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检索条件"机构=Dep. of Electronics and Computer Engineering"

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Enhancing the accuracy of fitness competitions results validation via designing and implementing of Fitness Auto Referee (FAR) evaluation system based on Internet of Things (IOT)

Enhancing the accuracy of fitness competitions results valid...

引用

2024 International Telecommunications Conference, ITC-Egypt 2024

作者： Fayad, Youssef Mahmoud, Ahmed Khairy Miligy, Ahmed F. El Monem, Mahmoud Ahmed Abd El Shafei, Ahmed Osama Mabrouk, Kareem Ehab Air defense college Alexandria University Electronics and Communication Dep. Alexandria Egypt Alexandria University Computer and communications engineering Dep. Alexandria Egypt

ISBN: (纸本)9798350351408

Internet of Things (IOT) has its importance in the field of data exchange and control, makes great effects when applied in the fitness field. Applying IOT technology in fitness field could introduce an auto judge for fitness competitions by counting the repetitions of any exercise accurately, and exchange data with an accurate preprepared data base. In this paper a novel fitness auto referee (FAR) system has been presented. This system could be emerged as a specialized groundbreaking assistive technology to enhance the accuracy of fitness competitions results validation. The algorithm of the presented system relies on the distinctive physical properties, gender, and age of each player as inputs. The golden ratio or the golden number 1.618 has been employed in this system to locate sensors accurately, also to count the correct repetitions as function of the player tall. This system has the ability to deal with multiplayers simultaneously indoor or outdoor. Last but not least it provides a flexible means to carry out exercises with different groups (males/females - special forces/combat forces - adults/children - athletes/nonathletes. etc.) accurately within a short period of time. © 2024 IEEE.

关键词： Electronic health record

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Energy Access Assessment Report Focusing on Energy Poverty and Energy Demand for Rwanda 3

Energy Access Assessment Report Focusing on Energy Poverty a...

引用

3rd IEEE International Maghreb Meeting of the Conference on Sciences and Techniques of Automatic Control and computer engineering, MI-STA 2023

作者： Asiegbu, Adimchinobi Almaktoof, Ali Khalat, Abdurazag Cape Peninsula University of Technology Dep. of Electrical Electronics and Computer Engineering Cape Town South Africa Sabratha University Dep. of Electrical and Electronics Engineering Sabratha Libya

ISBN: (纸本)9798350319897

This technical report is a researched document that examines energy access in Rwanda, focusing on energy poverty assessment and energy demand assessment. The key findings and observations can help mitigate the energy access problems and improve access to modern energy in Rwanda. Studies have shown that sub-Saharan African countries have poor access or no access to modern energy, Rwanda being part of sub-Saharan Africa also has Poor access to modern and clean energy. To carefully study these energy access problems and proffer solutions to increase energy access in Rwanda based on realities in today's Africa, (where most households fall below poverty lines, with social issues, low Purchasing Power Parity (PPP) and Gross Domestic Power (GDP)), an assessment of these energy problems is done based on Energy Poverty Assessment (EPA) and Energy Demand Assessment (EDA) Composite Multidimensional Indicators (CMDI), respectively. Based on the findings of this research, non-poor energy households increased their energy consumption by 29.26% to 0.68 billion kWh. The clean cooking gas usage in Rwanda is very low at 2.1%, The 2016 estimated electricity consumption is 0.5273 billion kWh, growing by 57.41% to 0.83 billion kWh for energy-poor households in 2021, compared to 52.6% predicted by International Energy Agency (IEA). Finally, there is a direct relationship between energy access and economic development including quality of life, standard of living, per capita income, urbanization, modern infrastructure, etc. Thus, the key findings and observations in this research report will improve economic and infrastructural development in Rwanda, if implemented. © 2023 IEEE.

关键词： Smart power grids

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AbotalebNet: A Novel Deep Learning Architecture for Time Series Forecasting with Convolutional and LSTM Layers Enhanced by Multi-Head Attention 2

AbotalebNet: A Novel Deep Learning Architecture for Time Ser...

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2nd IEEE International Conference on Trends in Quantum Computing and Emerging Business Technologies, TQCEBT 2024

作者： Abotaleb, Mostafa Makarovskikh, Tatiana Mijwil, Maad M. Bala, Indu Hiran, Kamal Kant Doshi, Ruchi South Ural State University Dep. of System Programming Chelyabinsk454080 Russia Baghdad College of Economic Sciences University Computer Techniques Engineering Department Baghdad Iraq Lovely Professional University Electrical and Electronics Engineering Punjab India Sir Padampat Singhania University Dep. of Computer Science Udaipur India Universidad Azteca Dep. of Computer Science and Engineering Chalco Mexico

ISBN: (纸本)9798350384277

This paper introduces AbotalebNet, a novel deep learning architecture optimized for time series forecasting, with a particular focus on the complexities of COVID-19 data. AbotalebNet's architecture is mathematically represented as:\begin{equation*}\hat Y = {\text{Attention}}(\operatorname{LSTM} ({\text{CNN}}(X)))\tag{1}\end{equation*}Here, X represents the input time series data, and Ŷ is the forecasted output. AbotalebNet integrates the feature extraction capabilities of Convolutional Neural Networks (CNNs) with the sequential data processing power of Long Short-Term Memory (LSTM) networks, further enhanced by a Multi-Head Attention *** address overfitting, a key challenge in deep learning, the model incorporates regularization strategies, dropout mechanisms, and batch normalization:\begin{equation*}\mathop {\min }\limits_\Theta ( Loss (X,\hat Y;\Theta ) + \lambda R(\Theta ))\tag{2}\end{equation*}In this equation, Θ denotes the model parameters, R(Θ) represents regularization terms, and λ is the regularization coefficient. These additions aid in preventing the model from overfitting to the training data, ensuring robust performance on unseen *** evaluation on COVID-19 time series data demonstrates AbotalebNet's enhanced predictive accuracy over traditional models, solidifying its potential for advanced non-linear time series analysis. © 2024 IEEE.

关键词： Convolutional neural networks

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Fault-Tolerant FPGA-Based System for Mitigating SEUs in Configuration and User Bits

Fault-Tolerant FPGA-Based System for Mitigating SEUs in Conf...

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2024 International Conference on Microelectronics, ICM 2024

作者： Amer, O.T. Alkady, G.I. Mohamed, F.Y. Mahmoud, A.W. Amer, H.H. Daoud, R.M. Cairo University Communication & Computer Eng. Dep. Faculty of Engineering Cairo Egypt American University in Cairo Electronics & Communications Engineering Department Sead Group Cairo Egypt

ISBN: (纸本)9798350379396

This paper addresses two issues. First, in the context of factory automation, a low-cost fault-tolerant sensor system is proposed along with its FPGA-based voter. Second, the voter itself is designed to recover from failures affecting its own circuitry. The sensor system consists of one high-quality sensor used for measurement along with two functionally identical, but lower cost sensors used for monitoring purposes. A biased voter circuit is designed and implemented on the same Field Programmable Gate Array (FPGA) used to house the controller. It is shown that the voter can detect and recover from Single Event Upsets (SEUs) which often occur in the harsh factory environments. Both FPGA configuration and user bits are protected. It was shown that the proposed design requires less resources and power than some other designs presented in the literature. Focusing next on recovering from SEUs in both user and configuration FPGA bits, another use case is investigated, namely an I2C master;it is shown that the proposed design again requires less resources than some other commonly used techniques. Both proposed designs were implemented on the Xilinx Zynq-7000 (xc7z020clg400-1). © 2024 IEEE.

关键词： Field programmable gate arrays (FPGA)

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CCDF measurement with Multi Impairment of Wi-Fi 6 performance using MATLAB Simulink 3

CCDF measurement with Multi Impairment of Wi-Fi 6 performanc...

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3rd International Conference on Advances in Electrical, Computing, Communication and Sustainable Technologies, ICAECT 2023

作者： Al-Azzawi, Fatima Faydhe Abbas, Rasha Hassan Al-Azzawi, Zainab Faydhe Kamal, Kamal Y. Institute of Technology Electronics Technical Department Middle Technical University MTU Baghdad Iraq AL- Mansour University College Dep. Computer Technology Engineering Iraq

ISBN: (纸本)9781665494007

The current 802.11ac WiFi standard is built upon and improved by WiFi 6, commonly referred to as 'AX WiFi' or '802.11ax WiFiIn response to the increasing number of gadgets in the globe, WiFi 6 was first developed. If you have numerous smart home devices, VR equipment, or a lot of other gadgets, WiFi 6 employs a 160 MHz Channel to offer a channel that larger and providing by 1024-QAM to more data with one packed, increasing efficiency. In this paper 802.11 ax design and implemented in MATLAB Simulink where CCDF measurement (Complementary Cumulative Distribution Function measure the probability that a signal's instantaneous power may exceed its target level or average power) with multi-impairment performance illustrated using 1024-QAM to offer a packed signal with increasing data and a channel with160 MHz, 1024 QAM examined with AWGN and Rayliegh fading channel in term of bit error rate. © 2023 IEEE.

关键词： Fading channels

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Enhancing the accuracy of fitness competitions results validation via designing and implementing of Fitness Auto Referee (FAR) evaluation system based on Internet of Things (IOT)

Enhancing the accuracy of fitness competitions results valid...

引用

Telecommunications Conference (ITC-Egypt), International

作者： Youssef Fayad Ahmed Khairy Mahmoud Ahmed F. Miligy Mahmoud Ahmed Abd El monem Ahmed Osama El shafei Kareem Ehab Mabrouk Electronics and Communication Dep. Air defense college Alexandria University Alexandria Egypt Computer and communications engineering Dep. Alexandria University Alexandria Egypt

ISBN: (数字)9798350351408

ISBN: (纸本)9798350351415

关键词： Military communication Accuracy Costs Military computing Instruments Sensor systems Telecommunications

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Energy Access Assessment Report Focusing on Energy Poverty and Energy Demand for Rwanda

Energy Access Assessment Report Focusing on Energy Poverty a...

引用

Sciences and Techniques of Automatic Control and computer engineering MI-STA, IEEE International Maghreb Meeting of the Conference on

作者： Adimchinobi Asiegbu Ali Almaktoof Abdurazag Khalat Dep. of Electrical Electronics and Computer Engineering Cape Peninsula University of Technology Cape Town South Africa Dep. of Electrical and Electronics Engineering Sabratha University Sabratha Libya

This technical report is a researched document that examines energy access in Rwanda, focusing on energy poverty assessment and energy demand assessment. The key findings and observations can help mitigate the energy access problems and improve access to modern energy in Rwanda. Studies have shown that sub-Saharan African countries have poor access or no access to modern energy, Rwanda being part of sub-Saharan Africa also has Poor access to modern and clean energy. To carefully study these energy access problems and proffer solutions to increase energy access in Rwanda based on realities in today’s Africa, (where most households fall below poverty lines, with social issues, low Purchasing Power Parity (PPP) and Gross Domestic Power (GDP)), an assessment of these energy problems is done based on Energy Poverty Assessment (EPA) and Energy Demand Assessment (EDA) Composite Multidimensional Indicators (CMDI), respectively. Based on the findings of this research, non-poor energy households increased their energy consumption by 29.26% to 0.68 billion kWh. The clean cooking gas usage in Rwanda is very low at 2.1%, The 2016 estimated electricity consumption is 0.5273 billion kWh, growing by 57.41% to 0.83 billion kWh for energy-poor households in 2021, compared to 52.6% predicted by International Energy Agency (IEA). Finally, there is a direct relationship between energy access and economic development including quality of life, standard of living, per capita income, urbanization, modern infrastructure, etc. Thus, the key findings and observations in this research report will improve economic and infrastructural development in Rwanda, if implemented.

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A Support Vector Regression based Machine Learning method for on-chip Aging Estimation 4

A Support Vector Regression based Machine Learning method fo...

引用

4th IEEE International Conference on Computing and Information Sciences, ICCIS 2021

作者： Alnuayri, Turki Martinez, A. Leonel Hernandez Khursheed, Saqib Rossi, Daniele University of Liverpool Dep. of Electrical Engineering and Electronics Liverpool United Kingdom Dep. of Computer Engineering Taibah University Medina Saudi Arabia University of Pisa Dep. of Information Engineering Pisa Italy

ISBN: (纸本)9781665494410

Semiconductor supply chain industry is spread worldwide to reduce cost and to meet the electronic systems high demand for ICs, and with the era of internet of things (IoT), the estimated numbers of electronic devices will rise over trillions. This drift in the semiconductor supply chain produces high volume of e-waste, which affects integrated circuits (ICs) security and reliability through counterfeiting, i.e., recycled and remarked ICs. Utilising recycled IC as a new one or a remarked IC to upgrade its level into critical infrastructure such as defence or medical electronics may cause systems failure, compromising human lives and financial loss. This paper harvests aging degradation induced by BTI and HCI, observing frequency and discharge time affected by changes in drain current and sub-threshold leakage current over time, respectively. Such task is undertaken by Cadence simulations, implementing a 51-stage ring oscillator (51-RO) using 22nm CMOS technology library and aging model provided by GlobalFoundries (GF). Machine learning (ML) algorithm of support vector regression (SVR) is adapted for this application, using a training process that involves operating temperature, discharge time, frequency, and aging time. The data sampling is performed over an emulated 12 years period with four representative temperatures of 20° C, 40° C, 60° C, and 80° C with additional testing data from temperatures of 25° C and 50° C. The results demonstrate a high accuracy on aging estimation by SVR, reported as a normal distribution with the mean (μ) equal to 0.01 years (3.6 days) and a standard deviation (σ) of ±0.1 years (±36 days). © 2021 IEEE.

关键词： Integrated circuits

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AbotalebNet: A Novel Deep Learning Architecture for Time Series Forecasting with Convolutional and LSTM Layers Enhanced by Multi-Head Attention

AbotalebNet: A Novel Deep Learning Architecture for Time Ser...

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

作者： Mostafa Abotaleb Tatiana Makarovskikh Maad M. Mijwil Indu Bala Kamal Kant Hiran Ruchi Doshi Dep. of System Programming South Ural State University Chelyabinsk Russia Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Electrical and Electronics Engineering Lovely Professional University Punjab India Dep. of Computer Science Sir Padampat Singhania University Udaipur India Dep. of Computer Science and Engineering Universidad Azteca Chalco Mexico

ISBN: (数字)9798350384277

ISBN: (纸本)9798350384284

This paper introduces AbotalebNet, a novel deep learning architecture optimized for time series forecasting, with a particular focus on the complexities of COVID-19 data. AbotalebNet’s architecture is mathematically represented as:\begin{equation*}\hat Y = {\text{Attention}}(\operatorname{LSTM} ({\text{CNN}}(X)))\tag{1}\end{equation*}Here, X represents the input time series data, and Ŷ is the forecasted output. AbotalebNet integrates the feature extraction capabilities of Convolutional Neural Networks (CNNs) with the sequential data processing power of Long Short-Term Memory (LSTM) networks, further enhanced by a Multi-Head Attention *** address overfitting, a key challenge in deep learning, the model incorporates regularization strategies, dropout mechanisms, and batch normalization:\begin{equation*}\mathop {\min }\limits_\Theta ( Loss (X,\hat Y;\Theta ) + \lambda R(\Theta ))\tag{2}\end{equation*}In this equation, Θ denotes the model parameters, R(Θ) represents regularization terms, and λ is the regularization coefficient. These additions aid in preventing the model from overfitting to the training data, ensuring robust performance on unseen *** evaluation on COVID-19 time series data demonstrates AbotalebNet’s enhanced predictive accuracy over traditional models, solidifying its potential for advanced non-linear time series analysis.

关键词： COVID-19 Analytical models Solid modeling Time series analysis Training data Predictive models Data models

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Forecasting of Monkeypox Cases Using Optimized SARIMAX Based Model 3

Forecasting of Monkeypox Cases Using Optimized SARIMAX Based...

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3rd IEEE International Conference on Electronic engineering, ICEEM 2023

作者： Elshabrawy, Mahmoud Eid, Marwa M. Abdelhamid, Abdelaziz A. El-Kenawy, El-Sayed M. Ibrahim, Abdelhameed Delta Higher Institute of Engineering and Technology Dept. of Communications & Electronics Mansoura Egypt Delta University for Science and Technology Faculty of Artificial Intelligence Mansoura35111 Egypt Ain Shams University Faculty of Computer and Information Sciences Dep. of Computer Science Cairo Egypt University Mansoura Faculty of Engineering Computer Engineering and Control Systems Dept. Egypt

ISBN: (纸本)9798350323511

This study presents a dipper-throated-based ant colony optimization (DTACO) with the Seasonal Auto-Regressive Integrated Moving Average with eXogenous factor (SARIMAX) model (DTACO+SARIMAX) to forecast monkeypox cases. The work optimizes the SARIMAX model using grid search cross-validation and fine-tunes its hyperparameters using DTACO to improve prediction accuracy. The suggested model's consistency and accuracy are considerable compared to previous studies. Comparisons with state-of-the-art models validate the proposed model's predictions. DTACO+SARIMAX can be used to control disease and monitor monkeypox. Healthcare organizations and governments can better manage and track the pandemic's course by offering accurate predictions, reducing public panic, and enabling effective pandemic planning. The Analysis of Variance (ANOVA) and Wilcoxon signed-rank tests are conducted on the proposed DTACO-SARIMAX model and compared models. © 2023 IEEE.

关键词： Ant colony optimization

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