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Introduction of Fog Computations to Measuring System Collection and Accounting of Data of Distributed Internet of Things

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Journal of Physics: Conference Series 2021年第1期2096卷

作者： V D Munister A L Zolkin T G Aygumov V E Ozhiganov Mariupol State University Department of Enterprise Economics State educational institution of higher professional education "Donetsk National Technical University" Ph.D. in Engineering Science Senior Lecturer of Computer and Information Sciences Department of the Povolzhskiy State University of Telecommunications and Informatics Department of computer engineering software and automated systems Dagestan State Technical University Federal State Unitary Enterprise RUSSIAN FEDERAL NUCLEAR CENTER All-Russian Research Institute of Experimental Physics FSUE RFNC – VNIIEF

The article deals with the problems of speed and timeliness of the development of control actions from digital management systems. Generalization of researches is given. The role of mathematical applications of game theory for the role in the implementation of fog and boundary computations in the paradigm of the development of digital signal processing systems as well as their optimization value are substantiated.A particular generalization is developed and is given from the processing of an encoded signal sequence that demonstrates the operation of a humidity sensor from the viewpoint of bringing it to the basis of an antagonistic game in order to determine the optimal strategy of the device's behaviour at a given time *** practical significance of the study is confirmed by a new approach to the implementation of economic methods for analysis of probabilistic outcomes within the framework of game theory in an algorithm for analysis of a digital signal for a linear comparator of recorded physical quantities in the form of digital levels. The value of such analysis metrics in network-centric distributed systems of the Internet of things in multiplexing environments is given.

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Preface

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CEUR Workshop Proceedings 2022年 3091卷

作者： Kovalev, Igor Semenkin, Eugene Schwenker, Friedhelm Shaidurov, Vladimir Ganchev, Todor Škraba, Andrej Stanimirović, Predrag Kolehmainen, Mikko Kronberger, Gabriel Affenzeller, Michael Brester, Christina Ryzhikov, Ivan Kazakovtsev, Lev Sopov, Evgenii Semenkina, Maria Akhmedova, Shakhnaz Stanovov, Vladimir Krasnoyarsk Regional Science and Technology City Hall Department of Computer Science of Siberian Federal University Krasnoyarsk Russia Siberian Institute of Applied System Analysis named after A.N. Antamoshkin The Department ofsystem analysis and operations research of Reshetnev University Krasnoyarsk Russia Institute of Neural Information Processing Ulm University Ulm Germany The Institute of Computational Modeling SB RAS Krasnoyarsk Russia Technical University of Varna Varna Bulgaria University of Maribor UM Faculty of Organizational Sciences Cybernetics and Decision Support Systems Laboratory Slovenia Department of Computer Science Niš Serbia Department of Environmental and Biological Sciences Environmental Informatics University of Eastern Finland Kuopio Finland University of Applied Sciences Upper Austria Hagenberg Austria Department of Software Engineering School of Informatics Communications and Media University of Applied Sciences Upper Austria Hagenberg Austria University of Eastern Finland UEF · Department of Environmental Science Kuopio Finland The Department of Systems Analysis Reshetnev University of Science and Technology Krasnoyarsk Russia Siberian Institute of Applied System Analysis Krasnoyarsk Russia University of Applied Sciences Upper Austria and Softwarepark Hagenberg Austria Reshetnev Siberian States University of Science and Technology Siberian Institute of Applied System Analysis Krasnoyarsk Russia SibSAU · Institute of computer science and telecommunications Krasnoyarsk Russia

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Machine learning methods in electrocardiography classification 8

Machine learning methods in electrocardiography classificati...

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8th International Conference Advanced computer Information Technologies, ACIT 2018

作者： Mykoliuk, Iryna Jancarczyk, Daniel Karpinski, Mikolaj Kifer, Viktor Department of Computer Science and Automatics University of Bielsko-Biala 2 Willowa St Bielsko-Biala Poland Faculty of Computer Information Systems and Software Engineering Ternopil Ivan Puluj National Technical University 56 Ruska St Ternopil Ukraine

This document reports results that tend to confirm the applicability of the machine learning combined with signal processing for automatic atrial fibrillation detection from a short single lead electrocardiography rec... 详细信息

ISBN: (纸本)9789666544899

关键词： Electrocardiograms

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Where is your field going? A machine learning approach to study the relative motion of the domains of physics

arXiv

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arXiv 2019年

作者： Palmucci, Andrea Liao, Hao Napoletano, Andrea Zaccaria, Andrea Department of Physics "Sapienza" University of Rome Italy Guangdong Province Key Laboratory of Popular High Performance Computers College of Computer Science and Software Engineering Shenzhen University Shenzhen China Institute for Complex Systems UOS Sapienza Rome Italy

We propose an original approach to describe the scientific progress in a quantitative way. Using innovative Machine Learning techniques we create a vector representation for the PACS codes and we use them to represent the relative movements of the various domains of Physics in a multidimensional space. This methodology unveils about 25 years of scientific trends, enables us to predict innovative couplings of fields, and illustrates how Nobel Prize papers and APS milestones drive the future convergence of previously unrelated fields. Copyright © 2019, The Authors. All rights reserved.

关键词： Vector spaces

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Business Model Canvas Should Pay More Attention to the software Startup Team

Business Model Canvas Should Pay More Attention to the Softw...

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Euromicro Conference on software engineering and Advanced Applications (SEAA)

作者： Kai-Kristian Kemell Atte Elonen Mari Suoranta Anh Nguyen-Duc Juan Garbajosa Rafael Chanin Jorge Melegati Usman Rafiq Abdullah Aldaeej Nana Assyne Afonso Sales Sami Hyrynsalmi Juhani Risku Henry Edison Pekka Abrahamsson Faculty of Information Technology University of Jyvaskyla Jyvaskyla Finland School of Business and Economics University of Jyväskylä Jyvaskyla Finland Department of Business and IT University of Southeast Norway Bø Norway Universidad Politécnica de Madrid Madrid Spain School of Technology PUCRS Porto Alegre Brazil Faculty of Computer Science Free University of Bozen-Bolzano Bolzano Italy Department of Information Systems University of Maryland Baltimore Country Baltimore United States Department of Software Engineering LUT University Lahti Finland Lero NUI Galway Galway Ireland

ISBN: (数字)9781728195322

ISBN: (纸本)9781728195339

Business Model Canvas (BMC) is a tool widely used to describe startup business models. Despite the various business aspects described, BMC pays a little emphasis on team- related factors. The importance of team-related factors in software development has been acknowledged widely in literature. While not as extensively studied, the importance of teams in software startups is also known in both literature and among practitioners. In this paper, we propose potential changes to BMC to have the tool better reflect the importance of the team, especially in a software startup environment. Based on a literature review, we identify various components related to the team, which we then further support with empirical data. We do so by means of a qualitative case study of five startups.

关键词： Business software Interviews Tools Task analysis software engineering Ontologies

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Incident detection based on mobile crowd sensing for smart city

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Journal of computers (Taiwan) 2019年第1期30卷 96-104页

作者： Zhang, Peng Zhang, Zhenjiang Chao, Han-Chieh Department of Electronic and Information Engineering Key Laboratory of Communication and Information Systems Beijing Municipal Commission of Education Beijing Jiaotong University Beijing100044 China Department of Software Engineering Key Laboratory of Communication and Information Systems Beijing Municipal Commission of Education Beijing Jiaotong University Beijing100044 China Department of Electrical Engineering National Dong Hwa University Hualien97401 Taiwan School of Information Science and Engineering Fujian University of Technology Fuzhou350118 China School of Mathematics and Computer Science Wuhan Polytechnic University Wuhan430023 China Department of Computer Science and Information Engineering National Ilan University Yilan County260 Taiwan

In recent years, mobile devices have taken a significant role in improving the quality of people’s life. In order to enhance the usability of those devices, more and more sensors have been built in. Furthermore, the data storage and data processing capacity are becoming larger and larger. To capture more information about the city conditions and make full use of the computing resource, mobile crowd sensing has been proposed. Incident detection is an important component of the safety monitoring system, however, existing incident detection methods are mostly based on computer vision which is vulnerability to environmental impacts. This paper proposes an automatic incident detection model based on Mobile Crowd Sensing to deal with the problem above. The incident detection algorithm in the model is based on the human activity recognition and the crowd density. The simulation results show that this model is feasible and effective. 2019 © computer Society of the Republic of China. All Rights Reserved.

关键词： Environmental impact

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Energy-efficient hybrid precoding design for integrated multicast-unicast millimeter wave communications with SWIPT

arXiv

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arXiv 2020年

作者： Hao, Wanming Sun, Gangcan Zhou, Fuhui Mi, De Shi, Jia Xiao, Pei Leung, Victor C.M. School of Information Engineering Zhengzhou University Zhengzhou450001 China 5G Innovation Center Institute of Communication Systems University of Surrey GuildfordGU2 7XH United Kingdom 5G Innovation Center Institute of Communication Systems University of Surrey GuildfordGU2 7XH United Kingdom College of Electronic and Information Engineering Nanjing University of Aeronautics and Astronautics Nanjing210000 China State Key Laboratory of Integrated Services Networks Xi'dian University Xian710071 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T1Z4 Canada

In this paper, we investigate the energy-efficient hybrid precoding design for integrated multicast-unicast millimeter wave (mmWave) system, where the simultaneous wireless information and power transform is considered at receivers. We adopt two sparse radio frequency chain antenna structures at the base station (BS), i.e., fully-connected and subarray structures, and design the codebook-based analog precoding according to the different structures. Then, we formulate a joint digital multicast, unicast precoding and power splitting ratio optimization problem to maximize the energy efficiency of the system, while the maximum transmit power at the BS and minimum harvested energy at receivers are considered. Due to its difficulty to directly solve the formulated problem, we equivalently transform the fractional objective function into a subtractive form one and propose a two-loop iterative algorithm to solve it. For the outer loop, the classic Bi-section iterative algorithm is applied. For the inner loop, we transform the formulated problem into a convex one by successive convex approximation techniques and propose an iterative algorithm to solve it. Meanwhile, to reduce the complexity of the inner loop, we develop a zero forcing (ZF) technique-based low complexity iterative algorithm. Specifically, the ZF technique is applied to cancel the inter-unicast interference and the first order Taylor approximation is used for the convexification of the non-convex constraints in the original problem. Finally, simulation results are provided to compare the performance of the proposed algorithms under different schemes. Copyright © 2020, The Authors. All rights reserved.

关键词： Energy efficiency

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A cascade deep learning model for diagnosing pharyngeal acid reflux episodes using hypopharyngeal multichannel intraluminal Impedance-pH signals

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Intelligence-Based Medicine 2023年 8卷 100131-100131页

作者： Fu, Jachih Lee, Ping-Huan Wang, Chen-Chi Lin, Ying-Cheng Chuang, Chun-Yi Tsou, Yung-An Chen, Yen-Yang Yang, Sheng-Shun Lien, Han-Chung Computer Aided Measurement and Diagnostic Systems Laboratory Department of Industrial Engineering and Management National Yunlin University of Science and Technology Yunlin Taiwan Division of Gastroenterology Taichung Veterans General Hospital Taichung Taiwan Department of Otolaryngology Taichung Veterans General Hospital Taichung Taiwan School of Medicine National Yang Ming Chiao Tung University Taipei Taiwan School of Speech Language Pathology & Audiology Chung Shan Medical University Taichung Taiwan Department of Otolaryngology Chung Shan Medical University Hospital Taichung Taiwan School of Medicine Chung Shan Medical University Taichung Taiwan Department of Otolaryngology-Head and Neck Surgery China Medical University Hospital Taichung Taiwan Institute of Biomedical Sciences National Chung Hsing University Taichung Taiwan Department of Post-Baccalaureate Medicine College of Medicine National Chung Hsing University Taichung Taiwan

AbstractDetecting pharyngeal acid reflux (PAR) episodes from 24-h ambulatory hypopharyngeal multichannel intraluminal impedance-pH (HMII-pH) signals is crucial for diagnosing laryngopharyngeal reflux (LPR). Currently, a lack of effective software for PAR episode detection requires time-consuming manual interpretation, which is prone to inter-rater variability. This study introduces a deep learning-based artificial intelligence (AI) system for PAR episode detection and diagnosis using HMII-pH signals. Ninety patients with suspected LPR and 28 healthy volunteers underwent HMII-pH testing in three Taiwanese medical centers. Candidate PAR episodes were defined as esophagopharyngeal pH drops exceeding 2 units, with nadir pH below 5 within 30 seconds during esophageal acidification. A consensus review by three experts validated 84 PAR episodes in 17 subjects. Data preprocessing identified 225 candidate PAR episodes, including 84 PAR episodes and 141 swallows/artifacts, were divided into training, validation, and test datasets (6:2:2 ratio). Three cascade deep learning AI models were trained. Among them, the cascade Multivariate Long Short-Term Memory with Fully Convolutional Network (MLSTM-FCN) model performed best in the test dataset. At the episode level, this model achieved 0.936 accuracy, 0.941 precision, 0.889 recall, 0.966 specificity, 0.914 F 1 score, and 0.864 Matthew's correlation coefficient (MCC). For subject-level evaluation, the corresponding metrics were 0.917 accuracy, 1.000 precision, 0.818 recall, 1.000 specificity, 0.900 F 1 score, and 0.842 MCC. In conclusion, the cascade MLSTM-FCN model demonstrates robust accuracy in diagnosing PAR episodes from HMII-pH signals, offering a promising tool for efficient and consistent PAR episode detection in LPR diagnosis.

关键词： Pharyngeal acid reflux episodes Hypopharygneal multichannel intraluminal impedance-pH Laryngopharyngeal reflux Artificial intelligence Deep learning

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Selective kernel networks

arXiv

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arXiv 2019年

作者： Li, Xiang Wang, Wenhai Hu, Xiaolin Yang, Jian PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology China Momenta National Key Lab for Novel Software Technology Nanjing University Department of Computer Science and Technology Tsinghua University China

In standard Convolutional Neural Networks (CNNs), the receptive fields of artificial neurons in each layer are designed to share the same size. It is well-known in the neuroscience community that the receptive field size of visual cortical neurons are modulated by the stimulus, which has been rarely considered in constructing CNNs. We propose a dynamic selection mechanism in CNNs that allows each neuron to adaptively adjust its receptive field size based on multiple scales of input information. A building block called Selective Kernel (SK) unit is designed, in which multiple branches with different kernel sizes are fused using softmax attention that is guided by the information in these branches. Different attentions on these branches yield different sizes of the effective receptive fields of neurons in the fusion layer. Multiple SK units are stacked to a deep network termed Selective Kernel Networks (SKNets). On the ImageNet and CIFAR benchmarks, we empirically show that SKNet outperforms the existing state-of-the-art architectures with lower model complexity. Detailed analyses show that the neurons in SKNet can capture target objects with different scales, which verifies the capability of neurons for adaptively adjusting their receptive field sizes according to the input. The code and models are available at https://***/implus/SKNet. Copyright © 2019, The Authors. All rights reserved.

关键词： Neurons

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Fuzzy Adaptive Teaching Learning-based Optimization Strategy for GUI Functional Test Cases Generation

Fuzzy Adaptive Teaching Learning-based Optimization Strategy...

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2018年第七届软件与计算机应用国际会议

作者： Fakhrud Din Kamal Z.Zamli Faculty of Computer Systems & Software Engineering Universiti Malaysia Pahang Department of Computer Science & IT University of Malakand KPK

Graphical User Interface(GUI) visualizes computer programs for the purpose of facilitating interaction between users and various computing devices. Today's computers, smart phones and even small devices such as watches are equipped with GUIs. Unlike command based interaction, GUI uses images, labels, push buttons, radio buttons, etc. for the effective communication of users with a software system. GUI testing is a critical part of software testing as it is the door to the actual functionality of software. For the quality assurance, GUI functional testing of a software validates proper interaction between the interface and the user without considering any coding details. In this paper, a strategy based on fuzzy Adaptive Teaching Learning-based Optimization(ATLBO) algorithm, a variant of the basic Teaching Learning-based Optimization(TLBO) algorithm, for GUI functional testing is proposed. ATLBO utilizes Event-Interaction Graph(EIG) for the generation of quality test cases. The proposed strategy has produced competitive experimental results against the basic TLBO and other test case generation algorithms.

关键词： GUI Functional Testing Teaching Learning-based Optimization Mamdani Fuzzy Inference System Model-based testing

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