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2023 IEEE International Conference on Electro Information Technology, eIT 2023

作者： Singh, Gurpreet Shekhar, Abhishek Raj Yu, Xinrui Saniie, Jafar Illinois Institute of Technology Embedded Computing and Signal Processing Research Laboratory Department of Electrical and Computer Engineering ChicagoIL United States

ISBN: (纸本)9781665493765

The new era of technology is being greatly influenced by the field of artificial intelligence. computer vision and deep learning have become increasingly important due to their ability to process vast amounts of data and provide insights and solutions in a variety of fields. computer vision, deep learning and signal analysis have been used in a growing number of applications and services including smart devices, image, and speech recognition, healthcare, etc., one such device is an infant monitoring system. It monitors the daily activities of the infant such as their sleeping patterns, sounds, and movements. In this paper, deep learning and computer vision libraries were used to develop algorithms to detect whether the infant was in any uncomfortable situation such as sleeping on its back, face being covered and whether the infant was awake. The smart infant monitoring system detects the infant's unsafe resting situation in real time and sent immediate alerts to the caretaker's device. This paper presents the design flow of a smart infant monitoring system consisting of a night vision camera, a Jetson Nano, and a Wi-Fi internet connection. The pose estimation and awake detection algorithms were developed and tested successfully for different infant resting/sleeping situations. The smart infant monitoring system provides significant benefits for safety and an improved understanding of infants' sleep patterns and behavior. © 2023 IEEE.

关键词： Face recognition

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Twitter sentiment analysis using conditional generative adversarial network

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International Journal of Cognitive computing in engineering 2024年第1期5卷 161-169页

作者： Mahalakshmi, V. Shenbagavalli, P. Raguvaran, S. Rajakumareswaran, V. Sivaraman, E. Department of Information Technology Sengunthar College of Engineering Tiruchengode Tiruchengode637205 India Assistant Professor Karunya Institute of Technology and Sciences Coimbatore India Assistant Professor Department of Computational Intelligence School of Computing SRM Institute of Science and Technology SRM Nagar Kattankulathur Kanchipuram Chennai Tamil Nadu 603203 India Assistant Professor Department of Computer Science and Design Erode Sengunthar Engineering College Tamil Nadu Thuduppathi India Department of Electrical and Computer Engineering Curtin University Miri Malaysia

Sentiment analysis, which aims to extract information from textual data indicating people's ideas or attitudes about a particular problem, has developed into one of the most exciting study issues in natural language processing (NLP) with the development of social media. Twitter is a social network with an extensive audience that expresses their thoughts and opinions clearly and readily. Due to the prevalence of slang phrases and incorrect spellings in short phrase styles, Twitter data analysis is more challenging than data analysis from other social networks. Automated feature selection still has several limitations, such as higher computing costs that rise with the number of characteristics. Deep learning, which is self-learned and more accurate at processing vast amounts of data, is utilized to overcome these challenges. This paper introduces a conditional generative adversarial network (GAN) for Twitter sentiment analysis, whereas a convolutional neural network (CNN) has been used to extract traits from Twitter data. Compared to existing works, the proposed work has outperformed in accuracy, recall, precision, and F1 score. The suggested method is the most accurate, with a classification accuracy of 93.33 %. © 2024

关键词： Sentiment analysis

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Vaxina: Decentralized Vaccination Tracking System 9

Vaxina: Decentralized Vaccination Tracking System

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9th International Conference on Information Technology Trends, ITT 2023

作者： Almobaideen, Wesam Mohammed, Mohammad Rochester Institute of Technology Dubai Electrical Engineering and Computing Sciences Department United Arab Emirates The University of Jordan Computer Science Department Amman Jordan

ISBN: (纸本)9798350327502

With the epidemic of Covid-19, a realisation has come into effect that the healthcare industry requires a more efficient method of storing non-tamperable vaccination certificates as well as sharing them with the relevant parties when required. As such, the need for efficient and secure vaccination tracking has led to the development of decentralized solutions. This paper aims to bring forth a Ethereum platform-based vaccination blockchain certificate system which supports authenticated consensual vaccination certificate sharing through the use of solidity smart contracts. We analyse the system architecture, implementation and evaluation, which will support the system's ability to resolve some of the security issues associated with centralised database systems such as low availability, integrity of the vaccination information as well as confidentiality and privacy of patient medical records. Vaxina system is a real-world implementation of a decentralized vaccination tracking system based on the Ethereum platform. It focuses more on the practical implementation and addresses some of the limitations of the previous proposed systems. It has been implemented and tested on the Ethereum blockchain and is available on GitHub for public use. The paper provides valuable insights for future work in the field of decentralized healthcare solutions. © 2023 IEEE.

关键词： Smart contract

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Coherent ring VCSELs for high power, narrow linewidth applications 29

Coherent ring VCSELs for high power, narrow linewidth applic...

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Vertical-Cavity Surface-Emitting Lasers XXIX 2025

作者： Graham, Luke Guenter, James Khurana, Pritha Tatum, Jim Khurana, Arjun Castillo, Freddie Dallas Quantum Devices 1575 Redbud Suite 212 McKinneyTX75069 United States Department of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA30308 United States

ISBN: (数字)9781510685178

ISBN: (纸本)9781510685161

The coherent ring VCSEL (CR-VCSEL) structure developed at Dallas quantum Devices is a monolithic, narrow linewidth high power device configuration that can be fabricated using typical VCSEL epitaxy and conventional processing techniques. So far, CR-VCSELs have demonstrated CW output power greater than 500mW with linewidths less than 0.2nm, power conversion efficiency greater than 30%, with full angle divergence angle less than 10 degrees. In applications where they are appropriate, CR-VCSELs should enjoy significant cost advantages over other high power, narrow linewidth semiconductor laser configurations. © 2025 SPIE.

关键词： Semiconductor lasers

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Generative Adversarial Networks for Spatio-Spectral Compression of Hyperspectral Images 14

Generative Adversarial Networks for Spatio-Spectral Compress...

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14th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing, WHISPERS 2024

作者： Fuchs, Martin Hermann Paul Byju, Akshara Preethy Walda, Alisa Rasti, Behnood Demir, Begum Amrita School of Computing Amrita Vishwa Vidyapeetham Department of Computer Science and Engineering Amritapuri India Technische Universität Berlin Faculty of Electrical Engineering and Computer Science Germany BIFOLD - Berlin Institute for the Foundations of Learning and Data Germany

ISBN: (纸本)9798331513139

Deep learning-based hyperspectral image (HSI) compression has recently attracted great attention in remote sensing due to the growth of hyperspectral data archives. Most of the existing models achieve either spectral or spatial compression and do not jointly consider the spatio-spectral redundancies present in HSIs. To address this problem, in this paper, we propose High Fidelity Compression (HiFiC)-based models for spatio-spectral compression of HSIs. In detail, we introduce two new models: i) HiFiC using Squeeze and Excitation (SE) blocks (denoted as HiFiCsE);and ii) HiFiC with 3D convolutions (denoted as HiFiC3D) in the framework of compression of HSIs. We analyze the effectiveness of HiFiCsE and HiFiC3D in compressing the spatio-spectral redundancies with channel attention and inter-dependency analysis. Experimental results show the efficacy of the proposed models in performing spatio-spectral compression, while reconstructing images at reduced bitrates with higher reconstruction quality. The code of the proposed models is publicly available at https://***/rsim/HSI-SSC. © 2024 IEEE.

关键词： Generative adversarial networks

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Fractional Order Differential Evolution

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IEEE Transactions on Evolutionary Computation 2024年第3期29卷 1-1页

作者： Wang, Kaiyu Gao, Shangce Zhou, MengChu Zhan, Zhi-Hui Cheng, Jiujun Faculty of Engineering University of Toyama Toyama-shi Japan Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA College of Artificial Intelligence Nankai University Tianjin China Ministry of Education Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

Differential evolution (DE) is a widely recognized method to solve complex optimization problems as shown by many researchers. Yet, non-adaptive versions of DE suffer from insufficient exploration ability and uses no historical information for its performance enhancement. This work proposes Fractional Order Differential Evolution (FODE) to enhance DE performance from two aspects. Firstly, a bi-strategy co-deployment framework is proposed. The population-based and parameter-based strategies are combined to leverage their respective advantages. Secondly, the fractional order calculus is first applied to the differential vector to enhance DE’s exploration ability by using the historical information of populations, and ensures the diversity of population in an evolutionary process. We use the 2017 IEEE Congress on Evolutionary Computation (CEC) test functions, and CEC2011 real-world problems to evaluate FODE’s performance. Its sensitivity to parameter changes is discussed and an ablation study of multi-strategies is systematically performed. Furthermore, the variations of exploration and exploitation in FODE are visualized and analyzed. Experimental results show that FODE is superior to other state-of-the-art DE variants, the winners of CEC competitions, other fractional order calculus-based algorithms, and some powerful variants of classic algorithms. IEEE

关键词： Particle swarm optimization (PSO)

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HiRISE: High-Resolution Image Scaling for Edge ML via In-Sensor Compression and Selective ROI 24

HiRISE: High-Resolution Image Scaling for Edge ML via In-Sen...

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61st ACM/IEEE Design Automation Conference, DAC 2024

作者： Reidy, Brendan Tabrizchi, Sepehr Mohammadi, Mohammadreza Angizi, Shaahin Roohi, Arman Zand, Ramtin Department of Computer Science and Engineering University of South Carolina ColumbiaSC United States School of Computing University of Nebraska-Lincoln LincolnNE United States Department of Electrical and Computer Engineering New Jersey Institute of Technology NewarkNJ United States

ISBN: (纸本)9798400706011

With the rise of tiny IoT devices powered by machine learning (ML), many researchers have directed their focus toward compressing models to fit on tiny edge devices. Recent works have achieved remarkable success in compressing ML models for object detection and image classification on microcontrollers with small memory, e.g., 512kB SRAM. However, there remain many challenges prohibiting the deployment of ML systems that require high-resolution images. Due to fundamental limits in memory capacity for tiny IoT devices, it may be physically impossible to store large images without external hardware. To this end, we propose a high-resolution image scaling system for edge ML, called HiRISE, which is equipped with selective region-of-interest (ROI) capability leveraging analog in-sensor image scaling. Our methodology not only significantly reduces the peak memory requirements, but also achieves up to 17.7× reduction in data transfer and energy consumption. © 2024 Copyright held by the owner/author(s).

关键词： Image compression

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Observing quantum Measurement Collapse as a Learnability Phase Transition

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Physical Review X 2024年第4期14卷 041012页

作者： Utkarsh Agrawal Javier Lopez-Piqueres Romain Vasseur Sarang Gopalakrishnan Andrew C. Potter Santa Barbara California 93106 USA Department of Physics Department of Electrical and Computer Engineering Department of Physics and Astronomy and Quantum Matter Institute

During a quantum measurement, superpositions of states with different observable properties probabilistically collapse into one with a sharp value of the measured observable. In macroscopic quantum systems, this collapse arises via a continuous measurement-induced phase transition (MIPT) at a critical value of the strength of interaction with the measurement apparatus. MIPTs lie outside established paradigms for equilibrium or nonequilibrium critical phenomena and delineate distinct, stable dynamical and computational phases of matter. quantum computers enable programmable simulation of the interaction of a measurement apparatus with a dynamical quantum system, to explore MIPT phenomena over a range of system sizes while retaining quantum coherence. Yet, existing experimental protocols rely on fundamentally nonscalable postselection techniques or direct classical simulation of quantum circuits. Here, we report the scalable observation of finite-size scaling evidence for an observable-sharpening MIPT in monitored quantum circuits in a chain of Yb+171 ions in Quantinuum’s H1-1 trapped-ion quantum processor. By leveraging an equivalent description as a statistical physics problem, we implement scalable classical algorithms to infer the value of the measured observable from a single experimental shot. This technique enables a truly scalable protocol to observe observable-sharpening MIPTs in generic classes of circuits that cannot be directly classically simulated and also provides enhanced means to detect and suppress errors in the quantum simulation.

关键词： Dynamical phase transitions quantum circuits quantum entanglement quantum measurements quantum phase transitions

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Complex Permeability Determination for EE Core Based on Flexible PCB Fixture and Magnetic Flux Path Length Analysis 2

Complex Permeability Determination for EE Core Based on Flex...

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2nd China Power Supply Society Electromagnetic Compatibility Conference, CPEMC 2024

作者： Jie, Huamin Zhao, Zhenyu Chang, Yongqi Wang, Changdong Jiang, Shiqi Gao, Richard Xian-Ke See, Kye Yak School of Electrical and Electronic Engineering Nanyang Technological University Singapore Singapore Department of Electrical and Computer Engineering National Univeristy of Singapore Singapore Singapore Department of Control Science and Engineering Harbin Institute of Technology Harbin China School of Electronics and Information Engineering Harbin Institute of Technology Harbin China School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China Institute of High Performance Computing A*STAR Singapore Singapore

ISBN: (纸本)9798331509552

The complex permeability of magnetic cores acts as a vital factor in electromagnetic interference (EMI) filtering choke design and optimization. Many efforts have been reported for toroid cores, but fewer target those with other shapes, such as EE, EI, UU, and UI cores. Owing to their commonly used in planar EMI filters, determining the complex permeability of them is of significance. Targeting EE cores, this paper presents a flexible printed circuit board (PCB)-based fixture adapter tailored designed for performing the measurement, while the corresponding core permeability can be calculated through magnetic flux path length analysis. The experiments validate the effectiveness and accuracy of the proposed methods, spanning a wide frequency range up to 120 MHz. ©2024 IEEE.

关键词： Magnetic flux

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Joint Optimization of Model Splitting and Device Task Assignment for Private Multi-hop Split Learning 59

Joint Optimization of Model Splitting and Device Task Assign...

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59th Annual Conference on Information Sciences and Systems, CISS 2025

作者： Wei, Dongyu Zhang, Qianqian Liu, Yuchen Chen, Mingzhe Rowan University Department of Electrical and Computer Engineering GlassboroNJ08028 United States Nc State University Department of Computer Science RaleighNC27695 United States University of Miami Department of Electrical and Computer Engineering Coral GablesFL33146 United States University of Miami Frost Institute for Data Science and Computing Coral GablesFL33146 United States

ISBN: (纸本)9798331513269

In this paper, a deceptive signal-assisted private split learning is investigated. In our model, several edge devices jointly perform collaborative training, and some eavesdroppers aim to collect the model and data information from devices. To prevent the eavesdropper from collecting model and data information, a subset of devices can transmit deceptive signals. Therefore, it is necessary to determine the subset of devices used for deceptive signal transmission, the subset of model training devices, and the models assigned to each model training device. This problem is formulated as an optimization problem whose goal is to minimize the eavesdropped information while meeting the model training energy consumption and delay constraints. To solve this problem, we propose an actor-critic deep reinforcement learning (AC-DRL) framework that enables a centralized agent to dynamically determine the model training devices, the deceptive signal transmission devices, the transmit power, and sub-models assigned to each model training device while considering network topology and device properties (e.g., energy and privacy constraints). The proposed AC-DRL framework integrates an actor network to generate optimal actions and a critic network to stabilize the learning process by evaluating the cumulative rewards. Simulation results demonstrate that the proposed method improves the convergence rate by 3× and the accumulated reward by up to 40% compared with the proximal policy optimization (PPO) algorithm. © 2025 IEEE.

关键词： Deep reinforcement learning

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