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1st Zimbabwe Conference of Information and Communication Technologies, ZCICT 2022

作者： Mahachi, Shawn Zvarevashe, Kudakwashe Nyandoro, Leslie Kudzai University of Zimbabwe Faculty of Computer Engineering Informatics and Communications Analytics and Informatics Department Harare Zimbabwe

ISBN: (纸本)9781665475761

In recent years, the COVID-19 pandemic has spread all over the world. Due to its rapid transmission, techniques that automatically detect COVID-19 infections and distinguish it from other forms of pneumonia are crucial. The scientific community has embarked on finding solutions to quick detection of COVID-19 through implementation of deep learning(DL) techniques that can diagnose COVID-19 using computed tomography (CT) lung scans. The use of CT images has been widely accepted in medical imaging and it is a pertinent screening tool due to its higher sensitivity in recognizing early pneumonic changes. Also, most developed DL models developed have been end-to-end from feature extraction to categorization of the COVID19 infected images. The proposed model results showed high accuracy rates on both training and testing of the model in COVID-19 classification. A customised ResNet-50 architecture has the best results in classifying the images and achieved state of art accuracy of 97% on training and testing using the COVID dataset with 200 epochs. This work presented a computationally efficient and highly accurate model for multi-class classification of normal and infected individuals. The model can help in effective early screening of COVID-19 cases hence reducing the burden on healthcare systems. © 2022 IEEE.

关键词： COVID-19

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A Deep Learning-Based Intelligent Irrigation System Utilizing Multimodal Information Fusion 16

A Deep Learning-Based Intelligent Irrigation System Utilizin...

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16th International Congress on Image and Signal Processing, BioMedical engineering and Informatics, CISP-BMEI 2023

作者： Tang, Chengyu Luan, Jiale Lu, Yingzhu Fan, Yawen Nanjing Forestry University Department of Computer Science and Technology Nanjing China Nanjing University of Posts and Telecommunications Department of Computer Science and Technology Nanjing China Nanjing University of Posts and Telecommunications School of Communications and Information Engineering Nanjing China

ISBN: (纸本)9798350330755

This paper presents an intelligent agricultural irrigation system that leverages deep learning technology. The system, centered around the Raspberry Pi 4B, integrates computer vision technology, sensors, and control programs. Addressing the problem of relying on empirical methods for agricultural irrigation, this study utilizes the Raspberry Pi 4B as the core system and employs a deep neural network to identify target objects. The system also monitors the temperature and humidity of air and soil using sensors. By implementing a control program, the system autonomously determines irrigation requirements based on preset temperature and humidity ranges. This approach enables the development of a cost-effective and user-friendly irrigation system, conserving water resources and providing multiple benefits. © 2023 IEEE.

关键词： Deep neural networks

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Comparative Analysis of ML algorithms for Predictive Prenatal Monitoring 4

Comparative Analysis of ML algorithms for Predictive Prenata...

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4th IEEE Eurasia Conference on Biomedical engineering, Healthcare and Sustainability, ECBIOS 2022

作者： Bautista, John Mark Reyes, Rosula S.J. Ateneo de Manila University Department of Electronics Computer and Communications Engineering Quezon City Philippines

ISBN: (数字)9781728195797

ISBN: (纸本)9781728195797

Despite the rapid advancements in prenatal health care services a lot of low-income sectors are experiencing high fetal mortality rates because of inaccessible prenatal health services. The reasons include financial incapability, pregnant women residing in isolated regions cannot access reliable healthcare services, and insufficient healthcare equipments in certain areas. To assess the shortcomings of the current prevalent methods, this study proposes an accurate non-invasive process of prenatal health care assessment by using a trained machine learning algorithm in a telemedicine setup. This setup uses a mobile app for patients and doctors connected to a cloud storage database where the patient information is stored. The predictive model would then be able to predict whether a patient is a high-risk or low-risk pregnancy based on the patient information inputted in the app. The Machine Learning algorithms to be compared are Random Forest Decision Tree, Decision Tree, K-nearest neighbor, and SVM. After pre-processing the dataset, the predictive model was created by inputting the dataset of patient information to multiple machine learning algorithms and assessing their performance parameters. Based on the testing results, the preferred algorithm to be used is the Random Decision Tree Algorithm which had better overall performance than the previous model of Bautista and Quiwa. The study showed the further potential of the Machine Learning algorithm as a healthcare tool as data can now be easily attained using current technologies. Further improvement with the telemedicine setup could aid women who do not have sufficient access to healthcare services. © 2022 IEEE.

关键词： Decision trees

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Embedded Coded Relay System for Molecular communications

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computers, Materials & Continua 2022年第8期72卷 2729-2748页

作者： Eman S.Attia Ashraf A.M.Khalaf Fathi E.Abd El-Samie Saied M.Abd El-atty Konstantinos A.Lizos Osama Alfarraj Farid Shawki Imran Khan Ki-Il Kim Department of Electrical Engineering Faculty of EngineeringMinia UniversityMiniaEgypt Department of Electronics and Electrical Communications Faculty of Electronic EngineeringMenoufia UniversityMenoufiaEgypt Department of Informatics Faculty of Mathematics and Natural SciencesUniversity of Oslo(UiO)Norway Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Electrical Engineering University of Engineering&TechnologyPeshawar814Pakistan Department of Computer Science and Engineering Chungnam National UniversityDaejeon34134Korea

With the emergence of the COVID-19 pandemic,the World Health Organization(WHO)has urged scientists and industrialists to exploremodern information and communication technology(ICT)as a means to reduce or even eliminate *** World Health Organization recently reported that the virus may infect the organism through any organ in the living body,such as the respiratory,the immunity,the nervous,the digestive,or the cardiovascular *** the abovementioned goal,we envision an implanted nanosystem embedded in the intra living-body *** main function of the nanosystem is either to perform diagnosis and mitigation of infectious diseases or to implement a targeted drug delivery system(i.e.,delivery of the therapeutic drug to the diseased tissue or targeted cell).The communication among the nanomachines is accomplished via communication-based molecular *** control/interconnection of the nanosystem is accomplished through the utilization of Internet of bio-nano things(IoBNT).The proposed nanosystem is designed to employ a coded relay nanomachine disciplined by the decode and forward(DF)principle to ensure reliable drug delivery to the targeted ***,both the sensitivity of the drug dose and the phenomenon of drug molecules loss before delivery to the target cell site in long-distance due to the molecules diffusion process are taken into *** this paper,a coded relay NM with conventional coding techniques such as RS and Turbo codes is selected to achieve minimum bit error rate(BER)performance and high signal-to-noise ratio(SNR),while the detection process is based on maximum likelihood(ML)probability and minimum error probability(MEP).The performance analysis of the proposed scheme is evaluated in terms of channel capacity and bit error rate by varying system parameters such as relay position,number of released molecules,relay and receiver *** results are validated through simulation and demonstrate that the proposed scheme can

关键词： Molecular communication nanonetwork internet of bio-nano things coded relay scheme coding

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A Lightweight Integrated Narrowband Interference Detection and Suppression Scheme for OTFS

A Lightweight Integrated Narrowband Interference Detection a...

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IEEE Conference on Vehicular Technology (VTC)

作者： Yuchen Wu Zhenni Pan Shigeru Shimamoto Department of Communications and Computer Engineering Tokyo Japan

Orthogonal Time Frequency Space (OTFS) has been proven to have strong robustness to Doppler shifts and is regarded as a potential candidate for Orthogonal Frequency Division Multiplexing (OFDM) in the next generation wireless communication. However, narrowband interference (NBI) may become a headache for OTFS-based wireless systems. In OTFS, NBI will be spread over the whole delay-Doppler (DD) domain, and thus cause serious interference. Such interference is very dangerous in OTFS-based high-mobility communication for it will let the fast-moving user equipment (UE) in danger of losing control. Previous work infers OTFS to have natural robustness to NBI. In this work, we conduct further analysis and come to the conclusion that such robustness is only capable when NBI is weak, but when NBI gets stronger, OTFS exhibits vulnerability to it. Then we propose an NBI suppression scheme for OTFS to detect and eliminate NBI with a low computation load. Simulation results prove our analysis and indicate the proposed scheme can prevent serious interference resulting from NBI to some extent.

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Gauss Gradient and SURF Features for Landmine Detection from GPR Images

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computers, Materials & Continua 2022年第6期71卷 4457-4486页

作者： Fatma M.El-Ghamry Walid El-Shafai Mahmouad I.Abdalla Ghada M.El-Banby Abeer D.Algarni Moawad I.Dessouky Adel S.Elfishawy Fathi E.Abd El-Samie Naglaa F.Soliman Department of Electronics and Communications Engineering Faculty of EngineeringZagazig UniversityZagazigSharqia44519Egypt Department of Electronics and Electrical Communications Engineering Faculty of Electronic EngineeringMenoufia UniversityMenoufia32952Egypt Department of Industrial Electronics and Control Engineering Faculty of Electronic EngineeringMenoufia UniversityMenouf32952Egypt Department of Information Technology College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhSaudi Arabia

Recently,ground-penetrating radar(GPR)has been extended as a well-known area to investigate the subsurface ***,its output has a low resolution,and it needs more processing for more *** paper presents two algorithms for landmine detection from GPR *** first algorithm depends on a multi-scale technique.A Gaussian kernel with a particular scale is convolved with the image,and after that,two gradients are estimated;horizontal and vertical ***,histogram and cumulative histogram are estimated for the overall gradient *** bin values on the cumulative histogram are used for discrimination between images with and without ***,a neural classifier is used to classify images with cumulative histograms as feature *** second algorithm is based on scale-space analysis with the number of speeded-up robust feature(SURF)points as the key parameter for *** addition,this paper presents a framework for size reduction of GPR images based on decimation for efficient *** further classification steps can be performed on images after *** sensitivity of classification accuracy to the interpolation process is studied in detail.

关键词： GPR images cumulative histogram gradient image neural classifier SURF

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Enhancing the Reliability of NR-V2X Sidelink Broadcast through Relay

Enhancing the Reliability of NR-V2X Sidelink Broadcast throu...

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Asia-Pacific Conference on communications

作者： Shiro Aoki Suhua Tang Department of Computer and Network Engineering The University of Electro-Communications Tokyo Japan

ISBN: (数字)9798331517816

ISBN: (纸本)9798331517823

NR-V2X sidelink (SL) broadcast is used for the real-time exchange of position and other information between adjacent vehicles. However, its reliability degrades much in non-line-of-sight environments, where Blind ReTransmission (BReTX) does not work well. Relay can effectively address this issue, and a relaybased SL broadcast method was proposed in previous work. However, in this method, each vehicle must detect communication Link Quality (LQ) and exchange LQ Indicators (LQIs) with its neighbors to select a proper relay, which causes much overhead. This paper aims to improve the relay-based SL broadcast from two aspects. (i) To reduce the overhead, we suggest aggregating LQIs per direction, making the overhead of sharing LQIs irrelevant to the number of vehicles. (ii) To further improve reliability, we use an explicit ACK to deal with the potential failure of the transmission from the source vehicle to the relay. Using explicit ACKs allows the source vehicle to retransmit the packet until the relay vehicle correctly receives it, after which the relay vehicle handles the remaining retransmissions. Simulation results confirm that the proposed method, RReTX-ACK, improves the packet dissemination rate by approximately 8.30% within a 200 m distance from the source vehicle in an intersection scenario, compared to BReTX without using relay vehicles.

关键词： Simulation Asia Sidelink Real-time systems Reliability Relays

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Optimal Access Point Centric Clustering for Cell-Free Massive MIMO Using Gaussian Mixture Model Clustering

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in communications and Networking 2024年 2卷 675-687页

作者： Biswas, Pialy Mallik, Ranjan K. Letaief, Khaled B. Technische Universität Dresden Vodafone Chair Mobile Communications Systems Dresden01062 Germany Indian Institute of Technology Delhi Department of Electrical Engineering New Delhi Hauz Khas110016 India The Hong Kong University of Science and Technology Department of Electronic and Computer Engineering New Territories Hong Kong

This paper proposes a Gaussian mixture model (GMM) based access point (AP) clustering technique in cell-free massive MIMO (CFMM) communication systems. The APs are first clustered on the basis of large-scale fading coefficients, and the users are assigned to each cluster depending on the channel gain. As the number of clusters increases, there is a degradation in the overall data rate of the system, causing a trade-off between the cluster number and average rate per user. To address this problem, we present an optimization problem that optimizes both the upper bound on the average downlink rate per user and the number of clusters. The optimal number of clusters is intuitively determined by solving the optimization problem, and then grouping the APs and users. As a result, the computation expense is much lower than the current techniques, since the existing methods require evaluations of the network performance in multiple iterations to find the optimal number of clusters. In addition, we analyze the performance of both balanced and unbalanced clustering. Numerical results will indicate that the unbalanced clustering yields a superior rate per user while maintaining a lower level of complexity compared to the balanced one. Furthermore, we investigate the statistical analysis of the spectral efficiency (SE) per user in the clustered CFMM. The findings reveal that the SE per user can be approximated by the logistic distribution. © 2023 CCBY.

关键词： Fading channels

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A Proactive Crowdsourcing Framework for Fake Base Station Detection and Avoidance

A Proactive Crowdsourcing Framework for Fake Base Station De...

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IEEE Annual Computing and Communication Workshop and Conference (CCWC)

作者： Omar A. Saad Ahmed A. Etman Mai A. Abdel-Malek Mohamed Azab Department of Computer and Communications Engineering Faculty of Engineering Alexandria University Alexandria Egypt Department of Electrical and Computer Engineering The University of Arizona AZ USA Department of Computer and Information Sciences Virginia Military Institute VA USA

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

With next-generation communication technologies becoming more integrated into critical infrastructure, securing these networks against sophisticated threats has become paramount. One of the most persistent threats to this communication is the growing presence of fake base stations (FBS). These FBSs are malicious entities that pose as legitimate base stations to intercept, manipulate, or exploit mobile communications, leading to serious privacy breaches. While some existing literature addresses the detection of such malicious entities, detecting FBSs has traditionally been the responsibility of network operators, who rely on tools like signal strength analysis and radio frequency (RF) monitoring to identify unusual patterns or rogue signals. However, these centralized methods often struggle to detect FBSs in complex environments characterized by high mobility, dense urban areas, and sophisticated spoofing techniques. This paper proposes a scalable and pervasive blockchain-based crowd-sourcing mechanism to enable extensive device participation in identifying FBSs. In a fully automated and distributed manner, the proposed platform uses vetted and verified information recorded on the blockchain to identify and report suspicious FBSs in the area. This allows other users to dissociate from these malicious entities automatically. A reputation management system is also employed to ensure the reliability and accountability of participating users. Using qualitative and quantitative evaluations, the proposed approach demonstrated effectiveness and efficiency in achieving all targeted goals.

关键词： Crowdsourcing Radio frequency Base stations Accuracy Urban areas Privacy breach Blockchains Security Reliability Radiofrequency identification

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Achievable Rate Analysis and Optimization of Double-RIS Assisted Spatially Correlated MIMO with Statistical CSI

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China communications 2025年第1期22卷 139-157页

作者： Xu Kaizhe Guo Jiajia Zhang Jun Jin Shi Ma Shaodan Department of Communications and Networking Xi’an Jiaotong-Liverpool UniversitySuzhou 215123China State Key Laboratory of Internet of Things for Smart City University of MacaoMacao SARChina Jiangsu Key Laboratory of Wireless Communications Nanjing University of Posts and TelecommunicationsNanjing 210003China National Mobile Communications Research Laboratory Southeast UniversityNanjing 210096China State Key Laboratory of Internet of Things for Smart City and the Department of Electrical and Computer Engineering University of MacaoMacao SARChina

Reconfigurable intelligent surface(RIS)is a novel meta-material which can form a smart radio environment by dynamically altering reflection directions of the impinging electromagnetic *** the prior literature,the inter-RIS links which also contribute to the performance of the whole system are usually neglected when multiple RISs are *** this paper we investigate a general double-RIS assisted multiple-input multiple-output(MIMO)wireless communication system under spatially correlated non line-of-sight propagation channels,where the cooperation of the double RISs is also *** design objective is to maximize the achievable ergodic rate based on full statistical channel state information(CSI).Specifically,we firstly present a closedform asymptotic expression for the achievable ergodic rate by utilizing replica method from statistical *** a full statistical CSI-enabled optimal design is proposed which avoids high pilot training overhead compared to instantaneous CSI-enabled *** further reduce the signal processing overhead and lower the complexity for practical realization,a common-phase scheme is proposed to design the double *** results show that the derived asymptotic ergodic rate is quite accurate even for small-sized antenna *** the proposed optimization algorithm can achieve substantial gain at the expense of a low overhead and ***,the cooperative double-RIS assisted MIMO framework is proven to achieve superior ergodic rate performance and high communication reliability under harsh propagation environment.

关键词： common reflection pattern double RISs large system analysis MIMO replica method

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