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arXiv 2020年

作者： Xiong, Zhaohan Xia, Qing Hu, Zhiqiang Huang, Ning Bian, Cheng Zheng, Yefeng Vesal, Sulaiman Ravikumar, Nishant Maier, Andreas Yang, Xin Heng, Pheng-Ann Ni, Dong Li, Caizi Tong, Qianqian Si, Weixin Puybareau, Elodie Khoudli, Younes Géraud, Thierry Chen, Chen Bai, Wenjia Rueckert, Daniel Xu, Lingchao Zhuang, Xiahai Luo, Xinzhe Jia, Shuman Sermesant, Maxime Liu, Yashu Wang, Kuanquan Borra, Davide Masci, Alessandro Corsi, Cristiana De Vente, Coen Veta, Mitko Karim, Rashed Preetha, Chandrakanth Jayachandran Engelhardt, Sandy Qiao, Menyun Wang, Yuanyuan Tao, Qian Nuñez-Garcia, Marta Camara, Oscar Savioli, Nicolo Lamata, Pablo Zhao, Jichao Auckland Bioengineering Institute University of Auckland Auckland New Zealand State Key Lab of Virtual Reality Technology and Systems Beihang University Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China SenseTime Inc Shenzhen China Tencent Jarvis Laboratory Shenzhen China Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Shenzhen China School of Computer Science Wuhan University Wuhan China Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Epita Research and Development Laboratory Paris France Department of Computing Imperial College London London United Kingdom School of Naval Architecture Shanghai Jiao Tong University Ocean & Civil Engineering Shanghai China School of Data Science Fudan University Shanghai China Epione Research Group Universite Cote d'Azur Inria Sophia Antipolis France Harbin Institute of Technology School of Computer Science and Technology Harbin China Department of Electric University of Bologna Electronic and Information Engineering Cesena Italy Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands School of Biomedical Engineering and Imaging Sciences Kings College London London United Kingdom Faculty of Electrical Engineering and Information Technology University of Magdeburg Magdeburg Germany Heidelberg University Hospital Germany Biomedical Engineering Center Fudan University Shanghai China Department of Radiology Leiden University Medical Center Leiden Netherlands Department of Information and Communication Technologies Universitat Pompeu Fabra Barcelona Spain D

Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI)widely used for visualizing diseased cardiacstructures, is a crucial first step for clinical diagnosis and treatment. However, direct segmentation ofLGE-MRIsischallenging due to *** most clinical studies have relied on manual and labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning *** address this, we organized the "2018 Left Atrium Segmentation Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset, and associated labels of the left atriumsegmented by three medical experts,ultimately attractingthe participation of27 international teams. In this paper, extensive analysis of the submitted algorithms using technical and biological metrics was performed by undergoing subgroup analysis and conducting hyper-parameter analysis,offeringan overall picture ofthe major design choices of convolutional neural networks(CNNs) and practical considerations for achieving state-of-the-art left atrium segmentation. Results show the top methodachieved a dice score of 93.2%and a mean surface to a surface distance of 0.7mm, significantly outperforming prior state-of-the-art. Particularly, our analysisdemonstrated that double, sequentially usedCNNs, in which a first CNN is used for automatic region-of-interestlocalization and a subsequent CNN is used for refinedregional segmentation, achieved far superiorresults than traditional methods and pipelinescontaining *** findings from this study can potentially be extended to other imaging datasets and modalities, having an impact on the wider medical imaging *** large-scale benchmarking study makes a significant step towards much-improved segmentation methods for cardiac LGE-MRIs, and will serveas animportant benchmark for evaluating and comparing the future works in the field. Copyright © 2020, Th

关键词： Magnetic resonance imaging

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BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2023年第6期20卷 824-835页

作者： Linus Manubens-Gil Zhi Zhou Hanbo Chen Arvind Ramanathan Xiaoxiao Liu Yufeng Liu Alessandro Bria Todd Gillette Zongcai Ruan Jian Yang Miroslav Radojević Ting Zhao Li Cheng Lei Qu Siqi Liu Kristofer E Bouchard Lin Gu Weidong Cai Shuiwang Ji Badrinath Roysam Ching-Wei Wang Hongchuan Yu Amos Sironi Daniel Maxim Iascone Jie Zhou Erhan Bas Eduardo Conde-Sousa Paulo Aguiar Xiang Li Yujie Li Sumit Nanda Yuan Wang Leila Muresan Pascal Fua Bing Ye Hai-Yan He Jochen F Staiger Manuel Peter Daniel N Cox Michel Simonneau Marcel Oberlaender Gregory Jefferis Kei Ito Paloma Gonzalez-Bellido Jinhyun Kim Edwin Rubel Hollis T Cline Hongkui Zeng Aljoscha Nern Ann-Shyn Chiang Jianhua Yao Jane Roskams Rick Livesey Janine Stevens Tianming Liu Chinh Dang Yike Guo Ning Zhong Georgia Tourassi Sean Hill Michael Hawrylycz Christof Koch Erik Meijering Giorgio A Ascoli Hanchuan Peng Institute for Brain and Intelligence Southeast University Nanjing China. Microsoft Corporation Redmond WA USA. Tencent AI Lab Bellevue WA USA. Computing Environment and Life Sciences Directorate Argonne National Laboratory Lemont IL USA. Kaya Medical Seattle WA USA. University of Cassino and Southern Lazio Cassino Italy. Center for Neural Informatics Structures and Plasticity Krasnow Institute for Advanced Study George Mason University Fairfax VA USA. Faculty of Information Technology Beijing University of Technology Beijing China. Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing China. Nuctech Netherlands Rotterdam the Netherlands. Janelia Research Campus Howard Hughes Medical Institute Ashburn VA USA. Department of Electrical and Computer Engineering University of Alberta Edmonton Alberta Canada. Ministry of Education Key Laboratory of Intelligent Computation and Signal Processing Anhui University Hefei China. Paige AI New York NY USA. Scientific Data Division and Biological Systems and Engineering Division Lawrence Berkeley National Lab Berkeley CA USA. Helen Wills Neuroscience Institute and Redwood Center for Theoretical Neuroscience UC Berkeley Berkeley CA USA. RIKEN AIP Tokyo Japan. Research Center for Advanced Science and Technology (RCAST) The University of Tokyo Tokyo Japan. School of Computer Science University of Sydney Sydney New South Wales Australia. Texas A&M University College Station TX USA. Cullen College of Engineering University of Houston Houston TX USA. Graduate Institute of Biomedical Engineering National Taiwan University of Science and Technology Taipei Taiwan. National Centre for Computer Animation Bournemouth University Poole UK. PROPHESEE Paris France. Department of Neuroscience Columbia University New York NY USA. Mortimer B. Zuckerman Mind Brain Behavior Institute Columbia University New York NY USA. Department of Computer Science Northern Illinois Universit

BigNeuron is an open community bench-testing platform with the goal of setting open standards for accurate and fast automatic neuron tracing. We gathered a diverse set of image volumes across several species that is representative of the data obtained in many neuroscience laboratories interested in neuron tracing. Here, we report generated gold standard manual annotations for a subset of the available imaging datasets and quantified tracing quality for 35 automatic tracing algorithms. The goal of generating such a hand-curated diverse dataset is to advance the development of tracing algorithms and enable generalizable benchmarking. Together with image quality features, we pooled the data in an interactive web application that enables users and developers to perform principal component analysis, t-distributed stochastic neighbor embedding, correlation and clustering, visualization of imaging and tracing data, and benchmarking of automatic tracing algorithms in user-defined data subsets. The image quality metrics explain most of the variance in the data, followed by neuromorphological features related to neuron size. We observed that diverse algorithms can provide complementary information to obtain accurate results and developed a method to iteratively combine methods and generate consensus reconstructions. The consensus trees obtained provide estimates of the neuron structure ground truth that typically outperform single algorithms in noisy datasets. However, specific algorithms may outperform the consensus tree strategy in specific imaging conditions. Finally, to aid users in predicting the most accurate automatic tracing results without manual annotations for comparison, we used support vector machine regression to predict reconstruction quality given an image volume and a set of automatic tracings.

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Methods of obtaining modifying supplement based on calcium hydrosilicates for lime dry mixes

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IOP Conference Series: Materials Science and Engineering 2018年第1期451卷

作者： I S Pyshkina K V Zhegera G V Martyashin Department of Information-Computing Systems Penza State University of Architecture and Construction 28 Titov Str. Penza 440028 Russia Department of Quality Control and Construction Technology Penza State University of Architecture and Construction 28 Titov Str. Penza 440028 Russia Department of Computer Science Penza State University 40 Krasnaya Penza 440026 Russia

The article considers the possibility of using a supplement, based on calcium hydrosilicates (CHS), synthesized in the presence of diatomite, as a modifying additive for finishing lime dry mixes. The results of the physicochemical analysis of the supplement are given. The impact of the method of obtaining CHS supplements on the compressive strength of lime samples was researched. The X-ray phase analysis of supplements, based on calcium hydrosilicates, is presented. The technological and operational characteristics of lime finishing mixes with the use of calcium hydrosilicates, synthesized in the presence of diatomite in the their recipe, are given.

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Publisher Correction: The Scales Project, a cross-national dataset on the interpretation of thermal perception scales

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Scientific data 2020年第1期7卷 11页

作者： Marcel Schweiker Amar Abdul-Zahra Maíra André Farah Al-Atrash Hanan Al-Khatri Rea Risky Alprianti Hayder Alsaad Rucha Amin Eleni Ampatzi Alpha Yacob Arsano Montazami Azadeh Elie Azar Bannazadeh Bahareh Amina Batagarawa Susanne Becker Carolina Buonocore Bin Cao Joon-Ho Choi Chungyoon Chun Hein Daanen Siti Aisyah Damiati Lyrian Daniel Renata De Vecchi Shivraj Dhaka Samuel Domínguez-Amarillo Edyta Dudkiewicz Lakshmi Prabha Edappilly Jesica Fernández-Agüera Mireille Folkerts Arjan Frijns Gabriel Gaona Vishal Garg Stephanie Gauthier Shahla Ghaffari Jabbari Djamila Harimi Runa T Hellwig Gesche M Huebner Quan Jin Mina Jowkar Renate Kania Jungsoo Kim Nelson King Boris Kingma M Donny Koerniawan Jakub Kolarik Shailendra Kumar Alison Kwok Roberto Lamberts Marta Laska M C Jeffrey Lee Yoonhee Lee Vanessa Lindermayr Mohammadbagher Mahaki Udochukwu Marcel-Okafor Laura Marín-Restrepo Anna Marquardsen Francesco Martellotta Jyotirmay Mathur Gráinne McGill Isabel Mino-Rodriguez Di Mou Bassam Moujalled Mia Nakajima Edward Ng Marcellinus Okafor Mark Olweny Wanlu Ouyang Ana Ligia Papst de Abreu Alexis Pérez-Fargallo Indrika Rajapaksha Greici Ramos Saif Rashid Christoph F Reinhart Ma Isabel Rivera Mazyar Salmanzadeh Karin Schakib-Ekbatan Stefano Schiavon Salman Shooshtarian Masanori Shukuya Veronica Soebarto Mohammad Tahsildoost Federico Tartarini Despoina Teli Priyam Tewari Samar Thapa Maureen Trebilcock Jörg Trojan Ruqayyatu B Tukur Conrad Voelker Yeung Yam Liu Yang Gabriela Zapata-Lancaster Yongchao Zhai Yingxin Zhu Zahra Sadat Zomorodian Building Science Group Karlsruhe Institute of Technology Englerstr. 7 76131 Karlsruhe Germany. marcel.schweiker@kit.edu. WIN-Kolleg Thermal comfort and pain Heidelberg Academy of Sciences and Humanities Karlstr. 4 69117 Heidelberg Germany. marcel.schweiker@kit.edu. Mechanical Engineering Department University of Technology Iraq Al sinaea 10066 Baghdad Iraq. CTC/Department of Civil Engineering Federal University of Santa Catarina Rua João Pio Duarte Silva s/n. Córrego Grande 88.040-901 Florianópolis SC Brazil. School of Architecture and Built Environment German Jordanian University Darat Othman Bdeir 11180 Amman Jordan. Civil and Architectural Engineering Department Sultan Qaboos University Al-Khod P.O. Box: 33 PC: 123 Muscat Oman. Laboratory of Building Technology Department of Architecture Institut Teknologi Bandung Ganesha 10 40132 Bandung Indonesia. Department of Building Physics Bauhaus-University Weimar Coudraystr. 11A 99423 Weimar Germany. Energy & Climate Change Group Faculty of Engineering & Physical Sciences University of Southampton University Road SO17 1BJ Southampton United Kingdom. Welsh School of Architecture Cardiff University King Edward VII Avenue CF10 3NB Cardiff United Kingdom. Department of Architecture Massachusetts Institute of Technology Massachusetts Ave 02139 Cambridge United States of America. Faculty of Engineering Environment and Computing BNE Research Centre Coventry University Gulson road CV1 2JH Coventry United Kingdom. Industrial and Systems Engineering Khalifa University Masdar Campus Building 1B 54224 Abu Dhabi United Arab Emirates. Architecture University of Tehran Kish International Campus Niyayesh St. Mirmohanna Blvd 79416-55665 Kish Iran. Department of Architecture Ahmadu Bello University Zaria Nigeria Samaru Zaria Nigeria. WIN-Kolleg Thermal comfort and pain Heidelberg Academy of Sciences and Humanities Karlstr. 4 69117 Heidelberg Germany. Department of Cognitive and Clinical Neurosci

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

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Author Correction: BigNeuron: a resource to benchmark and predict performance of algorithms for automated tracing of neurons in light microscopy datasets

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Nature methods 2024年第10期21卷 1959页

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On energy efficiency of liquid cooled HPC datacenters

On energy efficiency of liquid cooled HPC datacenters

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Intersociety Conference on Thermal and Thermomechanical Phenomena in Electronic systems (ITHERM)

作者： Michael K. Patterson Shankar Krishnan John M. Walters Technical Computing Group Systems Architecture and Path finding Intel Corporation Dupont Washington USA India Institute of Technology Mumbai India Walters Engineering LLC Hillsboro Oregon USA

HPC Data Center's performance and growth are now being limited by both cost and power. A cost-efficient data center and an energy-efficient data center are all too often mutually exclusive, but they do not have to be. Liquid cooling is one area that, when done right, can improve both costs and energy efficiency. The design for liquid cooling systems generally begins with the ASHRAE liquid cooling datacenter classes. These provide guidance to both datacenter facility cooling system designers and electronic equipment manufacturers by providing a common baseline and understanding of the interface conditions between the cooling and the IT equipment. Further, the liquid cooling classes also suggest possible cooling equipment for a given datacenter class. Due to the aforementioned cooling equipment prescription, perception exists that moving from W1/W2 class environments to W3 or W4 classes represent increased energy efficiency during IT equipment operation. In this paper we show this not to be the case universally and explore a more detailed, technical approach to optimizing both cost and energy efficiency. The range of parameters includes geographical and climate conditions, state of the existing data center cooling infrastructure (greenfield, retrofit, cluster change-out, expansion), and IT level liquid cooling architecture. Through this analysis we show that for energy efficient operation of the IT equipment there exists an optimum liquid operating temperature that can also provide the lowest TCO. This temperature can drive the right capital investment as well as reduce facility operational expense and IT operational expense. We also explore the impact on reliability, the controls architecture, use of efficiency metrics, cluster compute performance, and opportunities for energy re-use.

关键词： Liquid cooling Energy efficiency Poles and towers Temperature distribution Guidelines Meteorology

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Influence of Synthesis Mode of Supplement Based on Calcium Hydrosylicates on the Structure and Properties of Lime Compositions

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IOP Conference Series: Materials Science and Engineering 2017年第1期262卷

作者： V I Loganina I S Pyshkina Sub-Department of Quality Management and Construction Technology Penza State University of Architecture and Construction 28 Herman Titov St. Penza 440028 Russia Sub-Department of Information-Computing Systems Penza State University of Architecture and Construction 28 Herman Titov St. Penza 440028 Russia

It was proposed to use synthesized calcium hydrosilicates in finishing lime dry mixes as a modifying supplement. The effect of substances containing amorphous silica which are used for synthesis on the activity of the modifying supplement was established. The effect of the synthesis mode of supplement on the structure formation of lime compositions was illustrated. It was found that the injection of supplements of hydrosilicates accelerates the increase of mechanical strength. The efficiency of the modifying supplements of amorphous silica, such as diatomite, in the synthesis was shown.

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Changing life expectancy in European countries 1990–2021: a subanalysis of causes and risk factors from the Global Burden of Disease Study 2021

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The Lancet Public Health 2025年第3期10卷 e172-e188页

作者： Steel, Nicholas Bauer-Staeb, Clarissa Maria Mercedes Ford, John A. Abbafati, Cristiana Abdalla, Mohammed Altigani Abdelkader, Atef Abdi, Parsa Zuñiga, Roberto Ariel Abeldaño Abiodun, Olugbenga Olusola Abolhassani, Hassan Abu-Gharbieh, Eman Abukhadijah, Hana J. Abu-Zaid, Ahmed Addo, Isaac Yeboah Addolorato, Giovanni Adekanmbi, Victor Adetunji, Juliana Bunmi Adeyeoluwa, Temitayo Esther Agardh, Emilie E. Agyemang-Duah, Williams Ahmad, Danish Ahmed, Ayman Ahmed, Syed Anees Akinosoglou, Karolina Akkaif, Mohammed Ahmed Al Awaidy, Salah Al Hasan, Syed Mahfuz Al Zaabi, Omar Ali Mohammed Aldridge, Robert W. Algammal, Abdelazeem M. Al-Gheethi, Adel Ali Saeed Ali, Abid Ali, Mohammed Usman Ali, Syed Shujait Ali, Waad Alicandro, Gianfranco Alif, Sheikh Mohammad Al-Jumaily, Adel Allebeck, Peter Alrawashdeh, Ahmad Al-Rifai, Rami H. Alsabri, Mohammed A. Alshahrani, Najim Z. Aluh, Deborah Oyine Al-Wardat, Mohammad Al-Zyoud, Walid A. Amiri, Sohrab Anderlini, Deanna Andrei, Catalina Liliana Anil, Abhishek Anvari, Saeid Anyasodor, Anayochukwu Edward Appiah, Seth Christopher Yaw Aquilano, Michele Arabloo, Jalal Arafat, Mosab Areda, Demelash Aremu, Abdulfatai Armani, Keivan Armocida, Benedetta Ärnlöv, Johan Asaduzzaman, Muhammad Astell-Burt, Thomas Aujayeb, Avinash Ausloos, Marcel Azadnajafabad, Sina Aziz, Shahkaar Azzam, Ahmed Y. Babu, Giridhara Rathnaiah Badache, Andreea Corina Badiye, Ashish D. Bahramian, Saeed Baig, Atif Amin Baker, Jennifer L. Bansal, Hansi Bärnighausen, Till Winfried Barone, Mark Thomaz Ugliara Barrow, Amadou Barteit, Sandra Bashir, Shahid Bashiru, Hameed Akande Basso, João Diogo Bastan, Mohammad-Mahdi Basu, Sanjay Batra, Kavita Bauckneht, Matteo Baune, Bernhard T. Beghi, Massimiliano Beiranvand, Maryam Béjot, Yannick Bell, Michelle L. Bello, Olorunjuwon Omolaja Belo, Luis Beloukas, Apostolos Beneke, Alice A. Bettencourt, Paulo J.G. Bhagavathula, Akshaya Srikanth Bhala, Neeraj Bhaskar, Sonu Bisulli, Francesca Bjørge, Tone Bodunrin, Aadam Olalekan Carvajal, Alejandro Botero Bouaoud, Souad Brayne, Carol Brenner, H Department of Primary Care and Public Health University of East Anglia Norwich United Kingdom Office for Health Improvement and Disparities Department of Health and Social Care London United Kingdom Department of Legal and Economic Studies La Sapienza University Rome Italy Rome Italy Hull York Medical School University of Hull Hull United Kingdom Department of Mathematics and Sciences Ajman University Ajman United Arab Emirates Department of Medicine Memorial University St. John's NL Canada Postgraduate Department University of Sierra Sur Miahuatlan de Porfirio Diaz Mexico Yhteiskuntadatatieteen keskus (Centre for Social Data Science) Helsinki Finland Department of Internal Medicine Federal Medical Centre Abuja Nigeria Research Center for Immunodeficiencies Tehran University of Medical Sciences Tehran Iran Department of Medical Biochemistry and Biophysics Karolinska Institutet (Karolinska Institute) Stockholm Sweden Department of Clinical Sciences University of Sharjah Sharjah United Arab Emirates Department of Biopharmaceutics and Clinical Pharmacy University of Jordan Amman Jordan Medical Research Center Hamad Medical Corporation Doha Qatar Department of Biochemistry and Molecular Medicine Alfaisal University Riyadh Saudi Arabia College of Graduate Health Sciences University of Tennessee Memphis TN United States School of Medicine Sydney NSW Australia Centre for Social Research in Health University of New South Wales Sydney NSW Australia Internal Medicine and Alcohol Related Disease Unit Fondazione Policlinico Universitario A. Gemelli IRCCS Rome Italy Department of Medical and Surgical Sciences Università Cattolica di Roma (Catholic University of Rome) Rome Italy Department of Obstetrics and Gynecology University of Texas Medical Branch Galveston TX United States Department of Biochemistry Osun State University Osogbo Nigeria Department of Pharmacology and Therapeutics University of Medical Sciences Ondo Ondo Nigeria Department of Veteri

Background: Decades of steady improvements in life expectancy in Europe slowed down from around 2011, well before the COVID-19 pandemic, for reasons which remain disputed. We aimed to assess how changes in risk factors and cause-specific death rates in different European countries related to changes in life expectancy in those countries before and during the COVID-19 pandemic. Methods: We used data and methods from the Global Burden of Diseases, Injuries, and Risk Factors Study 2021 to compare changes in life expectancy at birth, causes of death, and population exposure to risk factors in 16 European Economic Area countries (Austria, Belgium, Denmark, Finland, France, Germany, Greece, Iceland, Ireland, Italy, Luxembourg, the Netherlands, Norway, Portugal, Spain, and Sweden) and the four UK nations (England, Northern Ireland, Scotland, and Wales) for three time periods: 1990–2011, 2011–19, and 2019–21. Changes in life expectancy and causes of death were estimated with an established life expectancy cause-specific decomposition method, and compared with summary exposure values of risk factors for the major causes of death influencing life expectancy. Findings: All countries showed mean annual improvements in life expectancy in both 1990–2011 (overall mean 0·23 years [95% uncertainty interval [UI] 0·23 to 0·24]) and 2011–19 (overall mean 0·15 years [0·13 to 0·16]). The rate of improvement was lower in 2011–19 than in 1990–2011 in all countries except for Norway, where the mean annual increase in life expectancy rose from 0·21 years (95% UI 0·20 to 0·22) in 1990–2011 to 0·23 years (0·21 to 0·26) in 2011–19 (difference of 0·03 years). In other countries, the difference in mean annual improvement between these periods ranged from –0·01 years in Iceland (0·19 years [95% UI 0·16 to 0·21] vs 0·18 years [0·09 to 0·26]), to –0·18 years in England (0·25 years [0·24 to 0·25] vs 0·07 years [0·06 to 0·08]). In 2019–21, there was an overall decrease in mean annual life expectancy a

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Behavioral modeling of Wireless Sensor nodes using meta-data

Behavioral modeling of Wireless Sensor nodes using meta-data

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Intelligent Sensors, Sensor Networks and Information Processing Conference (ISSNIP)

作者： Hashan M Wanniarachchi Kasun S Perera M.D.J.S Goonatillake University of Colombo School of Computing Sri Lanka Database Technology Group Institute of Systems Architecture Dresden TU Germany

Many Wireless Sensor Network systems are deployed under extreme conditions. Thus, it's important to maximize their lifespan by optimal use of network's resources. Battery lifespan of a node is a crucial resource that needs to be used carefully. Energy aware routing & scheduled sensing have been introduced for careful use of battery. It is necessary to optimize battery usage by predicting it's future behavior. This will lead the users to take early decisions, thus minimizing network downtime. Hence, we explore the possibility of using meta-data on each node, to represent and predict node behavior using machine learning models. In this research, we use node voltage level as an indicator of the energy used, as voltage is proportional to available energy. Node energy consumption is modeled and predicted by ARIMA models using these voltage readings. We also classify nodes as high, medium & low use, with respect to its current and future usage, thus allowing user to take early decisions maximizing network throughput (lifetime). After evaluating predicted node behavior against a created base set of behavioral classifications, we achieved a 80% accuracy rate. Using different and increasing window sizes, we evaluated the validity of our model. In these experiments, our prediction method produced highly accurate results for all considered prediction windows. By being able to predict a node's energy consumption behavior at a higher accuracy rate, WSN users can make optimization decisions beforehand to increase the network lifetime.

关键词： Wireless sensor networks Predictive models Time series analysis Data models Energy consumption Accuracy Voltage measurement

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A New Algorithm for Non-rigid Point Matching Using Geodesic Graph Model

A New Algorithm for Non-rigid Point Matching Using Geodesic ...

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IEEE International Conference on Mechatronics and Automation

作者： Deheng Qian Tianshi Chen Hong Qiao State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of Sciences Beijing 100190 China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing 100190 China

ISBN: (纸本)9781479970995

Point matching is a problem of finding the optimum matching between two sets of key points which are extracted from the surfaces of objects. A popular approach represents the features of a set of points with a graph model. Traditionally, the measurement applied in the graph model is the Euclidian distance, which is not suitable for objects with non-rigid deformations. In this paper, we propose a novel graph model called the geodesic graph model (GGM) which uses a geodesic-like distance as its measurement. GGM can better tackle non-rigid deformations because the geodesic-like distance is a kind of invariant structural feature during non-rigid deformations. The building process of the GGM is justified under the assumption that all the key points are spanning on a manifold. To further handle the deviations of key point locations, we come up with a feature weighting process to increase our algorithm's robustness. We conduct several experiments on different kinds of deformations over several widely used datasets. Experimental results demonstrate the effectiveness of our algorithm.

关键词： Point matching non-rigid deformation manifold geodesic distance

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