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2024 IEEE Middle East Conference on Communications and Networking, MECOM 2024

作者： Siddig, Ali A. Al-Dweik, Arafat Iraqi, Youssef Pandey, Anshul Giacalone, Jean-Pierre Hamila, Ridha Al-Rimawi, Ashraf Khalifa University of Science and Technology Dept. of Computer and Information Eng. PO Box 127788 Abu Dhabi United Arab Emirates Mohammed VI Polytechnic College of Computing Benguerir Morocco Secure Systems Research Center Abu Dhabi United Arab Emirates Qatar University Dept. of Electrical Eng. Doha Qatar Birzeit University Dept. of Electrical and Computer Eng. Birzeit Palestine

ISBN: (纸本)9798350376715

To achieve the ultimate performance of intelligent reflecting surface (IRS), an accurate channel estimation is required, which in turns induces significant signaling overhead. This work presents a novel blind channel estimation scheme for IRS-assisted heterogeneous communications and sensing networks. The proposed scheme consists of two transmission stages, one for sensing and the other for communications. By noting that the sensing information have generally less stringent quality of service (QoS) requirements, the sensing stage is used for joint blind sensing data detection and blind channel estimation for the subsequent communications stage. Channel estimation is achieved using a specific frame structure where the modulated sensing symbols cooperate to estimate the channel coefficients for all IRS reflecting elements blindly and jointly. The simulation results obtained show that the proposed blind channel estimation accuracy is comparable to pilot-based schemes. © 2024 IEEE.

关键词： Channel estimation

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B2G (Buggy-to-Grid): Vehicle-to-Grid (V2G) concept in microgrids with strong penetration of electric vehicles

B2G (Buggy-to-Grid): Vehicle-to-Grid (V2G) concept in microg...

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2022 International Young Engineers Forum in electrical and computer engineering, YEF-ECE 2022

作者： Santos, Guilherme Pina, Joao Murta Belem, Ricardo Nova School of Science and Technology Fct Nova Dept. of Electrical and Computer Engineering Caparica Portugal Uninova Caparica Portugal Technical Director Dom Pedro Investimentos Lisboa Portugal

ISBN: (纸本)9781665467315

This paper aims to evaluate the potential of integrating Vehicle-to-Grid (V2G) technology into a golf course, together with a self-consumption Photovoltaic (PV) system, by using the surplus generated energy to charge the golf buggies and later discharging it according to the course needs. The Dom Pedro Golf Course, located in the south of Portugal, is used as a case study. Its PV system compromises 244 solar panels with 97.6 kWp total, and a predicted annual first-year generation of 176.70 MWh and 9.34 MWh of surplus, that the buggies could discharge through V2G technology. With these integrated technologies, the first year of investment results includes 87% Performance Ratio (PR), 100% of Self-Consumption Ratio (SCR), and 35% Self-Sufficiency Ratio (SSR), while the Benefit-Cost Ratio (BCR) of the V2G technology alone for the whole considered investment horizon of ten years is 1.79. An economic assessment of the project showed that results followed the same positive trend, with a Net Present Value (NPV) of 50,353.42€, an Internal Rate of Return (IRR) of 13%, a Payback Period (PP) of seven years, and a Levelized Cost of Energy (LCOE) of 0.1315 €/kWh, suggesting a beneficial and feasible result for the investment period. © 2022 IEEE.

关键词： Energy efficiency

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A Comparative Analysis of Two Dielectric Nanostructures to Enhance Efficiency of Perovskite Solar Cells

A Comparative Analysis of Two Dielectric Nanostructures to E...

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2022 International Conference on Advancement in electrical and Electronic engineering, ICAEEE 2022

作者： Hasen, Nayem Rahman, Aminur Singha, Showmik Chowdhury, Tahmid Aziz Shahjalal University of Science and Technology Dept. of Electrical and Electronic Engineering Sylhet3114 Bangladesh Rensselaer Polytechnic Institute Dept. of Electrical Computer and Systems Engineering TroyNY12180 United States

ISBN: (数字)9781665469449

ISBN: (纸本)9781665469449

Perovskite (CH3NH3PbI3) solar cells (PSCs) have recently been invented due to their desirable characteristics such as high absorption, low cost, ease of fabrication, and rapidly improving efficiencies. In recent years, the effectiveness of organometallic halide perovskite as an absorber layer has already surpassed 20%. Dielectric nanostructures are being considered as a possible option for third generation photovoltaics, such as PSCs, due to lower losses and simpler fabrication. Dielectric nanostructured antireflection coatings, on the other hand, have recently emerged as a method for reducing reflection and improving the power conversion efficiency (PCE) of a thin-film absorber that was previously inefficient. In this research, two different types of nanostructures are used on the perovskite absorber layer that reduced light reflection and enhanced the power absorption, short-circuit current, and efficiency of the solar cells. Furthermore, as demonstrated how changing the nanostructure size has an effect on the absorption enhancement. Utilizing the finite difference time domain (FDTD) and coupled optical-electrical models, the proposed optimum nanostructures exhibit greater than 90% power absorption, with the maximum enhanced photocurrent. Total photocurrent increased by 17-20% compared to conventional planar PSCs, with a maximum PCE of 16.78%, representing 37% enhancement throughout the entire wavelength range. © 2022 IEEE.

关键词： Perovskite

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Predicting Lifespan of Ground-to-Air Multipath Components in mmWave UAV Channels

Predicting Lifespan of Ground-to-Air Multipath Components in...

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IEEE Conference on Wireless Communications and Networking

作者： Wahab Khawaja Rune H. Jacobsen Sajid Hussain Ismail Guvenc Dept. Electrical and Computer Engineering Aarhus University Aarhus Denmark Dept. Computer Systems Engineering Mirpur Univ. Science and Technology Mirpur AJK Pakistan Dept. Electrical and Computer Engineering North Carolina State University Raleigh NC USA

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

In mobile ground-to-air (GA) propagation channels, the birth and death of multipath components (MPCs) are frequently observed, and the wide-sense stationary uncorrelated scattering (WSSUS) assumption does not always hold. Several methods exist for tracking the birth and death of MPCs, however, to the best of knowledge of authors, there is no existing literature that addresses the prediction of the lifespan of the MPCs in non-WSSUS GA propagation channels. In this work, we consider the GA channel as non-WSSUS and individual MPCs across receiver positions are represented as time series based on the Euclidean distance between channel parameters of the MPCs. These time series representations, referred to as path bins, are analyzed using a semi-Markov chain model. The channel parameter variations and dependencies between path bins are used to predict the lifespan of path bins using weighted sum method, machine learning classifiers, and deep neural networks. For comparison, the birth and death of path bins are also modeled using a Poisson distribution and a Markov chain. Simulation results demonstrate that deep neural networks offer highly accurate predictions for the lifespan (including death) of MPC path bins in the considered GA propagation scenario.

关键词： Analytical models Simulation Time series analysis Scattering Artificial neural networks Machine learning Receivers Euclidean distance Autonomous aerial vehicles Millimeter wave communication

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Visual Field Prediction for Fundus Image with Generative AI 18

Visual Field Prediction for Fundus Image with Generative AI

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18th International Conference on Ubiquitous Information Management and Communication, IMCOM 2024

作者： Kang, Honggu Ko, Seonghyeon Kim, Juchan Le, Duc-Tai Bum, Junghyun Han, Jongchul Choo, Hyunseung Dept. of AI Systems Engineering Sungkyunkwan University Suwon Korea Republic of College of Computing and Informatics Sungkyunkwan University Suwon Korea Republic of Sungkyun AI Research Institute Sungkyunkwan University Suwon Korea Republic of Dept. of Ophthalmology Samsung Medical Center Seoul Korea Republic of Dept. of Electrical and Computer Engineering Sungkyunkwan University Suwon Korea Republic of

ISBN: (纸本)9798350331011

Accurate diagnosis of glaucoma is crucial due to its high risk of blindness, but the long examination time often undermines the reliability of results by examinee's subjective factors. We propose a method to reduce examination time by generating static perimetry results with Conventional Fundus Images (CFIs) utilizing the CFI2GM technique, which leverages multimodal data. Based on data from 3,306 glaucoma patients at Samsung Medical center in Seoul, we conducted ophthalmic image translation utilizing the Pix2Pix model. Our method, combining cGAN, L1, and SSIM loss, achieved MSE 57.9886 and PSNR 30.6057 dB. Furthermore, we received positive feedback from ophthalmologist regarding the high practical applicability of the images generated by our method. This indicates that CFI2GM can enhance the reliability of glaucoma examination results as well as reduce testing time. © 2024 IEEE.

关键词： Diagnosis

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Low-Latency and Energy-Efficient Task Scheduling for End-Edge-Cloud Collaborative Computing

Low-Latency and Energy-Efficient Task Scheduling for End-Edg...

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2024 IEEE International Conference on systems, Man, and Cybernetics, SMC 2024

作者： Li, Jingyao Yuan, Haitao Ma, Yaofei Bi, Jing Yang, Jinhong Zhang, Jia School of Automation Science and Electrical Engineering Beihang University Beijing100191 China College of Computer Beijing University of Technology Beijing100124 China Cssc Systems Engineering Research Institute Beijing China Southern Methodist University Dept. of Computer Science DallasTX75275 United States

ISBN: (纸本)9781665410205

Mobile edge computing (MEC) is a new paradigm that improves the quality of service compared with traditional cloud computing. In MEC, computational tasks are submitted by numerous end users and are partially offloaded to edge servers or a central cloud. However, the characteristics of tasks are different from each other, and the limited resources of computational nodes are also heterogeneous, which brings great challenges to computation offloading and resource allocation for MEC. This work establishes an end-edge-cloud collaborative computing network, which consists of end devices, edge servers, and a central cloud. Task execution location and CPU running frequency determine the execution time and energy consumption to finish the tasks. Considering the aforementioned factors, a multi-objective constrained optimization problem is formulated. To solve the problem, an improved Non-dominated Sorting Genetic Algorithm II (NSGA-II) with self-adaptive crossover and mutation rates is proposed, which is called Improved NSGA-II with _Self-adaptive Crossover and Mutation (INSCM). The total execution time and energy consumption can be jointly minimized with our proposed INSCM. Numerous experiments are carried out to test the performance of INSCM. Simulation results show that INSCM effectively improves the performance of NSGA-II and surpasses random offloading and NSGA-III, which shows practical use in real-life scenarios. © 2024 IEEE.

关键词： Mobile edge computing

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Task Reverse Offloading with Deep Reinforcement Learning in Multi-Access Edge Computing 9

Task Reverse Offloading with Deep Reinforcement Learning in ...

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9th International Conference on computer and Communication engineering, ICCCE 2023

作者： Saeed, Mamoon M. Saeed, Rashid A. Mokhtar, Rania A. Khalifa, Othman O. Ahmed, Zeinab E. Barakat, Mohammed Elnaim, Areeg Ali Faculty of Engineering Department of Communications and Electronics Engineering Yemen Taif University College of Computers and Information Technology Department of Computer Engineering P.O. Box 11099 Taif21944 Saudi Arabia International Islamic University Malaysia Department of Electrical and Computer Engineering Malaysia Libyan Center for Engineering Research and Information Technology Bani Waleed Libya International Islamic University Malaysia Electrical and Computer Engineering Dept. Malaysia University of Gezira Computer Engineering Dept. Sudan Alneelain University College of Engineering Khartoum Sudan

ISBN: (纸本)9798350325218

The Multi-access Edge Computing (MEC) technology's quick development greatly benefits the Collaborative Mobile Infrastructure System (CMIS). To combine the data and produce tasks, crowd-sensing data will be transferred to the MEC server in CMIS. Nevertheless, if there are too many devices, it becomes extremely difficult for MEC to decide appropriately based on the data from the devices and infrastructure. This study builds a framework for reverse offloading that carefully balances the relationship between task completion time and user mobile energy consumption. Moreover, to decrease system use generally, an adaptive optimal reverse offloading method based on Deep Q-Network is created (DQN). The results of the simulations demonstrate that the suggested approach may successfully minimize energy consumption and work latency when compared to full local and fixed offloading techniques. © 2023 IEEE.

关键词： Reinforcement learning

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Modified Convolutional Neural Network Algorithm for Categorization of Solar PV Panel Soiling 2

Modified Convolutional Neural Network Algorithm for Categori...

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2nd International Conference on Smart Technologies for Power and Renewable Energy, SPECon 2024

作者： Chaki, Abhyudoy Bhatia, Rachna Mishra, Pallavi Singh, R. Raja Rajasekar, N. Vellore Institute of Technology School of Computer Science Vellore India Vellore Institute of Technology School of Advanced Sciences Dept. of Mathematics Vellore India Vellore Institute of Technology Automotive Research Center Vellore India Vellore Institute of Technology School of Electrical Engineering Vellore India

ISBN: (纸本)9798350383041

The identification and categorization of soiling using artificial intelligence is essential for improving solar photovoltaic (PV) system efficiency. It is possible to use focused and effective cleaning techniques on solar panels by classifying and comprehending the particular types of pollutants. This research proposes a modified convolutional neural network (CNN) approach to efficiently classify soiling on solar PV panels. In contrast to the conventional method, the suggested CNN model is created using simple images rather than images plus time series data. As a result, processing speed is increased. Additionally, the suggested model achieved 96.91 percent accuracy. This success establishes our suggested methodology as a ground-breaking development in soiling type detection, providing improved accuracy and a unique strategy in contrast to current approaches. The trained and tested model is deployed on the web using the Flask. Flask is a powerful and flexible microweb framework for Python, Finally, a comparison is made between the existing method and the proposed method. © 2024 IEEE.

关键词： Image classification

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Predicting Lifespan of Ground-to-Air Multipath Components in mmWave UAV Channels

arXiv

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arXiv 2025年

作者： Khawaja, Wahab Jacobsen, Rune H. Hussain, Sajid Guvenc, Ismail Dept. Electrical and Computer Engineering Aarhus University Aarhus8000 Denmark Dept. Computer Systems Engineering Mirpur Univ. Science and Technology AJK Mirpur10250 Pakistan

关键词： Markov chains

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Optimization of Imprecise Multiplier Circuits by using Binary Decision Diagram 9

Optimization of Imprecise Multiplier Circuits by using Binar...

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9th IEEE International Symposium on Smart Electronic systems, iSES 2023

作者： Choudhary, Pooja Bhargava, Lava Vinod, G.U. Suhag, Ashok Kumar Fujita, Mashahiro Singh, Virendra Swami Keshvanand Institute of Technology Management and Gramothan Dept. of Electronics and Communication Engineering Jaipur India Malaviya National Institute of Technology Jaipur India Malaviya National Institute of Technology Dept. of Electronics and Communication Engineering Jaipur India Qualcomm Bangalore India Bml Munjal University Dept. of Electronics and Communication Engineering Haryana India Vlsi Design and Education Center University of Tokyo Japan Indian Institute of Technology Dept. of Electrical Engineering Dept. of Computer Science Engineering Bombay India

ISBN: (纸本)9798350383249

Imprecise multipliers are employed to enhance the efficiency and reduce the power consumption of arithmetic circuits while tolerating acceptable levels of inaccuracy in the outcomes. The multiplier circuit has a broader range of applications within embedded systems, particularly in the fields of digital signal processing, IoT and multimedia, etc. We proposed a systematic formal approach utilizing the Look-Up Table (LUT) employing the Burch method to evaluate the imprecise multiplier based on the Binary Decision Diagram (BDD) considering Worst Case Error constraint. Implemented 13 bit multiplier using normal and proposed imprecise multiplier shows 98% reduction in number of nodes. The proposed 25 bit imprecise multiplier based on Burch HL shows an 89% decrement in a number of nodes w.r.t. Burch LH method. BDD generation for imprecise multiplier is 75% faster than normal. © 2023 IEEE.

关键词： Timing circuits

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