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28. VDE ITG-Fachtagung Mobilkommunikation: Technologien und Anwendungen - 28th VDE ITG Conference on Mobile Communication: Technologies and Applications

作者： John, Fabian Hauschild, Sebastian Hellbrück, Horst University of Applied Sciences Lübeck – Technische Hochschule Lübeck Germany Department of Electrical Engineering and Computer Science Center of Excellence CoSA Germany

ISBN: (纸本)9783800763832

We present a low-cost 5G-SA system based on edge-computing hardware with open-source components and off-the-shelf hardware in this paper. Our system is suitable for nomadic and ad-hoc 5G-SA use cases that require low-power consumption and small space. We show a complete development covering how to prepare the operating system for the installation of the 5G next-generation node B (gNB) and how to install and deploy the 5G-SA software components. We evaluate the performance, indoor coverage range, and power consumption of our system with measurements. We show that our system achieves acceptable data rates, and is very space and energy-efficient with an average power consumption of ≈ 24.81 W. We provide detailed and maintained documentation on the deployment of our system and discuss possible extensions for future work. © 2024 Mobilkommunikation: Technologien und Anwendungen - 28. VDE ITG-Fachtagung. All rights reserved.

关键词： Software radio

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Intelligent Smart Grid Stability Predictive Model for Cyber-Physical Energy Systems

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computer Systems science & engineering 2023年第2期44卷 1219-1231页

作者： Ashit Kumar Dutta Manal Al Faraj Yasser Albagory Mohammad zeid M Alzamil Abdul Rahaman Wahab Sait Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia

A cyber physical energy system(CPES)involves a combination of pro-cessing,network,and physical *** smart grid plays a vital role in the CPES model where information technology(IT)can be related to the physical *** the same time,the machine learning(ML)modelsﬁnd useful for the smart grids integrated into the CPES for effective decision ***,the smart grids using ML and deep learning(DL)models are anticipated to lessen the requirement of placing many power plants for electricity *** this aspect,this study designs optimal multi-head attention based bidirectional long short term memory(OMHA-MBLSTM)technique for smart grid stability predic-tion in *** proposed OMHA-MBLSTM technique involves three subpro-cesses such as pre-processing,prediction,and hyperparameter *** OMHA-MBLSTM technique employs min-max normalization as a pre-proces-sing ***,the MBLSTM model is applied for the prediction of stability level of the smart grids in *** the same time,the moth swarm algorithm(MHA)is utilized for optimally modifying the hyperparameters involved in the MBLSTM *** ensure the enhanced outcomes of the OMHA-MBLSTM technique,a series of simulations were carried out and the results are inspected under several *** experimental results pointed out the better outcomes of the OMHA-MBLSTM technique over the recent models.

关键词： Stability prediction smart grid cyber physical energy systems deep learning data analytics moth swarm algorithm

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Physics-guided hierarchical Neural Networks for Maxwell's equations in metamaterials

Physics-guided hierarchical Neural Networks for Maxwell's eq...

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CLEO: Fundamental science, CLEO:FS 2024 - Part of Conference on Lasers and Electro-Optics, CLEO 2024

作者： Lynch, Sean LaMountain, Jacob Fan, Bo Bu, Jie Raju, Amogh Wasserman, Daniel Karpatne, Anuj Podolskiy, Viktor Department of Physics and Applied Physics University of Massachusetts LowellMA01854 United States Department of Computer Science Virginia Tech U VA United States Department of Electrical and Computer Engineering University of Texas AustinTX78758 United States

We develop a hierarchical approach to building a Physics-guided neural network (PGNN) for scalable solutions of Maxwell equations with high spatial resolution and illustrate the developed formalism on a metamaterial p... 详细信息

ISBN: (纸本)9781957171395

关键词： Maxwell equations

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Enhancing Security with Machine Learning-based Finger-Vein Biometric Authentication System 5

Enhancing Security with Machine Learning-based Finger-Vein B...

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5th International Conference on Mobile Computing and Sustainable Informatics, ICMCSI 2024

作者： Babu, G. Prakash Aswal, Upendra Singh Sindhu, M. Upadhye, Vijay Jagdish Natrayan, L. Patil, Harshal Acharya Institute of Technology Department of Computer Science and Engineering Karnataka Bangalore560 107 India Graphic Era Deemed to be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India R.M.K. Engineering College Department of Computer Science and Business Systems Tamil Nadu Thiruvallur601 206 India Parul Institute of Applied Sciences Research and Development Cell Parul University Department of Microbiology Gujarat Waghodia391760 India SIMATS Saveetha School of Engineering Department of Mechanical Engineering Tamil Nadu Chennai602105 India Computer Science and Engineering Symbiosis Institute of Technology Maharashtra Pune412 115 India

ISBN: (纸本)9798350395235

Biometric authentication systems have gained popularity as the necessity for secure individual identification has expanded. Iris, hand geometry, fingerprints, retina, vein patterns on the fingers and palms, and voice patterns are all common biometric features. Biometric recognition systems based on finger-veins are becoming more common due to their numerous advantages, including low danger of duplication or fraud, minimum invasiveness, and excellent identification accuracy. Motivated by these advantages, our research looks into the subject of finger-vein authentication. Similar to identification, exploration of hidden patterns within data is essential for effective finger-vein recognition. The study employs two feature extraction approaches, the Local Binary Pattern (LBP) and the Gabor filter, as well as two machine learning models, the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Support Vector Machine (SVM). Using finger-vein images from the SDUMLA HMT database, this study evaluates four distinct feature extraction and ML model combinations. The simulation results highlight the excellent accuracy of 97.72%, False Acceptance Rate (FAR) of 3.1%, and False Rejection Rate (FRR) of 1.41% attained by LBP with ANFIS. In addition to performance measurements, the study examines the amount of time required for each task. © 2024 IEEE.

关键词： Local binary pattern

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Kernel Granulometric Texture Analysis and Light RES-ASPP-UNET Classification for Covid-19 Detection

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computers, Materials & Continua 2022年第4期71卷 651-666页

作者： A.Devipriya P.Prabu K.Venkatachalam Ahmed Zohair Ibrahim Department of Computer Science and Engineering KPR Institute of Engineering and TechnologyCoimbatore641407India Department of Computer Science CHRIST(Deemed to be University)Bangalore560029India Department of Applied Cybernetics Faculty of ScienceUniversity of Hradec Králové50003Hradec KrálovéCzech Republic Computer Sciences Department College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadhKSA.P.o.Box:84428Postal Code:11671

This research article proposes an automatic frame work for detectingCOVID -19 at the early stage using chest X-ray image. It is an undeniable factthat coronovirus is a serious disease but the early detection of the virus presentin human bodies can save lives. In recent times, there are so many research solutions that have been presented for early detection, but there is still a lack in needof right and even rich technology for its early detection. The proposed deeplearning model analysis the pixels of every image and adjudges the presence ofvirus. The classifier is designed in such a way so that, it automatically detectsthe virus present in lungs using chest image. This approach uses an imagetexture analysis technique called granulometric mathematical model. Selectedfeatures are heuristically processed for optimization using novel multi scaling deep learning called light weight residual–atrous spatial pyramid pooling(LightRES-ASPP-Unet) Unet model. The proposed deep LightRES-ASPPUnet technique has a higher level of contracting solution by extracting majorlevel of image features. Moreover, the corona virus has been detected usinghigh resolution output. In the framework, atrous spatial pyramid pooling(ASPP) method is employed at its bottom level for incorporating the deepmulti scale features in to the discriminative mode. The architectural workingstarts from the selecting the features from the image using granulometricmathematical model and the selected features are optimized using LightRESASPP-Unet. ASPP in the analysis of images has performed better than theexisting Unet model. The proposed algorithm has achieved 99.6% of accuracyin detecting the virus at its early stage.

关键词： Deep residual learning convolutional neural network COVID-19 X-ray principal component analysis granulo metrics texture analysis

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An Efficient Three-Party Authenticated Key Exchange Procedure Using Chebyshev Chaotic Maps with Client Anonymity

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computers, Materials & Continua 2023年第6期75卷 5337-5353页

作者： Akshaykumar Meshram Monia Hadj Alouane-Turki N.M.Wazalwar Chandrashekhar Meshram Department of Applied Mathematics Yeshwantrao Chavan College of EngineeringNagpur441110MaharashtraIndia Department of Statistics Rashtrasant Tukadoji Maharaj Nagpur UniversityNagpur440033MaharashtraIndia Department of Computer Science College of Computer ScienceKing Khalid UniversityAbhaSaudi Arabia Department of Post Graduate Studies and Research in Mathematics Jaywanti Haksar Government Post-Graduate CollegeCollege of Chhindwara UniversityBetul460001Madhya PradeshIndia

Internet of Things(IoT)applications can be found in various industry areas,including critical infrastructure and healthcare,and IoT is one of several technological *** a result,tens of billions or possibly hundreds of billions of devices will be linked *** smart devices will be able to gather data,process it,and even come to decisions on their *** is the most essential thing in these *** IoT infrastructure,authenticated key exchange systems are crucial for preserving client and data privacy and guaranteeing the security of data-in-transit(e.g.,via client identification and provision of secure communication).It is still challenging to create secure,authenticated key exchange *** majority of the early authenticated key agreement procedure depended on computationally expensive and resource-intensive pairing,hashing,or modular exponentiation *** focus of this paper is to propose an efficient three-party authenticated key exchange procedure(AKEP)using Chebyshev chaotic maps with client anonymity that solves all the problems mentioned *** proposed three-party AKEP is protected from several *** proposed three-party AKEP can be used in practice for mobile communications and pervasive computing applications,according to statistical experiments and low processing *** protect client identification when transferring data over an insecure public network,our three-party AKEP may also offer client ***,the presented procedure offers better security features than the procedures currently available in the literature.

关键词： Client anonymity Chebyshev chaotic maps authenticated key exchange statistical experiment Galois fields

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On Error-Based Step Size Control for Discontinuous Galerkin Methods for Compressible Fluid Dynamics

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应用数学与计算数学学报 2025年第1期7卷 3-39页

作者： Hendrik Ranocha Andrew R.Winters Hugo Guillermo Castro Lisandro Dalcin Michael Schlottke-Lakemper Gregor J.Gassner Matteo Parsani Applied Mathematics University of HamburgBundesstr.5520146 HamburgGermany Department of Mathematics Applied Mathematics Linköping UniversityLinköpingSweden Extreme Computing Research Center(ECRC) Computer Electrical and Mathematical Science and Engineering Division(CEMSE)King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Applied and Computational Mathematics RWTH Aachen UniversityAachenGermany High-Performance Computing Center Stuttgart University of StuttgartStuttgartGermany Department of Mathematics and Computer Science University of CologneCologneGermany Center for Data and Simulation Science University of CologneCologneGermany Extreme Computing Research Center(ECRC) Computer Electrical and Mathematical Science and Engineering Division(CEMSE)King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia Physical Science and Engineering Division(PSE) King Abdullah University of Science and Technology(KAUST)Thuwal 23955-6900Saudi Arabia

We study a temporal step size control of explicit Runge-Kutta(RK)methods for com-pressible computational fluid dynamics(CFD),including the Navier-Stokes equations and hyperbolic systems of conservation laws such as the Euler *** demonstrate that error-based approaches are convenient in a wide range of applications and compare them to more classical step size control based on a Courant-Friedrichs-Lewy(CFL)*** numerical examples show that the error-based step size control is easy to use,robust,and efficient,e.g.,for(initial)transient periods,complex geometries,nonlinear shock captur-ing approaches,and schemes that use nonlinear entropy *** demonstrate these properties for problems ranging from well-understood academic test cases to industrially relevant large-scale computations with two disjoint code bases,the open source Julia pack-ages *** with *** and the C/Fortran code SSDC based on PETSc.

关键词： Explicit Runge-Kutta(RK)methods Step size control Compressible fluid dynamics Adaptivity in space and time Shock capturing

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Enhancing Cloud Security: A Comprehensive Review of Machine Learning Approaches 2

Enhancing Cloud Security: A Comprehensive Review of Machine ...

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2nd International Conference on Cyber Resilience, ICCR 2024

作者： Safi, Wassim Ghwanmeh, Sameh Mahfuri, Mahmoud Al-Sit, Waleed T. Higher Institute For Applied Sciences And Technology-HIAST Damascus Syria American University In The Emirates Department Of Computer Science United Arab Emirates International University For Science And Technology Computer And Information Technology Department Damascus Syria College Of Engineering Mu'tah University Department Of Computer Engineering Al-karak Jordan Higher Colleges Of Technology Dubai United Arab Emirates

ISBN: (纸本)9798350394962

Cloud Computing (CC) has ushered in a paradigm shift in the provisioning of IT resources, offering users enhanced cost-efficiency and streamlined infrastructure management. However, this growth in CC adoption has also brought forth a myriad of security challenges, encompassing threats to availability, integrity, and confidentiality. To combat these challenges, Cloud Service Providers (CSPs) are increasingly turning to Machine Learning (ML) to diminish the necessity for human intervention in the identification and resolution of security issues. ML's capacity to process extensive datasets and make highly accurate predictions holds the potential to revolutionize CSPs' approach to security. This paper delves into recent advancements in the realm of ML-based security within Cloud Computing. It assesses the attributes and efficacy of various ML algorithms, underscoring their distinctive strengths and possible limitations. Our objective is to present a comprehensive overview of the current landscape of ML in cloud security, illuminating the promising prospects this emerging field holds. © 2024 IEEE.

关键词： Cloud security

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Gamification of E-Learning Apps via Acceptance Requirements Analysis 19

Gamification of E-Learning Apps via Acceptance Requirements ...

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19th International Conference on Evaluation of Novel Approaches to Software engineering, ENASE 2024

作者： Calabrese, Federico Piras, Luca Al-Obeidallah, Mohammed Ghazi Egbikuadje, Benedicta Oghenevoke Alkubaisy, Duaa Department of Information Engineering and Computer Science University of Trento Trento Italy Department of Computer Science Middlesex University London United Kingdom Department of Software Engineering Al Ain University Abu Dhabi United Arab Emirates College of Applied Studies and Community Service Imam Abdulrahman Bin Faisal University Dammam Saudi Arabia

ISBN: (纸本)9789897586965

In the last few years, and particularly during and after the COVID-19 pandemic, E-Learning has become a very important and strategic asset for our society, relevant both for academic and industry settings, involving participants ranging from students to professionals. Different applications have been developed to support E-Learning to be an effective tool, particularly in relation to the software engineering and programming areas. However, in order to be effective, in particular within academic settings, such tools require students to be continuously engaged and motivated to learn both practical and theoretical aspects. The integration of gamification in educational environments has gained considerable prominence as a potential mean to augment students’ motivation and involvement, providing them with immediate feedback and reinforcement, bolstering their sense of accomplishment and motivation to persist in their studies. However, to design gamified applications that can effectively engage and motivate users, as the literature has demonstrated, it is required to consider psychological, sociological, and human behavioural aspects, often referred to as Acceptance Requirements. This study presents a case study, where a Goal Modeling-based, Systematic, Acceptance Requirements Analysis and Gamification Design process, has been applied, by using the Agon framework, to prototype a gamified tool, aiming at engaging students towards learning both theory and practice related to a "Web-Based Mobile App Development" university module. To evaluate our proposed prototype, students were involved to use our proposed gamified prototype. The results show that our gamification solution can engage and motivate students towards learning both theoretical and practical aspects of the module. © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Requirements engineering

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Optimal Lot Size and Backordered Quantity Under Carbon Tax

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International Journal of applied and Computational mathematics 2024年第4期10卷 1-16页

作者： Raiya, Geetanjali Mittal, Mandeep Department of Mathematics Amity Institute of Applied Sciences Amity University Uttar Pradesh Noida 201313 India Department of Mathematics School of Computer Science Engineering and Technology Bennett University Uttar Pradesh Greater Noida India

Researchers and industry are working together to develop low carbon inventory models that comply with carbon pricing regulations while maintaining company profits. However, this task becomes more challenging when companies also face issues related to product quality imperfections. To address this issue, a new study has developed an economic order quantity (EOQ) model that takes into account carbon emission tax, defective rates, selling prices, imperfect items, scrap items and backordering. The objective of this mathematical model is reduce overall expenses, encompassing emissions-related costs. A numerical example is come up to adorn the model, based on previous data. Sensitivity analysis has also been performed to validate the model and understand system characteristics, identifying the factors that affect total emissions. © The Author(s), under exclusive licence to Springer Nature India Private Limited 2024.

关键词： Carbon tax Defective items EOQ Optimization Shortages

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