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arXiv 2023年

作者： Zhang, Chuang Sun, Geng Wu, Qingqing Li, Jiahui Liang, Shuang Niyato, Dusit Leung, Victor C.M. The College of Computer Science and Technology Jilin University Changchun130012 China The Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun130012 China The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai China Pillar of Engineering Systems and Design Singapore University of Technology and Design 487372 Singapore The School of Information Science and Technology Northeast Normal University Changchun130024 China The School of Computer Science and Engineering Nanyang Technological University 639798 Singapore The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The Department of Electrical and Computer Engineering University of British Columbia VancouverBCV6T 1Z4 Canada

Unmanned aerial vehicles (UAVs) as aerial relays are practically appealing for assisting Internet of Things (IoT) network. In this work, we aim to utilize the UAV swarm to assist the secure communication between the micro base station (MBS) equipped with the planar array antenna (PAA) and the IoT terminal devices by collaborative beamforming (CB), so as to counteract the effects of collusive eavesdropping attacks in time-domain. Specifically, we formulate a UAV swarm-enabled secure relay multi-objective optimization problem (US2RMOP) for simultaneously maximizing the achievable sum rate of associated IoT terminal devices, minimizing the achievable sum rate of the eavesdropper and minimizing the energy consumption of UAV swarm, by jointly optimizing the excitation current weights of both MBS and UAV swarm, the selection of the UAV receiver, the position of UAVs and user association order of IoT terminal devices. Furthermore, the formulated US2RMOP is proved to be a non-convex, NP-hard and large-scale optimization problem. Therefore, we propose an improved multi-objective grasshopper algorithm (IMOGOA) with some specific designs to address the problem. Simulation results exhibit the effectiveness of the proposed UAV swarm-enabled collaborative secure relay strategy and demonstrate the superiority of IMOGOA. Copyright © 2023, The Authors. All rights reserved.

关键词： Multiobjective optimization

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European Spine Journal 2025年 1-9页

作者： Alhosanie, Tasneem N. Klaib, Ahmad F. Al Hadidi, Fadi Abdelraheem Kanaan, Tareq Mohammad, Khuzama Al-zurgan, Aya Yousef, Noor Tareq Oweis, Liyana Hasan, Salah M. Eftiha, Majed Alkubaisi, Fatima Abdulsattar Tarawneh, Maher Ahmad Altarazi, Abdelrahman Barakat, Motaz AbuHijlah, Iyas Al-Sabbag, Qusai Tarawneh, Emad Software Engineering Department King Hussein School of Computing Sciences Princess Sumaya University for Technology Amman Jordan Information Systems Department Faculty of Information Technology and Computer Sciences Yarmouk University Irbid Jordan Department of Orthopedic Surgery School of Medicine University of Jordan Amman Jordan Department of Neurosurgery School of Medicine University of Jordan Amman Jordan Psychiatry Department Jordanian Royal Medical Services Amman Jordan Jordanian Royal Medical Services Amman Jordan Department of Radiology School of Medicine University of Jordan Amman Jordan Department of Maxillofacial School of Dentistry University of Jordan Amman Jordan Oral and Maxillofacial Surgeon Head and Neck Department King Hussein Cancer Center Amman Jordan

Lumbar spondylolisthesis (LS) defined as the displacement of a vertebral body relative to the vertebra below it. Moreover, it typically affects the lower lumbar spine, particularly in women above 50 years of age. Many studies showed that the prevalence of LS increases with age and reported a higher number of cases in women. This study aimed to determine the prevalence of lumbar spondylolisthesis in adults and elderly people in the Jordanian community. A total of 2000 patients with low back pain, aged 18–94 years, with records of their age, gender, and lumbar spine X-ray from Jordan University Hospital were included in this retrospective cross-sectional study. The anterior-posterior (AP) and lateral lumbosacral X-ray images of every subject under study were read using SYNAPSE Viewer. Finally, the IBM SPSS Statistics program was used for statistical analysis. Among the 1,458 subjects included in the study, 910 (62.4%) were females and 548 (37.6%) were males. Their mean age was (53.2 ± 16.1) years. A total of 385 subjects had lumbar spondylolisthesis, with a total incidence of 26.4%. The percentage of Spondylolisthesis among males was (16.1%) and (32.6%) among females. The rates of spondylolisthesis (P = 0.00) were significantly higher in females. There was no significant difference between prevalence of males and females in patients under 50 years old. But the prevalence of LS in patients who are over 50 years old increased significantly, compared to other age groups. So our results showed that as the age of the patients increased, the prevalence increased in both males and females (P = 0.01). There were 385 cases that had slipped at different level, sorting in prevalence as follows L4-L5 > L5-S1 > L3-L4 > L2-L3 > L1-L2. We noticed that the L4-L5 in females took the largest proportion, but in males L4-L5 and L5-S1 took almost the same proportion. The majority of our patients had LS only at one level, with a percentage of 94.3% among all cases. The total pr

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A Novel Tri-Band Antenna Design for Wireless LAN Applications

A Novel Tri-Band Antenna Design for Wireless LAN Application...

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IEEE Conference on Antenna Measurements & Applications (CAMA)

作者： Sarosh Ahmad Hichem Boubakar Wael Jaafar Halim Yanikomeroglu Department of Signal Theory and Communications Universidad Carlos III de Madrid Spain Laboratory of Information Processing and Telecommunications (LTIT) University of Bechar Bechar Algeria Software and Information Technology Engineering Department Ecole de Technologie Supérieure QC Canada Systems and Computer Engineering Department Carleton University ON Canada

ISBN: (纸本)9781665490382

In this paper, we propose the design of a novel tri-band compact patch antenna. This antenna is resonant within frequencies 2.45 GHz, 3.5 GHz, and 5.8 GHz, making it compatible with wireless local area network (WLAN), world interoperability for microwave access (WiMAX), and Bluetooth. The flexible Roger RT5880 substrate with a thickness of 0.8 mm is used to construct the antenna, and a 50 Ω microstrip line is used as its feeding technique. The final structure has a small size of 30 × 20 × 0.8 mm 3 compared to state-of-the-art designs. Through simulations, we demonstrate the stable and satisfying performances of the novel antenna design over the three targeted frequency bands, thus favoring its use for integrated systems that operate within WLAN, WiMAX, and Bluetooth wireless bands.

关键词： Antenna measurements Microwave antennas Wireless LAN Bluetooth Patch antennas Resonant frequency Microstrip antennas

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Device Activity Detection in mMTC with Low-Resolution ADC: A New Protocol

arXiv

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arXiv 2023年

作者： Wang, Zhaorui Liu, Ya-Feng Wang, Ziyue Liu, Liang Pan, Haoyuan Cui, Shuguang The Future Network of Intelligence Institute The School of Science and Engineering The Chinese University of Hong Kong Shenzhen518172 China The State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China The Department of Electronic and Information Engineering The Hong Kong Polytechnic University Hong Kong College of Computer Science and Software Engineering Shenzhen University Shenzhen China

This paper investigates the effect of low-resolution analog-to-digital converters (ADCs) on device activity detection in massive machine-type communications (mMTC). The low-resolution ADCs induce two challenges on the device activity detection compared with the traditional setup with the assumption of infinite ADC resolution. First, the codebook design for signal quantization by the low-resolution ADC is particularly important since a good design of the codebook can lead to small quantization error on the received signal, which in turn has significant influence on the activity detector performance. To this end, prior information about the received signal power is needed, which depends on the number of active devices K. This is sharply different from the activity detection problem in traditional setups, in which the knowledge of K is not required by the BS as a prerequisite. Second, the covariance-based approach achieves good activity detection performance in traditional setups while it is not clear if it can still achieve good performance in this paper. To solve the above challenges, we propose a communication protocol that consists of an estimator for K and a detector for active device identities: 1) For the estimator, the technical difficulty is that the design of the ADC quantizer and the estimation of K are closely intertwined and doing one needs the information/execution from the other. We propose a progressive estimator which iteratively performs the estimation of K and the design of the ADC quantizer;2) For the activity detector, we propose a custom-designed stochastic gradient descent algorithm to estimate the active device identities. Numerical results demonstrate the effectiveness of the communication protocol. © 2023, CC BY.

关键词： Stochastic systems

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HealthEdge: A Machine Learning-Based Smart Healthcare Framework for Prediction of Type 2 Diabetes in an Integrated IoT, Edge, and Cloud Computing System

arXiv

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arXiv 2023年

作者： Hennebelle, Alain Materwala, Huned Ismail, Leila Melbourne Australia Research Laboratory Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates University United Arab Emirates National Water and Energy Center United Arab Emirates University United Arab Emirates Lab School of Computing and Information Systems The University of Melbourne Australia

Diabetes Mellitus has no permanent cure to date and is one of the leading causes of death globally. The alarming increase in diabetes calls for the need to take precautionary measures to avoid/predict the occurrence of diabetes. This paper proposes HealthEdge, a machine learning-based smart healthcare framework for type 2 diabetes prediction in an integrated IoT-edge-cloud computing system. Numerical experiments and comparative analysis were carried out between the two most used machine learning algorithms in the literature, Random Forest (RF) and Logistic Regression (LR), using two real-life diabetes datasets. The results show that RF predicts diabetes with 6% more accuracy on average compared to LR. © 2023, CC BY-NC-ND.

关键词： Edge computing

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Review of Approaches to the Use of UAVs, Remote Sensing and GIS in Humanitarian Demining: Ukrainian Case

SSRN

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SSRN 2024年

作者： Hutsul, Taras Khobzei, Mykola Tkach, Vladyslav Krulikovskyi, Oleh Moisiuk, Oleksandr Ivashko, Victor Samila, Andrii Department of Geomatics Land and Agromanagement Yuriy Fedkovych Chernivtsi National University Kotsyubynsky 2 Chernivtsi58002 Ukraine Department of Radio Engineering and Information Security Yuriy Fedkovych Chernivtsi National University Kotsyubynsky 2 Chernivtsi58002 Ukraine Department of Computer Sciences Yuriy Fedkovych Chernivtsi National University Kotsyubynsky 2 Chernivtsi58002 Ukraine Integrated Center for Research Development and Innovation in Advanced Materials Nanotechnologies and Distributed Systems for Fabrication and Control Stefan cel Mare University of Suceava Street University 13 Suceava720229 Romania Faculty of Electrical Engineering and Computer Science Stefan cel Mare University of Suceava Street University 13 Suceava720229 Romania

The history of the use of mines dates back almost two centuries. The geography of their use and the associated social harm have made them, without exaggeration, a global problem. At the same time, searches were underway for safe methods of their neutralization using various technical means. In so doing, until now, none of the existing methods provides a 100% guarantee of cleaning the territory, which determines the purpose of finding innovative methods and the possibility of combining them with existing ones. Unmanned aerial vehicles (UAVs) are becoming a real modern breakthrough in the field of intellectual achievements. Obtaining optimal results when solving a wide range of different problems, together with the development of composite materials, software, and the latest navigation equipment, make the tasks assigned to them and the expected results more and more difficult. UAVs allow people not to be in life-threatening conditions, to conduct activities beyond their physiological and psychophysiological abilities. The combination of the ability to collect spatial data during flight in various ranges of remote sensing with the possibility of carrying variants of useful equipment opens up prospects for their use in the field of demining territories. Supplementing UAV technologies with modern information systems for processing and analysis of information (expert systems, machine learning, computational intelligence, distributed artificial intelligence, neural networks, etc.), including spatial (GIS), opens up great prospects in the field of humanitarian demining of territories. © 2024, The Authors. All rights reserved.

关键词： Geographic information systems

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SEVGGNET-LSTM: A FUSED DEEP LEARNING MODEL FOR ECG CLASSIFICATION

arXiv

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arXiv 2022年

作者： He, Tongyue Chen, Yiming Chen, Junxin Wang, Wei Zhou, Yicong College of Medicine and Biological Information Engineering Northeastern University Shenyang China School of Software Dalian University of Technology Dalian China School of Intelligent Systems Engineering Sun Yat-sen University Shenzhen China Department of Computer and Information Science University of Macau China

This paper presents a fused deep learning algorithm for ECG classification. It takes advantages of the combined convolutional and recurrent neural network for ECG classification, and the weight allocation capability of attention mechanism. The input ECG signals are firstly segmented and normalized, and then fed into the combined VGG and LSTM network for feature extraction and classification. An attention mechanism (SE block) is embedded into the core network for increasing the weight of important features. Two databases from different sources and devices are employed for performance validation, and the results well demonstrate the effectiveness and robustness of the proposed algorithm. Copyright © 2022, The Authors. All rights reserved.

关键词： Electrocardiograms

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Correction to: Fuzzy logic and CPSO-optimized key management for secure communication in decentralized IoT networks: A lightweight solution

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Peer-to-Peer Networking and Applications 2024年第6期17卷 4396-4396页

作者： Vellingiri, J. Vedhavathy, T.R. Senthil Pandi, S. Bala Subramanian, C. Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India Department of Networking and Communications School of Computing SRM Institute of Science and Technology Kattankulathur India Department of Computer Science and Engineering Rajalakshmi Engineering College Thandalam Chennai India Department of Computer Science Engineering Kalasalingam Academy of Research and Education Krishnankoil India

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Parallel Implementation of Real-Time Object Detection using OpenMP

Parallel Implementation of Real-Time Object Detection using ...

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2021 International Conference on Information Science and Communications Technologies, ICISCT 2021

作者： Rakhimov, Mekhriddin Elov, Jamshid Khamdamov, Utkir Aminov, Shavkatjon Javliev, Shakhzod Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Department of Artificial Intelligence Tashkent Uzbekistan Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Department of Hardware and Software of Management Systems in Telecommunication Tashkent Uzbekistan Chonnam National University Department of Computer Engineering Gwangju Korea Republic of Tashkent University of Information Technologies Named after Muhammad Al-Khwarizmi Department of Computer System Tashkent Uzbekistan

ISBN: (纸本)9781665432580

One of the urgent problems of the modern world is the detection and recognition of objects on a video stream in real-time. To work with a video stream, you need to split it into frames that are more convenient for processing. There is a possibility of using multi-core processors for image processing. Typically, the operating system allows only one core of multi-core processors to execute a task. But there are such parallelization tools that allow parallelizing execution when processing data on multi-core processors. And OpenMP is one of them that can be used in real-time object detection processing procedures. But in this case, the main thing is to find that part of the algorithm that is suitable for parallelization. Using OpenMP simply does not give an efficient accelerated result. In this paper, we discuss the implementation of OpenMP technology in real-time object detection algorithms on multi-core processors. Experimental results show that the use of OpenMP technology significantly improves the processing speed for real-time object detection on multi-core processors, when there are no GPUs. © 2021 IEEE.

关键词： Object detection

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A mesh quality discrimination method based on convolutional neural network

A mesh quality discrimination method based on convolutional ...

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2020 IEEE International Conference on Artificial Intelligence and computer Applications, ICAICA 2020

作者： Xu, Zhenpeng Chen, Xinhai Chi, Lihua Liu, Jie Gong, Chunye Sci. and Technol. on Parallel and Distributed Proc. Lab. Lab. of Software Eng. for Complex Systems National University of Defense Technology Changsha China College of Computer Science and Electronic Engineering Hunan University Changsha China

ISBN: (纸本)9781728170046

With the rapid development of highperformance computing, computational fluid dynamics (CFD) has become an important part of hydrodynamics and aerodynamics. Mesh quality is the key factor that affects the accuracy and efficiency of CFD numerical calculation. However, the current the process of mesh quality discrimination is very time-consuming. The manpower time needed for this process takes up a large proportion in the whole numerical calculation process. A large number of artificial intelligence algorithms have been put forward to replace the human to efficiently complete all kinds of tedious tasks. In this paper, we propose a convolutional neural network (CNN) based mesh quality discrimination method, MeshNet. MeshNet uses residual neural network structure to learn mesh features and automatically judge the mesh quality. The experimental results show that the proposed network can greatly save labor time cost and achieve an accuracy of 94.41% for mesh quality discrimination. © 2020 IEEE.

关键词： Convolutional neural networks

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