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Frontiers in Optics + Laser Science 2023, FiO, LS 203: Part of Frontiers in Optics + Laser Science 2023

作者： Zhu, Chen Huang, Jie Research Center for Optical Fiber Sensing Zhejiang Laboratory Hangzhou311100 China Department of Electrical and Computer Engineering Missouri University of Science and Technology RollaMO65409 United States

An optical fiber curvature sensor based on a no-core fiber (NCF) cascaded with a hollow-core fiber (HCF), realizing simultaneously high sensitivity and a broad dynamic range with the assistance of machine learning ana... 详细信息

ISBN: (纸本)9781957171296

关键词： Curvature sensors Fiber Bragg gratings Fiber optic sensors Long period fiber gratings Machine learning optical fibers

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ElectrodeNet - A Deep-Learning-Based Sound Coding Strategy for Cochlear Implants

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IEEE Transactions on Cognitive and Developmental Systems 2024年第1期16卷 346-357页

作者： Huang, Enoch Hsin-Ho Chao, Rong Tsao, Yu Wu, Chao-Min Department of Electrical Engineering National Central University Taoyuan320317 Taiwan Research Center for Information Technology Innovation Academia Sinica Taipei115201 Taiwan Department of Computer Science and Information Engineering National Cheng Kung University Tainan701401 Taiwan Department of Electrical Engineering Chung Yuan Christian University Taoyuan320314 Taiwan

ElectrodeNet, a deep-learning-based sound coding strategy for the cochlear implant (CI), is proposed to emulate the advanced combination encoder (ACE) strategy by replacing the conventional envelope detection using various artificial neural networks. The extended ElectrodeNet-CS strategy further incorporates the channel selection (CS). Network models of deep neural network (DNN), convolutional neural network (CNN), and long short-term memory (LSTM) were trained using the fast Fourier transformed bins and channel envelopes obtained from the processing of clean speech by the ACE strategy. Objective speech understanding using short-time objective intelligibility (STOI) and normalized covariance metric (NCM) was estimated for ElectrodeNet using CI simulations. Sentence recognition tests for vocoded Mandarin speech were conducted with normal-hearing listeners. DNN, CNN, and LSTM-based ElectrodeNets exhibited strong correlations to ACE in objective and subjective scores using mean squared error (MSE), linear correlation coefficient (LCC), and Spearman's rank correlation coefficient (SRCC). The ElectrodeNet-CS strategy was capable of producing N-of-M compatible electrode patterns using a modified DNN network to embed maxima selection, and to perform in similar or even slightly higher average in STOI and sentence recognition compared to ACE. The methods and findings demonstrated the feasibility and potential of using deep learning in the CI coding strategy. © 2023 IEEE.

关键词： Cochlear implants

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Fiber Ring Resonator Based on Microwave Photonics and Sensitivity-Enhanced Sensing

Fiber Ring Resonator Based on Microwave Photonics and Sensit...

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Optica Sensing Congress 2023, AIS, FTS, HISE, Sensors, ES 2023 - Part of optical Sensors and Sensing Congress 2023

A new concept of microwave-photonic (MWP) fiber ring resonator is introduced and a method for sensitivity-enhanced sensing based on the system is proposed and experimentally demonstrated. © 2023 The Author (s).

ISBN: (纸本)9781957171241

关键词： Microwave photonics

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Anticipating optical Availability in Hybrid RF/FSO Links Using RF Beacons and Deep Learning

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2024年 2卷 1369-1388页

作者： Ibrahim, Mostafa Ahmad, Arsalan Ekin, Sabit Lopresti, Peter Altunc, Serhat Kegege, Obadiah O'Hara, John F. Texas A&M University Department of Engineering Technology College StationTX77843 United States Oklahoma State University School of Electrical and Computer Engineering StillwaterOK74075 United States Texas A&M University Department of Electrical and Computer Engineering College StationTX77843 United States The University of Tulsa Department of Electrical and Computer Engineering TulsaOK74104 United States Goddard Space Flight Center NASA GreenbeltMD20771 United States

Radiofrequency (RF) communications offer reliable but low data rates and energy-inefficient satellite links, while free-space optical (FSO) promises high bandwidth but struggles with disturbances imposed by atmospheric effects. A hybrid RF/FSO architecture aims to achieve optimal reliability along with high data rates for space communications. Accurate prediction of dynamic ground-to-satellite FSO link availability is critical for routing decisions in low-earth orbit constellations. In this paper, we propose a system leveraging ubiquitous RF links to proactively forecast FSO link degradation prior to signal drops below threshold levels. This enables pre-calculation of rerouting to maximally maintain high data rate FSO links throughout the duration of weather effects. We implement a supervised learning model to anticipate FSO attenuation based on the analysis of RF patterns. Through the simulation of a dense lower earth orbit (LEO) satellite constellation, we demonstrate the efficacy of our approach in a simulated satellite network, highlighting the balance between predictive accuracy and prediction duration. An emulated cloud attenuation model is proposed to provide insight into the temporal profiles of RF signals and their correlation to FSO channel dynamics. Our investigation sheds light on the trade-offs between prediction horizon and accuracy arising from RF beacon numbers and proximity. © 2024 The Authors.

关键词： Satellite links

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Fabry-Perot Interferometer in a Coreless Fiber Fabricated by aFsLaser for High-Temperature Applications

Fabry-Perot Interferometer in a Coreless Fiber Fabricated by...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

An optical fiber in-line Fabry-Perot interferometer (FPI) fabricated in a no-core fiber using the direct femtosecond laser writing technique for high-temperature sensing applications is reported. © Optica Publish... 详细信息

ISBN: (纸本)9781957171258

关键词： Fabry-Perot interferometers

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Gradual Domain Adaptation via Manifold-Constrained Distributionally Robust Optimization 38

Gradual Domain Adaptation via Manifold-Constrained Distribut...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Saberi, Amirhossein Najafi, Amir Behjati, Amin Emrani, Ala Zolfi, Yasaman Shadrooy, Mahdi Motahari, Abolfazl Khalaj, Babak H. Department of Electrical Engineering Sharif University of Technology Tehran Iran Department of Computer Engineering Sharif University of Technology Tehran Iran Sharif Center for Information Systems and Data Science Sharif University of Technology Tehran Iran

The aim of this paper is to address the challenge of gradual domain adaptation within a class of manifold-constrained data distributions. In particular, we consider a sequence of T ≥ 2 data distributions P1, ..., PT undergoing a gradual shift, where each pair of consecutive measures Pi, Pi+1 are close to each other in Wasserstein distance. We have a supervised dataset of size n sampled from P0, while for the subsequent distributions in the sequence, only unlabeled i.i.d. samples are available. Moreover, we assume that all distributions exhibit a known favorable attribute, such as (but not limited to) having intra-class soft/hard margins. In this context, we propose a methodology rooted in Distributionally Robust Optimization (DRO) with an adaptive Wasserstein radius. We theoretically show that this method guarantees the classification error across all Pis can be suitably bounded. Our bounds rely on a newly introduced compatibility measure, which fully characterizes the error propagation dynamics along the sequence. Specifically, for inadequately constrained distributions, the error can exponentially escalate as we progress through the gradual shifts. Conversely, for appropriately constrained distributions, the error can be demonstrated to be linear or even entirely eradicated. We have substantiated our theoretical findings through several experimental results. © 2024 Neural information processing systems foundation. All rights reserved.

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Equipment Parameter Investigation for a Proposed Power Conductor Hot Spot Identification system 10

Equipment Parameter Investigation for a Proposed Power Condu...

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10th International Conference on Modern Power Systems, MPS 2023

作者： Polycarpou, Alexis Komodromos, Michael Kalli, Kyriacos Argyrou, Maria C. Ioannou, Andreas Computer Engineering and Informatics Frederick University Frederick Research Center Deparment of Electrical Nicosia Cyprus Cyprus University of Technology PhOSLab Department of Electrical Engineering/Computer Engineering and Informatics Limassol Cyprus

ISBN: (纸本)9798350326826

Reliability, efficiency and continuity of power energy supplied is an area which receives increasing attention as the main infrastructure of power transmission and distribution systems in many countries is ageing. Hot spot related cable failures and power interruptions have a big financial impact on the power provider. The operational parameters of a method to identify in real time hot spots on cables is investigated in this paper, which can result to timely fault prevention. This can be achieved through exploiting the optical sensing capabilities of the existing optical fibre on-grid network. The resulting parameters aim to be advantageous for the system as they increase accuracy and will enable the Remote, automated, continuous, and real time monitoring of the grid infrastructure integrity. © 2023 IEEE.

关键词： Cables

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Tunable defect-engineered nanohybrid heterostructures: exfoliated 2D WSe2-MoS2 nanohybrid sheet covered on 1D ZnO nanostructures for self-powered UV photodetectors

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Journal of Materials Chemistry C 2023年第18期11卷 6082-6088页

作者： Saravanan, Adhimoorthy Huang, Bohr-Ran Kathiravan, Deepa Sakalley, Shikha Chen, Sheng-Chi Graduate Institute of Electro-Optical Engineering and Department of Electronic and Computer Engineering National Taiwan University of Science and Technology Taipei106 Taiwan Department of Mechanical Engineering National Taiwan University of Science and Technology Taipei106335 Taiwan Department of Materials Engineering and Center for Plasma and Thin Film Technologies Ming Chi University of Technology New Taipei City24301 Taiwan College of Engineering and Center for Green Technology Chang Gung University Taoyuan333323 Taiwan

Future innovation of optoelectronic industrial applications for energy saving devices such as self-powered sensors and photodetectors have gained much research attention. Here, for the first time, two-dimensional (2D) WSe2@MoS2 nanohybrid sheet heterostructures covering one-dimensional (1D) ZnO nanorod (ZNR) based self-powered ultraviolet (UV) photodetectors are fabricated using a combination of the liquid exfoliation process and a hydrothermal technique. The WSe2@MoS2-ZNR device shows a high on/off current ratio (up to 17.2 × 103), and a fast response time (0.05/0.1 s) at 365 nm illumination under self-bias. In addition, an enhanced UV responsivity (1.6 A W−1) and outstanding photosensitivity (1.16 × 106%) were also observed. The excellent photoresponse, photosensitivity and responsivity were achieved due to strong interactions of the optical materials, enhanced light absorption and efficient charge separation of the present nanohybrid structure. This work provides reliable methodology for the fabrication of a unique 2D nanohybrid covered 1D nanostructure for use in self-powered optoelectronic devices and, useful for energy conservation. © 2023 The Royal Society of Chemistry.

关键词： Selenium compounds

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A Triple-mode Wideband Filtering Antenna with Planar Configuration by Combining One SIW cavity and Two Half-mode Patch Resonators

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IEEE Antennas and Wireless Propagation Letters 2025年第6期24卷 1512-1516页

作者： Liu, Qianwen Li, Chengyu Zhu, Lei Tang, Yiming Lu, Wen-Jun Lyu, Yun-Peng Huang, Feng Nanjing University of Posts and Telecommunications College of Telecommunications and Information Engineering Nanjing210003 China State Key Laboratory of Millimeter Waves Nanjing210003 China Nanjing University of Posts and Telecommunications College of Electronic and Optical Engineering Nanjing210023 China University of Macau Department of Electrical and Computer Engineering Faculty of Science and Technology China

In this letter, a triple-mode wideband filtering antenna with planar configuration is presented. The designed antenna has a single substrate layer, which consists of one half-mode circular patch resonator, one half-mode rectangular patch resonator, and one rectangular SIW cavity, and it is fed through the conventional coaxial probe. On the basis of the field analysis, these three types of resonators are compactly integrated without disturbing the resonant properties of their dominant modes. Given this, three resonant modes of half-TM11, half-TM10, and TE11 are well generated, so as to form a widened operating band. Meanwhile, four radiation nulls are produced from far-field radiation cancellation with the increased radiation boundaries. For validation, a prototype antenna with the center frequency of 2.8 GHz is designed and fabricated. The measured results indicate that the proposed antenna achieves an improved fractional bandwidth of 15.7% under three in-band resonances. Besides, four radiation nulls created at 2.32, 3.22, 3.49, and 3.89 GHz in stopband effectively enhance the out-of-band suppression level. Moreover, low profile of 0.037λ0 is also implemented. © 2002-2011 IEEE.

关键词： Microstrip antennas

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Insect Identification in Pulsed Lidar Images Using Changepoint Detection Algorithms

Insect Identification in Pulsed Lidar Images Using Changepoi...

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2023 Annual Intermountain engineering, technology and Computing, IETC 2023

作者： Sweeney, Nathaniel Xu, Caroline Shaw, Joseph A. Hocking, Toby D. Whitaker, Bradley M. Montana State University Electrical and Computer Engineering Department United States University of Michigan Electrical Engineering and Computer Science Department United States Montana State University Optical Technology Center United States School of Informatics Computing and Cyber Systems Northern Arizona University United States

ISBN: (纸本)9798350335903

Noninvasive entomological insect monitoring often utilizes a variety of tools such as LiDAR to gather information without interfering with the insects in their habitat. These collection methods often result in large amounts of data that can be te-dious and lengthy to interpret and analyze. Machine learning has been previously used in the past in order to analyze Li-DAR images to detect insects, but often suffers from pitfalls such as long training times and large computational power requirements. In an attempt to offer an alternative that takes little to no training on the data and much less computational power, this paper looks at the use of changepoint detection algorithms to analyze LiDAR images containing insects. By analyzing the rows or columns of a LiDAR image, the algorithms should be able to detect abrupt changes in the image that would represent the insects. While not as accurate, the changepoint detection algorithms give comparable results to a machine learning algorithm tested on the same dataset without the need for supervised training. © 2023 IEEE.

关键词： Anomaly detection

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