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Lower limb joint angular phase plane coordination analysis on standard maximum vertical jump

Gait & Posture 2021年 90卷 205-206页

作者： Rodrigues, C. Correia, M. Abrantes, J. Rodrigues, B. Nadal, J. INESC TEC - Institute for Systems and Computer Engineering- Technology and Science C-BER - Centre for Biomedical Engineering Research Porto Portugal University of Porto Faculty of Engineering Porto Portugal Lusófona University CICANT Centre for Research in Applied Communication Culture and New Technologies- MovLab Laboratory of Technologies for Interactions and Interfaces Lisbon Portugal Federal University of Pernambuco Department of Electronic and Systems Recife Brazil Federal University of Rio de Janeiro Biomedical Engineering Program Rio de Janeiro Brazil

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Computational approaches to analyzing and generating comics 42

Computational approaches to analyzing and generating comics

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42nd Annual Meeting of the Cognitive Science Society: Developing a Mind: Learning in Humans, Animals, and Machines, CogSci 2020

作者： Upadhyayula, Aditya Martens, Chris Cardona-Rivera, Rogelio E. Hendrickson, Andrew T. Cohn, Neil Department of Psychological and Brain Sciences Johns Hopkins University United States Department of Computer Science North Carolina State University United States School of Computing The Entertainment Arts and Engineering Program University of Utah United States Department of Cognitive Science and Artificial Intelligence Tilburg University Netherlands Department of Cognition and Communication Tilburg University Netherlands

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CubeSat single-photon detector module for investigating in-orbit laser annealing to heal radiation damage

arXiv

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arXiv 2024年

作者： Sultana, Nigar Krynski, Joanna Lim, Jin Gyu Floyd, John Lembeck, Michael Makarov, Vadim Kwiat, Paul Jennewein, Thomas Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada University of Illinois Urbana-Champaign ChampaignIL United States Vigo Quantum Communication Center University of Vigo Vigo Spain Quantum Information Science Program Canadian Institute for Advanced Research TorontoONM5G 1Z8 Canada

Single-photon avalanche photodiodes (SPADs) based on silicon are widely considered for quantum satellite communications but suffer from an increasing dark count rate (DCR) due to displacement damage in their active areas induced by proton radiation. When the DCR of SPADs exceeds a certain threshold, they become unusable for quantum communication protocols. Previous laboratory experiments have demonstrated that laser annealing of SPADs’ active area with about 1 W optical power can significantly reduce radiation-induced DCR of synthetically irradiated SPADs. To assess the feasibility of in-orbit laser annealing on constantly irradiated SPADs in low-Earth orbit, we developed a module with a CubeSat form factor capable of both laser and thermal annealing of four silicon SPADs. Here we report the design and ground testing of this module, investigating laser annealing in a simulated space environment. Our results pave the way for an in-orbit trial that may prove this technology useful for future satellite missions with quantum receivers on board. Copyright © 2024, The Authors. All rights reserved.

关键词： Radiation damage

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Airplane type identification based on mask RCNN and drone images

arXiv

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arXiv 2021年

作者： Alshaibani, Waleed Tawfik Abdullah Helvaci, Mustafa Shayea, Ibraheem Saad, Sawsan A. Azizan, Azizul Yakub, Fitri Institute of Informatics Satellite Communication and Remote Sensing Program Istanbul Technical University ITU Ayazaga Campus Institute of Informatics Building Sariyer Istanbul34469 Turkey Istanbul34469 Turkey Faculty of Engineering and Built Environment Universiti Sains Islam Malaysia Negeri Sembilan Bandar BaruNilai71800 Malaysia Department of Computer Engineering College of Computer Science and Engineering University of Ha'il Saudi Arabia Advance Informatics Department Razak Faculty of Technology and Informatics Universiti Teknologi Malaysia Kuala Lumpur54100 Malaysia

For dealing with traffic bottlenecks at airports, aircraft object detection is insufficient. Every airport generally has a variety of planes with various physical and technological requirements as well as diverse service requirements. Detecting the presence of new planes will not address all traffic congestion issues. Identifying the type of airplane, on the other hand, will entirely fix the problem because it will offer important information about the plane's technical specifications (i.e., the time it needs to be served and its appropriate place in the airport). Several studies have provided various contributions to address airport traffic jams;however, their ultimate goal was to determine the existence of airplane objects. This paper provides a practical approach to identify the type of airplane in airports depending on the results provided by the airplane detection process using mask region convolution neural network. The key feature employed to identify the type of airplane is the surface area calculated based on the results of airplane detection. The surface area is used to assess the estimated cabin length which is considered as an additional key feature for identifying the airplane type. The length of any detected plane may be calculated by measuring the distance between the detected plane's two furthest points. The suggested approach's performance is assessed using average accuracies and a confusion matrix. The findings show that this method is dependable. This method will greatly aid in the management of airport traffic congestion. Copyright © 2021, The Authors. All rights reserved.

关键词： Aircraft detection

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Multi-class classification of COVID-19 and other infections using machine learning model with wavelet and laws features

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AIP Conference Proceedings 2024年第1期2919卷

作者： Shaik Mahaboob Basha Aloísio Vieira Lira Neto Mohamed Abd Elaziz Shaik Hashmitha Mohisin Victor Hugo C. e Albuquerque Department of Electronics& Communication Engg N.B.K.R. Institute of Science &Technology Vidyanagar India Graduation Program in Telecommunication Engineering Federal Institute of Ceará Fortaleza-CE Brazil Faculty of Computer Science and Engineering Galala University Suez 435611 Egypt Department of Avionics Indian Institute of Space Science and Technology Thiruvananthapuram India Department of Teleinformatics Engineering Federal University of Ceará Fortaleza-CE Brazil

Corona virus disease (COVID-19) was responsible for inevitable chaos in the human life due to its inherent nature of high contagious across the globe. This very reason also hindered the physicians and healthcare support systems to provide in-time medical aid to the suffering *** diagnosis of COVID-19 using clinical information is the prime source for detection and treatment. However, computerised tomography (CT) and Chest X-ray (CXR) images have found useful for screening purposes due to the successful usage of image based analysis by physicians which was aided by image processing based *** means of screening, diagnosis and prognostic studies using CT and CXR with the help of comprehensive image processing pipelines could invariably augment the efficiency in the respective tasks. The main objective of this study is to implement multi-class classification of COVID-19, viral-pneumonia (VP) and lung opacity (LO) using threshold-based segmented CXR images further transformed using wavelet transform (WT)and Laws based texture representation. Machine learning (ML) based implementation and comparison was carried using Random Forest (RF) and Logistic regression (LR) techniques with of the WT and Laws features individually as well as with combined feature vectors. The ML based implementation was aimed to classify the CXR image based on the individual or combined features into COVID-19, VP or LO pathology. The validation of the ML techniques was further carried using confusion matrix and other validation measures. The performance of the RF classifier was superior with WT features than Laws texture features on individual category of features than LR technique. Amarginal improvement was observed in the classifier performance when WT and Laws based features were combined.

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Global mapping of scientific information exchange using altmetric data

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Quality and Quantity 2019年第2期53卷 935-955页

作者： Park, Hyo Chan Youn, Jonghee M. Park, Han Woo Interdisciplinary Program of Digital Convergence Business Yeungnam University Gyeongsan South Korea Department of Computer Engineering Yeungnam University Gyeongsan South Korea Department of Media and Communication Interdisciplinary Program of Digital Convergence Business Cyber Emotions Research Institute Yeungnam University Gyeongsan South Korea

This study seeks to provide new insights into a hidden pattern in the cross-national scholarly network structure from an altmetric perspective. We investigate and visualize the dynamic and complex nature of altmetric-based referrals using a social network algorithm that measures societal use (via Twitter) and scientific impact (via Mendeley). We also use a variety of network indices and a QAP (quadratic assignment procedure) to validate the structural relationships between inter-country sharing networks. Finally, we identify several groups including Asia, Europe, and North America through a transcontinental sharing analysis. The findings suggest that policymakers need to track the international routes of word-of-mouth, which constitute socially mediated data-sharing, regarding scientific publications. © 2018, Springer Nature B.V.

关键词： Altmetrics Big data Global mapping Network analysis Social media

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A Vehicle Forensics framework with Post-Quantum Cryptography and Blockchain

A Vehicle Forensics framework with Post-Quantum Cryptography...

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IEEE International Symposium on Intelligent Signal Processing and communication Systems (ISPACS)

作者： Chien-Lung Hsu Yi-Cheih Hsu Yu-Jen Shih Cheng-Wei Wu Fu-Hau Hsu Graduate Institute of Management Chang Gung University Taiwan Taoyuan R.O.C Department of Information Management Chang Gung University Taiwan Taoyuan R.O.C Healthy Aging Research Center Chang Gung University Taiwan Taoyuan R.O.C Department of Visual Communication Design Ming-Chi University of Technology Taiwan Taoyuan R.O.C Chang-Gung Memorial Hospital Taiwan Taoyuan R.O.C Department of Master of Science Degree Program in Innovation for Smart Medicine Chang Gung University Taiwan Taoyuan R.O.C Computer Science& Information Engineering National Central University Taiwan Taoyuan R.O.C Information Management Chang Gung University Taiwan Taoyuan R.O.C

ISBN: (数字)9798350389210

ISBN: (纸本)9798350389227

With the recent rapid development of the Internet of Vehicles and autonomous driving technology, the demand for digital vehicle identification has also increased. Coupled with the gradual maturity of quantum technology, the Internet of Vehicles, which mainly uses wireless communication technology for data transmission, will face the threat of quantum computing and privacy issues. This research proposes the Vehicle Forensics Cloud System (VFCS), a framework combining post-quantum cryptography and blockchain technology to address these issues and provide a comprehensive solution for vehicle digital forensics.

关键词： Wireless communication Quantum computing Digital forensics Insurance Signal processing Blockchains Safety Autonomous vehicles Interoperability Internet of Vehicles

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Analysis and Comparative Between Profile Matching and SAW Method in Decision Support

Analysis and Comparative Between Profile Matching and SAW Me...

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International Conference on Cyber and IT Service Management (CITSM)

作者： Helmi Kurniawan Ashari P Swondo Eka Purnama Sari Khairul Ummi Muhammad Rusdi Tanjung Yusfrizal Faculty of Engineering and Computer Science Universitas Potensi Utama Medan Indonesia English Education Study Program Universitas Potensi Utama Medan Indonesia Faculty of Economy and Business Universitas Potensi Utama Medan Indonesia Visual Communication Design Study Program Universitas Potensi Utama Medan Indonesia

ISBN: (数字)9781728177014

ISBN: (纸本)9781728177021

A decision support system is a solution to help provide decision recommendations in determining which options to choose as a solution, in this decision support system research, namely analyzing and comparing the Matching Profile method and the SAW (Simple Additive Weighting) method in providing the best choice solution in the case of determining teaching staff acceptance. Determination of Admission, The selected teaching staff, are teaching staff who are in accordance with the criteria and quality. In an educational institution, namely TPQ Anbata, it is a very important issue to get elected teaching staff. One way to get teaching staff who is in accordance with the cafeteria is determined by selecting or determining prospective teaching staff by paying attention to various assessment criteria so that the selected teaching staff is feasible. From the tests that have been carried out, the results obtained include all test cases, and the testing is appropriate. The results of the system calculation accuracy test show that the calculation of Matching Profile and SAW (Simple Additive Weighting Method carried out by the system with manual calculations is correct and the feasibility test. Appropriate to use in making decisions determining the recruitment of teachers than the profile matching method in Anbata TPQ. The difference is in the highest value in each method. In the profile matching method, the highest value is Salman Al Fariz with a value of 4.70 while in the SAW method the highest value is Ali Gufron with a value of 8,778., it can be concluded that the most appropriate method in determining the recruitment of teaching staff at TPQ Anbata is the Profile Matching Method.

关键词： Education Surface acoustic waves Decision support systems Additives Recruitment Decision making Writing

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A novel evolution-based recommendation system 12

A novel evolution-based recommendation system

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12th International Conference on Ubi-Media Computing, Ubi-Media 2019

作者： Chen, Yi-Cheng Chu, Yen-Lung Hui, Lin Chen, Sheng-Chih Thaipisutikul, Tipajin Weng, Kai-Ze Department of Information Management National Central University Taiwan Department of Computer Science and Information Engineering Tamkang University Taiwan Department of Innovative Information and Technology Tamkang University Taiwan Master's Program of Digital Content and Technologies College of Communication National ChengChi University Taiwan Department of Computer Science and Information Engineering National Central University Taiwan

ISBN: (纸本)9781728128207

Matrix factorization (MF) technique has been widely utilized in recommendation systems due to the precise prediction of users' interests. Prior MF-based methods adapt the overall rating to make the recommendation by extracting latent factors from users and items. However, in real applications, people's preferences usually vary with time;the traditional MF-based methods could not properly capture the change of users' interests. In this paper, by incorporating the recurrent neural network (RNN) into MF, we develop a novel recommendation system, M-RNN-F, to effectively describe the preference evolution of users over time. A learning model is proposed to capture the evolution pattern and predict the user preference in the future. The experimental results show that M-RNN-F performs better than other state-of-the-art recommendation algorithms. In addition, we conduct the experiments on real world dataset to demonstrate the practicability. © 2019 IEEE.

关键词： Recommender systems

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The matter of deep reinforcement learning towards practical ai applications 12

The matter of deep reinforcement learning towards practical ...

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12th International Conference on Ubi-Media Computing, Ubi-Media 2019

作者： Thaipisutikul, Tipajin Chen, Yi-Cheng Hui, Lin Chen, Sheng-Chih Mongkolwat, Pattanasak Shih, Timothy K. Department of Computer Science and Information Engineering National Central University Taiwan Department of Information Management National Central University Taiwan Department of Innovative Information and Technology Tamkang University Taiwan Master's Program of Digital Content and Technologies College of Communication National ChengChi University Taiwan Faculty of Information and Communication Technology Mahidol University Thailand

ISBN: (纸本)9781728128207

Reinforcement Learning (RL) is an extraordinarily paradigm that aims to solve a complex problem. This technique leverages the traditional feedforward networks with temporal-difference learning to overcome supervised and unsupervised real-world problems. However, RL is one of state-of-the-art topic due to the opaque aspects in design and implementation. Also, in which situation we will get performance gain from RL is still unclear. Therefore, This study firstly examines the impact of Experience Replay in Deep Q-Learning agent with Self-Driving Car application. Secondly, The impact of Eligibility Trace in RNN A3C agents with Breakout AI game application is studied. Our results indicated that these two techniques enhance RL performance by more than 20 percent as compared with traditional RL methods. © 2019 IEEE.

关键词： Reinforcement learning

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