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IEEE SENSORS

作者： Guilherme H. Weber Danilo F. Gomes Eduardo N. dos Santos Ana L. B. Santana Jean C. C. da Silva Cicero Martelli Daniel R. Pipa Rigoberto E. M. Morales Sérgio T. C. Manoel F. da Silva Marco J. da Silva Graduate Program in Electrical and Computer Engineering Universidade Tecnológica Federal do Paraná Curitiba Brazil Multiphase Flow Research Center Universidade Tecnológica Federal do Paraná Curitiba Brazil Petrobras Rio de Janeiro Brazil Johannes Kepler University Linz Linz Austria

The occurrence of leaks in the transport of fluids in the oil and gas industry is a recurring problem. Compounds subject to reactions with the environment flowing under high pressure and temperature, through long pipes in places of difficult access exposed to the weather create several conditions for leaks. Distributed Acoustic Sensing (DAS) technology perfectly addresses those challenges as it provides long-range remote sensing through a dense network of sensors with high sensitivity and wide frequency response. In this work, we demonstrate the use of a DAS system simultaneously operating in acoustic and temperature gradient modes to detect a 1 mm diameter hole leak in a 1 inch-pipeline under water-air slug flow. The measurement data demonstrate the effects of the leakage in both acoustic and thermal perspectives. The results determine the system's ability to identify the integrity of the pipeline, as well as the exact moment the leak started, its location, characteristics in both time and frequency domain, and thermal effects as well. This research contributes to advance DAS technology as a sensing tool capable of detecting mm-sized leak hole in fluid transport pipelines.

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Demonstration of a polarization-agnostic geometric phase in nonlocal metasurfaces

Demonstration of a polarization-agnostic geometric phase in ...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Adam C. Overvig Y. Kasahara Andrea Alù Photonics Initiative Advanced Science Research Center City University of New York New York NY USA Department of Electrical and Computer Engineering The University of Texas at Austin Austin TX USA Physics Program Graduate Center of the City University of New York New York NY USA

We theoretically propose and experimentally demonstrate nonlocal metasurfaces rationally designed using symmetry-breaking principles to manipulate the optical wavefront with a geometric phase selective to custom ellip... 详细信息

ISBN: (纸本)9781957171258

关键词： Circular polarization Constructive interference Fano resonance Polarization Polarized light Thermal emission

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Trends, Opportunities, and Challenges in Detecting Depressive Disorders Through Mobile Devices: A Review

Trends, Opportunities, and Challenges in Detecting Depressiv...

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computer System, Information Technology, and electrical engineering (COSITE), International Conference on

作者： Gregorius Natanael Elwirehardja Mahmud Isnan Anzaludin Samsinga Perbangsa Kahlil Muchtar Bens Pardamean Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Information System Department School of Information Systems Bina Nusantara University Jakarta Indonesia Department of Electrical and Computer Engineering Universitas Syiah Kuala Banda Aceh Indonesia Computer Science Department BINUS Graduate Program -Master of Computer Science Bina Nusantara University Jakarta Indonesia

Depressive Disorders (DD) is one of the most prevalent mental disorders in the world that may lead to suicide cases. To prevent the latter, ubiquitous early detection systems may be effective. Recent studies have since researched the development of such systems by exploiting several forms of data, including video, audio, Ecological Momentary Assessments (EMA), and passive sensing data using sensors embedded in mobile devices. To summarize the trends, opportunities, and existing challenges in this field, this study reviewed 15 papers to answer four research questions. EMA was the most popular data to be used in this task, but other approaches, such as using video, audio, and typing behaviors, may be considered due to the subjectivity of EMA. These data were typically recorded using smartphones and analyzed using Machine Learning (ML). However, most of the developed systems had yet to be implemented. Overall, it was concluded that further studies may need to explore usages of more objective data in multimodal approaches as well as consider using Mobile Cloud Computing (MCC) to deploy these systems to provide more effective and efficient diagnoses. Future studies must also take into account the existing challenges of the data and infrastructures, such as the weaknesses of several data types, limitations of mobile devices, as well as the challenges of diagnosis approaches.

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Multi-wavelength edge detection based on nonlocal multilayer GaAs-AlAs thin films

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Optics letters 2025年第12期50卷 3856-3859页

作者： Sina Mohammadi Matthew Markowitz Francesco Monticone Mohammad Ali Miri Maria Tamargo Ph.D. Program in Physics The Graduate Center of the City University of New York New York New York 10016 USA Department of Physics Queens College of the City University of New York Queens New York 11367 USA School of Electrical and Computer Engineering Cornell University Ithaca New York 14853 USA Department of Chemistry The City College of New York New York New York 10031 USA

We demonstrate a compact multilayer GaAs-AlAs structure for passive optical edge detection at multiple wavelengths. Through the inverse design of the layer thicknesses, this structure manipulates spatial frequency components of an incoming wavefront, selectively reflecting high-frequency features while suppressing low-frequency intensity variations. Simulations reveal a reflectance transition from minimal to near-total as a function of numerical aperture, a property leveraged for enhancing edge contrast in optical imaging. For the first time, to our knowledge, we utilize molecular beam epitaxy (MBE) to fabricate edge detection devices, ensuring structural fidelity. Material characterization confirms high-quality interfaces, precise thickness control, and excellent uniformity, validating the suitability of MBE for this application. Experimental angle-resolved reflectance measurements closely align with theoretical predictions, demonstrating the feasibility of this approach for real-time, hardware-based optical image processing. The proposed design automatically works for at least two wavelengths and can be readily extended to operate at multiple wavelengths simultaneously. This work opens new possibilities for employing multilayer interference structures in high-performance optical imaging and real-time signal processing.

关键词： Digital image processing Edge detection Inverse design Optical imaging Signal processing Thin films

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Generative AI-enabled Vehicular Networks: Fundamentals, Framework, and Case Study

arXiv

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arXiv 2023年

作者： Zhang, Ruichen Xiong, Ke Du, Hongyang Niyato, Dusit Kang, Jiawen Shen, Xuemin Poor, H. Vincent The Engineering Research Center of Network Management Technology High Speed Railway of Ministry of Education School of Computer and Information Technology Beijing Jiaotong University Beijing100044 China The Collaborative Innovation Center of Railway Traffic Safety Beijing Jiaotong University Beijing100044 China The National Engineering Research Center of Advanced Network Technologies Beijing Jiaotong University Beijing100044 China The School of Computer Science and Engineering The Energy Research Institute @ NTU Interdisciplinary Graduate Program Nanyang Technological University Singapore The School of Automation Guangdong University of Technology China The Department of Electrical and Computer Engineering University of Waterloo Canada The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Recognizing the tremendous improvements that the integration of generative AI can bring to intelligent transportation systems, this article explores the integration of generative AI technologies in vehicular networks, focusing on their potential applications and challenges. Generative AI, with its capabilities of generating realistic data and facilitating advanced decision-making processes, enhances various applications when combined with vehicular networks, such as navigation optimization, traffic prediction, data generation, and evaluation. Despite these promising applications, the integration of generative AI with vehicular networks faces several challenges, such as real-time data processing and decision-making, adapting to dynamic and unpredictable environments, as well as privacy and security concerns. To address these challenges, we propose a multi-modality semantic-aware framework to enhance the service quality of generative AI. By leveraging multi-modal and semantic communication technologies, the framework enables the use of text and image data for creating multi-modal content, providing more reliable guidance to receiving vehicles and ultimately improving system usability and efficiency. To further improve the reliability and efficiency of information transmission and reconstruction within the framework, taking generative AI-enabled vehicle-to-vehicle (V2V) as a case study, a deep reinforcement learning (DRL)-based approach is proposed for resource allocation. Finally, we discuss potential research directions and anticipated advancements in the field of generative AI-enabled vehicular networks. Copyright © 2023, The Authors. All rights reserved.

关键词： Reinforcement learning

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A 3D GAN Architecture for Volumetric Synthetic Aperture Sonar

A 3D GAN Architecture for Volumetric Synthetic Aperture Sona...

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OCEANS

作者： Greg D. Vetaw Albert Reed Daniel C. Brown Suren Jayasuriya School of Electrical Computer and Energy Engineering Arizona State University The Applied Research Laboratory Graduate Program in Acoustics Pennsylvania State University

ISBN: (纸本)9781665427883

Synthetic aperture sonar (SAS) is used extensively in underwater imaging for visualizing the seafloor and objects present on it. However, processing SAS images can be time-consuming and tedious, with machine learning techniques being ineffective due to the lack of available data. In particular, automated target recognition (ATR) with 3D SAS data for machine learning is challenging in many ways due to the complexity with working with 3D volumetric data. Recently, researchers have introduced generative adversarial networks (GANs) to help perform 2D SAS image generation for data augmentation. Following this line of work in this paper, we introduce a 3D-GAN architecture to generate photorealistic 3D SAS data which matches the fidelity of real data. In particular, we discuss novel latent space sampling and normalization to help 3D GANs overcome mode collapse for generating volumetric SAS information. Experimental results are shown on real 3D SAS data, showing the potential of using 3D GANs for dataset augmentation in the future.

关键词： Three-dimensional displays Target recognition Image synthesis Sea floor Imaging Data visualization Machine learning

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High-Resolution Hydrogen Molecule Detection Utilizing a Nanocandle-Based Optical Sensor

High-Resolution Hydrogen Molecule Detection Utilizing a Nano...

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Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： Nu-Ri Park Min-Joong Kim Jongsu Lee Minah Seo Yong-Sang Ryu Myung-Ki Kim KU-KIST Graduate School of Converging Science and Technology Korea University Seoul Republic of Korea Sensor System Research Center Korea Institute of Science and Technology Seoul Republic of Korea Department of Electrical Engineering and Computer Science Northwestern University Evanston IL USA School of Biomedical Engineering Korea University Seoul Republic of Korea Interdisciplinary Program in Precision Public Health Korea University Seoul Republic of Korea

Our research introduces a novel method for safe hydrogen detection. We've developed an advanced nano-candle sensor, combining Pd with nano-candles, enabling accurate detection of low-concentration hydrogen (<3%... 详细信息

ISBN: (数字)9798350372076

ISBN: (纸本)9798350372083

关键词： Electric potential Accuracy Hydrogen Lasers and electrooptics Optical sensors Electro-optical waveguides

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Deep and shallow data science for multi-scale optical neuroscience

arXiv

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arXiv 2024年

作者： Mishne, Gal Charles, Adam Halıcıoğlu Data Science Institute Department of Electrical and Computer Engineering the Neurosciences Graduate Program UC San Diego 9500 Gilman Drive La Jolla CA92093 United States Department of Biomedical Engineering Kavli Neuroscience Discovery Institute Center for Imaging Science Department of Neuroscience Mathematical Institute for Data Science Johns Hopkins University BaltimoreMD21287 United States

Optical imaging of the brain has expanded dramatically in the past two decades. New optics, indicators, and experimental paradigms are now enabling in-vivo imaging from the synaptic to the cortex-wide scales. To match the resulting flood of data across scales, computational methods are continuously being developed to meet the need of extracting biologically relevant information. In this pursuit, challenges arise in some domains (e.g., SNR and resolution limits in micron-scale data) that require specialized algorithms. These algorithms can, for example, make use of state-of-the-art machine learning to maximally learn the details of a given scale to optimize the processing pipeline. In contrast, other methods, however, such as graph signal processing, seek to abstract away from some of the details that are scale-specific to provide solutions to specific sub-problems common across scales of neuroimaging. Here we discuss limitations and tradeoffs in algorithmic design with the goal of identifying how data quality and variability can hamper algorithm use and dissemination. © 2024, CC BY.

关键词： Neuroimaging

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Industrial Semiconductor GPT: A Question-and-Answer System that Provides Professional Advice and Problem-Solving Methods for Semiconductor and Factory Equipment and Process

Industrial Semiconductor GPT: A Question-and-Answer System t...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Yi-Cheng Yu Cheng-Fu Liou Shang-Wen Chuang Chen-Yi Lee Graduate Degree Program of College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Research Labs (MZAIR Labs) Meta Zen Artificial Intelligence Tainan Taiwan Artificial Intelligence and Industrial Internet of Things Research Center Advantech Co. Ltd. Taipei Taiwan Department of Electronics Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan

ISBN: (数字)9798350394085

ISBN: (纸本)9798350394092

Research on PHM(Predictive Health Management) for detecting or predicting abnormalities in semiconductors and factory equipment and processes typically focuses on improving accuracy and reducing model size. However, in actual factory settings, on-site personnel not only need to accurately detect and predict abnormalities in processes and equipment but also require an understanding of how to handle the encountered abnormal problems when they occur. This study proposes Industrial Semiconductor GPT technology, which embeds relevant professional technical documents of process equipment into a vector database and generates possible professional questions and answers. When on-site personnel inquire, a natural language processing algorithm is used to compare the generated answers with actual answers. Furthermore, this study verifies the collected expert data through prompting engineering, resulting in an approximately $9.39 \%$ increase in similarity, ensuring that factory personnel can obtain accurate and helpful answers when facing abnormal situations.

关键词： Semiconductor device modeling Accuracy Databases Search methods Production facilities Vectors Semiconductor process modeling

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Diffusion cascade in a model of interacting random walkers

arXiv

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arXiv 2024年

作者： Raj, Abhishek Glorioso, Paolo Gopalakrishnan, Sarang Oganesyan, Vadim Physics Program and Initiative for the Theoretical Sciences The Graduate Center CUNY New YorkNY10016 United States Department of Physics and Astronomy College of Staten Island CUNY Staten IslandNY10314 United States Zyphra Palo AltoCA94306 United States Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

We consider the relaxation of finite-wavevector density waves in a facilitated classical lattice gas. Linear hydrodynamics predicts that such perturbations should relax exponentially, but nonlinear effects were predicted to cause subexponential relaxation via nonperturbative long-time tails [1]. We present a detailed numerical study of this effect. While our results clearly indicate the importance of nonlinear effects, we find that the wavevector-dependence of the late-time relaxation is clearly inconsistent with theoretical predictions. We discuss manifestations of hydrodynamic nonlinearities in mesoscopic samples and at short times. © 2024, CC BY.

关键词： Density of gases

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