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arXiv 2020年

作者： Ullah, Amin Muhammad Anwar, Syed Bilal, Muhammad Majid Mehmood, Raja Software Engineering Department University of Engineering and Technology Taxila Punjab47050 Pakistan OrlandoFL32816 United States Division of Computer and Electronics Systems Engineering Hankuk University of Foreign Studies Yongin-si17035 Korea Republic of Information and Communication Technology Department School of Electrical and Computer Engineering Xiamen University Malaysia Sepang43900 Malaysia

The electrocardiogram (ECG) is one of the most extensively employed signals used in the diagnosis and prediction of cardiovascular diseases (CVDs). The ECG signals can capture the heart’s rhythmic irregularities, commonly known as arrhythmias. A careful study of ECG signals is crucial for precise diagnoses of patients’ acute and chronic heart conditions. In this study, we propose a two-dimensional (2-D) convolutional neural network (CNN) model for the classification of ECG signals into eight classes;namely, normal beat, premature ventricular contraction beat, paced beat, right bundle branch block beat, left bundle branch block beat, atrial premature contraction beat, ventricular flutter wave beat, and ventricular escape beat. The one-dimensional ECG time series signals are transformed into 2-D spectrograms through short-time Fourier transform. The 2-D CNN model consisting of four convolutional layers and four pooling layers is designed for extracting robust features from the input spectrograms. Our proposed methodology is evaluated on a publicly available MIT-BIH arrhythmia dataset. We achieved a state-of-the-art average classification accuracy of 99.11%, which is better than those of recently reported results in classifying similar types of arrhythmias. The performance is significant in other indices as well, including sensitivity and specificity, which indicates the success of the proposed *** Codes 68T10, 62H30 Copyright © 2020, The Authors. All rights reserved.

关键词： Diseases

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LSTM-Based Channel Prediction for Secure Massive MIMO Communications Under Imperfect CSI

LSTM-Based Channel Prediction for Secure Massive MIMO Commun...

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IEEE International Conference on Communications (ICC)

作者： Tenghui Peng Rongqing Zhang Xiang Cheng Liuqing Yang School of Software Engineering Tongji University Shanghai China State Key Laboratory of Advanced Optical Communication Systems and Networks Peking University Beijing China Department of Electrical and Computer Engineering Colorado State University CO USA

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

In recent years, massive multiple-input multiple-output (MIMO) has been regarded as a promising technique in the fifth-generation (5G) communication systems. With the ability of focusing transmission beams on users, massive MIMO has a natural advantage in the field of physical layer security to improve the system secrecy performance. However, in practical mobile systems, the imperfect channel state information (CSI) caused by the channel estimation error and the transmission and processing delay will have a non-negligible impact on the system performance. In this paper, we investigate secure communications in a multi-user massive MIMO-enabled vehicular communication networks. Considering the influence of imperfect CSI on the secrecy performance, we derive a tight asymptotic lower bound of the system secrecy capacity under both perfect and imperfect CSI. Moreover, we further analyze the impact of vehicle speed on the system secrecy performance and propose a channel prediction scheme based on (Long Short-Term Memory) LSTM model to compensate for the negative effects of imperfect CSI, which can improve the system secrecy performance in high mobility scenario. Simulation results show that the imperfect CSI severely reduces the system secrecy capacity, but its negative effects can be effectively alleviated through the designed LSTM-based channel prediction and compensation scheme.

关键词： MIMO communication Channel estimation Precoding Interference Network security Physical layer Antennas

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ER Model Partitioning: Towards Trustworthy Automated systems Development

SSRN

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SSRN 2020年

作者： Pieris, Dhammika Wijegunesekera, M.C. Dias, N.G.J. Department of Human Resource Management Faculty of Commerce and Management Studies University of Kelaniya Sri Lanka Department of Software Engineering Faculty of Computing and Technology University of Kelaniya Sri Lanka Department of Computer Systems Engineering Faculty of Computing and Technology University of Kelaniya Sri Lanka

In database development, a conceptual model is created, in the form of an Entity-relationship(ER) model, and transformed to a relational database schema (RDS) to create the database. However, some important information represented on the ER model may not be transformed and represented on the RDS. This situation causes a loss of information during the transformation process. With a view to preserving information, in our previous study, we standardized the transformation process as a one-to-one and onto mapping from the ER model to the RDS. For this purpose, we modified the ER model and the transformation algorithm resolving some deficiencies existed in them. Since the mapping was established using a few real-world cases as a basis and for verification purposes, a formal-proof is necessary to validate the work. Thus, the ongoing research aiming to create a proof will show how a given ER model can be partitioned into a unique set of segments and use it to represent the ER model itself. How the findings can be used to complete the proof in the future will also be explained. Significance of the research on automating database development, teaching conceptual modeling, and using formal methods will also be discussed. © 2020, The Authors. All rights reserved.

关键词： Mapping

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ER model Partitioning: Towards Trustworthy Automated systems Development

arXiv

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arXiv 2020年

关键词： Mapping

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Semantic-Preserving Adversarial Text Attacks

TechRxiv

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TechRxiv 2021年

作者： Yang, Xinghao Gong, Yongshun Liu, Weifeng Bailey, James Zhu, Tianqing Tao, Dacheng Liu, Wei The School of Computer Science University of Technology Sydney 15 Broadway UltimoNSW2007 Australia The School of Software Shandong University Jinan China Qingdao266580 China The Department of Computing and Information Systems University of Melbourne Australia The JD Explore Academy China The Digital Science Institute Faculty of Engineering The University of Sydney DarlingtonNSW2008 Australia

Deep learning models are known immensely brittle to adversarial image examples, yet their vulnerability in text classification is insufficiently explored. Existing text adversarial attack strategies can be roughly divided into three categories, i.e., character-level attack, word-level attack, and sentence-level attack. Despite the success brought by recent text attack methods, how to induce misclassification with the minimal text modifications while keeping the lexical correctness, syntactic soundness, and semantic consistency simultaneously is still a challenge. To examine the vulnerability of deep models, we devise a Bigram and Unigram based adaptive Semantic Preservation Optimization (BU-SPO) approach which attacks text documents not only at a unigram word level but also at a bigram level to avoid generating meaningless sentences. We also present a hybrid attack strategy that collects substitution words from both synonyms and sememe candidates, to enrich the potential candidate set. Besides, a Semantic Preservation Optimization (SPO) method is devised to determine the word substitution priority and reduce the perturbation cost. Furthermore, we constraint the SPO with a semantic Filter (dubbed SPOF) to improve the semantic similarity between the input text and the adversarial example. To estimate the effectiveness of our proposed methods, BU-SPO and BU-SPOF, we attack four victim deep learning models trained on three real-world text datasets. Experimental results demonstrate that our approaches accomplish the highest semantics consistency and attack success rates by making the minimal word modifications compared with competitive methods. © 2021, CC BY.

关键词： Semantics

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A modeling and simulation framework for UAVs utilizing 4G-LTE cellular networks

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International Journal of Modeling, Simulation, and Scientific Computing 2018年第5期9卷 108-139页

作者： Shafagh Jafer Stephen Jones Ashok Vardhan Raja Department of Electrical ComputerSoftwareand Systems Engineering Embry-Riddle Aeronautical UniversityDaytona Beach32114 FLUSA

Only a small amount of work has been published on the topic of exploiting existing Long-Term Evolution-Advanced(LTE)cellular communication network infrastructure for Unmanned Aerial Vehicles(UAV)data *** paper documents a modeling and simulation(m&s)framework that has been developed utilizing the powerful OMNeT++simulation tool for assessing the feasibility and effectiveness of this prospect in various UAV *** multiple scenarios,we have studied the data rate requirements for communications between small and medium-sized UAVs and base *** this framework,we have shown that the data rate requirements for the links are within the data throughput achieved by LTE *** developed framework implements a propagation model endorsed by the 3GPP LTE project team and also accurately models the high mobility of *** framework is highly configurable and extensible and boasts of automatic aggregation of results and chart *** outcomes of this research may be utilized by industry for rapidly deploying highly mobile,low-cost UAVs in a wide range of applications and scenarios.

关键词： Unmanned air vehicle communication UAV Data Link OMNET++ 4GLTE modeling and simulation.

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Numerical Evaluation of the Traffic Flow Indicators Using Super-Resolution Satellite Imagery

Numerical Evaluation of the Traffic Flow Indicators Using Su...

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2019 International Multi-Conference on Industrial engineering and Modern Technologies, FarEastCon 2019

作者： Pugachev, I.N. Markelov, G.Y. Tormozov, V.S. Nosenko, A.O. Pacific National University PNU Khabarovsk Russia Pacific National University PNU Computer Engineering Department Khabarovsk Russia Pacific National University PNU Department of Computer Software and Automated Systems Khabarovsk Russia Pacific National University PNU Foreign Language Department Khabarovsk Russia

ISBN: (纸本)9781728100616

The article is devoted to methods of numerical evaluation of traffic flow indicators (TFP) using super-resolution satellite imagery. The method includes several main stages: the stage of extracting images of the space intervals, the stage of detection and classification of vehicles, and the stage of numerical calculation of the intensity and combination of the traffic flow based on the vehicles distributing on the space interval. The main tasks and problems associated with the detection and classification of vehicles are given. © 2019 IEEE.

关键词： Image recognition

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Optimization of Neural Network Parameters Based on a Genetic Algorithm for Prediction of Time Series

Optimization of Neural Network Parameters Based on a Genetic...

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2020 International Multi-Conference on Industrial engineering and Modern Technologies, FarEastCon 2020

作者： Tormozov, V.S. Zolkin, A.L. Vasilenko, K.A. Pacific National University Senior Lecturer of the Software Engineering for Computers and Computer-based Systems Sub-faculty Tikhookeanskaya St. 136 Khabarovsk680054 Russia Ph.D. in Engineering Science Povolzhskiy State University of Telecommunications and Informatics Senior Lecturer of Computer and Information Sciences Department of the Povolzhskiy State University of Telecommunications and Informatics Samara443010 Russia Highest Category Lecturer Service and Design College of the Vladivostok State University of Economics and Service Service and Design College of the Vladivostok State University of Economics and Service Vladivostok690092 Russia

ISBN: (纸本)9781728169514

The article proposes a method that allows to solve the complex combinatorial problem of structural optimization of an artificial neural network with a large dimension of the space of optimization parameters using the stochastic mutation function with a constant coefficient of variation. A study on assessment of applicability of this approach to solve the problem is given. This method is proposed to be used for the forecasting of time series values. © 2020 IEEE.

关键词： Structural optimization

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REFUGE2 CHALLENGE: A TREASURE TROVE FOR MULTI-DIMENSION ANALYSIS AND EVALUATION IN GLAUCOMA SCREENING

arXiv

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arXiv 2022年

作者： Fang, Huihui Li, Fei Wu, Junde Fu, Huazhu Sun, Xu Son, Jaemin Yu, Shuang Zhang, Menglu Yuan, Chenglang Bian, Cheng Lei, Baiying Zhao, Benjian Xu, Xinxing Li, Shaohua Fumero, Francisco Sigut, José Almubarak, Haidar Bazi, Yakoub Guo, Yuanhao Zhou, Yating Baid, Ujjwal Innani, Shubham Guo, Tianjiao Yang, Jie Orlando, José Ignacio Bogunović, Hrvoje Zhang, Xiulan Xu, Yanwu The REFUGE2 Challenge Australia State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-Sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Intelligent Healthcare Unit Baidu Inc. Beijing China The Institute of High Performance Computing Agency for Science Technology and Research Singapore Yatiris Group PLADEMA Institute CONICET UNICEN Tandil Argentina Christian Doppler Lab for Artificial Intelligence in Retina Department of Ophthalmology and Optometry Medical University of Vienna Vienna Austria VUNO Inc Seoul Korea Republic of Tencent HealthCare Tencent Shenzhen China Computer Vision Institute College of Computer Science and Software Engineering of Shenzhen University Shenzhen China School of Biomedical Engineering Health Science Center Shenzhen University China Xiaohe Healthcare ByteDance Guangdong Guangzhou510000 China School of Biomedical Engineering Shenzhen University China College of Computer Science & Software Engineering Shenzhen University China Department of Computer Science and Systems Engineering Universidad de La Laguna Spain Saudi Electronic University Saudi Arabia King Saud University Saudi Arabia Institute of Automation Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China SGGS Institute of Engineering and Technology India Institute of Medical Robotics Shanghai Jiao Tong University China Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

With the rapid development of artificial intelligence (AI) in medical image processing, deep learning in color fundus photography (CFP) analysis is also evolving. Although there are some open-source, labeled datasets of CFPs in the ophthalmology community, large-scale datasets for screening only have labels of disease categories, and datasets with annotations of fundus structures are usually small in size. In addition, labeling standards are not uniform across datasets, and there is no clear information on the acquisition device. Here we release a multi-annotation, multi-quality, and multi-device color fundus image dataset for glaucoma analysis on an original challenge-Retinal Fundus Glaucoma Challenge 2nd Edition (REFUGE2). The REFUGE2 dataset contains 2000 color fundus images with annotations of glaucoma classification, optic disc/cup segmentation, as well as fovea localization. Meanwhile, the REFUGE2 challenge sets three sub-tasks of automatic glaucoma diagnosis and fundus structure analysis and provides an online evaluation framework. Based on the characteristics of multi-device and multi-quality data, some methods with strong generalizations are provided in the challenge to make the predictions more robust. This shows that REFUGE2 brings attention to the characteristics of real-world multi-domain data, bridging the gap between scientific research and clinical application. © 2022, CC BY-NC-ND.

关键词： Color

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Branch-based Link Planning for Time-varying Space-air Integrated networks

Branch-based Link Planning for Time-varying Space-air Integr...

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IEEE International Conference on Communications (ICC)

作者： Feng Wang Dingde Jiang Houbing Song Lei Shi School of Astronautics and Aeronautic University of Electronic Science and Technology of China Chengdu China Department of Electrical Computer Software and Systems Engineering ERAU Daytona Beach FL USA Department of Computing Carlow Institute of Technology Carlow Ireland

ISBN: (数字)9781728150895

ISBN: (纸本)9781728150901

The space-air integrated networks (SAIN) has been a valuable architecture due to its characteristics of wide coverage and high survey accuracy. However, it is not easy to design routing strategy in SAIN, considering complex relative motion of low-earth-orbit (LEO) satellites and unmanned aerial vehicles (UAV). Specifically, the main problem is how to find optimal links to realize stable and efficient UAV data transmission in time-varying SAIN. To address the problem above, this paper first analyzes the motion characteristics of satellites and UAVs to find the optimal accessing control satellites (ACSs) for the UAV. Then, different from traditional routing, a branch-based link planning strategy (BLPS) is proposed to realize efficient and stable inter-satellite link (ISL) deployment between ACSs, which can guarantee timely transmission of UAV data. Simulation results show that the proposed BLPS strategy is feasible and effective.

关键词： Satellites Gold Attenuation Low earth orbit satellites Data communication Unmanned aerial vehicles Routing

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