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IEEE International Conference on Group IV Photonics

作者： John Rollinson Asif Chowdhury Robert Karlicek Mona Hella Dept. of Electrical and Computer Systems Engineering Rensselaer Polytechnic Institute Troy NY USA

We demonstrate a method for improving Ge-on-Si avalanche photodiode (APD) sensitivity by varying the geometry of the charge and multiplication regions to achieve lower noise current. The APD's, fabricated in a commercial silicon photonic process, demonstrate an improved signal-to-noise ratio (SNR) at 940nm wavelength.

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Optimized Rewinding of Oversized Induction Motors

Optimized Rewinding of Oversized Induction Motors

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IEEE International Conference on Electric Machines and Drives (IEMDC)

作者： Fernando J. T. E. Ferreira Dept. of Electrical and Computer Engineering Institute of Systems and Robotics University of Coimbra Coimbra Portugal

When an oversized and/or faulty three-phase squirrel-cage induction motor (SCIM) needs to be rewound, there is an opportunity to redesign its stator winding as a function of its actual load cycle, aiming at improving its efficiency, power factor, and reliability. This paper proposes a systematic methodology to optimize the stator winding of in-operation, line-operated, fixed-speed SCIMs, with two goals, namely, (i) optimization of the fundamental component of magnetizing flux to the actual load cycle, reducing the original core saturation level and nominal power of the motor, and (ii) optimization of the pitch and turns of the coils to minimize the winding copper losses and the airgap magnetomotive force harmonic content. Hybrid computing approaches using the first principles and nondeterministic methods can be used for winding optimization. The proposed methodology has been applied to three oversized 5.5 kW, 4-pole SCIMs, driving industrial ceramic paste mixers, and led to a 30-45% reduction in the line current, a 10-25% reduction in the input active power, and a 20-35% increase in the power factor, enabling an upgrade of the efficiency class from "IE0"/IE1 to IE3/IE4. The decrease of the motor line current reduces the Joule losses in the upstream cables and transformers, freeing power transmission capacity in the power supply network. The proposed strategy is a cost-effective alternative to the replacement of old oversized motors with new well-sized motors, avoiding mechanical readaptation of the fixing and coupling systems and allowing for higher efficiency and power factor levels than those of new well-sized motors. It also fosters copper recycling, the electric motor circular economy, and the local economic activity associated with the motor repair/rewinding sector.

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An Automated Calibration Scheme for PLL in Time-to-Digital Converters 5

An Automated Calibration Scheme for PLL in Time-to-Digital C...

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5th International Conference on Circuits and systems, ICCS 2023

作者： Xu, Jiayuan Yu, Hang Li, Yan Xi, Daoming Ge, Binjie Liu, Hongzhao College of Computer Science and Software Engineering Shenzhen University Shenzhen Laboratory of Ic Design for Iot Shenzhen China Institute of Artificial Intelligence Hefei Comprehensive National Science Center Hefei China Shenzhen MSU-BIT University Dept. of Electronics and Computer Engineering Shenzhen China Quality and Standards Academy Shenzhen Technology University Shenzhen China

ISBN: (纸本)9798350308273

In high performance Time-to-Digital Converters (TDCs), PLL can be adopted to generate the required reference clock, which is in ∼ 100 MHz range. In order to improve the phase noise performance, the full VCO frequency range in such a PLL is generally divided into multiple sectors, which can be selected by digital code. This paper proposes an automated calibration scheme to set the digital code for optimized VCO performance. Primarily based on successive approximation algorithm, the proposed scheme completes the automated calibration with only minimal steps. By co-simulating with a typical PLL, the efficacy of the proposed scheme is fully validated. Tentatively synthesized using CMOS 180nm technology, the automated calibration scheme only occupies 100× 100um2 of silicon area, and consists of 144 logical gates. The power consumption is only ∼ 100 uW. © 2023 IEEE.

关键词： Approximation algorithms

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Impact on mental health due to COVID-19 pandemic: A cross-sectional study in Bangladesh

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Clinical eHealth 2023年第1期6卷 42-52页

作者： Pathan, Refat Khan Biswas, Munmun Yasmin, Suraiya Uddin, Mohammad Amaz Das, Anindya Khandaker, Mayeen Uddin Faruque, M.R.I. Sarker, Moniruzzaman Dept. of Computing and Information Systems School of Engineering and Technology Sunway University Selangor Bandar Sunway 47500 Malaysia Dept. of Computer Science and Engineering BGC Trust University Bangladesh Chittagong 4381 Bangladesh Department of Computer and Information Science Tokyo University of Agriculture and Technology Koganei - 184-0012 Tokyo Japan Dept. of Computer Science and Engineering Chittagong University of Engineering and Technology Chittagong 4349 Bangladesh Dept. of Electrical and Electronic Engineering Chittagong University of Engineering and Technology Chittagong 4349 Bangladesh Center for Applied Physics and Radiation Technologies School of Engineering and Technology Sunway University Selangor Bandar Sunway 47500 Malaysia Space Science Centre Universiti Kebangsaan Malaysia 43600 UKM Selangor Bangi Malaysia Department of Marketing Sunway University Business School Sunway University Selangor Bandar Sunway 47500 Malaysia Faculty of Graduate Studies Daffodil International University Daffodil Smart City Birulia Savar Dhaka – 1216 Bangladesh

The government of Bangladesh has implemented the “Stay Home” policy following the WHO recommendation to resist the community transmission of Covid-19. As a result, the routine activities of all government, semi-government establishments, including educational institutions, are severely affected, and the country's economic growth becomes slowed down. To overcome such a situation, the relevant authorities have introduced the “Work from Home” policy for the employees and “Remote Education” for students. However, due to the persistence of multi-dimensional socio-economic problems, many employees and students face big challenges in performing their regular jobs while adopting such a policy. Consequently, enormous psychological anxiety has been developed for all people, including students, parents, employees, etc., and concurrently created severe changes in their behavior. This study aims to analyze the reasons for the behavioral changes of the employees, students, academic staff, and family members of different ages due to psychological anxiety, stress, or physical issues. A comprehensive online-based survey has been carried out on people working in various sectors in Bangladesh. A modified Apriori Algorithm has been used to sort out the associations between the causes and types of behavioral changes. Analyzed data revealed a massive human behavioral change in most participants. This finding indicates that the negligence of those significant human behavioral changes may cause a higher risk of creating psychological imbalance. Therefore, there is a need to have a solid understanding of the reasons for the behavioral changes and set up standard guidelines to maintain “Work from Home” in this Covid-19 situation to avoid psychological imbalance. Based on this study, some suggestions have been given for implementation by the government on an urgent basis. © 2023

关键词： Apriori algorithm Behavioral changes Covid-19 outbreak Family crisis Work from home

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Demand Side Management for Commercial Buildings: Multiple Heating systems Based Optimization in Real-Time

Demand Side Management for Commercial Buildings: Multiple He...

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IEEE International Conference on engineering, Technology and Innovation

作者： Ahmet Köse Ivan Sukhanov Juri Belikov Aleksei Tepljakov Eduard Petlenkov Product development R8 Technologies OÜ Tallinn Estonia Dept. of Software Science Tallinn University of Technology Tallinn Estonia Dept. of Computer Systems Tallinn University of Technology Tallinn Estonia

ISBN: (数字)9798350362435

ISBN: (纸本)9798350362442

The real estate sector has a dramatic impact on global carbon dioxide emissions, approximately 70% of the emissions are produced by or are due to building operations. The increasing demand for heating buildings poses a significant challenge in terms of emissions, and the European Parliament has set ambitious targets in response to this challenge, including a 40% reduction in final energy consumption and a 42.5% cut in primary energy consumption by 2030. With rising global electricity costs, there is significant interest in exploring energy-efficient alternatives for building operations. Although comprehensive heating and heating systems are widespread throughout the European Union, most technical systems lack robust applications of machine learning algorithms in daily operations. This work presents a data-driven approach that optimizes in real-time the operation of heating systems in multiple segments of the real estate sector. The application is based on a supervised learning model. More than 60% reduction in heating energy costs is achieved by implementing the nonlinear model with proactive optimization with hourly electricity market prices, weather forecasts, and estimated occupancy profiles in buildings. At the same time, the work describes a data-driven approach that targets the use of existing hardware and infrastructure, avoiding additional investments while achieving significant energy savings, and also reducing the load of human operations. The proposed application is deployed during the heating season and is driven by real data collected at 15-minute intervals.

关键词： Heating systems Energy consumption Demand side management Buildings Heat engines Europe Weather forecasting Real-time systems Energy efficiency Optimization

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Securing Digital Media in the Age of AI and Deepfakes: A Hardware-Based Solution

Securing Digital Media in the Age of AI and Deepfakes: A Har...

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IEEE Southeastcon

作者： Eric Scott Sisson Steven C. Puckett Dept. of Electrical and Computer Engineering Mississippi State University Starkville AL USA Computer Science and Information Systems University of North Alabama Florence AL USA

ISBN: (数字)9798331504847

ISBN: (纸本)9798331504854

The current capabilities of deepfake and Artificial Intelligence (AI) technologies make it difficult to distinguish what is original from what is fabricated. Electronic documents can be digitally signed to verify and authenticate their information, making them admissible as evidence in court. In contrast, images and videos from Internet of Things (IoT) devices-such as police body cameras, security cameras, and smartphones are susceptible to deepfake manipulation, which can undermine their authenticity in legal proceedings. This research hypothesizes that digitally signing image and video data utilizing hardware cryptographic coprocessors could render them admissible in court. Utilizing our processes will also identify if any manipulation of the image data has occurred. Our approach addresses a critical need in the justice system to combat the challenges deepfakes pose and could pave the way for enhanced trust in digital evidence, bolstering the integrity of the judicial process.

关键词： Deepfakes Law enforcement Media Cameras Hardware Cryptography Internet of Things Artificial intelligence Digital signatures Coprocessors

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On the Feasibility of a 3D-Printable Conductive Filament-Based Antenna Array for Conformal Applications

On the Feasibility of a 3D-Printable Conductive Filament-Bas...

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Antennas and Propagation & USNC/URSI National Radio Science Meeting

作者： Hemachandra Konduru Benjamin Callies Dipankar Mitra Sayan Roy Shuvashis Dey Rahul Gomes Dept. of Computer Science and Computer Engineering University of Wisconsin La Crosse WI USA Dept. of Physics and Astronomy University of Wisconsin La Crosse WI USA Electrical and Computer Engineering Division of Energy Matter and Systems University of Missouri Kansas City MO USA Dept. of Electrical and Computer Engineering North Dakota State University Fargo ND USA Dept. of Computer Science University of Wisconsin Eau Claire WI USA

ISBN: (数字)9798350369908

ISBN: (纸本)9798350369915

NASA is actively addressing communication challenges, particularly in developing phased array antenna systems (PAAS) within the 2-2.5 GHz frequency band. The initial stages involve designing individual flexible antenna elements and non-phased antenna arrays. To address the issues of conformal phased array antennas, this work presents design enhancements for a 3D-printable microstrip patch antenna using a conductive Electrifi filament. The modifications, informed by recent research, are evaluated for the antenna's return loss and surface current distributions. The adopted modifications demonstrated superior performances in terms of gain and input reflection (S 11 ). The investigation extends to a 1x4 antenna array based on the modified patch antenna element. Notably, the conformal antennas and the antenna array exhibit low return loss alongside high gain and directivity, suggesting substantial potential for efficient performance in conformal surface applications.

关键词： Phased arrays Microstrip antenna arrays Electric potential Patch antennas NASA Resonant frequency Microstrip antennas

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Exploring the Efficacy of Spatial Subsampling Methods for 3D Image Projection for 2D Classification

Exploring the Efficacy of Spatial Subsampling Methods for 3D...

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IEEE International Workshop on Imaging systems and Techniques (IST)

作者： Alif Aditya Wicaksono I Ketut Eddy Purnama Reza Fuad Rachmadi Feby Artwodini Muqtadiroh Ardyono Priyadi Mauridhi Hery Purnomo Dept. of Electrical Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Dept. of Computer Engineering Institut Teknologi Sepuluh Nopember Surabaya Indonesia Dept. of Information Systems Institut Teknologi Sepuluh Nopember Surabaya Indonesia

ISBN: (数字)9798350378214

ISBN: (纸本)9798350378221

In this paper we purpose several methods for creating 2D projection image from a 3D scan such as from CT or MR scans, and then look at different types of 2.5D projections with a growing window sizes and see its effects on the efficacy of the machine learning model when combined with the selection algorithms. As such we are using Shi-Tomasi Detectors, Sobel Edge Detectors, Sum of Pixel Intensity, Center Select, and Random Selection as selection algorithms from which we select the most interesting index for a given scan, from which depending on the window size, we stack the neighboring slices in the axial plane to the channel dimension creating a 2.5D image from which we could pass into our ResNet-9 model configured for classification and train the model for a given number of epochs, under the same conditions for all of our tests, this ensures that for all of our experiments the main effector is window size and the selection algorithm. From our tests we found that the Center Select on average outperforming the other selection algorithm, this result indicate that medical information is mostly centered around the scan, and when combined with the saving of 60% less parameters, the purposed method shows a new way of processing 3D Scans for processing with a 2D CNN model.

关键词： Solid modeling Three-dimensional displays Machine learning algorithms Image edge detection Computed tomography Neural networks Detectors Machine learning Classification algorithms Indexes

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A Deep Learning for Predicting Possibility of Bluetooth Low Energy Tag Signals

A Deep Learning for Predicting Possibility of Bluetooth Low ...

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2024 International computer Symposium, ICS 2024

作者： Teng, Hao-Pei Tseng, Vincent S. Ying, Josh Jia-Ching National Chung Hsing University Dept. Management Information Systems Taichung Taiwan National Yang Ming Chiao Tung University Department of Computer Science and Information Engineering Hsinchu City Taiwan

ISBN: (纸本)9798331509071

With the rapid growth of IoT technology, using Bluetooth Low Energy (BLE) gateways to track the trajectory of elderly individuals can help prevent them from getting lost. However, BLE tag signals do not persist, and BLE gateways often enter standby mode for extended periods, resulting in unnecessary energy waste and reducing the lifespan of the devices. To address this issue, predicting the likelihood of BLE tag signals is essential to conserve energy. This paper presents a deep learning approach to enhance the operational efficiency and lifespan of BLE gateways deployed in an IoT system. The system consists of two major modules: the Number of BLE Tags Estimation Module and the Next Accessed BLE Gateway Prediction Module. The Number of BLE Tags Estimation Module leverages a CNN-GRU (Convolutional Neural Network - Gated Recurrent Unit) architecture to predict the time lapse between the sensing of signals from BLE tags. The Next Accessed BLE Gateway Prediction Module employs a self-attention mechanism to predict which specific BLE gateway will receive the BLE tag signal, further optimizing system performance. Experimental results show that the proposed prediction model effectively reduces unnecessary energy consumption, thereby improving the efficiency and service life of the entire IoT system. © 2024 IEEE.

关键词： Bluetooth Low Energy Gateway Gated Recurrent Convolutional Neural Network Multi-head Self-attention Next Accessed BLE Gateway Prediction Number of Signals Estimation

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Cascaded Deep CNN Network for Breast Cancer Screening from Ultrasound Images Using Differential Evolution Algorithm 4

Cascaded Deep CNN Network for Breast Cancer Screening from U...

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4th International Conference on Digital Society and Intelligent systems, DSInS 2024

作者： Rahman, Wahidur Sakib, Md Shadman Hosen, Md Solaiman Azad, Mir Mohammad Alshahrani, Hani Sulaiman, Adel Al Reshan, Mana Saleh Shaikh, Asadullah Uttara University Dept. of Computer Science and Engineering Uttara Dhaka Bangladesh University of Information Technology and Sciences Dept. of Computer Science and Engineering Dhaka Bangladesh Iowa State University Department of Computer Science United States Department of Computer Science and Engineering Dhaka Bangladesh College of Computer Science and Information Systems Najran University Department of Computer Science Saudi Arabia Saudi Arabia College of Computer Science and Information Systems Najran University Department of Information Systems Saudi Arabia

ISBN: (纸本)9798331528829

Because of poor blood flow and susceptibility to infections, breast cancer can be challenging to heal and, if not treated, might cause major problems, including limb amputation and a lowered quality of life. Though several systems exist to identify breast cancer, few combine machine learning (ML), deep learning (DL), and optimization strategies. This work presents a novel method leveraging complex algorithms to precisely identify breast cancer from ultrasound images. The study utilizes a dataset from a secondary source, categorizing it into three identical classes: normal (Class 0), benign (Class 1), and malignant (Class 2). It extracts features using pre-trained convolutional neural networks (CNNs), optimizes these features using the Differential Evolution Algorithm (DEA), and classifies images using standard machine learning algorithms. To show how well it detects breast cancer, the approach combines seven conventional ML classifiers with ten pre-trained CNN models. Using DenseNet121 + DEA + Xtreme Gradient Boosting (XGB) Classifier, DEA chooses important features derived by CNNs with a high accuracy of 97.76%. However, the proposed method can be very effective for real-time breast cancer screening using ultrasound images. © 2024 IEEE.

关键词： Convolutional neural networks

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