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Energy-Efficient Resource Optimization for Massive MIMO Networks Considering Network Load

computers, Materials & Continua 2022年第4期71卷 871-888页

作者： Samira Mujkic Suad Kasapovic Mohammed Abuibaid Department of Telecommunications Faculty of Electrical EngineeringUniversity of TuzlaTuzla75000Bosnia Herzegovina 2Department of Systems and Computer Engineering Faculty of Engineering and DesignCarleton UniversityOttawaK1S 5B6Canada

This paper investigates the resource optimization problem for a multi-cell massive multiple-input multiple-output(MIMO)network in which each base station(BS)is equipped with a large number of antennas and each base station(BS)adapts the number of antennas to the daily load profile(DLP).This paper takes into consideration user location distribution(ULD)variation and evaluates its impact on the energy efficiency of load adaptive massive MIMO *** variation is modeled by dividing the cell into two coverage areas with different user densities:boundary focused(BF)and center focused(CF)*** cells are assumed identical in terms of BS configurations,cell loading,and ULD variation and each BS is modeled as an M/G/m/m state dependent queue that can serve a maximum number of users at the peak *** with energy efficiency(EE)we analyzed deployment and spectrum efficiency in our adaptive massive MIMO system by evaluating the impact of cell size,available bandwidth,output power level of the BS,and maximum output power of the power amplifier(PA)at different cell *** also analyzed average energy consumption on an hourly basis per BS for the model proposed for data traffic in Europe and also the model proposed for business,residential,street,and highway areas.

关键词： Massive MIMO traffic load energy efficiency user location distribution optimization

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The Truncated Cone Effect in AFM Nanoindentation on Soft Materials

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Micro and Nanosystems 2023年第2期15卷 153-158页

作者： Kontomaris, Stylianos-Vasileios Malamou, Anna Department of Research and Development BioNanoTec LTD Nicosia Cyprus Faculty of Engineering and Architecture Metropolitan College Athens Greece Radar Systems and Remote Sensing Lab of the School of Electrical & Computer Engineering of National Technical University of Athens Athens Greece

Background: Atomic Force Microscopy (AFM) nanoindentation is the principal method for the characterization of soft materials at the nanoscale. In most cases, pyramidal tips are used and approximated to perfect cones. However, the extended use of the AFM tip may alter its sharp-ness. Objective: In many cases, a truncated cone shape is appropriate for tip modeling. In this technical note, the equation that relates the force with the indentation depth when indenting an elastic half-space using a truncated cone is derived. Methods: The nanoindentation equation for a truncated cone tip is derived using the fundamental differential equation that relates the sample’s contact stiffness with Young’s modulus. Results: When fitting Sneddon’s equation (which is valid for a perfect cone) on data obtained using a truncated cone-shaped AFM tip, the results show a ‘pseudo-softening’ behavior. Conclusion: The AFM tip's sharpness in nanoindentation experiments is a crucial parameter for obtaining the correct mechanical patterns of unknown samples. © 2023 Bentham Science Publishers.

关键词： Biological materials

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Analysis and Assessment of Wind Energy Potential of Socotra Archipelago in Yemen

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computers, Materials & Continua 2022年第1期70卷 1177-1193页

作者： Murad A.Almekhlafi Fahd N.Al-Wesabi Imran Khan Nadhem Nemri Khalid Mahmood Hany Mahgoub Noha Negm Amin M.El-Kustaban Ammar Zahary Department of Electrical Engineering–Communication Engineering Sana’a UniversityYemen Department of Computer Science King Khalid UniversityMuhayel AseerKingdom of Saudi Arabia Faculty of Computer and IT Sana’a UniversitySana’aYemen Department of Electrical Engineering University of Engineering and TechnologyPeshawarP.O.B 814Pakistan Department of Information Systems King Khalid UniversityMayahel AseerKSA Computer Science Department King Khaled UniversityKSA&Faculty of Computers and InformationComputer Science DepartmentMenoufia UniversityEgypt Computer Science Department King Khaled UniversityKSA&Faculty of ScienceDepartment of Mathematics and Computer ScienceMenoufia UniversityEgypt Electronics Department Faculty of EngineeringUSTSana’aYemen Faculty of Computer and IT Sana’a UniversityYemen

The increasing use of fossil fuels has a significant impact on the environment and ecosystem,which increases the rate of *** the high potential of renewable energy sources inYemen and the absence of similar studies in the region,this study aims to examine the potential of wind energy in Socotra *** was done by analyzing and evaluating wind properties,determining available energy density,calculating wind energy extracted at different altitudes,and then computing the capacity factor for a number of wind turbines and determining the *** average wind speed in Socotra Island was obtained from the Civil Aviation and Meteorology Authority data,only for the five-year data currently *** results showed high wind speeds from June to September(9.85-14.88 m/s)while the wind speed decreased for the rest of the *** average wind speed in the five years was 7.95 m/*** average annual wind speed,wind energy density,and annual energy density were calculated at different altitudes(10,30,and 50 m).According to the International Wind Energy Rating criteria,the region of Socotra Island falls under Category 7 and is classified as‘Superb’for most of the *** study provides useful information for developing wind energy and an efficient wind approach.

关键词： Energy potential wind characteristics Weibull distribution wind power density Socotra island

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Project Assessment in Offshore Software Maintenance Outsourcing Using Deep Extreme Learning Machines

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computers, Materials & Continua 2023年第1期74卷 1871-1886页

作者： Atif Ikram Masita Abdul Jalil Amir Bin Ngah Saqib Raza Ahmad Salman Khan Yasir Mahmood Nazri Kama Azri Azmi Assad Alzayed Faculty of Ocean Engineering Technology and Informatics University Malaysia TerengganuKuala NerusMalaysia Department of Computer Science&Information Technology Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Department of Software Engineering Faculty of Information TechnologyThe University of LahoreLahore54000Pakistan Advanced Informatics Department Razak Faculty of Technology and InformaticsUniversiti Teknologi Malaysia54100Kuala LumpurMalaysia Computer Science and Information Systems Department College of Business Studies Public Authority for Applied Education and Training(PAAET)AdailiyahKuwait Department of Computer Science Sharif College of Engineering&TechnologyLahore54000Pakistan

Software maintenance is the process of fixing,modifying,and improving software deliverables after they are delivered to the *** can benefit from offshore software maintenance outsourcing(OSMO)in different ways,including time savings,cost savings,and improving the software quality and *** of the hardest challenges for the OSMO vendor is to choose a suitable project among several clients’*** goal of the current study is to recommend a machine learning-based decision support system that OSMO vendors can utilize to forecast or assess the project of OSMO *** projects belong to OSMO vendors,having offices in developing countries while providing services to developed *** the current study,Extreme Learning Machine’s(ELM’s)variant called Deep Extreme Learning Machines(DELMs)is used.A novel dataset consisting of 195 projects data is proposed to train the model and to evaluate the overall efficiency of the proposed *** proposed DELM’s based model evaluations achieved 90.017%training accuracy having a value with 1.412×10^(-3) Root Mean Square Error(RMSE)and 85.772%testing accuracy with 1.569×10^(-3) RMSE with five DELMs hidden *** results express that the suggested model has gained a notable recognition rate in comparison to any previous *** current study also concludes DELMs as the most applicable and useful technique for OSMO client’s project assessment.

关键词： Software outsourcing deep extreme learning machine(DELM) machine learning(ML) extreme learning machine assessment

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Evaluating the Impact of Pulse Oximetry Bias in Machine Learning Under Counterfactual Thinking 3rd

Evaluating the Impact of Pulse Oximetry Bias in Machine L...

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3rd International Workshop on Applications of Medical Artificial Intelligence, AMAI 2024 held in conjunction with the 27th International Conference on Medical Image Computing and computer Assisted Intervention, MICCAI 2024

作者： Martins, Inês Matos, João Gonçalves, Tiago Celi, Leo A. Wong, An-Kwok Ian Cardoso, Jaime S. Faculty of Engineering University of Porto Porto Portugal Institute for Systems and Computer Engineering Technology and Science Porto Portugal Duke University Durham United States Massachusetts Institute of Technology Cambridge United States

ISBN: (纸本)9783031820069

Algorithmic bias in healthcare mirrors existing data biases. However, the factors driving unfairness are not always known. Medical devices capture significant amounts of data but are prone to errors;for instance, pulse oximeters overestimate the arterial oxygen saturation of darker-skinned individuals, leading to worse outcomes. The impact of this bias in machine learning (ML) models remains unclear. This study addresses the technical challenges of quantifying the impact of medical device bias in downstream ML. Our experiments compare a "perfect world", without pulse oximetry bias, using SaO2 (blood-gas), to the "actual world", with biased measurements, using SpO2 (pulse oximetry). Under this counterfactual design, two models are trained with identical data, features, and settings, except for the method of measuring oxygen saturation: models using SaO2 are a "control" and models using SpO2 a "treatment". The blood-gas oximetry linked dataset was a suitable test-bed, containing 163,396 nearly-simultaneous SpO2 - SaO2 paired measurements, aligned with a wide array of clinical features and outcomes. We studied three classification tasks: in-hospital mortality, respiratory SOFA score in the next 24 h, and SOFA score increase by two points. Models using SaO2 instead of SpO2 generally showed better performance. Patients with overestimation of O2 by pulse oximetry of ≥ 3% had significant decreases in mortality prediction recall, from 0.63 to 0.59, P © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Oximeters

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TCN-MLP: A Hybrid Model for Short-Term Electrical Load Forecasting 1

TCN-MLP: A Hybrid Model for Short-Term Electrical Load Forec...

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1st International Conference of Intelligent Methods, systems and Applications, IMSA 2023

作者： Farid, Karim S. Ali, A.A. Salem, Sameh A. Mohamed, Amr E. Egyptian Electricity Holding Company Dept. Information Systems & Communications Cairo Egypt Helwan University - Faculty of Engineering Dept. Electrical Power Cairo Egypt Helwan University - Faculty of Engineering Dept. Computer & Systems Engineering Cairo Egypt

ISBN: (纸本)9798350335569

Using artificial intelligence (AI) to anticipate electrical consumption is one of the used applications of this technology. The analysis and training of AI models are done using data related to electricity consumption, including consumption itself, temperatures, working hours, the number of inhabitants, etc. AI models can help to reduce electricity expenses and consumption, which helps in identifying peak use periods and creating more efficient energy plans. Another use of artificial intelligence is to advise users on how to use less energy in a more efficient manner. Based on such insight, this study suggests a hybrid model based on a temporal convolutional network and multilayer perceptron (TCN-MLP), to forecast hourly power consumption. The results show that the recommended model can account for the patterns in the historical data relating to hourly power usage. TCN-MLP hybrid model has proved to be better than competing approaches in the coefficient of determination (R2) and the mean absolute percentage error (MAPE%) metrics. © 2023 IEEE.

关键词： Electric power utilization

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CONV1D-GRU: A Hybrid Model for Short-Term Electrical Load Forecasting

CONV1D-GRU: A Hybrid Model for Short-Term Electrical Load Fo...

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2023 International Telecommunications Conference, ITC-Egypt 2023

ISBN: (纸本)9798350326062

With the development of plans to replace conventional meters with smart meters to enhance the functionality and performance of power system, forecasting power consumption is still a challenge due to nonlinearity patterns, different types of noise in the datasets, and sometimes lack of data. Artificial Intelligence plays an important role in utilizing the data sent from smart meters, particularly in the area of short-term load forecasting, which is required to meet the needs of consumers. Based on such insight, this study suggests a hybrid model based on a one-dimension convolutional neural network and gated recurrent units (CONV1D-GRU), to forecast hourly power consumption. The outcome demonstrates that the suggested model is capable of encompassing the patterns in a historical data pertaining the hourly power use. The suggested model has outperformed the competing strategies in the mean absolute percentage error (MAPE%), and the coefficient of determination (R^2) metrics. © 2023 IEEE.

关键词： Smart meters

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Recognition of American sign language using modified deep residual CNN with modified canny edge segmentation

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Multimedia Tools and Applications 2025年 1-28页

作者： Hariharan, U. Devarajan, Devikanniga Kumar, P. Santhosh Rajkumar, K. Meena, M. Akilan, T. Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Punjab Mohali140413 India Department of Computer Science and Engineering GITAM University Karnataka Bengaluru561203 India Department of Information Technology Faculty of Engineering and technology SRM Institute of Science and Technology Ramapuram Tamil Nadu Chennai600089 India Department of Data Science and Business Systems School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamil Nadu Chennai603203 India Department of Computer Science and Engineering Sri Sairam Engineering College Tamil Nadu Tambaram602109 India Research Coordinator Dept. of IQAC Amity University Kolkata West Bengal 700135 India

American Sign Language (ASL) recognition aims to recognize hand gestures, and it is a crucial solution to communicating between the deaf community and hearing people. However, existing sign language recognition algorithms still have some drawbacks, such as difficulty recognizing hand movements and low recognition accuracy for most sign language recognition. A Modified Convolutional Neural Network (MCNN) deep residual 101 classifier-based American Sign Language identification system has been developed to address this problem. There are three main parts presents in the method. The first part is pre-processing the images to remove the noise, enhance the contrast of the picture, adjust the contrast level and smoothen the picture using various filters. The second part is the segmentation, and it’s used to partition the image using a modified canny edge detection method by removing all weak edges present in the image. Finally, classification will be done using the Modified CNN deep residual 101 classifiers. By classifying the image, the American Sign Language is accurately identified. This process is conducted through images. The outcome shows that the suggested approach has a 0.97% accuracy and a 0.05% False Positive Rate. Other CNN architectures such as resNet 50 and resNet 18 reached 0.90% and 0.80% accuracy, respectively, which is lower than our proposed method. In addition, the resNet 101 classifier effectively recognizes the difficult hand gestures through the image data. It obtains high recognition accuracy for 36 signs is 0 to 9 numbers and a to z alphabets from American Sign Language. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Convolutional neural networks

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Trustworthiness Evaluation of Deep Learning Accelerators Using UVM-based Verification with Error Injection

Trustworthiness Evaluation of Deep Learning Accelerators Usi...

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2024 Design and Verification Conference and Exhibition Europe, DVCon Europe 2024

作者： Aboudeif, Randa Awaad, Tasneem A. AbdElsalam, Mohamed Ismail, Yehea Electronics and Communications Engineering Department The American University in Cairo Cairo Egypt Siemens Digital Industries Software Cairo Egypt Department of Computer and Systems Engineering Faculty of Engineering Ain Shams University Cairo Egypt

ISBN: (纸本)9783800764396

Testing the reliability and trustworthiness of high-performance computing (HPC) applications has made Deep Learning Accelerators (DLAs) verification critically important. In this paper, we introduce a hardware verification framework with an error injection methodology based on the Universal Verification Methodology (UVM) for DLAs that is scalable, reusable, and efficient to test the robustness and resilience of Deep Neural Networks (DNNs) running on various DLA designs. Furthermore, the error injection methodology is applicable to simulation and hardware-assisted verification (HAV) platforms for emulation and FPGA prototyping. Our proposed error injection mechanism is evaluated using Nvidia Deep Learning Accelerator (NVDLA), an open-source DLA core. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Reusability

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A Neutrosophic based C-Means Approach for Improving Breast Cancer Clustering Performance

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Neutrosophic Sets and systems 2023年 53卷 317-330页

作者： Abdelhafeez, Ahmed Mohamed, Hoda K Maher, Ali Abdelmonem, Ahmed Faculty of Information Systems and Computer Science October 6th University Cairo12585 Egypt Faculty of Engineering Ain shams University Cairo11566 Egypt Military Technical College Cairo18711 Egypt Faculty of computers and informatics Zagazig University Zagazig11544 Egypt

Breast cancer is among the most prevalent cancers, and early detection is crucial to successful treatment. One of the most crucial phases of breast cancer treatment is a correct diagnosis. Numerous studies exist about breast cancer classification in the literature. However, analyzing the cancer dataset in the context of clusterability for unsupervised modeling is rare. This work analyzes pointedly the breast cancer dataset clusterability via applying the widely used c-means clustering algorithm and its evolved versions fuzzy and neutrosophic ones. An in-depth comparative study is conducted utilizing a set of quantitative and qualitative clustering efficiency metrics. The study's outcomes divulge the presented neutrosophic c-means clustering superiority in segregating similar breast cancer instances into clusters © 2023, Neutrosophic Sets and *** Rights Reserved.

关键词： Diseases

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