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International Conference on Emerging Trends in networks and computer communications (ETNCC)

作者： Emmanuel Adetiba Jamiu R. Olasina Obiseye Obiyemi Dept. of Electrical and Information Engineering Covenant Applied Informatics and Communication Africa Center of Excellence Covenant University Ota Nigeria HRA Institute for Systems Science Durban University of Technology Durban South Africa Dept. of Computer Engineering Federal Polytechnic Ilaro Ilaro Nigeria Dept. of Electrical Power Engineering Space Science Centre Durban University of Technology Durban South Africa

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

Access to well-curated large datasets remains a significant bottleneck in AI-based research within wireless communication. Rapid advancements in neighbouring fields, such as computer vision and natural language processing, are largely due to the availability of extensive open-access datasets. However, similar progress has not been observed in wireless communication. To address this gap, we curated a comprehensive dataset for fifteen digital modulation schemes, including 4QAM, 16QAM, 64QAM, 256QAM, 8PSK, 16PSK, 32PSK, 64PSK, 128PSK, 256PSK, CPFSK, DBPSK, DQPSK, GFSK, and GMSK. Our dataset considers Rayleigh and Rician channel models under Additive White Gaussian Noise (A WGN) with SNRs ranging from -20dB to + 20dB in 5dB increments. The data samples were converted to constellation signal images and carefully pre-processed. Named RadioModRec, this dataset provides a valuable resource for researchers to train and evaluate AI models. It is freely accessible on Kaggle, promoting further innovation in the wireless communication domain.

关键词： Wireless communication Technological innovation Transfer learning Stochastic processes Rician channels Market research Natural language processing Optimization Residual neural networks Radio spectrum management

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Broadband Corrugated Modified Vivaldi Antenna for Microwave based Imaging Applications 24

Broadband Corrugated Modified Vivaldi Antenna for Microwave ...

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24th International Conference on computer and Information Technology, ICCIT 2021

作者： Talukder, Md Siam Samsuzzaman, Md Paul, Liton Chandra Masud, Md Abdul Azim, Rezaul Moniruzzaman, Md Patuakhali Science and Technology University Faculty of Computer Science and Engineering Patuakhali Bangladesh Pabna University of Science and Technology Dept. of ElectricalElectronic and Communication Engineering Pabna Bangladesh University of Chittagong Dept. of Physics Chittagong Bangladesh Universiti Kebangsaan Malaysia Dept. of ElectricalElectronic and Systems Engineering Bangi Malaysia

ISBN: (纸本)9781665494359

This paper explored a Broadband Corrugated Modified Vivaldi Antenna for head imaging applications using microwaves. The antenna is made up of a tapered feeding circular slotted patch and a rectangular modified wing shape ground with an electrical dimension of $0.49\lambda\times 0.41\lambda\times 0.009\lambda$ at lower frequency of 1.67 GHz. Front and back radiator are placed on a substrate of FR-4 material having a permittivity of 4.3 and a thickness of 1.5 mm, which are less expensive and more widely accessible. The designed antenna works between 1.67 and 6.37 GHz, allowing the imaging system to function over a large fractional bandwidth of about 116.92 %. With an appropriate impedance matching and a directionally stable radiation characteristics, the antenna has a decent gain of 9 dBi. A 50$\Omega$ microstrip line feeds the antenna. The simulation tools CST was used to evolve and optimize the design. © 2021 IEEE.

关键词： Microstrip antennas

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Machine Learning for Climate Change Impact Assessment and Adaptation Planning

Machine Learning for Climate Change Impact Assessment and Ad...

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Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), International Conference on

作者： T. Suresh Balakrishnan Prabhakar Krishnan U. Samson Ebenezar M. Mohammed Nizarudeen N. Kamal Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Center for Cyber Security Systems and Networks Amrita Vishwa Vidyapeetham Amritapuri-Campus India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India Department of Computer Science and Engineering CARE College of Engineering Trichy Tamil Nadu Dept. of Computer Science and Engineering GRT Institute of Engineering and Technology Tiruttani Tamil Nadu India

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

The presented research introduces a new advanced data analytics methodology for climate change impact assessment and adaptive planning that utilizes machine learning and deep-learning techniques. Quantitative analysis was performed over several databases such as GHCN, ERA5, National Centers for Environmental Prediction (NCEP) and JRA-55 in addition to CRU for a thorough assessment of the proposed methodology. In climate modeling, the Convolutional Neural networks (CNN) showed better accuracy than ERA5 and JRA-55 which outperformed Global Historical Climatology Network (GHCN) with lower Mean Absolute Error (MAE), Root Mean Squared Error values. The Early Warning System, which was built using the LSTM network algorithm, had a higher accuracy of 0.90 and recall rate of 0.88 especially with JRA-55 proving its effectiveness in early detection for extreme weather events before they occur. The RL-based adaptive planning component performed well across all datasets, with ERA5 reaching the best feedback loop performance score of 94%. The precision agriculture optimization, which was enabled by Random Forest revealed different accuracy and AUC-ROC values across various datasets showing flexibility. Against international climate agreements, the overall alignment score was 0.78 with ERA5 being slightly better at 0.80 using Support Vector Machines (SVM). These quantitative assessments accentuate the practicality, efficacy, and applicability of this methodology in various datasets making it an apt tool for evidence-based decisions on climate resilience strategies at the global level.

关键词： Climate change Machine learning Planning Data analysis Quality assessment Meteorology Support vector machines Convolutional neural networks Statistical analysis Random forests Long short term memory Feedback loop Evidence theory

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Secure the 5G and Beyond networks with Zero Trust and Access Control systems for Cloud Native Architectures

Secure the 5G and Beyond Networks with Zero Trust and Access...

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ACS/IEEE International Conference on computer systems and Applications

作者： Hisham A. Kholidy Keven Disen Andrew Karam Elhadj Benkhelifa Mohammad A. Rahman Atta-Ur Rahman Ibrahim Almazyad Ahmed F. Sayed Rakia Jaziri Dept. of Networks and Computer Security College of Engineering SUNY Polytechnic Institute Utica NY USA Dept. of Networks and Comp. Security SUNY Polytechnic Institute Utica NY USA The Air Force Research Laboratory (AFRL RIGB) Rome NY USA Smart Systems AI and Cybersecurity Research Centre Staffordshire Univ. UK Dept of Electrical and Computer Engineering Florida International University USA Dept. of Computer Science Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia Dept. of Electrical and Computer Engineering Univ. of Arizona Tucson AZ USA Transmission Dept. Telecom Egypt Fayoum Egypt Paragraphe Research Lab. Univ. of Paris VIII France

5G networks are highly distributed, built on an open service-based architecture that requires multi-vendor hardware and software development environments, all of which create a high attack surface in the 5G networks than other proprietary fixed-function networks. Besides that, cloud-native architectures also present new security challenges. Cloud-native separates monolithic virtual machines into microservice pods, resulting in higher volumes of signaling and communication flowing through and between microservices. In addition, secure connections in monolithic applications have been replaced by untrusted communication between microservice pods, requiring additional cybersecurity capabilities. Access control systems were created to provide reliability and limit access to an organization’s assets. However, due to technology's constant evolution and dynamicity, these conventional security systems lack the security to protect an organization’s information because they were created to address access control for known users. For 5G based cloud native technology, these access controls need to be taken further by implementing a Zero Trust model to secure one’s essential assets for all users within the system. Zero Trust is implemented in an access control system under the concept "Never Trust, Always Verify". In this paper, we implement zero trust as a factor within access control systems by combining the principles of access control systems and zero-trust security by factoring in the user’s historical behavior and recommendations into the mix.

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Vulnerability Testing on IoT networks Using Machine Learning Algorithm

Vulnerability Testing on IoT Networks Using Machine Learning...

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Advances in Data Engineering and Intelligent Computing systems (ADICS), International Conference on

作者： R. Mohandas D.S.John Deva Prasanna Baiju B V S. Nathiya T. Dhiliphan Rajkumar C. Sivashankar Department of Computational Intelligence SRM Institiute of Science and Technology Chennai India Department of Datascience and Business systems SRM Institiute of Science and Technology Chennai India School of Computer Science and Engineering(SCOPE) VIT University Vellore TamilNadu India Department of Electronics and Communication Engineering Prince shri Venkateshwara Padmavathi Engineering College Chennai Dept. Of Computer Science and Engineering Kalasalingam Academy Of Research And Education Tamilnadu Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences (SIMATS) Chennnai India

ISBN: (数字)9798350364828

ISBN: (纸本)9798350364835

The increasing growth of Internet of Things (IoT) devices has created a wide attack surface for cyber criminals to carry out more destructive cyber attacks; therefore, the number of cyber attacks in the information security industry is increasing rapidly. As attackers use new and innovative methods to launch cyber-oriented attacks, many of these strikes have succeeded in their malicious goals. Anomaly-based intrusion detection systems (IDS) use machine learning techniques to detect and classify attacks on IoT networks. Faced with unpredictable network technologies and various infiltration methods, traditional machine learning technologies are powerless. In abounding areas of research, kernel learning methods prove that they accurately determine pathological abilities.

关键词： Learning systems Industries Pathology Machine learning algorithms Intrusion detection Machine learning Internet of Things

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Team Precoding Towards Scalable Cell-free Massive MIMO networks

arXiv

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arXiv 2022年

作者： Miretti, Lorenzo Björnson, Emil Gesbert, David Communication Systems Dept. EURECOM Sophia Antipolis France Dept. of Computer Science KTH Royal Institute of Technology Stockholm Sweden Dept. of Electrical Engineering Linköping University Linköping Sweden

In a recent work, we studied a novel precoding design for cell-free networks called team minimum mean-square error (TMMSE) precoding, which rigorously generalizes centralized MMSE precoding to distributed operations based on transmitter-specific channel state information (CSI). Despite its flexibility in handling different cooperation regimes at the CSI sharing level, TMMSE precoding assumes network-wide sharing of the data bearing signals, and hence it is inherently not scalable. In this work, inspired by recent advances on scalable cell-free architectures based on user-centric network clustering techniques, we address this issue by proposing a novel version of the TMMSE precoding design covering partial message sharing. The obtained framework is then successfully applied to derive a variety of novel, optimal, and efficient precoding schemes for a user-centric cell-free network deployed using multiple radio stripes. Numerical simulations of a typical industrial internet-of-things scenario corroborate the gains of TMMSE precoding over competing schemes in terms of spectral efficiency under different power constraints. Although presented in the context of downlink precoding, the results of this paper may be applied also on the uplink. © 2022, CC BY.

关键词： Mean square error

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Intra-Datacenter Load Balancing in a Federated Cloud with Throttled Algorithm

Intra-Datacenter Load Balancing in a Federated Cloud with Th...

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2024 International Conference on science, Engineering and Business for Driving Sustainable Development Goals, SEB4SDG 2024

作者： Akinola, Damola Gideon Adetiba, Emmanuel Abayomi, Abdultaofeek Nnaji, Uche Thakur, Surendra Moyo, Sibusiso Covenant University Covenant Applied Informatics and Communications African Center of Excellence Department of Electrical and Information Engineering Ota Nigeria Durban University of Technology HRA Institute for Systems Science Durban South Africa Mangosuthu University of Technology Department of Information and Communication Technology Durban South Africa Covenant University Dept. of Computer and Information Science Covenant Applied Informatics and Communications African Center of Excellence Ota Nigeria Durban University of Technology KZN E-Skills CoLab Durban South Africa Stellenbosch University School for Data Science and Computational Thinking Department of Mathematical Sciences Stellenbosch7602 South Africa

ISBN: (纸本)9798350358155

The emergence of cloud computing technology has motivated a very high number of users in different organizations to access its services in running and delivering their various operations and services. However, this surge of cloud users has led to the problem of uneven load sharing among the cloud computing infrastructures. As a result, federated cloud infrastructure was initiated to provide more cloud computing resources to accommodate more cloud users' requests and also to provide equal requests mapping with the available cloud resources. Nevertheless, the issue of unequal allocation of requests within the datacenter(s) that makes up the federated cloud environment still persists. This research work presents an intra-datacenter load balancing algorithm in a federated cloud environment named;the intra-balancer adapted throttled algorithm to evenly distribute requests in each datacenter that formulate the federated cloud infrastructures. The simulation of the intra-balancer was carried out in CloudAnalyst, the results of the implementation showed that the intra-balancer outperformed the Round-Robin and ESCE with 98.93ms and 2.26ms for the response and processing time respectively. The results assert that the intra-balancer algorithm provides a better load balancing solution in a federated cloud environment. © 2024 IEEE.

关键词： Cloud platforms

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Optimization of Home Health Vehicle Routes

Optimization of Home Health Vehicle Routes

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IEEE International Symposium on Innovations in Intelligent systems and Applications (INISTA)

作者： Moez Balti Bouhadida Rahma Abderrazak Jemai Telecom Dept. Electronic Systems and Communications Networks Laboratory (SERCOM) Polytechnic School of Tunisia Carthage University Tunis Tunisia Computer Science Dept. of Faculty of Mathematical Physical and Natural Sciences of Tunis Electronic Systems and Communications Networks Laboratory (SERCOM) Polytechnic School of Tunisia Carthage University Tunis Tunisia Tunisia Polytechnic School SERCOM-Lab. INSAT Carthage University Tunis Tunisia

Home health care (HHC) defines the set of services that are provided to a patient at home. They have the advantage of being less expensive and less stressful for patients. However, this practice requires in most cases the displacement of a medical staff for a patient's home intervention, which generates the problem of effective management at lower cost of these visits. Internet of Things (IoT) technologies is enabling the medical sector as well as the transportation sector to witness a huge change in the way they operate. The implementation of intelligent solutions, building a bridge linking the Health 4.0 sector to that of Intelligent Transport systems (ITS), allows the resolution of new problems and guarantees an improvement in the quality of services. This article provides a vehicle routing solution for home healthcare based on CPLEX solver. The proposed solution provides efficient route optimization for vehicles to assist patients in their treatment.

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Comparison of DAT with DCT in a Viewpoint of Current Image Processing and Analysis Trends

Comparison of DAT with DCT in a Viewpoint of Current Image P...

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IEEE International Conference on Dependable systems, Services and Technologies (DESSERT)

作者： Viktor Makarichev Vladimir Lukin Iryna Brysina Dept. of Computer Systems Networks and Cybersecurity National Aerospace University “KhAI” Kharkiv Ukraine Dept. of Information-Communication Technologies National Aerospace University “KhAI” Kharkiv Ukraine Dept. of Higher Mathematics and System Analysis National Aerospace University “KhAI” Kharkiv Ukraine

ISBN: (纸本)9798350333053

Digital images are a type of data that has many applications. There are many constructive tools for their analysis and processing. In particular, various discrete transforms are used in order to get useful data features. Here, discrete atomic transform (DAT) and discrete cosine transform (DCT), which are discrete data transforms based, respectively, on atomic and trigonometric functions, are compared in a viewpoint of current image processing and analysis trends. Nowadays, due to a combination of challenges, it is of particular importance to develop such algorithms that provide data compression and protection features in combination with artificial intelligence oriented format. For different reasons, in particular functional properties, application of such non-classic tools as atomic functions to solving this problem seems to be promising. Trigonometric functions are widely used in image processing and can be de-facto considered as a standard. In this research, we provide a comprehensive comparison of DAT and DCT using different criteria, as well as discuss their strengths and weaknesses in the context of the problem considered.

关键词： Atomic measurements Image coding Market research Distortion Loss measurement Time measurement Discrete cosine transforms

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A Neural Network-based System Identification Model to Predict Output Current and Voltage of Solar Photovoltaic Panels

A Neural Network-based System Identification Model to Predic...

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science, Engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Emmanuel Adetiba Olumide O. Oluleye Ayodele H. Ifijeh Victoria Oguntosin Olatayo M. Olaniyan Olubunmi Adewale Akinola Gbenga Afolabi Jacob O. Odetola. Abdultaofeek Abayomi Dept. of Electrical & Information Engineering Covenant University Ota Ogun State Nigeria HRA Institute for -Systems Science Durban University of Technology Durban South Africa Dept. of Computer Engineering Federal University Oye-Ekiti Ekiti State Nigereia Dept. of Information and Communication Technology Mangosuthu University of Technology Durban South Africa

Solar irradiance is the energy per unit area received by the Sun as electromagnetic radiation. It is one of the most important renewable energy sources. Photovoltaic or other solar technologies are used to generate power more accurately than direct sun irradiation. Solar irradiance research and measurement have a variety of critical applications, including forecasting power generation from solar power plants, climate modeling, and weather forecasting. This paper presents a neural network-based system identification model developed using measured parameters from solar panels with various wattage specifications, namely, 10W, 20W, 40W, and 100W. The parameters that were measured to train the ANN model for the prediction of the output current and voltage include the angle of panel orientation, panel temperature, ambient temperature, irradiance, and wattage. Several training experiments were conducted and the best ANN model produced at 500 epochs gave an accuracy of 99.81% and a loss of 0.1940. The model was deployed into an intelligent Web App that was also developed in this study. This app could be a potential tool for renewable energy engineers and researchers.

关键词： Temperature measurement Renewable energy sources Voltage measurement Current measurement Weather forecasting Solar energy Predictive models

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