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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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EMGTTL: Transformers-Based Transfer Learning for Classification of ADL using Raw Surface EMG Signals

arXiv

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arXiv 2024年

作者： Kareemulla, Ashraf Ali Sanodiya, Rakesh Kumar Turlapaty, Anish Chand Naidu, Surya Dept. of Computer Science and Engineering Indian Institute of Information Technology Sri City India Dept. of Electronics and Communication Engineering Indian Institute of Information Technology Sri City India

Surface Electromyography (sEMG) is widely studied for its applications in rehabilitation, prosthetics, robotic arm control, and human-machine interaction. However, classifying Activities of Daily Living (ADL) using sEMG signals often requires extensive feature extraction, which can be time-consuming and energy-intensive. The objective of this study is stated as follows. Given sEMG datasets, such as electromyography analysis of human activity databases (DB1 and DB4), with multi-channel signals corresponding to ADL, is it possible to determine the ADL categories without explicit feature extraction from sEMG signals. Further is it possible to learn across the datasets to improve the classification performances. A classification framework, named EMGTTL, is developed that uses transformers for classification of ADL and the performance is enhanced by cross-data transfer learning. The methodology is implemented on EMAHA-DB1 and EMAHA-DB4. Experiments have shown that the transformer architecture achieved 64.47% accuracy for DB1 and 68.82% for DB4. Further, using transfer learning, the accuracy improved to 66.75% for DB1 (pre-trained on DB4) and 71.04% for DB4 (pre-trained on DB1). © 2024, CC BY.

关键词： Adversarial machine learning

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Human Breathing Rate Monitoring Using Modulated Scatterer-Based Radar technology

Human Breathing Rate Monitoring Using Modulated Scatterer-Ba...

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IEEE Instrumentation and Measurement technology Conference

作者： Mithaa Alhanaee Baker Mohammad Mohamed A. Abou-Khousa Dept. Electrical Engineering and Computer Science Khalifa University of Science and Technology Abu Dhabi United Arab Emirates

ISBN: (数字)9798350380903

ISBN: (纸本)9798350380910

Remote monitoring of human vital signs using Radar concepts has attracted considerable attention lately. However, the proposed methods thus far are susceptible to background clutter and random body motion which impact their utility. This paper demonstrates the application of the modulated scattering technique in remote monitoring of human breathing rate. A scatterer used as the breathing rate monitoring tag was built and modulated in order to have two different states. The states are then subtracted from each other to get an accurate measurement of the breathing rate by eliminating as much random body movement and clutter information as possible.

关键词： Wireless communication Accuracy Noise Modulation Scattering Radar Medical services

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Utilizing AI-powered Drones for Efficient Flood Rescue Operations

Utilizing AI-powered Drones for Efficient Flood Rescue Opera...

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Automation, Computing and Renewable Systems (ICACRS), International Conference on

作者： M. Jaswanth Kumar Dhanush Bitra Rohan Titus Dill Jazz Cinu C Kiliroor Dept. of Electronics and Communication Indian Institute of Information Technology Kottayam Kottayam Kerala India Dept. of Computer Science Indian Institute of Information Technology Kottayam Kottayam Kerala India

ISBN: (数字)9798331532420

ISBN: (纸本)9798331532437

In times of severe earthquakes and floods, rapid and effective aid is crucial. Traditional methods often face challenges due to accessibility issues and the vast scale of *** proposed Autonomous Aerial Humanitarian Assistance and Disaster Relief (A2-HADR) System aims to transform disaster response through the use of drone technology. The proposed solution addresses the obstacles in rescue operations by employing drones equipped with Artificial Intelligence (AI) to provide immediate support. The advanced system utilizes state-of-the-art computer Vision technology to autonomously detect people and obstacles from heights of 50-100 meters at various angles and deliver essential supplies like food, clothing, and rescue gear to those in need. We have executed and assessed a range of advanced object detection algorithms, such as YOLOv8, YOLOv9, and Detectron2. After thorough evaluation, Detectron2 emerged as the most effective model among those tested, showcasing exceptional accuracy and resilience.

关键词： Deep learning computer vision Accuracy Disasters Computational modeling Object detection Disaster management Transforms Artificial intelligence Drones

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Enhancing Power Quality in Distribution Systems through Bi-directional Solid-State Transformers

Enhancing Power Quality in Distribution Systems through Bi-d...

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On-Line Conference (ONCON), Industrial Electronics Society Annual

作者： Khaled Esamil SH Ghambirlou Adel Ali Abosnina Gerry Moschopoulos Dept. Electrical and Computer Engineering Western University London Canada College of Electrical and Electronics Technology Benghazi Libya

ISBN: (数字)9798331540319

ISBN: (纸本)9798331540326

Power quality issues, including voltage sags, swells, harmonic distortion, oscillatory voltage transients, etc. can degrade equipment performance, leading to inefficiencies and costly disruptions. Studying these issues and introducing solutions are essential for maintaining the reliability and efficiency of modern distribution systems. This paper examines and contrasts the benefits of a bidirectional three-stage solid-state transformer (SST) in mitigating power quality issues to traditional transformers. SST introduces reactive power support to improve the system quality by controlling active and reactive power. This paper investigates key power quality issues-voltage sag, swell, harmonic distortion, and oscillatory transients-arising from capacitor bank energizing, focusing on the novel application of solid-state transformers (SSTs) in mitigating these effects. Additionally, it examines SST-based fault isolation across various fault types (three-phase, double line-to-ground, and line-to-ground). Several simulations have been performed using the MATLAB/Simulink platform to highlight the benefits of the Solid-State Transformer (SST) over conventional transformers and cascade multiple-active-bridge SST (CMABSST) types. The proposed SST employs active and reactive power control to alleviate these effects, allowing the SST controller to manage reactive power effectively and provide the required support that makes the system stable and reliable.

关键词： Reactive power Reactive power control Power quality Power system stability Transformers Stability analysis Power system reliability Harmonic distortion Transient analysis MATLAB

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Electricity Price Forecasting Enhancement Using Combined Model

Electricity Price Forecasting Enhancement Using Combined Mod...

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Annual North-American Power Symposium

作者： Eric Lu Mingze Li Dept. of Electrical and Computer Engineering University of Houston TX USA Engineering Technology University of Houston TX USA

ISBN: (数字)9798331521035

ISBN: (纸本)9798331521042

Electricity price forecasting is an important tool for market participants to design bidding strategies in the electricity market. This paper introduces a combined neural network (c-NN) model that integrates Temporal Convolutional Networks (TCNs), Long Short-Term Memory (LSTM) networks, CNN-GRU hybrids, and Transformer models to address the volatility and nonlinear patterns of electricity prices. Our evaluation uses historical data from ERCOT Hub North prices to demonstrate that this integrated approach significantly outperforms individual models in accuracy. The findings suggest substantial financial benefits and support for renewable energy transitions, offering valuable insights for energy traders, grid operators, and policy makers. This research underscores the potential of combining machine learning (ML) models to enhance energy market analytics.

关键词： Renewable energy sources Accuracy Computational modeling Electricity Predictive models Transformers Data models Convolutional neural networks Forecasting Long short term memory

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Design and Optimization Techniques of Wireless Transfer System for Readout of in-Vivo Devices 8

Design and Optimization Techniques of Wireless Transfer Syst...

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8th IEEE International Conference on computer and Communications, ICCC 2022

作者： Yu, Mengxia Ye, Terry Tao Dept. Electrical and Computer Engineering National University of Singapore Singapore Singapore Southern University of Science and Technology Dept. Electrical and Electronic Engineering Shenzhen China Southern University of Science and Technology Jiaxing Research Institute Jiaxing China

ISBN: (纸本)9781665450515

in vivo devices have become an important diagnostic and treatment tool for physicians and life science researchers. The precise design of wireless battery-free system for communicating with in vivo devices is a great challenge that needs to be urgently addressed in the field of wireless communication engineering and bioelectromagnetics. This project aims to investigate and design an efficiency enhanced near-field communication (NFC) system by studying the characterizations of coupling coils applied for wireless energy transmission and signal readout of in vivo devices. The proposed work could be a guideline for designing an inductive power supply platform with high transmission efficiency. © 2022 IEEE.

关键词： Near field communication

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UniFaaS: Programming across Distributed Cyberinfrastructure with Federated Function Serving

UniFaaS: Programming across Distributed Cyberinfrastructure ...

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International Symposium on Parallel and Distributed Processing (IPDPS)

作者： Yifei Li Ryan Chard Yadu Babuji Kyle Chard Ian Foster Zhuozhao Li Dept. of Computer Science and Engineering Southern University of Science and Technology Shenzhen China Data Science and Learning Division Argonne National Laboratory Lemont IL USA Dept. of Computer Science University of Chicago Chicago IL USA

ISBN: (数字)9798350387117

ISBN: (纸本)9798350387124

Modern scientific applications are increasingly decomposable into individual functions that may be deployed across distributed and diverse cyberinfrastructure such as supercomputers, clouds, and accelerators. Such applications call for new approaches to programming, distributed execution, and function-level management. We present UniFaaS, a parallel programming framework that relies on a federated function-as-a-service (FaaS) model to enable composition of distributed, scalable, and high-performance scientific workflows, and to support fine-grained function-level management. UniFaaS provides a unified programming interface to compose dynamic task graphs with transparent wide-area data management. UniFaaS exploits an observe-predict-decide approach to efficiently map workflow tasks to target heterogeneous and dynamic resources. We propose a dynamic heterogeneity-aware scheduling algorithm that employs a delay mechanism and a re-scheduling mechanism to accommodate dynamic resource capacity. Our experiments show that UniFaaS can efficiently execute workflows across computing resources with minimal scheduling overhead. We show that UniFaaS can improve the performance of a real-world drug screening workflow by as much as 22.99% when employing an additional 19.48% of resources and a montage workflow by 54.41% when employing an additional 47.83% of resources across multiple distributed clusters, in contrast to using a single cluster.

关键词： Scheduling algorithms Parallel programming Heuristic algorithms Parallel processing Elasticity Dynamic scheduling Supercomputers

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Carotid plaque stroke risk assessment using multiscale AM-FM analysis based on DoG filterbanks

Carotid plaque stroke risk assessment using multiscale AM-FM...

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International Conference on Digital Signal Processing (DSP)

作者： Kyriacos P. Constantinou Ioannis P. Constantinou Marios S. Pattichis Efthyvoulos Kyriacou Constantinos S. Pattichis Dept. of Computer Science University of Cyprus Nicosia Cyprus Istognosis Ltd Nicosia Cyprus Dept. of Electrical and Computer Engineering University of New Mexico Albuquerque NM USA Dept. of Electrical Engineering Computer Engineering and Informatics Cyprus University of Technology Limassol Cyprus

The objective of this work was the investigation of multiscale Amplitude Modulation - Frequency Modulation (AM-FM) analysis based on Difference of Gaussians (DoG) filterbanks representations in order to predict the risk of stroke by analysing carotid plaques ultrasound images of individuals with asymptomatic carotid stenosis. We computed the instantaneous amplitude, instantaneous phase and the magnitude of instantaneous frequency to extract histogram features on each plaque region. The Support Vectors Machine classifier was implemented to classify asymptomatic versus symptomatic plaques. A dataset of 100 carotid plaque images (50 asymptomatic and 50 symptomatic) were tested, and showed that the AM-FM features based on DoG filterbanks and simple histograms performed better than the traditional AM-FM features. Best results were obtained when an eight scale filterbank with a combination of scales was used reaching the accuracy of 75%.

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Deep and Spatio-Temporal Detection for Abnormal Traffic in Cloud Data Centers

Deep and Spatio-Temporal Detection for Abnormal Traffic in C...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Yuan, Haitao Wang, Shen Bi, Jing Zhang, Jia School of Automation Science and Electrical Engineering Beihang University Beijing China Beijing University of Technology Faculty of Information Technology Beijing China Lyle School of Engineering at Southern Methodist University Dept. of Computer Science DallasTX United States

ISBN: (纸本)9798350337020

Current interactions of network traffic through cloud data centers have become an important process of network services. Precise and real-time detection and prediction of network traffic can assist system operators in effectively allocating resources, and assessing network performance based on actual service requirements, and analyzing network health. However, sources and distribution of network traffic are different, which makes accurate warnings of network attack traffic become a difficult problem. In recent years, neural networks have been proven to be effective in predicting time series data, particularly long short-term memory networks for capturing temporal features and convolutional methods for capturing spatial features. This work proposes a Deep Hybrid Spatio-Temporal (DHST) network method for abnormal traffic detection in cloud data centers, which combines a cooperative temporal convolutional network, an attention mechanism and a random inactivation method to capture the network traffic data's spatio-temporal features. It improves accuracy of abnormal traffic detection, and realizes classification of normal traffic and abnormal one. It achieves higher accuracy than typical detection methods when applied to a real-life dataset collected from Yahoo Webscope S5. © 2023 IEEE.

关键词： anomaly detection Deep learning network traffic spatio-temporal modeling temporal convolutional networks

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