This could actually come closer to the "Ultimate Display", which Ivan Sutherland had already sketched in 1965. Not available to the public yet, like the Ultimate Display, we want to take a look into the crystal ball in this perspective to see if it can overcome some clinical challenges that - especially - AR still faces in the medical domain, but also go beyond and discuss if the Vision Pro could support clinicians in essential tasks to spend more time with their patients. © 2023, CC BY.

关键词： Augmented reality

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Evaluation of algorithms for multi-modality whole heart segmentation: An open-access grand challenge

arXiv

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arXiv 2019年

作者： Zhuang, Xiahai Li, Lei Payer, Christian Štern, Darko Urschler, Martin Heinrich, Mattias P. Oster, Julien Wang, Chunliang Smedby, Örjan Bian, Cheng Yang, Xin Heng, Pheng-Ann Mortazi, Aliasghar Bagci, Ulas Yang, Guanyu Sun, Chenchen Galisot, Gaetan Ramel, Jean-Yves Brouard, Thierry Tong, Qianqian Si, Weixin Liao, Xiangyun Zeng, Guodong Shi, Zenglin Zheng, Guoyan Wang, Chengjia MacGillivray, Tom Newby, David Rhode, Kawal Ourselin, Sebastien Mohiaddin, Raad Keegan, Jennifer Firmin, David Yang, Guang School of Data Science Fudan University Shanghai200433 China Fudan-Xinzailing Joint Research Center for Big Data Fudan University Shanghai200433 China School of Biomedical Engineering Shanghai Jiao Tong University Shanghai200240 China Institute of Computer Graphics and Vision Graz University of Technology Graz8010 Austria Ludwig Boltzmann Institute for Clinical Forensic Imaging Graz8010 Austria Institute of Medical Informatics University of Lubeck Lubeck23562 Germany Inserm Université de Lorraine U1254 IADI Nancy France School for Technology and Health KTH Royal Institute of Technology StockholmSE-10044 Sweden School of Biomed. Eng. Health Science Centre Shenzhen University Shenzhen518060 China Dept. of Comp. Sci. and Eng. Chinese University of Hong Kong Hong Kong China University of Central Florida Orlando32816 United States School of Computer Science and Engineering Southeast University Nanjing210096 China Université de Tours 64 avenue Jean Portalis Tours37200 France School of Computer Science Wuhan University Wuhan430072 China Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology SIAT Shenzhen China Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Institute for Surgical Technology & Biomechanics University of Bern Bern3014 Switzerland BHF Centre for Cardiovascular Science University of Edinburgh Edinburgh United Kingdom Edinburgh Imaging Facility QMRI University of Edinburgh Edinburgh United Kingdom Department of Imaging Sciences & Biomedical Engineering Kings College London London United Kingdom Cardiovascular Research Centre Royal Brompton Hospital LondonSW3 6NP United Kingdom National Heart and Lung Institute Imperial College London London LondonSW7 2AZ United Kingdom

Knowledge of whole heart anatomy is a prerequisite for many clinical applications. Whole heart segmentation (WHS), which delineates substructures of the heart, can be very valuable for modeling and analysis of the anatomy and functions of the heart. However, automating this segmentation can be arduous due to the large variation of the heart shape, and different image qualities of the clinical data. To achieve this goal, a set of training data is generally needed for constructing priors or for training. In addition, it is difficult to perform comparisons between different methods, largely due to differences in the datasets and evaluation metrics used. This manuscript presents the methodologies and evaluation results for the WHS algorithms selected from the submissions to the Multi-Modality Whole Heart Segmentation (MM-WHS) challenge, in conjunction with MICCAI 2017. The challenge provides 120 three-dimensional cardiac images covering the whole heart, including 60 CT and 60 MRI volumes, all acquired in clinical environments with manual delineation. Ten algorithms for CT data and eleven algorithms for MRI data, submitted from twelve groups, have been evaluated. The results show that many of the deep learning (DL) based methods achieved high accuracy, even though the number of training datasets were limited. A number of them also reported poor results in the blinded evaluation, probably due to overfitting in their training. The conventional algorithms, mainly based on multi-atlas segmentation, demonstrated robust and stable performance, even though the accuracy is not as good as the best DL method in CT segmentation. The challenge, including provision of the annotated training data and the blinded evaluation for submitted algorithms on the test data, continues as an ongoing benchmarking resource via its homepage (***/zhuangxiahai/0/mmwhs/). Copyright © 2019, The Authors. All rights reserved.

关键词： Heart

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TimeTubes: Visual Exploration of Observed Blazar Datasets

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Journal of Physics: Conference Series 2018年第1期1036卷

作者： Issei Fujishiro Naoko Sawada Masanori Nakayama Hsiang-Yun Wu Kazuho Watanabe Shigeo Takahashi Makoto Uemura Center for Information and Computer Science Keio University 3-14-1 Hiyoshi Kohoku-ku Yokohama 223-8522 Japan Current address: Institute of Computer Graphics and Algorithms TU Wien Favoritenstr. 9 / 5. Stock (5th floor) A-1040 Vienna Austria Department of Computer Science and Engineering Toyohashi University of Technology 1-1 Hibarigaoka Tempaku-cho Toyohashi 441-8580 Japan School of Computer Science and Engineering University of Aizu Aizu Wakamatsu Fukushima 965-8580 Japan Astrophysical Science Center Hiroshima University 1-3-1 Kagamiyama Higashi-Hiroshima 739-8526 Japan

Blazars are attractive objects for astronomers to observe in order to burrow into the magnetic field in the relativistic jet. This paper presents TimeTubes as a novel visualization scheme that allows astronomers to interactively explore characteristic temporal variation patterns in observed blazar datasets. In the TimeTubes spatialization, the two Stokes parameters and their errors with a common timestamp are transformed into an ellipse. A series of such ellipses are aligned in parallel along the timeline to form a 3D volumetric tube. The resulting tube is then colorized by the observed intensities and colors of the blazar, and finally volume-rendered. A designated user interface is provided with visual exploration functions according to Shneiderman's Visual Information Seeking Mantra. In the latest version, an auxiliary mechanism, called visual data fusion, was incorporated to ameliorate data- and mapping-inherent uncertainties for more efficient and effective visual exploration.

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基于元胞自动机的单道交通模型研究

基于元胞自动机的单道交通模型研究

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第七届全国交通运输领域青年学术会议

作者： Yang Menglong 杨梦龙 Liu Yiguang 刘怡光 Institute of Image and Graphics School of Computer Science and Engineeting Sichuan UniversityChen 四川大学计算机学院图形图象研究所成都 610064 Center for Nonlinear and Complex Systems School of Electronic Engineering University of Electronic 电子科技大学电子工程学院非线性复杂系统中心成都 610054

对基于元胞自动机的单道交通模型进行研究改进，细化了元胞长度和时间步长，对静止车辆的加速能力进行了约束，用简单的方法解决了NS模型中车辆的速度演化规则与前车速度无关的缺点。同时，考虑了随机慢化概率对交通密度的反应，利用计... 详细信息

对基于元胞自动机的单道交通模型进行研究改进，细化了元胞长度和时间步长，对静止车辆的加速能力进行了约束，用简单的方法解决了NS模型中车辆的速度演化规则与前车速度无关的缺点。同时，考虑了随机慢化概率对交通密度的反应，利用计算机仿真，得到了一些与其他元胞自动机模型共有的性质。另外，与其他模型相比较，新模型得到的基本图更加接近实测数据。

关键词：单道交通模型交通流元胞自动机系统仿真

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Preface

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Lecture Notes in computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2001年 2037卷 V-VI页

作者： Boers, Egbert J. W. Cagnoni, Stefano Gottlieb, Jens Hart, Emma Lanzi, Pier Luca Raidl, Günther R. Smith, Robert E. Tijink, Harald Leiden Institute of Advanced Computer Science Leiden University Niels Bohrweg 1 Leiden Netherlands Dept. of Computer Engineering University of Parma Parco Area delle Scienze 181/a Parma Italy SAP AG Neurottstrasse 16 Walldorf Germany Napier University School of Computing 219 Colinton Road Edinburgh United Kingdom Polytechnic of Milan Piazza Leonardo da Vinci 32 Milan Italy Algorithms and Data Structur es Group Institute of Computer Graphics Vienna University of Technology Favoritenstrasse 9-11/186 Vienna Austria The Intelligent Computer Systems Centre The University of The West of England Coldharbour Lane Frenchay Bristol United Kingdom Data and Knowledge Systems Department National Aerospace Laboratory NLR P.O. Box 153 Emmeloord Netherlands

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Innovative approaches to the formation of environmental safety at the objects of oil and gas production

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IOP Conference Series: Materials Science and engineering 2020年第1期749卷

作者： L Shkitsa T Yatsyshyn M Lyakh O Sydorenko Ivano-Frankivsk National Technical University of Oil and Gas Department of Engineering and Computer Graphics Ivano-Frankivsk Ukraine Ivano-Frankivsk National Technical University of Oil and Gas Department of Ecology Ivano-Frankivsk Ukraine Ivano-Frankivsk National Technical University of Oil and Gas Department of Oil and Gas Equipment Ivano-Frankivsk Ukraine Research and Design Institute PJSC 'Ukrnafta' Ecological Researches Laboratory Ivano-Frankivsk Ukraine

The innovative approach to environmental safety upgrade of pumping-circulatory equipment of drilling rigs based on the analysis of life history and continuous perfection principles. Examples of implementation of innovative approach in environmental safety investigation of oil and gas wells in order to define environmentally hazardous units of equipment and technological processes are given. In order to solve the problem of environmental safety improvement and the effectiveness of flushing out of well that are being drilled a modernized scheme of drilling rig pumping-circulatory system has been designed. System approach of modernizing separate elements of pumping-circulatory system has been used. The maximum air-tight pumping-circulatory drilling rig system has been designed. The proposed model takes into account the necessity of hazardous evaporations outlet with the aim of their further utilization. Such an approach will avoid the entry harmful substances into the environment and prevent harmful effects of aggressive substances on health personnel of drilling rig, environment and on state of the technological equipment.

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Comparative cellular analysis of motor cortex in human, marmoset and mouse (vol 598, pg 111, 2021)

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NATURE 2022年第7904期604卷 E8-E8页

作者： Bakken, Trygve E. Jorstad, Nikolas L. Hu, Qiwen Lake, Blue B. Tian, Wei Kalmbach, Brian E. Crow, Megan Hodge, Rebecca D. Krienen, Fenna M. Sorensen, Staci A. Eggermont, Jeroen Yao, Zizhen Aevermann, Brian D. Aldridge, Andrew I. Bartlett, Anna Bertagnolli, Darren Casper, Tamara Castanon, Rosa G. Crichton, Kirsten Daigle, Tanya L. Dalley, Rachel Dee, Nick Dembrow, Nikolai Diep, Dinh Ding, Song-Lin Dong, Weixiu Fang, Rongxin Fischer, Stephan Goldman, Melissa Goldy, Jeff Graybuck, Lucas T. Herb, Brian R. Hou, Xiaomeng Kancherla, Jayaram Kroll, Matthew Lathia, Kanan van Lew, Baldur Li, Yang Eric Liu, Christine S. Liu, Hanqing Lucero, Jacinta D. Mahurkar, Anup McMillen, Delissa Miller, Jeremy A. Moussa, Marmar Nery, Joseph R. Nicovich, Philip R. Niu, Sheng-Yong Orvis, Joshua Osteen, Julia K. Owen, Scott Palmer, Carter R. Pham, Thanh Plongthongkum, Nongluk Poirion, Olivier Reed, Nora M. Rimorin, Christine Rivkin, Angeline Romanow, William J. Sedeno-Cortes, Adriana E. Siletti, Kimberly Somasundaram, Saroja Sulc, Josef Tieu, Michael Torkelson, Amy Tung, Herman Wang, Xinxin Xie, Fangming Yanny, Anna Marie Zhang, Renee Ament, Seth A. Behrens, M. Margarita Bravo, Hector Corrada Chun, Jerold Dobin, Alexander Gillis, Jesse Hertzano, Ronna Hof, Patrick R. Hollt, Thomas Horwitz, Gregory D. Keene, C. Dirk Kharchenko, Peter V. Ko, Andrew L. Lelieveldt, Boudewijn P. Luo, Chongyuan Mukamel, Eran A. Pinto-Duarte, Antonio Preiss, Sebastian Regev, Aviv Ren, Bing Scheuermann, Richard H. Smith, Kimberly Spain, William J. White, Owen R. Koch, Christof Hawrylycz, Michael Tasic, Bosiljka Macosko, Evan Z. McCarroll, Steven A. Ting, Jonathan T. Zeng, Hongkui Zhang, Kun Feng, Guoping Ecker, Joseph R. Linnarsson, Sten Lein, Ed S. Allen Institute for Brain Science Seattle WA USA Department of Physiology and Biophysics University of Washington Seattle WA USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Department of Bioengineering University of California San Diego La Jolla CA USA The Salk Institute for Biological Studies La Jolla CA USA Epilepsy Center of Excellence Department of Veterans Affairs Medical Center Seattle WA USA Stanley Institute for Cognitive Genomics Cold Spring Harbor Laboratory Cold Spring Harbor NY USA Department of Genetics Harvard Medical School Boston MA USA Broad Institute of MIT and Harvard Cambridge MA USA LKEB Department of Radiology Leiden University Medical Center Leiden The Netherlands Pattern Recognition and Bioinformatics group Delft University of Technology Delft The Netherlands J. Craig Venter Institute La Jolla CA USA Department of Pathology University of California San Diego CA USA Division of Vaccine Discovery La Jolla Institute for Immunology La Jolla CA USA Genomic Analysis Laboratory The Salk Institute for Biological Studies La Jolla CA USA Computer Science and Engineering Program University of California San Diego La Jolla CA USA Howard Hughes Medical Institute The Salk Institute for Biological Studies La Jolla CA USA Bioinformatics and Systems Biology Graduate Program University of California San Diego La Jolla CA USA Institute for Genomes Sciences University of Maryland School of Medicine Baltimore MD USA Center for Epigenomics Department of Cellular and Molecular Medicine University of California San Diego La Jolla CA USA Ludwig Institute for Cancer Research La Jolla CA USA Department of Computer Science University of Maryland College Park College Park MD USA Sanford Burnham Prebys Medical Discovery Institute La Jolla CA USA Biomedical Sciences Program School of Medicine University of California San Diego La Jolla CA USA University of Connecticut Storrs CT USA Department

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Means of atmospheric air pollution reduction during drilling wells

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IOP Conference Series: Materials Science and engineering 2016年第1期144卷

作者： L Shkitsa T Yatsyshyn M Lyakh O Sydorenko Ivano-Frankivsk National Technical University of Oil and Gas Engineering and Computer Graphics Department 15 Karpatska Street Ivano-Frankivsk 76019 Ukraine Ivano-Frankivsk National Technical University of Oil and Gas Department of Ecology 15 Karpatska Street Ivano-Frankivsk 76019 Ukraine Ivano-Frankivsk National Technical University of Oil and Gas Oil and Gas Equipment Department 15 Karpatska Street Ivano-Frankivsk 76019 Ukraine Reseach and Desing Institute PJSC 'Ukrnafta' Environmental Department 2 Pivnitchnuy Bulvar Ivano-Frankivsk 76019 Ukraine

The process of drilling oil and gas wells is the source of air pollution through drilling mud evaporation containing hazardous chemical substances. The constructive solution for cleaning device of downhole tool that contains elements covering tube and clean the surface from the mud in the process of rising from the well is offered. Inside the device is filled with magnetic fluid containing the substance neutralizing hazardous substances. The use of the equipment proposed will make it possible to avoid penetration of harmful substances into the environment and to escape the harmful effects of aggressive substances for staff health and increase rig's fire safety.

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CIRCA: comprehensible online system in support of chest X-rays-based COVID-19 diagnosis

arXiv

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arXiv 2022年

作者： Prazuch, Wojciech Suwalska, Aleksandra Socha, Marek Tobiasz, Joanna Foszner, Pawel Jaroszewicz, Jerzy Gruszczynska, Katarzyna Sliwinska, Magdalena Walecki, Jerzy Popiela, Tadeusz Przybylski, Grzegorz Cieszanowski, Andrzej Nowak, Mateusz Pawlowska, Malgorzata Flisiak, Robert Simon, Krzysztof Zapolska, Gabriela Gizycka, Barbara Szurowska, Edyta Marczyk, Michal Polanska, Joanna Department of Data Science and Engineering Silesian University of Technology Gliwice Poland Department of Computer Graphics Vision and Digital Systems Silesian University of Technology Gliwice Poland Department of Infectious Diseases and Hepatology Medical University of Silesia Katowice Poland Department of Diagnostic Imaging Voivodship Specialist Hospital Wroclaw Poland Department of Diagnostic Radiology Central Clinical Hospital of The Ministry of Internal Affairs and Administration Warsaw Poland Department of Radiology Jagiellonian University Medical College Krakow Poland Department of Lung Diseases Cancer and Tuberculosis Kujawsko-Pomorskie Pulmonology Center Bydgoszcz Poland Department of Imaging Diagnostics National Institute of Oncology Warsaw Poland Department of Radiology Silesian Hospital Cieszyn Poland Department of Infectious Diseases and Hepatology Collegium Medicum in Bydgoszcz Nicolaus Copernicus University Torun Poland Department of Infectious Diseases and Hepatology Medical University of Bialystok Bialystok Poland Department of Infectious Diseases and Hepatology Wroclaw Medical University Wroclaw Poland Department of Radiology Czerniakowski Hospital Warsaw Poland PDepartment of Imaging Diagnostics MEGREZ Hospital Tychy Poland 2nd Department of Radiology Medical University of Gdansk Poland Yale Cancer Center Yale School of Medicine New HavenCT United States

Background: Due to the large accumulation of patients requiring hospitalization, the COVID-19 pandemic disease caused a high overload of health systems, even in developed countries. Deep learning techniques based on medical imaging data can help in the faster detection of COVID-19 cases and monitoring of disease progression. However, regardless of the numerous proposed solutions using lung X-rays, none of them is a product that can be used in the clinic. Methods: Five different datasets (clinical POLCOVID and AIforCOVID databases, a public collection of images called COVIDx set, pneumonia patients from NIH, and artificially generated data) were used to construct a representative dataset of 23 799 CXRs for model training, 1 050 images as a hold-out test set and 44 247 CXRs as independent test set (BIMCV database). A U-Net-based model was developed to identify a clinically relevant region of the CXR that contains the lungs and each class (normal, pneumonia, and COVID-19) was divided into 3 subtypes using a 2D Gaussian mixture model. A decision tree was used to aggregate predictions from the InceptionV3 convolutional network based on processed CXRs and a dense 7-layer neural network on radiomic features determined independently for the upper, middle, and lower lung fragments. Results: The lung segmentation model gave the Sorensen-Dice coefficient of 94.86% in the validation dataset, and 93.36% in the testing dataset. In 5-fold cross-validation, the accuracy for all classes ranged from 91% to 93%, keeping slightly higher specificity than sensitivity. Also, NPV was higher than PPV for each class to guarantee a low rate of false negative cases. In the hold-out test set, the balanced accuracy ranged between 68% and 100%. The highest performance was obtained for the subtypes N1, P1, and C1, while images assigned to subtypes N3, P3, or C3 have been wrongly predicted among themselves. A similar performance was obtained on the independent dataset for normal and COVID-19 class

关键词： COVID-19

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