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Quantum-Dot-Based Optical Fiber Temperature Sensor Fabricated by Two-Photon Polymerization

激光与光电子学进展 2024年第23期61卷 431-437页

作者： Wang Chao Sun Shengzhi Qian Bin Peng Sida Feng Zeqi Qiu Jianrong Laboratory of Infrared Materials and Devices Research Institute of Advanced TechnologiesNingbo UniversityNingbo 315211ZhejiangChina Institute of Light+X Science and Technology Faculty of Electrical Engineering and Computer ScienceNingbo UniversityNingbo 315211ZhejiangChina State Key Laboratory of Fluid Power and Mechatronic Systems Zhejiang UniversityHangzhou 310027ZhejiangChina State Key Laboratory of Modern Optical Instrumentation Zhejiang UniversityHangzhou 310027ZhejiangChina

Temperature is a common and significant physical quantity,particularly under harsh conditions such as a high voltage or strong electric/magnetic *** this study,we developed an all-fiber-optic temperature sensor based on a fiber-end microstructure doped with quantum dots(QDs).The microstructure was fabricated on a fiber tip using twophoton polymerization *** results show that the fluorescence intensity of the QD microstructure is reduced with an increase in temperature,and the peak position of photoluminescence exhibits a linear change in the temperature range of 26-70℃.The developed sensor exhibits a high-temperature sensitivity of 135 pm/℃.The proposed method has enormous potential for the development of fiber-end temperature sensors.

关键词： two-photon polymerization femtosecond laser temperature sensors quantum dots optical fiber

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Load-Aware Hierarchical Information-Centric Routing for Large-Scale LEO Satellite Networks

Load-Aware Hierarchical Information-Centric Routing for Larg...

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IEEE Conference on Wireless Communications and Networking

作者： Fei Yan Zhiyuan Wang Shan Zhang Qingkai Meng Hongbin Luo School of Computer Science and Engineering Beihang University Beijing China Zhongguancun Laboratory Beijing China State Key Laboratory of Virtual Reality Technology and Systems Beijing China State Key Laboratory of Software Development Environment Beijing China Institute of Artificial Intelligence Beihang University Beijing China

ISBN: (数字)9798350303582

ISBN: (纸本)9798350303599

The emerging large-scale low earth orbit (LEO) constellation is expected to provide global Internet services. However, large-scale satellite networks meet the challenges of topology dynamics and continuous traffic variation. This paper proposes LoHi, a Load-aware Hierarchical Information-centric (LoHi) Routing Protocol based on constellation Partitioning and logic path identifier (PID), to address the above challenges. Specifically, LoHi divides a constellation into satellite groups and only keeps track of the inter-satellite link (ISL) state within each group instead of the entire constellation, stabilizing global routing. Moreover, LoHi adopts the PID to denote the logic connectivity of adjacent satellite groups, which corresponds to multiple physical ISLs. Accordingly, the hierarchical multipath routing is calculated based on the inner-group and inter-group connectivity information. Furthermore, LoHi adjusts forwarding decisions according to the real-time traffic load on the ISL and group-level inter-group paths to reduce packet drop and queuing delay in a hierarchical pattern. Packet-level experiment results show that LoHi achieves a higher packet delivery ratio than the state-of-the-art mechanisms (up to 132.12%).

关键词： Satellites Web and internet services Low earth orbit satellites Telecommunication traffic Routing Throughput Routing protocols

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Advancing Privacy and Accuracy with Federated Learning and Homomorphic Encryption

TechRxiv

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TechRxiv 2023年

作者： Nguyen, Tuy Tan Phan, Quoc Bao Nguyen, Dinh C. The School of Informatics Computing and Cyber Systems Northern Arizona University FlagstaffAZ86011 United States The Department of Electrical and Computer Engineering University of Alabama in Huntsville HuntsvilleAL35899 United States

In this paper, we present an integrated framework that combines Federated Learning (FL) with Homomorphic Encryption (HE) using the Artificial Intelligence (AI) models and the Cheon-Kim-Kim-Song (CKKS) algorithm to address the challenges of privacy and accuracy. FL facilitates collaborative training of Convolutional Neural Network (CNN) and MultiLayer Perceptron (MLP) models across decentralized devices, allowing for data privacy preservation without sharing raw data. The integration of the CKKS algorithm for HE ensures secure computation on encrypted data during the FL process. Our experimental results on three diverse datasets demonstrate the efficacy of this approach, achieving an impressive highest average accuracy of 97.3%. Additionally, the CKKS algorithm is used to achieve efficient computation, making it a promising solution for privacy-conscious machine learning applications, and paving the way for practical deployment in various real-world scenarios, thereby revolutionizing the landscape of privacy-preserving machine learning. © 2023, CC BY.

关键词： Multilayer neural networks

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VENUS: A Geometrical Representation for Quantum State Visualization

arXiv

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arXiv 2023年

作者： Ruan, Shaolun Yuan, Ribo Guan, Qiang Lin, Yanna Mao, Ying Jiang, Weiwen Wang, Zhepeng Xu, Wei Wang, Yong School of Computing and Information System Singapore Management University Singapore Department of Computer and Information Sciences University of Delaware United States Department of Computer Science Kent State University United States Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong Computer and Information Science Department Fordham University United States Electrical and Computer Engineering George Mason University United States Computational Science Initiative Brookhaven National Laboratory United States

Visualizations have played a crucial role in helping quantum computing users explore quantum states in various quantum computing applications. Among them, Bloch Sphere is the widely-used visualization for showing quantum states, which leverages angles to represent quantum amplitudes. However, it cannot support the visualization of quantum entanglement and superposition, the two essential properties of quantum computing. To address this issue, we propose VENUS, a novel visualization for quantum state representation. By explicitly correlating 2D geometric shapes based on the math foundation of quantum computing characteristics, VENUS effectively represents quantum amplitudes of both the single qubit and two qubits for quantum entanglement. Also, we use multiple coordinated semicircles to naturally encode probability distribution, making the quantum superposition intuitive to analyze. We conducted two well-designed case studies and an in-depth expert interview to evaluate the usefulness and effectiveness of VENUS. The result shows that VENUS can effectively facilitate the exploration of quantum states for the single qubit and two qubits. Copyright © 2023, The Authors. All rights reserved.

关键词： Visualization

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A deep learning system for predicting time to progression of diabetic retinopathy

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NATURE MEDICINE 2024年第2期30卷 358-359页

作者： [Anonymous] Shanghai Belt and Road International Joint Laboratory for Intelligent Prevention and Treatment of Metabolic Disorders Department of Computer Science and Engineering School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Department of Endocrinology and Metabolism Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai Diabetes Institute Shanghai Clinical Center for Diabetes Shanghai China MOE Key Laboratory of AI School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China Department of Ophthalmology Huadong Sanatorium Wuxi China Department of Ophthalmology Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Ophthalmology and Visual Sciences The Chinese University of Hong Kong Hong Kong China Singapore Eye Research Institute Singapore National Eye Centre Singapore Singapore Department of Computer Science and Engineering The Hong Kong University of Science and Technology Hong Kong China Department of Chemical and Biological Engineering The Hong Kong University of Science and Technology Hong Kong China State Key Laboratory of Ophthalmology Zhongshan Ophthalmic Center Sun Yat-sen University Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science Guangzhou China Department of Ophthalmology Peking Union Medical College Hospital Peking Union Medical College Chinese Academy of Medical Sciences Beijing China Medical Records and Statistics Office Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine Shanghai China Department of Geriatrics Tongji Hospital Tongji Medical College Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Tech

We developed and validated a deep learning system (termed DeepDR Plus) in a diverse, multiethnic, multi-country dataset to predict personalized risk and time to progression of diabetic retinopathy. We show that DeepDR Plus can be integrated into the clinical workflow to promote individualized intervention strategies for the management of diabetic retinopathy.

关键词： Diabetes complications Machine learning Predictive markers

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Tensor-Based Multi-Scale Correlation Anomaly Detection for AIoT-Enabled Consumer Applications

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IEEE Transactions on Consumer Electronics 2024年

作者： Zeng, Jiuzhen Yang, Laurence T. Wang, Chao Deng, Xianjun Yang, Xiangli Huazhong University of Science and Technology Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Wuhan430074 China Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan430074 China Zhengzhou University School of Computer and Artificial Intelligence Zhengzhou450001 China St. Francis Xavier University Department of Computer Science AntigonishNSB2G 2W5 Canada University of South China School of Electrical Engineering Hengyang421001 China

Artificial Intelligence of Things (AIoT) is an innovative paradigm expected to enable various consumer applications that is transforming our lives. While enjoying benefits and services from these applications, we also face serious security issues due to malicious cyber attacks on the massive growth of AIoT consumer devices. Accurate anomaly detection is one of the critical tasks for the trustworthy AIoT removing those obstacles. However, limited by the vector-based data pattern and ill-considered factors for anomalous samples analysis, existing methods suffer from the low detection performance. In this paper, a multi-scale correlation tensor convolutional Gaussian mixture network (named as MTCGM) is presented for ameliorating this actuality. Specifically, MTCGM suggests to construct the multi-scale correlation tensor by stacking one self-correlation matrix and multiple surrounding-correlations of different scales, which well characterizes the network status of AIoT. Subsequently, a 3D-convolutional autoencoder (3DCA) is designed for capturing inter-feature correlations, and followed with a Gaussian mixture probability (GMP) network for the observations likelihood estimation. Moreover, low-dimensional space features, relative Euclidean distance and tensor cosine similarity (TCS) are adopted in MTCGM as the multi-factor to boost the likelihood estimation. Extensive experiments on public benchmark datasets verify the validity of MTCGM, and demonstrate its superiority over the state-of-the-art baselines even in presence of contaminated training samples and input noise. © 1975-2011 IEEE.

关键词： Tensors

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Measurement of paracetamol concentration using a fiber laser system

Measurement of paracetamol concentration using a fiber laser...

引用

Bioengineering (ENBENG), Portuguese Meeting in

作者： Liliana Soares Rosa Ana Perez-Herrera Susana Novais António Ferreira Orlando Frazão Susana Silva Faculty of Engineering INESC TEC - Institute for Systems and Computer Engineering Technology and Science University of Porto Porto Portugal Department of Electrical Electronic and Communication Engineering Public Univ. of Navarra ISC - Institute of Smart Cities Public Univ. of Navarra Pamplona Spain INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal LEPABE-Laboratory for Process Engineering Environment Biotechnology and Energy University of Porto Porto Portugal ALiCE - Associate Laboratory in Chemical Engineering University of Porto Porto Portugal

A linear fiber laser system for measurements of paracetamol concentration is experimentally demonstrated. The cavity is based on a fiber loop mirror and an FBG centered at 1567.8 nm. The sensing head corresponds to a refractometric sensor, whose which principle of operation is based on Fresnel reflection in the fiber tip (FBG side). The system works at detected variations of paracetamol concentrations with a sensitivity of $[(8.74\pm 0.34)\times 10^{-5}]\ \ \mu \mathrm{W}/(\mathrm{g}/\text{kg})$ and a resolution of 2.77 g/kg. The results prove that the fiber laser system could be an asset for processing industries, specifically for non-invasive and real-time measurements of concentration.

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Assessing Respiratory Changes Induced by STN-DBS in a Rat Model

Assessing Respiratory Changes Induced by STN-DBS in a Rat Mo...

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International Conference on Emerging Technologies, ICET

作者： Shehla Inam Livia de Mesquita Teixeira Muhammad Waqar Muhammad Ishaq Shahzaib Iqbal Fasih Ullah Jitendran Muthuswamy School of Biological & Health Systems Engineering Arizona State University Tempe USA Dept. of Biomedical Engineering Balochistan University of Engineering & Technology Khuzdar Pakistan Dept. of Electrical Engineering National University of Computer & Emerging Sciences Islamabad Pakistan Dept. of Mechanical Engineering Balochistan University of Engineering & Technology Khuzdar Pakistan Dept. of Electrical Engineering CECOS University of IT & Emerging Sciences Peshawar Pakistan

ISBN: (数字)9798350379587

ISBN: (纸本)9798350379594

Deep brain stimulation of the subthalamic nucleus (STN-DBS) is a technique used to treat motor symptoms of movement disorders including Parkinson's Disease (PD), a neurogenerative disorder caused by the progressive loss of striatal dopamine in the brain that leads to tremors, rigidity, and postural instability. Despite its therapeutic effects, DBS is still known to cause some non-motor symptoms including breathlessness due to unknown reasons in PD patients. In this paper, the changes in breathing rates were evaluated from peak detection for 20 trials in the time domain for the preliminary data obtained from a rat model after the application of DBS on the STN for frequencies 10Hz and 130Hz with stimulation intensities of 150 µA and 450 µA. Results revealed a significant change in breathing rates for all intensities and corresponding frequencies, confirming that the degree of disruption in breathing rate may depend on the stimulation frequency. The study may further suggest potential neuronal networks that mediate these changes.

关键词： Time-frequency analysis Statistical analysis Parkinson's disease Deep brain stimulation Motors Brain modeling Data models Rigidity Time-domain analysis Biological neural networks

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Adaptive Formation Learning Control for Cooperative AUVs under Complete Uncertainty

arXiv

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arXiv 2024年

作者： Jandaghi, Emadodin Zhou, Mingxi Stegagno, Paolo Yuan, Chengzhi Department of Mechanical Industrial and Systems Engineering University of Rhode Island KingstonRI United States Graduate School of Oceanography University of Rhode Island KingstonRI United States Department of Electrical Computer and Biomedical Engineering University of Rhode Island KingstonRI United States

This paper introduces an innovative two-layer control framework for Autonomous Underwater Vehicles (AUVs), tailored to master completely uncertain nonlinear system dynamics in the presence of challenging hydrodynamic forces and torques. Distinguishing itself from prior studies, which only regarded certain matrix coefficients as unknown while assuming the mass matrix to be known, this research significantly progresses by treating all system dynamics, including the mass matrix, as unknown. This advancement renders the controller fully independent of the robot’s configuration and the diverse environmental conditions encountered. The proposed framework is universally applicable across varied environmental conditions that impact AUVs, featuring a first-layer cooperative estimator and a second-layer decentralized deterministic learning (DDL) controller. This architecture not only supports robust operation under diverse underwater scenarios but also adeptly manages environmental effects such as changes in water viscosity and flow, which influence the AUV’s effective mass and damping dynamics. The first-layer cooperative estimator fosters seamless inter-agent communication by sharing crucial system estimates without the reliance on global information, while the second-layer DDL controller utilizes local feedback to precisely adjust each AUV’s trajectory, ensuring accurate formation control and dynamic adaptability. Furthermore, the capability for local learning and knowledge storage through radial basis function neural networks (RBF NNs) is pivotal, allowing AUVs to efficiently reapply learned dynamics following system restarts. Comprehensive simulations validate the effectiveness of our groundbreaking framework, highlighting its role as a major enhancement in the field of distributed adaptive control systems for AUVs. This approach not only bolsters operational flexibility and resilience but is also essential for navigating the unpredictable dynamics of marine environments

关键词： Radial basis function networks

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A Decade of Video Analytics at Edge: Training, Deployment, Orchestration, and Platforms

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IEEE Communications Surveys and Tutorials 2025年

作者： Bai, Tong Zhao, Haoran Huang, Lei Wang, Zhipeng Kim, Dong In Nallanathan, Arumugam Beihang University School of Cyber Science and Technology Beijing100191 China State Key Laboratory of CNS/ATM Beijing100191 China Beihang University School of Artificial Intelligence Beijing100191 China Beihang University Hangzhou International Innovation Institute Hangzhou China Beihang University School of Electronic Information Engineering Beijing100191 China Sungkyunkwan University Department of Electrical and Computer Engineering Suwon16419 Korea Republic of Queen Mary University of London School of Electronic Engineering and Computer Science London United Kingdom Kyung Hee University Department of Electronic Engineering Gyeonggi-do Yongin-si17104 Korea Republic of

Video analytics (VA), capable of autonomously understanding events in video content, has demonstrated significant potential across various applications, from surveillance to self-driving cars and industrial automation. However, traditional VA, relying on either end-device or cloud-based solutions, faces limitations such as restricted on-device computing power and network congestion at cloud centers. Edge computing offers a promising solution, enabling low-latency, high-accuracy, and bandwidth-efficient performance, thus supporting the rapid growth of VA deployment. This article provides a comprehensive review of VA at the edge, examining aspects of model training, deployment, end-edge-cloud orchestration, and VA platforms. Specifically, we explore model training approaches conducted in the cloud, at the edge, and in hybrid cloud-edge configurations. We also discuss various model deployment techniques, including quantization and network pruning. Furthermore, the article surveys end-edge-cloud orchestration strategies, categorized into VA query offloading and query scheduling. We evaluate practical deployments and review the literature on VA platforms. Finally, we outline several promising future research directions for advancing this field. © 1998-2012 IEEE.

关键词： Computation offloading

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