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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Mefe Moses Haris Haralambous Christina Oikonomou Muhammad Mubasshir Shaikh Department of Geomatics Ahmadu Bello University Zaria Nigeria Frederick Research Center Nicosia Cyprus Department of Electrical Engineering Computer Engineering and Informatics Frederick University Nicosia Cyprus Cloudwater Ltd Nicosia Cyprus Department of Electrical Engineering University of Sharjah Sharjah UAE Space Weather and Ionosphere Laboratory Sharjah Academy for Astronomy Space Sciences & Technology University of Sharjah Sharjah UAE

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

This paper presents co-seismic earthquake-related ionospheric signatures observed by remote sensing instrumentation situated in the eastern Mediterranean and Middle-East and in particular in cyprus, Iraq and UAE by exploiting Total Electron Content measurements from calibrated 1-30 sec RINEX files NICO and ISBA IGS stations and ionograms from Nicosia and Sharjah ionosonde stations. Based on the analysis of measurements from these instruments we observe daytime acoustic ionospheric waves after the second (10:24 UTC) 7.6 (Mw) earthquake shock of the 6 February 2023 Turkey earthquake. Although the GNSS ionospheric signatures are only detectable in the near-field, multiple-cusp ionogram signatures generated by Rayleigh surface waves, generating acoustic waves propagating up to the ionosphere, appear as far as Sharjah.

关键词： Seismic measurements Global navigation satellite system Instruments Electric shock Earthquakes Surface acoustic waves Acoustic measurements

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A fast parallel classification model for the diagnosis of cancer

Recent Advances in Computer Science and Communications

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Recent Advances in computer Science and Communications 2021年第1期14卷 291-301页

作者： Jain, Divya Singh, Vijendra Department of Computer Science and Engineering School of Engineering and Technology The Northcap University Gurugram India Department of Informatics School of Computer Science University of Petroleum and Energy Studies Dehradun India

Background: In this era of voluminous data, there is a need to process data speedily in less time. Moreover, it is also essential to diminish the dimensionality of data as well as to apply parallel computations for classification. SVM is a prominent classification tool and is currently one of the most popular state-of-the-art models for solving various classification problems that makes use of parallel computations to speed up its processing. Objective: To develop a fast, promising classification system using optimized SVM classifier with hybridized dimensionality reduction for the diagnosis of cancer disease. Methods: The proposed approach comprises of two stages – the first stage presents a hybrid approach to reduce the dimensionality of cancer datasets, and the second stage presents an efficient classification method to optimize the SVM parameters and improve its accuracy. To lessen the execution time, the proposed approach uses GPUs to concurrently run different processes on machine workers. Results: The proposed method with the combination of dimensionality reduction & parallel classification using optimized SVM classifier is found to give excellent results based on ‘Classification Accuracy’, ‘Selected Features’ and ‘Execution Time’. Conclusion: The experimental findings with benchmark datasets indicate that the proposed diagnostic model yields significant improvement in terms of execution time when compared to the conventional approach. The proposed model can assist doctors and medical professionals in the quick selection of most significant risk factors for the diagnosis and prognosis of cancer diseases. © 2021 Bentham Science Publishers.

关键词： Classification (of information)

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AI Based Prediction for Heart Disease: A Comparative Analysis and an Improved Machine Learning Approach 6

AI Based Prediction for Heart Disease: A Comparative Analysi...

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6th Asian Conference on Artificial Intelligence technology, ACAIT 2022

作者： Raval, Jay Verma, Jai Prakash Islam, Sardar N.M. Jain, Rachna Thakur, Narina Institute of Technology Nirma University Computer Science Engineering Department Ahmedabad India Victoria University Applied Informatics Research Melbourne Australia Bhagwan Parshuram Institute of Technology Department of Computer Science and Engineering Delhi India

ISBN: (纸本)9781665453110

Heart disease problems are growing day by day in the world. Many factors are responsible for increasing the chance of heart attack and any other disease. Many countries have a low level of cardiovascular competence in predicting heart disease-related issues. Finding the best accurate machine learning classifiers for various diagnostic uses by data mining and machine learning techniques aids in predicting whether or not the heart disease-related issue will occur. To predict heart disease, a number of supervised machine-learning algorithms are used and their effectiveness are evaluated. With the exceptionof MLP and KNN, all applied algorithms had their estimated feature significance scores for each feature. This helps to find the main factors affecting heart disease and the accuracy of the model, which helps to get the best prediction. At the end of the research the support vector machine gives us 87.91 % highest testing accuracy compare with all applied machine learning algorithm. © 2022 IEEE.

关键词： Data mining

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Green hydrogen storage capacity estimations in depleted gas reservoirs

Green hydrogen storage capacity estimations in depleted gas ...

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International Conference on Energy Transition in the Mediterranean Area (SyNERGY MED)

作者： Charalampos Konstantinou Matheos Giakoumi Panos Papanastasiou Andreas V. Olympios Fanourios Kourougianni Alexandros Arsalis George E. Georghiou Department of Civil and Environmental Engineering University of Cyprus Nicosia Cyprus PHAETHON Centre of Excellence for Intelligent Efficient and Sustainable Energy Solutions Nicosia Cyprus Fondazione Eni Enrico Mattei Milan Italy Department of Electrical and Computer Engineering PV Technology Laboratory University of Cyprus Nicosia Cyprus

ISBN: (数字)9798350375923

ISBN: (纸本)9798350375930

Green hydrogen is poised to play a crucial role in the transition to renewable energy sources. It acts as a versatile energy storage carrier, helping to balance supply and demand. However, storing hydrogen presents significant challenges, as current technologies can only handle limited volumes. One promising solution involves injecting hydrogen into depleted oil & gas reservoirs, where it can be stored and later extracted when needed. This study explores this approach using the material balance method, which estimates the volumes within a reservoir. It is applied to a depleted natural gas system supported by an aquifer, taking into account pressure and flow rate histories. As the natural gas production rate increases, the hydrogen storage capacity also rises preventing the water to invade in the reservoir. For example, at a reference pressure of 2000 psi, there is no hydrogen storage capacity at a flow rate of 10 MMMCF/day, whereas a storage capacity of 314 BSCF is observed at a flow rate of 1000 MMMCF/day. These insights highlight the potential of utilising depleted reservoirs for hydrogen storage, offering a feasible path to overcoming the current limitations in hydrogen storage technology.

关键词： Water Hydrogen storage Hydrogen Production Reservoirs Green hydrogen Mathematical models History Natural gas Aquifers

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Ant Colony Optimization for Service Composition in Multi-cloud Environments 15

Ant Colony Optimization for Service Composition in Multi-clo...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Maurya, Mahesh Vidhya, G. Akram, Faiz Saravanan, M. Venkata Ramana, K. Rajakumari, S. K.C. College of Engineering Department of Computer Engineering Mith Bunder road Thane East Maharashtra Kopri India Sona College of Technology Department of Computer Science and Engineering Tamilnadu Salem India Jimma Institute of Technology Jimma University Faculty of Computing and Informatics Department of Computer Science Oromia Region Jimma Ethiopia Sri Eshwar College of Engineering Department of Ece Tamilnadu Coimbatore India Qis College of Engineering and Technology Department of Cse Andrapradesh Ongole India R.M.D. Engineering College Department of Mathematics India

ISBN: (纸本)9798350370249

the Ant Colony Optimization (ACO) algorithm is a famous metaheuristic technique that has been successfully applied in various optimization issues. It mimics the foraging conduct of ants to locate the shortest path among meals assets and their colony. Recently, ACO has been increasingly more applied for provider composition in multi-cloud environments. This involves the composition of offerings from different cloud carriers to satisfy the service necessities of a person. By way of using ACO, services can be correctly selected and composed from a couple of carriers, considering elements including cost, excellent, and availability. ACO has proven promising outcomes in enhancing the overall performance and reliability of carrier composition in multi-cloud environments, making it a precious technique in todays tremendously distributed computing landscape. © 2024 IEEE.

关键词： Distributed cloud

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FPGA design and implementation for montgomery multiplication algorithm using MATLAB HDL coder

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Bulletin of the National Research Centre 2024年第1期48卷 1-16页

作者： Elsayed, Ghada Abass, Eman S. Computer Science and Informatics Faculty The British University in Egypt Cairo Egypt Electrical Engineering Department Faculty of Engineering Modern University for Technology and information (MTI University) Cairo Egypt

Modular multiplication for large numbers is especially important in cryptography algorithms such as RSA and elliptic curves. The Montgomery algorithm is the most famous and efficient one for calculating it. Hardware implementation for cryptography co-processors is better than software implementation in terms of speed and security. Many FPGA designs for the Montgomery multiplication algorithm was published based on hardware description languages like VERILOG and VHDL. This paper proposes the FPGA design and implementation using MATLAB HDL Coder. The algorithm is modified such that it can fit any small/large FPGA by introducing scaling factor. The design is configurable in both modulus length and the scaling factor. This paper performs a comparison between the synthesizing results for different scales and for different modulus lengths. The synthesizing is performed up to 8K bit modulus length, and it can be increased easily. In this paper, implementation of different modulus lengths with different frequencies and with different area utilization can be easily achieved. The design utilizes different area resources for each configuration. The target is xc7vx330t-2ffg1157 Virtex-7 Xilinx FPGA. The maximum frequency is 80.81 MHz for 4096-bit modulus length with 8-bit data width and 2 for serialization factor. The minimum area utilization is achieved for minimum configurations, i.e., 1024-bit modulus length with 8-bit data width and for unity serialization factor. This paper proposes a scalable and configurable FPGA design for Montgomery multiplication co-processor-based HDL coder design.

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Fractal Fractional Order Operators in Computational Techniques for Mathematical Models in Epidemiology

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computer Modeling in engineering & Sciences 2024年第2期138卷 1385-1403页

作者： Muhammad Farman Ali Akgül Mir Sajjad Hashemi Liliana Guran Amelia Bucur Institue of Mathematics Khwaja Fareed University of Enginnering and Information TechnologyRaheem Yar KhanPakistan Department of Computer Science and Mathematics Lebanese American UniversityBeirutLebanon Department of Mathematics Art and Science FacultySiirt UniversitySiirt56100Turkey Department of Mathematics Mathematics Research CenterNear East UniversityNicosiaTurkey Department of Computer Engineering Biruni UniversityIstanbul34010Turkey Department of Hospitality Services Babes-Bolyai UniversityCluj-Napoca400174Romania Department of Pharmaceutical Sciences Vasile Goldi竤Western University of AradArad310025Romania Department of Mathematics and Informatics Faculty of SciencesLucian Blaga University of SibiuSibiu550012Romania Department of Electronics and Communication Engineering Saveetha School of EngineeringSIMATSChennaiTamilnaduIndia

New fractional operators, the COVID-19 model has been studied in this paper. By using different numericaltechniques and the time fractional parameters, the mechanical characteristics of the fractional order model areidentified. The uniqueness and existence have been established. Themodel’sUlam-Hyers stability analysis has beenfound. In order to justify the theoretical results, numerical simulations are carried out for the presented methodin the range of fractional order to show the implications of fractional and fractal *** applied very effectivenumerical techniques to obtain the solutions of themodel and simulations. Also, we present conditions of existencefor a solution to the proposed epidemicmodel and to calculate the reproduction number in certain state conditionsof the analyzed dynamic system. COVID-19 fractional order model for the case of Wuhan, China, is offered foranalysis with simulations in order to determine the possible efficacy of Coronavirus disease transmission in theCommunity. For this reason, we employed the COVID-19 fractal fractional derivative model in the example ofWuhan, China, with the given beginning conditions. In conclusion, again the mathematical models with fractionaloperators can facilitate the improvement of decision-making for measures to be taken in the management of anepidemic situation.

关键词： COVID-19 model fractal-fractional operator Ulam-Hyers stability existence and uniqueness numerical simulation

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Optimizing Resource Utilization Using Vector Databases in Green Internet of Things

Optimizing Resource Utilization Using Vector Databases in Gr...

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2023 IEEE Globecom Workshops, GC Wkshps 2023

作者： Kumari, Rani Sah, Dinesh Kumar Cengiz, Korhan Nauman, Ali Ivković, Nikola Mihaljević, Ivan Birla Institutue of Technology Department of Computer Science and Engineering Ranchi India Siksha 'o' Anusandhan Department of Computer Science and Engineering Bhubaneswar India Istinye University Department of Computer Engineering Istanbul Turkey University of Hradec Kralove Faculty of Informatics and Management Department of Information Technologies Kralove50003 Czech Republic Yeungnam University Department of Information and Communication Engineering 280 Daehak-Ro Gyeongsan Gyeongbuk38541 Korea Republic of University of Zagreb Faculty of Organization and Informatics Pavlinska 2 Varaždin42000 Croatia

ISBN: (纸本)9798350370218

With the rapid proliferation of Internet of Things (IoT) devices and the ever-increasing volume of sensor data, optimizing resource utilization has become crucial for building sustainable and efficient IoT systems. In this study, we propose a novel approach for optimizing resource utilization in Green IoT through efficient storage and retrieval in vector databases. Our approach leverages specialized data structures, including k-d trees and ball trees, to achieve improved storage efficiency and accelerated retrieval performance for high-dimensional sensor data. We conducted extensive experiments to evaluate the effectiveness of our proposal, comparing it with traditional database approaches. The results demonstrate significant improvements in storage efficiency, with vector databases requiring considerably less storage space compared to traditional databases. Moreover, our approach enables fast and accurate retrieval of high-dimensional sensor data, reducing query times and enhancing real-time data analysis and decision-making capabilities. The technical achievements of our proposal offer promising prospects for the development of sustainable and efficient IoT systems in various domains, such as environmental monitoring, healthcare, and smart cities. Our work contributes to advancing the field of Green IoT by addressing the challenges of resource utilization and query performance through efficient storage and retrieval in vector databases. © 2023 IEEE.

关键词： Green IoT Indexing Techniques Resource Utilization Specialized Data Structures Vector Databases

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Polarization Reconfigurable Intelligent Metasurface for X- & Ku-band Applications in Communications

Polarization Reconfigurable Intelligent Metasurface for X- &...

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Microwave and Antenna Symposium (IMAS), International

作者： Muhammad Sumaid Ahsaan Gul Hassan Nosherwan Shoaib Symeon Nikolaou School of Electrical Engineering and Computer Science (SEECS) National University of Sciences and Technology (NUST) Islamabad Pakistan Department of Electrical Computer Engineering and Informatics Frederick Research Center (FRC) Frederick University Nicosia Cyprus

This article presents a polarization reconfigurable intelligent metasurface (PRIM) to realize on demand switchable cross polarization conversion and circular polarization conversion by linearly incident electromagnetic (EM) wave through globally tuned PIN diode models. The unit cell structure of PRIM consists of I-Shaped structure supported with split circle and biasing lines to control the states of voltage-controlled PIN diodes. The modeled PIN diodes are incorporated in between split circle and on the edges of unit cells. The ON/OFF states of these PIN diodes can switch the polarization state of a wave between linear and circular polarizations. The novel approach ensures the provision of electronically controlled, ultra-wide band (UWB) reflective circular-polarization in one state and cross-polarization in the other, over same functional frequency band of X- and Ku-band. The results demonstrate that in open/off state the metasurface presents cross polarization ranging from 9 GHz to 19 GHz, while in short/on state metasurface presents circular polarization ranging from 10 GHz to 22 GHz. The designed metasurface is fabricated using hardwire-bond technique to replicate functionality of diodes and tested to validate the performance of PRIM.

关键词： Microwave antennas Polarization PIN photodiodes Switches Metasurfaces Stability analysis Distance measurement

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Signal-Interference Bandpass Filters Using Resonant Transversal Filtering Sections With Asymmetrical Transfer Function Characteristics

Signal-Interference Bandpass Filters Using Resonant Transver...

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European Conference on Microwave

作者： David Chatzichristodoulou Symeon Nikolaou Photos Vryonides Dimitra Psychogiou Frederick Research Center Cyprus RF and Microwave Solutions Cyprus Dept. of Electrical and Computer Engineering and Informatics Frederick University Cyprus School of Engineering University College Cork Ireland Tyndall National Institute Ireland

This paper introduces a new class of planar bandpass filters (BPFs) with quasi-elliptic transfer functions obtained by cascading resonant transversal filtering sections (TFS) with asymmetric transfer function characteristics. Each TFS includes a parallel-type resonator in one of its two RF signal paths which in turn leads to two transmission zeros (TZs) without increasing the circuit size. Furthermore, it enables high-order filtering transfer functions to be realized by directly cascading multiple TFSs without the need for inter-stage impedance inverters. Theoretical examples of spectrally asymmetrical BPF responses using linear-circuit analysis illustrate the resonator-based TFS BPF concept’s operating principles. Additionally, a microstrip prototype was manufactured and measured at 1 GHz to demonstrate the proof of concept. The prototype exhibited the following RF performance characteristics: a center frequency of 1 GHz, a 3-dB bandwidth of 150 MHz, a minimum in-band insertion loss of 0.8 dB, and in-band group-delay variation: 4.2-7.5 ns.

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