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A System for Positioning an Optical Tunnel Measuring Transducer of a Microoptoelectromechanical Micro-g Accelerometer 23

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A System for Positioning an Optical Tunnel Measuring Transdu...

23rd IEEE International Conference of Young Professionals in Electron Devices and Materials, EDM 2022

作者： Barbin, Evgenij S. Nesterenko, Tamara G. Koleda, Aleksej N. Shesterikov, Evgeniy V. Kulinich, Ivan V. Kokolov, Andrey A. The Tomsk State University of Control Systems and Radioelectronics Laboratory of Intelligent Computer Systems Institute of Optics and Atmosphere Siberian Branch of the Russian Academy of Sciences Laboratory of Radiophotonics Tomsk Russia The Tomsk State University of Control Systems and Radioelectronics Division for Electronic Engineering National Research Tomsk Polytechnic University Laboratory of Radiophotonics Tomsk Russia

ISBN: (纸本)9781665498043

The paper investigates a microoptoelectromechanical accelerometer that implements optical measuring transducer built based on the optical tunneling effect. The optical transducer is a directional optical coupler that contains moving and fixed waveguides from Si3 N4 with dimensions of 0.3 ×1 µm. The treatise studies the normalized transmission at the directional coupler output depending on the gap and coupling length. Due to the positioning system, the optical measuring transducer can have several operating points with different optical sensitivity. For instance, for a set coupling length of 100 µm, the sensitivity of the directional coupler may reach 6.25 × 106 m-1, 20 × 106 m-1 or 41.7 × 106 m-1 for initial gaps of 255, 120 and 70 nm, correspondingly. The comb-drive actuator has three independent stages with 600, 50 and 4 fingers. It allows setting a necessary gap (from 0 to 2.8 µm) between the waveguides. It also imitates microaccelerations (from 16 micro-g to 3 g) by using electrostatic forces. The study proposes a method for selecting parameters of the positioning system (electrode voltage, number of fingers, spring suspension stiffness and inertial mass). © 2022 IEEE.

关键词： Directional couplers

Reinforcement Learning based Edge Offloading Strategy for Port Infrastructure States Monitoring

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Reinforcement Learning based Edge Offloading Strategy for Po...

IEEE International Conference on Communications in China Workshops (ICCC)

作者： Jingzhe Wang Qingqing Pan Ziqi Liu I Yunlong Lu Songgui Chen Zhufang Kuang Siyu Lin School of Electronic and Information Engineering Beijing Jiaotong University Beijing China Beijing Engineering Research Center of High-speed Railway Broadband Mobile Communications and Collaborative Innovation Center of Railway Traffic Safety Beijing China National Marine Environmental Forecasting Center (NMEFC) Beijing China Tianjin Research Institute for Water Transport Enginieering M.O.T. Tianjin China School of Computer and Information Engineering Central South University of Forestry and Technology Changsha China

ISBN: (数字)9798350377675

ISBN: (纸本)9798350377682

It is necessary to establish an edge computing system to provide computing power for latency sensitive monitoring data process task of port infrastructure states monitoring sys-tem. Considering the difference between power supply mode of onshore and offshore edge computing servers in port, an edge computing system architecture for port infrastructure monitoring is designed. The monitoring devices task offloading problem is formulated as a Constrained Markov Decision Process (CMDP), whose objective is minimizing the weighted-sum of latency and energy consumption with computing power and latency constraints. A Deep Deterministic policy gradient-based task Offloading and Computing resource allocation (DDOC) algorithm is proposed to solve the CMDP problem. Simulation results show that compared with other baseline solutions, the proposed DDOC effectively realized joint optimization of latency and energy consumption.

关键词： Energy consumption Power supplies Simulation Systems architecture Reinforcement learning Servers Resource management Monitoring Optimization Edge computing

Sensitivity-Optimized Waveguide-Based Methane Gas Sensor in the Mid-IR

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Sensitivity-Optimized Waveguide-Based Methane Gas Sensor in ...

Conference on Lasers and Electro-Optics (CLEO)

作者： Pen-Sheng Lin Arne Quellmalz Po-Han Huang Shayan Parhizkar Nour Negm Stephan Suckow Floria Ottonello-Briano Max C. Lemme Frank Niklaus Kristinn B. Gylfason Division of Micro and Nanosystems School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Chair of Electronic Devices RWTH Aachen University Aachen Germany AMO GmbH Advanced Microelectronic Center Aachen Aachen Germany Senseair AB Delsbo Sweden

We demonstrate methane gas sensing using suspended silicon waveguides and experimentally validate the sensitivity optimization of waveguide-based gas sensors by varying waveguide lengths. This method enables applicati... 详细信息

ISBN: (纸本)9798350369311

关键词： Methane Integrated optics Sensitivity Optical design Waveguide lasers Silicon Optical sensors Optical waveguides Gas detectors Optimization

How to predict on-road air pollution based on street view images and machine learning: a quantitative analysis of the optimal strategy

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arXiv 2024年

作者： Zhong, Hui Chen, Di Wang, Pengqin Wang, Wenrui Shen, Shaojie Liu, Yonghong Zhu, Meixin Guangzhou China Department of Civil and Environmental Engineering The Hong Kong University of Science and Technology Hong Kong Interdisciplinary Programs Office The Hong Kong University of Science and Technology Hong Kong Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong Guangdong Provincial Key Lab of Integrated Communication Sensing and Computation for Ubiquitous Internet of Things Guangzhou China School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou510006 China

On-road air pollution exhibits substantial variability over short distances due to emission sources, dilution, and physicochemical processes. Integrating mobile monitoring data with street view images (SVIs) holds promise for predicting local air pollution. However, algorithms, sampling strategies, and image quality introduce extra errors due to a lack of reliable references that quantify their effects. To bridge this gap, we employed 314 taxis to monitor NO, NO2, PM2.5 and PM10 dynamically and sampled corresponding SVIs, aiming to develop a reliable strategy. We extracted SVI features from ~ 382,000 streetscape images, which were collected at various angles (0°, 90°, 180°, 270°) and ranges (buffers with radii of 100m, 200m, 300m, 400m, 500m). Also, three machine learning algorithms alongside the linear land-used regression (LUR) model were experimented with to explore the influences of different algorithms. Four typical image quality issues were identified and discussed. Generally, machine learning methods outperform linear LUR for estimating the four pollutants, with the ranking: random forest > XGBoost > neural network > LUR. Compared to single-angle sampling, the averaging strategy is an effective method to avoid bias of insufficient feature capture. Therefore, the optimal sampling strategy is to obtain SVIs at a 100m radius buffer and extract features using the averaging strategy. This approach achieved estimation results for each aggregation location with absolute errors almost less than 2.5 μg/m3 or ppb. Overexposure, blur, and underexposure led to image misjudgments and incorrect identifications, causing an overestimation of road features and underestimation of human-activity features, contributing to inaccurate NO, NO2, PM2.5 and PM10 estimation. These findings provide a better understanding and valuable support for developing image-based air quality models and other SVI-related research. Copyright © 2024, The Authors. All rights reserved.

关键词： Decision trees

Competition between Tcp and Bcc Clusters During Relaxation of Undercooled Liquid Zirconium

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SSRN

SSRN 2022年

作者： Tian, Zean Mo, Yunfei Zhou, Lili Liang, Yongchao Zhang, Xiefu Zhang, Haitao Tan, Zhiguang Wang, Shengjie Luo, Jinhua Zou, Yingchang Liu, Rangsu School of Electronic Information and Electrical Engineering Hunan Province Key Laboratory of Applied Environmental Photocatalysis Changsha University Changsha410022 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China School of Medical and Information Engineering Gannan Medical University Ganzhou341000 China School of Big Data and Information Engineering Guizhou University Guiyang550025 China

AbstractionThe details of dynamic competition between topologically closepacked (TCP) clusters and metastable body-centered cubic (BCC) clusters during the isothermal relaxation for undercooled liquid zirconium (Zr) have been investigated by molecular dynamics (MD) simulations. The structural evolution of the simulated system has been analyzed by using the system energy, the pair distribution function and the largest standard cluster analysis. It is found that the TCP clusters rather than icosahedral ones dominate in number in undercooled liquid. The TCP clusters with T5=n5/(n4+n5+n6)>1/3 stabilize the undercooled liquid and prevent crystallization;while that with T51/3 decreases and the average degree of interconnect among them weakens. © 2022, The Authors. All rights reserved.

关键词： Molecular dynamics

A Flexible Graphene-Based Antenna for RF Vortex Generation

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A Flexible Graphene-Based Antenna for RF Vortex Generation

IEEE International Conference on electronic Information and Communication Technology (ICEICT)

作者： Shoudong Gu Zuxian He Xiaolong Wang Volodymyr I. Fesenko Vladimir R. Tuz State Key Laboratory on Integrated Optoelectronics College of Electronic Science and Engineering International Center of Future Science Jilin University Changchun China Department of Electronics Engineering Division of Engineering at Campus Irapuato-Salamanca University of Guanajuato Salamanca Mexico Department of Microwave Electronics Institute of Radio Astronomy of NAS of Ukraine Kharkiv Ukraine School of Radiophysics Biomedical Electronics and Computer Systems V. N. Karazin Kharkiv National University Kharkiv Ukraine

ISBN: (数字)9798350384437

ISBN: (纸本)9798350384444

Electromagnetic waves with orbital angular momentum (OAM) have garnered significant interest by reason of their potential in designing advanced communication systems. This paper reports on a specific design of a flexible graphene-based antenna for RF vortex wave generation. The antenna is composed of a ring-shaped resonator made of a graphene film with high conductivity deposited on a thin polydimethylsiloxane substrate. The graphene film is characterized by greater strength to repeated mechanical bending than metallic counterparts while providing good conductivity of the patch. We use the commercial ANSYS HFSS 3D electromagnetic solver to conduct full-wave numerical simulations of the antenna performances and radiation conditions, simulating the generation of vortex waves at different bending radii of the antenna. The mode purity is evaluated with the use of the spiral spectrum algorithm to reveal the stability of the vortex wave generation by the antenna in bent states. Several pilot samples of the antenna working in the RF band are fabricated and tested. Calculated together with empirical results demonstrate that the flexible antenna can stably generate vortex waves even subjected to either outward or inward bending. The proposed flexible graphene-based antenna has the potential for application in wireless communication systems.

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Optimization Design for Federated Learning in Heterogeneous 6G Networks

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arXiv 2023年

作者： Luo, Bing Ouyang, Xiaomin Sun, Peng Han, Pengchao Ding, Ningning Huang, Jianwei The Data Science Research Center the Division of Natural and Applied Sciences Duke Kunshan University Jiangsu Kunshan China The department of Information Engineering The Chinese University of Hong Kong Hong Kong The College of Computer Science and Electronic Engineering Hunan University Changsha410082 China The School of Science and Engineering The Chinese University of Hong Kong Shenzhen China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen China The Department of Electrical and Computer Engineering Northwestern University EvanstonIL60208 United States

With the rapid advancement of 5G networks, billions of smart Internet of Things (IoT) devices along with an enormous amount of data are generated at the network edge. While still at an early age, it is expected that the evolving 6G network will adopt advanced artificial intelligence (AI) technologies to collect, transmit, and learn this valuable data for innovative applications and intelligent services. However, traditional machine learning (ML) approaches require centralizing the training data in the data center or cloud, raising serious user-privacy concerns. Federated learning, as an emerging distributed AI paradigm with privacy-preserving nature, is anticipated to be a key enabler for achieving ubiquitous AI in 6G networks. However, there are several system and statistical heterogeneity challenges for effective and efficient FL implementation in 6G networks. In this article, we investigate the optimization approaches that can effectively address the challenging heterogeneity issues from three aspects: incentive mechanism design, network resource management, and personalized model optimization. We also present some open problems and promising directions for future research. Copyright © 2023, The Authors. All rights reserved.

关键词： 5G mobile communication systems

Simplifying Multimodality: Unimodal Approach to Multimodal Challenges in Radiology with General-Domain Large Language Model

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arXiv 2024年

作者： Cho, Seonhee Kim, Choonghan Lee, Jiho Chilkunda, Chetan Choi, Sujin Yoon, Joo Heung Kim Jaechul Graduate School of AI KAIST Gyeonggi Seongnam Korea Republic of Moon Soul Graduate School of Future Strategy KAIST Daejeon Korea Republic of Department of Computer Science and Engineering Ewha Womans University Seoul Korea Republic of Department of Chemical Engineering Carnegie Mellon University PittsburghPA United States Division of Environmental Science and Engineering POSTECH Gyeongbuk Pohang Korea Republic of Division of Pulmonary Allergy Critical Care and Sleep Medicine Department of Medicine University of Pittsburgh PittsburghPA United States

Recent advancements in Large Multimodal Models (LMMs) have attracted interest in their generalization capability with only a few samples in the prompt. This progress is particularly relevant to the medical domain, where the quality and sensitivity of data pose unique challenges for model training and application. However, the dependency on high-quality data for effective in-context learning raises questions about the feasibility of these models when encountering with the inevitable variations and errors inherent in real-world medical data. In this paper, we introduce MID-M, a novel framework that leverages the in-context learning capabilities of a general-domain Large Language Model (LLM) to process multimodal data via image descriptions. MID-M achieves a comparable or superior performance to task-specific fine-tuned LMMs and other general-domain ones, without the extensive domain-specific training or pre-training on multimodal data, with significantly fewer parameters. This highlights the potential of leveraging general-domain LLMs for domain-specific tasks and offers a sustainable and cost-effective alternative to traditional LMM developments. Moreover, the robustness of MID-M against data quality issues demonstrates its practical utility in real-world medical domain applications. © 2024, CC BY.

关键词： Cost effectiveness

MetaFollower: Adaptable Personalized Autonomous Car Following

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arXiv 2024年

作者： Chen, Xianda Chen, Kehua Zhu, Meixin Yang, Hao Shen, Shaojie Wang, Xuesong Wang, Yinhai Guangzhou511400 China Interdisciplinary Programs Office The Hong Kong University of Science and Technology Hong Kong Guangdong Provincial Key Lab of Integrated Communication Sensing and Computation for Ubiquitous Internet of Things Guangzhou511400 China Department of Civil and System Engineering Whiting School of Engineering Johns Hopkins University BaltimoreMD21218 United States Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong School of Transportation Engineering Tongji University Shanghai201804 China Department of Civil and Environmental Engineering University of Washington SeattleWA98195 United States

Car-following (CF) modeling, a fundamental component in microscopic traffic simulation, has attracted increasing interest of researchers in the past decades. In this study, we propose an adaptable personalized car-following framework - MetaFollower, by leveraging the power of meta-learning. Specifically, we first utilize Model-Agnostic Meta-Learning (MAML) to extract common driving knowledge from various CF events. Afterward, the pre-trained model can be fine-tuned on new drivers with only a few CF trajectories to achieve personalized CF adaptation. We additionally combine Long Short-Term Memory (LSTM) and Intelligent Driver Model (IDM) to reflect temporal heterogeneity with high interpretability. Unlike conventional adaptive cruise control (ACC) systems that rely on predefined settings and constant parameters without considering heterogeneous driving characteristics, MetaFollower can accurately capture and simulate the intricate dynamics of car-following behavior while considering the unique driving styles of individual drivers. We demonstrate the versatility and adaptability of MetaFollower by showcasing its ability to adapt to new drivers with limited training data quickly. To evaluate the performance of MetaFollower, we conduct rigorous experiments comparing it with both data-driven and physics-based models. The results reveal that our proposed framework outperforms baseline models in predicting car-following behavior with higher accuracy and safety. To the best of our knowledge, this is the first car-following model aiming to achieve fast adaptation by considering both driver and temporal heterogeneity based on meta-learning. Copyright © 2024, The Authors. All rights reserved.

关键词： Adaptive cruise control