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6th International Conference on Energy, Power and Environment, ICEPE 2024

作者： Rajesh, K.S. Kalaiselvi, S. Sathyanathan, P. Sudha, K. Kavitha, G. Murugan, S. Madanapalle Institute of Technology & Science Department of Electrical and Electronics Engineering Andhra Pradesh Madanapalle India Kongu Engineering College Department of Computer Technology Tamil Nadu Perundurai India Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Electrical and Electronics Engineering Tamil Nadu Chennai India Department of Business Administration Tamil Nadu Mannargudi India Vinayaka Missions Kirupananda Variyar Engineering College Department of Computer Science and Engineering Tamil Nadu Salem India Saveetha University Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Biomedical Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350379297

The retail industry is transforming with the emergence of the Internet of Things (IoT) and embedded controllers with Support Vector Machines (SVM) algorithm. This research explores the advancements in vending machines, which combine technology and embedded controllers with traditional vending machines. These intelligent devices can collect data, analyze it, and make real-time decisions thanks to their sensors, touch screens, and sophisticated embedded controllers. Through this innovation, smart vending machines can offer consumers a shopping experience based on their purchases and interactions with the machine. With data analysis capabilities, these integrated controllers enable the machines to provide discounts, special offers, and even recommendations on what to buy. In addition, intelligent vending machines help streamline inventory management, preventing shortages of popular goods. Because of this, embedded controllers are transforming vending machine operations' future while radically altering the retail industry. By collecting and analyzing data in real-time, smart vending machines allow for data-driven decision-making, which improves inventory management and reduces waste. Since all payment methods are linked flawlessly, purchasers can be sure that their transactions will go through without a problem. © 2024 IEEE.

关键词： Inventory control

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FlexSATE: Flexible and Distributed Traffic engineering with Supervised Learning in Ultra-Dense Low-Earth-Orbit Satellite Networks

FlexSATE: Flexible and Distributed Traffic Engineering with ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Liu, Xiaoyu Zhang, Zitian Qin, Xiaohan Zhou, Haibo Zhao, Lian Nanjing University School of Electronic Science and Engineering Nanjing210023 China Zhejiang Gongshang University School of Information and Electronic Engineering Hangzhou China Toronto Metropolitan University Department of Electrical Computer Biomedical Engineering TorontoONM5B 2K3 Canada

ISBN: (纸本)9798350351255

The ultra-dense low earth orbit (UD-LEO) satellite network is being vigorously developed due to its great potential in providing global coverage and services. For the sake of improved network performance in resource-constrained satellite networks, multipath schemes are being explored. However, state-of-the-art multipath routing algorithms face the challenge when dealing with highly dynamic satellite network features (i.e., frequent traffic variation, link failures) and fail to exploit the simple grid topology to design fast yet efficient traffic engineering (TE) approaches. In this paper, we propose a novel distributed TE scheme called Flexible Satellite Traffic engineering (FlexSATE), which leverages global path computation coupled with distributed local routing decisions to improve the overall load balancing performance for ultra-dense LEO satellite networks. By constructing a minimum-hop binary tree (MHBT), we propose an MHBT-based k-segment Routing algorithm, which is capable of promptly discovering routing paths with low latency, high diversity, and good load balancing. To further enhance network transmission performance, we employ supervised learning into dynamic rate adaption, where FlexSATE employs centralized offline learning to derive insights from the globally optimal routing strategy and utilizes distributed deployment to predict the optimal distribution of traffic in real time. Our simulation results on a real-world typical Walker-delta type LEO constellation with 720 satellites show that FlexSATE outperforms some existing approaches with superior robustness and flexibility. © 2024 IEEE.

关键词： Binary trees

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Fast non-iterative algorithm for 3D point-cloud holography

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Optics Express 2023年第22期31卷 36468-36485页

作者： Ersaro, Nathan Tessema Yalcin, Cem Murray, Liz Kabuli, Leyla Waller, Laura Muller, Rikky Graduate Program in Bioengineering University of California Berkeley and University of California San Francisco BerkeleyCA94720 United States Department of Electrical Engineering and Computer Sciences University of California Berkeley BerkeleyCA94720 United States

Recently developed iterative and deep learning-based approaches to computer-generated holography (CGH) have been shown to achieve high-quality photorealistic 3D images with spatial light modulators. However, such approaches remain overly cumbersome for patterning sparse collections of target points across a photoresponsive volume in applications including biological microscopy and material processing. Specifically, in addition to requiring heavy computation that cannot accommodate real-time operation in mobile or hardware-light settings, existing sampling-dependent 3D CGH methods preclude the ability to place target points with arbitrary precision, limiting accessible depths to a handful of planes. Accordingly, we present a non-iterative point cloud holography algorithm that employs fast deterministic calculations in order to efficiently allocate patches of SLM pixels to different target points in the 3D volume and spread the patterning of all points across multiple time frames. Compared to a matched-performance implementation of the iterative Gerchberg-Saxton algorithm, our algorithm’s relative computation speed advantage was found to increase with SLM pixel count, reaching >100,000x at 512 × 512 array format. © 2023 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Iterative methods

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Multi-Context Recommendation Systems (CARS) in Autonomous Driving and Other Applications

Multi-Context Recommendation Systems (CARS) in Autonomous Dr...

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2022 International Telecommunications Conference, ITC-Egypt 2022

作者： Ali, Abdullahi Kashef, Rasha Ibrahim, Ahmed Fakhry Toronto Metropolitan University Electrical Computer and Biomedical Engineering Faculty of Engineering and Architectural Science TorontoON Canada Boston University Computer Science Department United States

ISBN: (数字)9781665488082

ISBN: (纸本)9781665488082

Recommendation systems (RS) are important for their instantaneous ability to suggest to users their desired items and for ensuring a smooth and worthwhile user experience. To optimize the user's experience with the RS, it can be programmed to be aware of the various contexts the user has while interacting with the system. Context-aware recommendation systems (CARS) aim to add more nuance to the process of predicting items by considering different factors such as location and time. This paper presents the foundations of recommendation systems, such as categories, evaluation metrics, datasets, and challenges. In this paper, we show the effectiveness of multi-contexts in autonomous driving. Three CARS models are developed and tested. Experimental results show that adding multi-contexts provides more personalized options for drivers, allowing intelligent decisions. © 2022 IEEE.

关键词： Recommender systems

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SFA-Net: A Selective Features Absorption Network for Object Detection in Rainy Weather Conditions

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IEEE Transactions on Neural Networks and Learning Systems 2023年第8期34卷 5122-5132页

作者： Huang, Shih-Chia Hoang, Quoc-Viet Le, Trung-Hieu National Taipei University of Technology Department of Electronic Engineering Taipei10608 Taiwan National Taipei University of Technology International Graduate Program in Electrical Engineering and Computer Science Taipei10608 Taiwan Hung Yen University of Technology and Education Faculty of Information Technology Hungyen 17000 Viet Nam

In recent years, object detection approaches using deep convolutional neural networks (CNNs) have derived major advances in normal images. However, such success is hardly achieved with rainy images due to lack of visibility. Aiming to bridge this gap, in this article, we present a novel selective features absorption network (SFA-Net) to improve the performance of object detection not only in rainy weather conditions but also in favorable weather conditions. SFA-Net accomplishes this objective by utilizing three subnetworks, where the feature selection subnetwork is concatenated with the object detection subnetwork through the feature absorption subnetwork to form a unified model. To promote further advancement in object detection impaired by rain, we propose a large-scale rainy image dataset, named srRain, which contains both synthetic rainy images and real-world rainy images for training and testing purposes. srRain is comprised of 25 900 rainy images depicting diverse driving scenarios in the presence of rain with a total of 181 164 instances interpreting five common object categories. Experimental results display that our SFA-Net reaches the highest mean average precision (mAP) of 77.53% on a normal image set, 62.52% on a synthetic rainy image set, 37.34% on a collected natural rainy image set, and 32.86% on a published real rainy image set, surpassing current state-of-the-art object detectors and the combination of image deraining and object detection models while retaining a high speed. © 2012 IEEE.

关键词： Feature extraction

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Measuring the Minimum Power Requirement of FPGA Architectural Specifications 25

Measuring the Minimum Power Requirement of FPGA Architectura...

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Proceedings of the 2025 ACM/SIGDA International Symposium on Field programmable Gate Arrays

作者： Andy Ye Anas Razzaq Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto Ontario Canada

ISBN: (纸本)9798400713965

Bypass capacitances bridge the gap between momentary surges of power demand caused by switching transistors and the ability of power supplies to respond to these surges. Even though bypass capacitances are not explicitly specified in FPGA architectural specifications, the gate capacitances of quiescent transistors in an FPGA architecture can act as symbiotic bypass capacitances. Since any additional dedicated bypass capacitances can increase the implementation area of an FPGA, it is important to measure the ability of the quiescent bypass capacitances in maintaining stable operating voltages while the FPGA is powered by voltage sources with limited power outputs. This work measures the ability of the quiescent bypass capacitances in maintaining stable supply voltages under limited power in the context of FPGA architectural investigations. We found that, for the 12-LUT logic cluster investigated in this work, to limit VDD value droop to 5% of the nominal VDD value, the power source must be able to produce 3 times of the average power consumed by the same logic cluster powered by an ideal voltage source. When the power output is further reduced, VDD value droop increases, resulting in significantly increased delay and reduced noise margin. In addition, the degree of parallel execution also has a significant effect on VDD value. In particular, at the nominal VDD value of 0.9 volts and the maximum power output of 28.1 uW, executing all 12 LUTs in parallel results in VDD value drooping to a minimum value of 0.370 volts while executing 1 LUT at a time results in VDD value drooping to a minimum of 0.690 volts, with both cases achieving similar computing time. These results suggest that FPGA architectural evaluations should take bypass capacitances and the power limit of voltage sources into consideration in order to design efficient FPGA architectures for low power applications.

关键词： power-limited fpga architecture

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SVR-Optimized ANN Model for Predicting Earthquake Risk in electrical Substations Based on Disaster Datasets in the Aceh Region, Indonesia

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IEEE Access 2025年 13卷 105027-105043页

作者： Elvy Sahnur Nasution Yuwaldi Away Syahrial Ira Devi Sara Andri Novandri Doctoral Program School of Engineering Universitas Syiah Kuala Banda Aceh Indonesia Department of Electrical Engineering Universitas Muhammadiyah Sumatera Utara Medan Indonesia Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia Department of Computer Engineering Universitas Serambi Mekkah Banda Aceh Indonesia

This paper proposes a predictive model based on an Artificial Neural Network (ANN) optimized with Support Vector Regression (SVR) to predict seismic parameters as part of earthquake risk mitigation for substations. The proposed model aims to improve the accuracy of seismic parameter predictions, including seismic energy, Peak Ground Acceleration (PGA), and Modified Mercalli Intensity (MMI). A case study was conducted in the Aceh region, Indonesia, which is located near the subduction zone between the Indo-Australian Plate and the Eurasian Plate, making it highly seismically active. Therefore, this model is developed to identify substations most at risk of earthquake impacts while supporting disaster mitigation planning through early warning systems. The dataset used in the modeling includes information on the epicenter and substation coordinates, earthquake magnitude, depth, and the distance between the two points. The ANN training process uses backpropagation with sigmoid and tanh activation functions. The ANN outputs are then used as input features for the SVR model, applying a Radial Basis Function (RBF) kernel to capture the non-linear relationships between variables. Evaluation results show that the hybrid SVR-ANN model significantly improves prediction accuracy compared to conventional ANN. This improvement is indicated by a reduction in error and an increase in the coefficient of determination ( $R^{2}$ ). The findings of this paper can serve as a foundation for developing more effective mitigation systems to reduce earthquake impacts on electrical infrastructure.

关键词： Earthquakes Substations Predictive models Computational modeling Artificial neural networks Electronics packaging Accuracy Data models Prevention and mitigation Earth

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A Novel Strategy to Estimate and Manage the Power Consumption of Household Appliances 7

A Novel Strategy to Estimate and Manage the Power Consumptio...

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7th International Conference on Inventive Computation Technologies, ICICT 2024

作者： Mano Jemila, M.R. Pushpalatha, K.S. Mithuna, H.R. Supriya, C. Maheswari, K.T. Rajendiran, M. Dr. N. G.P. Institute of Technology Department of Biomedical Engineering Tamil Nadu Coimbatore641048 India Acharya Institute of Technology Department of Information Science And Engineering Karnataka Bengaluru560107 India Bannari Amman Institute of Technology Department of Electrical And Electronics Engineering Tamil Nadu Sathyamangalam638401 India Panimalar Engineering College Department of Computer Science And Engineering Tamil Nadu Chennai600123 India

ISBN: (纸本)9798350359299

Residential and commercial buildings account for approximately 26.6% and 21.2% of worldwide electrical power consumption, respectively. Because of the increasing need for energy and the lack of present energy sources, there will be a significant challenge in the future. As a result, with the rise of Smart Cities and Smart Grids, it is becoming increasingly vital to have effective strategies for controlling and reducing the consumption of electricity. One method of achieving consumption management is to predict the power usage of household equipment. This study focuses on power usage prediction using data from the UCI repository. In the preprocessing step Checking for null values, eliminating outliers, and modifying data are performed. Following that, the essential features are selected for dimension reduction using the Principal Component Analysis (PCA) technique. The Deep Sequential Prediction Model (DSPM) is the suggested deep learning method for predicting power consumption. Finally the significance of feature extraction and the effectiveness of the proposed model is evaluated using the R2 score and MSE. The proposed model is compared with the standard Artificial Neural Network (ANN) model. To examine the importance of the PCA method, feature-extracted data (from a correlation plot) and pre-processed data (without feature extraction) are used. The simulation shows that power consumption prediction utilizing the PCA feature extraction module along with the proposed DSPM model provides an excellent R2 score. © 2024 IEEE.

关键词： Electric power utilization

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Robust Multiresolution and Multistain Background Segmentation in Whole Slide Images 23rd

Robust Multiresolution and Multistain Background Segmentatio...

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Proceedings of the 23rd Polish Conference on Biocybernetics and biomedical engineering, PCBEE 2023

作者： Jurgas, Artur Wodzinski, Marek Atzori, Manfredo Müller, Henning Information Systems Institute Sierre Switzerland Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Science and Technology Krakow Poland Department of Neuroscience University of Padova Padova Italy Medical Faculty University of Geneva Geneva Switzerland

ISBN: (纸本)9783031384295

Background segmentation is an important step in analysis of histopathological images. It allows one to remove irrelevant regions and focus on the tissue of interest. However, background segmentation is challenging due to the variability of stain colors and intensity levels across different images, modalities, and magnification levels. In this paper, we present a learning-based model for histopathology background segmentation based on convolutional neural networks. We compare two multiresolution approaches to deal with the variability of magnification in histopathology images: (i) model that uses upscaling of smaller patches of the image, and (ii) model simultaneously trained on multiple resolution levels. Our model is characterized by solid performance both in multiresolution and multistain dyes (H &E and IHC), achieving good performance on publicly available dataset. The quantitative scores are, in terms of the Dice score, close to 94.71. The qualitative analysis presents strong performance on previously unseen cases from different distributions and various dyes. We freely release the model, weights, and ground-truth annotations to promote the open science and reproducible research. © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Pathology

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High-quality Mid-infrared Chalcogenide Ring Resonator

High-quality Mid-infrared Chalcogenide Ring Resonator

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

作者： Lu, Bright Xu, Bo Krueper, Greg Zohrabi, Mo Gopinath, Juliet T. Park, Wounjhang Department of Electrical Computer and Energy Engineering University of Colorado BoulderCO80309 United States Department of Physics University of Colorado BoulderCO80309 United States Materials Science and Engineering Program University of Colorado BoulderCO80309 United States

ISBN: (纸本)9781957171395

We report Ge23Sb7S70 chalcogenide ring resonators with up to 8 × 104 quality factors operating around 3.6 µm wavelength fabricated through e-beam lithography. Their rib waveguide geometry can be engineered to support close-to-zero dispersion modes needed for mid-infrared microcomb generation. CLEO 2024 © Optica Publishing Group 2024 © 2024 The Author(s)

关键词： Ring lasers

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