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Multidisciplinary engineering and Applied Science (ICMEAS), International Conference on

作者： Yusuf Olayinka Imam-Fulani Nasir Faruk Aliyu D. Usman Abubakar Abdulkarim Abdoulie M.S Tekanyi Abdulmalik S. Yaro Emmanuel Alozie Salisu Garba Kayode S. Adewole Abdulkarim A. Oloyede Olugbenga A. Sowande Bashir Abdullahi Baba Abdulwaheed Musa Agbotiname Lucky Imoize Yinusa A. Adediran Hawau I. Olagunju Lawan S. Taura Department of Telecommunication Science University of Ilorin Ilorin Nigeria Department of Electronics and Telecommunication Engineering Ahmadu Bello University Zaria Nigeria Department of Information Technology Sule Lamido University Kafin Hausa Nigeria Directorate of Information and Communication Technology Sule Lamido University Kafin Hausa Nigeria Department of Electrical Engineering Ahmadu Bello University Zaria Nigeria Department of Electrical and Telecommunication Engineering Kampala International University Kampala Uganda Department of Informatics and Qualitative Methods Faculty of Informatics and Management University of Hradec Králové Hradec Králové Czech Republic Department of Software Engineering Sule Lamido University Kafin Hausa Nigeria Department of Computer Science University of Ilorin Ilorin Nigeria Department of Electrical and Computer Engineering Kwara State University Malete Nigeria Institute for Intelligent Systems University of Johannesburg Johannesburg South Africa Department of Electrical and Electronics Engineering University of Lagos Lagos Nigeria Department of Electrical Engineering and Information Technology Institute of Digital Communication Ruhr University Bochum Germany Department of Electrical and Electronics Engineering University of Ilorin Ilorin Nigeria Department of Physics Sule Lamido University Kafin Hausa Nigeria

5G communication systems provide an end-to-end wireless connection to billions of users and devices across the globe. The quality of signals received during radio communication is notably influenced by the behavior of the radio propagation channel. A major parameter used in characterizing this channel is the Path Loss Exponent (PLE). Several works that have estimated and analyzed the PLE mostly considered the effect of distance and carrier frequency. However, the effect of base station antenna height and channel bandwidth on the PLE for 5G networks have not been adequately considered. To address this, the impact of antenna height of base station and channel bandwidth on the PLE within the 5G Frequency Range 1 (FR1) frequencies was investigated in this study, specifically at 800, 3500, and 5900 MHz. The licensed EDX Signal Pro software® with Cirrus high resolution global terrain and clutter data base was utilized to model, simulate and analyze the PLE for Kano City, Nigeria. Results showed that for the tested frequencies, an increase in either base station height or channel bandwidth leads to a significant reduction in the PLE. This study can be utilized by network planning engineers and the wireless research community to further improve network implementation and optimization toward understanding the behavior of signal propagation in 5G networks and beyond.

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Corrections to “Prioritizing DevOps Implementation Guidelines for Sustainable software Projects”

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IEEE Access 2025年 13卷 20246-20246页

作者： Muhammad Zohaib Ahmed Alsanad Areej Abdullah Alhogail Software Engineering Department Lappeenranta-Lahti University of Technology Lappeenranta Finland Department of Information Systems College of Computer and Information Sciences STCs Artificial Intelligence Chair King Saud University Riyadh Saudi Arabia

Presents corrections to the paper, (Corrections to “Prioritizing DevOps Implementation Guidelines for Sustainable software Projects”).

关键词： DevOps software Guidelines

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Anytime diagnosis for reconfiguration

arXiv

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arXiv 2021年

作者： Felfernig, Alexander Walter, Rouven Galindo, José A. Benavides, David Erdeniz, Seda Polat Atas, Müslüm Reiterer, Stefan Applied Software Engineering Group Institute for Software Technology TU Graz Austria Symbolic Computation Group WSI Informatics Universität Tübingen Germany Computer Languages and Systems Department University of Sevilla Spain SelectionArts Austria

Many domains require scalable algorithms that help to determine diagnoses efficiently and often within predefined time limits. Anytime diagnosis is able to determine solutions in such a way and thus is especially useful in real-time scenarios such as production scheduling, robot control, and communication networks management where diagnosis and corresponding reconfiguration capabilities play a major role. Anytime diagnosis in many cases comes along with a trade-off between diagnosis quality and the efficiency of diagnostic reasoning. In this paper we introduce and analyze FLEXDIAG which is an anytime direct diagnosis approach. We evaluate the algorithm with regard to performance and diagnosis quality using a configuration benchmark from the domain of feature models and an industrial configuration knowledge base from the automotive domain. Results show that FLEXDIAG helps to significantly increase the performance of direct diagnosis search with corresponding quality tradeoffs in terms of minimality and accuracy. Copyright © 2021, The Authors. All rights reserved.

关键词： Scheduling

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Intelligent Machine Learning Techniques for Condition Assessment of Power Transformers

Intelligent Machine Learning Techniques for Condition Assess...

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2020 International Conference on Power, Energy and Innovations, ICPEI 2020

作者： Leauprasert, Kunanya Suwanasri, Thanapong Suwanasri, Cattareeya Poonnoy, Nitchamon Electrical and Software Systems Engineering Bangkok Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok Thailand King Mongkut's University of Technology North Bangkok Faculty of Technical Education Department of Teacher Training in Electrical Engineering Bangkok Thailand

ISBN: (纸本)9781728172408

This paper introduces a condition assessment of power transformer in term of percentage of health index (%HI) by using regression models. The conditions of major components of power transformer are assessed by using input datasets from visual inspection, electrical test as well as paper and oil insulation test. 90 features of these input datasets are tested in regression models for determining the predicted HI. Six regression models such as linear regression, Ridge regression and Lasso regression, random forest regression, support vector regression, and deep neural network regression are tested to predict %HI. Actual input datasets related to actual %HI of 317 power transformers are used to teach such learning regression models. The random forest regression performs the best model providing the best output dataset with the lowest errors. © 2020 IEEE.

关键词： Deep neural networks

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A Novel Motion Accumulation and Selection Network for Rgb-Based Action Recognition

SSRN

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SSRN 2022年

作者： Wang, Huafeng Li, Hanlin Liu, Wanquan Gu, Xianfeng School of Information Technology North China University of Technology Beijing China School of Software Beihang University Beijing China School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou China Department of Computer Science Stony BrookNY11794-2424 United States

A large number of existing methods have demonstrated the significance of motion information for action recognition in video. In the literature, most of the previous methods rely heavily on the temporal differences of features extracted by convolutional networks (CNN) to represent the motion. However, this type of motion representation approach may have two potential drawbacks: 1) The difference operation may cause the incompleteness of the extracted moving target contour;2) Treating all the extracted motion features equally may lead to situations where some motion features will not contribute to the classification or even produce negative incentives. Inspired by the fact that the human eye is often a cumulative and selective process of visual attention when observing a video sequence, we propose a motion accumulation and selection network (MAS-Net) based on a novel embedded 2D CNN. According to the experimental results on typical video datasets such as Something-Something V1&V2 and Kinetics-400, it is shown that MAS-Net has achieved the state-of-the-arts on Something-Something V1&V2 and the competitive results on Kinetics-400, while the computational load is kept at a relatively low level. © 2022, The Authors. All rights reserved.

关键词： Motion estimation

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Energy Efficient Behavior Modelling for Demand Side Recommender System in Solar Microgrid Applications Using Multi Agent Reinforcement Learning Model

SSRN

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SSRN 2022年

作者： Onile, Abiodun Emmanuel Belikov, Juri Levron, Yoash Petlenkov, Eduard Department of Software Science Tallinn University of Technology Akadeemia tee 15a Tallinn12618 Estonia The Andrew and Erna Viterbi Faculty of Electrical Engineering Technion—Israel Institute of Technology Haifa3200003 Israel Department of Computer Systems Tallinn University of Technology Akadeemia tee 15a Tallinn12618 Estonia

Electricity consumers are often faced with challenges relating the choice of optimal energy saving plan, increasing integration of transient’s renewable sources of energy promises tantalizing solution which also constitutes emerging stability challenges for the electricity Grid. Demand side management using Battery storage as grid balancing measures are crucial towards extending physical limits of existing electricity grid. On the one hand, battery storage is assuming a critical role in decarbonization efforts partly due to its tendencies to store excess electricity obtained via renewable energy sources thus enabling unlimited potentials for seasonal storage. On the other hand, while solving the identified problem, other problems relating consumer behavior towards adoption of energy/battery efficiency technologies, consumers comfort-feedback and control loop integration exist. In this study, we presented electricity energy storage (EES) technologies which are embedded into the grid and further enhanced with the use of Reinforcement learning control and recommendation system technologies for improving the grid management, the grid reliability and sustainability, mitigated operational issues, improved BESS utilization and attain self-consumption goals. The result of the study shows that operating the BESS using the multi-agent RL control strategy achieved a maximum peak load reduction of about 24.5% alongside 94% comfort improvements in certain loads. © 2022, The Authors. All rights reserved.

关键词： Energy efficiency

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Allied Power Constraint Optimization and Optimal Beam Tracking Scheme for Mobile mmWave Massive MIMO Communications

TechRxiv

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TechRxiv 2021年

作者： Ghosh, Joydev Ra, In-Ho Singh, Saurabh Haci, Hüseyin Al-Utaibi, Khaled A. Sait, Sadiq M. School of Computer Science and Robotics Tomsk Polytechnic University Tomsk Russia School of Computer Information and Communication Engineering Kunsan National University Gunsan Korea Republic of Department of Industrial & Systems Engineering Dongguk University Korea Republic of Department of Electrical and Electronics Engineering Near East University Mersin 10 Turkey Computer Science and Software Engineering Department University of Ha’il Saudi Arabia Department of Computer Engineering King Fahd University of Petroleum & Minerals Dhahran Saudi Arabia

The fifth generation (5G) networks and internet of things (IoT) promise to transform our lives by enabling various new applications from driver-less cars to smart cities. These applications will introduce enormous amount of data traffic and number of connected devices in addition to the current wireless networks. Thus 5G networks require many researches to develop novel telecommunication technologies to accommodate these increase in data traffic and connected devices. In this paper, novel power constraint optimization and optimal beam tracking schemes are proposed for mobile mmWave massive MIMO communications. A recently published novel channel model that is different from other widely used ones is considered. The channel model considers the number of clusters and number of rays within each cluster as varying due to user mobility. The proposed power constraint optimization scheme harmonizes conventional total power constraint (TPC) and uniform power constraint (UPC) schemes into a new one called allied power constraint (APC) that can significantly improve the system performance in 5G networks while achieving fairness among users. TPC and UPC have major drawbacks with respect to fairness and achieving quality-of-service (QoS) for users in dense networks. Thus APC aims to harmonize TPC and UPC by adjusting each antenna element’s constraint to adapt for some power resilience to a specific antenna element, hence proposing an intermediate solution between the two extreme case power constraint optimization schemes. Three optimal beam tracking schemes: (i) conventional exhaustive search (CES), (ii) multiobjective joint optimization codebook (MJOC), and (iii) linear hybrid combiner (LHS) scheme, have been provided for the mobile mmWave massive MIMO system with the proposed APC scheme. For the proposed APC scheme a comprehensive performance analysis is provided and compared with TPC and UPC. Spectral efficiency (SE), bit-error-rate (BER), Jain’s fairness index, channel occup

关键词： Channel state information

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Optimization of wear properties on AA7075/Sic/Mos2hybrid metal matrix composite by response surface methodology

Optimization of wear properties on AA7075/Sic/Mos2hybrid met...

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2020 International Conference on Materials, Manufacturing and Mechanical engineering for Sustainable Developments, ICMSD 2020

作者： Umanath, K. Palanikumar, K. Sankaradass, Veeramalai Uma, K. Deaprtment of Mechanical Engineering Acadamy of Maritime Education and Training Chennai603112 India Sri SairamInstitute of Technology Chennai600044 India Department of Computer Science and Engineering ISL Engineering College Hyderabad Telangana500005 India Department of Software and Systems Engineering School of Information Technology and Engineering VIT University Vellore632014 India

Nowadays, composites are profoundly thriving as the replacement of ferrous alloys. The Aluminum manifested into a great substitute due to its properties like lightweight, low density, high strength, and corrosion resistance. The process of fusing the non-ceramics reinforcement like Silicon carbide, Boron carbide, Aluminum oxide, Titanium diboride with a non-metal like Graphite, Molybdenum disulfide or with other non-ceramic to form a hybrid metal matrix composite. In the present paper, an investigation carried out on Aluminum 7075 material by the process of stir casting where the Silicon Carbide (SiC) with three different weight % (i.e., 3%, 6%, 9%) and Molybdenum Disulphide (MoS2) with 1% constant weight % added to the base material. The tensile test and hardness test were then done on a UTM and Rockwell Hardness testing machine, respectively, for all three prepared materials. As the percentage of SiC increases in the material, the hardness value and tensile strength are increasing. The Al7075+9%SiC+1%MoS2is the highest of all. © 2020 Elsevier Ltd. All rights reserved.

关键词： Sulfur compounds

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Using an Expert Panel to Validate the Malaysian SMEs-software Process Improvement Model (MSME-SPI) 4th

Using an Expert Panel to Validate the Malaysian SMEs-Softwar...

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4th Computational Methods in systems and software, CoMeSySo 2020

作者： Almomani, Malek A. Basri, Shuib Almomani, Omar Capretz, Luiz Fernando Balogun, Abdullateef Husni, Moath Gilal, Abdul Rehman Department of Software Engineering The World Islamic Sciences and Education University Amman11947 Jordan Department of Computer and Information Sciences Universiti Teknologi PETRONAS Seri Iskandar Perak32610 Malaysia Department of Computer Network and Information Systems The World Islamic Sciences and Education University Amman11947 Jordan Department of Electrical and Computer Engineering Western University LondonON Canada Department of Computer Science University of Ilorin PMB 1515 Ilorin Nigeria Department of Computer Science Sukkur IBA University Sukkur65200 Pakistan

ISBN: (纸本)9783030633219

This paper presents the components of a newly developed Malaysian SMEs - software Process Improvement model (MSME-SPI) that can assess SMEs software development industry in managing and improving their software processes capability. The MSME-SPI is developed in response to practitioner needs that were highlighted in an empirical study with the Malaysian SME software development industry. After the model development, there is a need for independent feedback to show that the model meets its objectives. Consequently, the validation phase is performed by involving a group of software process improvement experts in examining the MSME-SPI model components. Besides, the effectiveness of the MSME-SPI model is validated using an expert panel. Three criteria were used to evaluate the effectiveness of the model namely: usefulness, verifiability, and structure. The results show the model effective to be used by SMEs with minor modifications. The validation phase contributes towards a better understanding and use of the MSME-SPI model by the practitioners in the field. © 2020, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： software design

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Can Foundation Models Really Segment Tumors? A Benchmarking Odyssey in Lung CT Imaging

arXiv

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arXiv 2025年

作者： Ayllón, Elena Mulero Mantegna, Massimiliano Shen, Linlin Soda, Paolo Guarrasi, Valerio Tortora, Matteo Unit of Computer Systems and Bioinformatics Department of Engineering Università Campus Bio-Medico di Roma Rome Italy College of Computer Science and Software Engineering Shenzhen University Shenzhen China Department of Diagnostics and Intervention Radiation Physics Biomedical Engineering Umeå University Umeå Sweden Department of Naval Electrical Electronics and Telecommunications Engineering University of Genoa Genoa Italy

Accurate lung tumor segmentation is crucial for improving diagnosis, treatment planning, and patient outcomes in oncology. However, the complexity of tumor morphology, size, and location poses significant challenges for automated segmentation. This study presents a comprehensive benchmarking analysis of deep learning-based segmentation models, comparing traditional architectures such as U-Net and DeepLabV3, self-configuring models like nnUNet, and foundation models like MedSAM, and MedSAM 2. Evaluating performance across two lung tumor segmentation datasets, we assess segmentation accuracy and computational efficiency under various learning paradigms, including few-shot learning and fine-tuning. The results reveal that while traditional models struggle with tumor delineation, foundation models, particularly MedSAM 2, outperform them in both accuracy and computational efficiency. These findings underscore the potential of foundation models for lung tumor segmentation, highlighting their applicability in improving clinical workflows and patient outcomes. © 2025, CC BY-NC-SA.

关键词： Image segmentation

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