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International Conference on Wireless and Optical Communications ( WOCC)

作者： Hanyu Gao Liang Zhang Bin Zhang Xiaoliang Chen Zhaohui Li Guangdong Provincial Key Labratory of Optoelectronic Information Processing Chips and Systems School of Electronics and Information Technology Sun Yat-sen University Guangzhou China Southern Marine Science and Engineering Guangdong Laboratory Zhuhai China Department of Electrical and Computer Engineering George Mason University Fairfax VA USA

This paper presents a composable machine learning method for generalizing the quality-of-transmission (QoT) metric estimation in optical networks. The composable machine learning approach characterizes this metric for lightpaths of arbitrary lengths by compositions of launch, propagation and readout modules. Results verify the feasibility of the design and show its successful application in facilitating autonomous lightpath provisioning.

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Generating tripartite entanglement via spatial overlap of identical particles

Generating tripartite entanglement via spatial overlap of id...

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CLEO: science and Innovations, S and I 2022

作者： Lee, Donghwa Pramanik, Tanumoy Cho, Young-Wook Lim, Hyang-Tag Chin, Seungbeom Kim, Yong-Su Seoul02792 Korea Republic of Division of Nano & Information Technology KIST School Korea University of Science and Technology Seoul02792 Korea Republic of State Key Laboratory for Mesoscopic Physics Collaborative Innovation Center of Quantum Matter Peking University Beijing100871 China Department of Physics Yonsei University Seoul03722 Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

We report experimental generation of all the genuine three-party entangled states, i.e., GHZ and W states, of identical particles via spatial overlap. It shows that the indistinguishability is crucial for generating e...

ISBN: (纸本)9781557528209

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Distributionally Robust Variational Quantum Algorithms with Shifted Noise

arXiv

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arXiv 2023年

作者： He, Zichang Peng, Bo Alexeev, Yuri Zhang, Zheng Department of Electrical and Computer Engineering University of California Santa Barbara Santa BarbaraCA93106 United States Physical and Computational Sciences Directorate Pacific Northwest National Laboratory RichlandWA99352 United States Computational Science Division Argonne National Laboratory LemontIL60439 United States

Given their potential to demonstrate near-term quantum advantage, variational quantum algorithms (VQAs) have been extensively studied. Although numerous techniques have been developed for VQA parameter optimization, it remains a significant challenge. A practical issue is that quantum noise is highly unstable and thus it is likely to shift in real time. This presents a critical problem as an optimized VQA ansatz may not perform effectively under a different noise environment. For the first time, we explore how to optimize VQA parameters to be robust against unknown shifted noise. We model the noise level as a random variable with an unknown probability density function (PDF), and we assume that the PDF may shift within an uncertainty set. This assumption guides us to formulate a distributionally robust optimization problem, with the goal of finding parameters that maintain effectiveness under shifted noise. We utilize a distributionally robust Bayesian optimization solver for our proposed formulation. This provides numerical evidence in both the Quantum Approximate Optimization Algorithm (QAOA) and the Variational Quantum Eigensolver (VQE) with hardware-efficient ansatz, indicating that we can identify parameters that perform more robustly under shifted noise. We regard this work as the first step towards improving the reliability of VQAs influenced by shifted noise from the parameter optimization perspective. Copyright © 2023, The Authors. All rights reserved.

关键词： Probability density function

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Multi-Scale Terahertz Image Reconstruction ⋆,⋆⋆

SSRN

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SSRN 2024年

作者： Su, Zhipeng Zhang, Yixiong Zhou, Jianyang Shi, Jianghong Qi, Feng Zhang, Xiao-Ping Ji, Chunlin School of Informatics Xiamen University National Demonstrative Software School Xiamen University China School of Electronic Science and Engineering National Model Microelectronics College Xiamen University China Shenyang Institute of Automation Chinese Academy of Sciences China Department of Electrical Computer & Biomedical Engineering Finance Department The Ted Rogers School of Management Toronto Metropolitan University Canada Kuang-Chi Institute of Advanced Technology State Key Laboratory of Meta-RF Electromagnetic Modulation Technology Shen Zhen China

This paper proposes a novel terahertz (THz) image recovery algorithm and a new THz image dataset is publicly available. Because of transmission noise, artificial errors and problems with diffraction phenomena are among the main problems that cause degradation of THz images. The point spread function (PSF) in real transmission is first obtained using a terahertz imaging system. Experiments can generate degraded images and clean image datasets for supervised training of neural networks via terahertz PSF. A Depthwise Dense Instantiation Normalization Block (DwDIN Block) has been proposed to recover the datasets. We constructed a powerful multi-stage network (DwDINet) at each scale by utilizing the DwDIN module. For input and output, the results of each stage of the network and the original images are taken as input into the next layer in order to enhance the background information. DwDINet achieves state-of-the-art (SOTA) results on the two main THz datasets. To verify the generality of the model, we performed experiments on various image recovery tasks. According to the experimental results, the proposed model is of great potential for the field of low-level image clarification. © 2024, The Authors. All rights reserved.

关键词： Optical transfer function

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Recent Advancements in Solid State Transformer-based EV Fast Charging Stations

Recent Advancements in Solid State Transformer-based EV Fast...

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International Conference on Power Electronics and Drive Systems (PEDS)

作者： Ruvini De Seram Anindita Golder Sheldon S. Williamson Smart Transportation Electrification and Energy Research (STEER) Group Ontario Tech University Oshawa ON Canada Advanced Storage Systems and Electric Transportation (ASSET) Laboratory Ontario Tech University Oshawa ON Canada Ontario Tech-Automotive Center of Excellence (ACE) Ontario Tech University Oshawa ON Canada Department of Electrical and Computer Engineering Faculty of Engineering and Applied Science Ontario Tech University Oshawa ON Canada

Growing deployments of ultra fast charging stations (XFCS) are putting a stress on the existing line-frequency transformer (LFT)-based medium voltage (MV) grid due to the high charging demands of exponentially growing electric vehicles (EV). The conventional LFT occupies a large space in the XFCS installation which introduces challenges in facilitating the XFCS infrastructure. As a result, solid-state transformer (SST)-based XFCS are being developed which can be directly connected to the MV grid and several advantages over LFT-based systems such as compactness, intelligence and availability of the DC link to connect renewable energy sources and storage system can be realized. As such, a detailed analysis of current developments in SST-based XFCS is presented and compared with one another in this paper. The challenges associated with the configurations are examined and direction for future research is identified.

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Text2Earth: Unlocking Text-driven Remote Sensing Image Generation with a Global-Scale Dataset and a Foundation Model

arXiv

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arXiv 2025年

作者： Liu, Chenyang Chen, Keyan Zhao, Rui Zou, Zhengxia Shi, Zhenwei The Department of Aerospace Intelligent Science and Technology School of Astronautics The State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China The Key Laboratory of Spacecraft Design Optimization and Dynamic Simulation Technologies Ministry of Education China Shanghai Artificial Intelligence Laboratory Shanghai200232 China Shen Yuan Honors College of Beihang University Beijing100191 China The Department of Electrical and Computer Engineering National University of Singapore Singapore117583 Singapore

Recently, generative foundation models have significantly advanced large-scale text-driven natural image generation and have become a prominent research trend across various vertical domains. However, in the remote sensing field, there is still a lack of research on large-scale text-to-image (text2image) generation technology. Existing remote sensing image-text datasets are small in scale and confined to specific geographic areas and scene types. Besides, existing text2image methods have struggled to achieve global-scale, multi-resolution controllable, and unbounded image generation. To address these challenges, this paper presents two key contributions: the Git-10M dataset and the Text2Earth foundation model. Git-10M is a global-scale image-text dataset comprising 10.5 million image-text pairs, 5 times larger than the previous largest one. The dataset contains essential resolution information and covers a wide range of geographic scenes and contains essential geospatial metadata, significantly surpassing existing datasets in both size and diversity. Building on Git-10M, we propose Text2Earth, a 1.3 billion parameter generative foundation model based on the diffusion framework to model global-scale remote sensing scenes. Text2Earth integrates a resolution guidance mechanism, enabling users to specify image resolutions. A dynamic condition adaptation strategy is proposed for training and inference to improve image generation quality. Text2Earth not only excels in zero-shot text2image generation but also demonstrates robust generalization and flexibility across multiple tasks, including unbounded scene construction, image editing, and cross-modal image generation. This robust capability surpasses previous models restricted to the basic fixed size and limited scene types. On the previous text2image benchmark dataset, Text2Earth outperfoms previous models with a significant improvement of +26.23 FID and +20.95% Zero-shot Cls-OA metric. Our project page is https://chen-y

关键词： Optical remote sensing

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Seasonal characteristics analysis and uncertainty measurement for wind speed time series

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Energy engineering: Journal of the Association of Energy engineering 2020年第5期117卷 289-299页

作者： Deng, Xing Shao, Haijian Wang, Xia School of Computer Science Jiangsu University of Science and Technology Zhenjiang212003 China School of Automation Key Laboratory of Measurement and Control for CSE Ministry of Education Southeast University Nanjing210096 China School of Information Science and Technology Nantong University Nantong226019 China Department of Electrical and Computer Engineering National University of Singapore 117583 Singapore

Wind speed’s distribution nature such as uncertainty and randomness imposes a challenge in high accuracy forecasting. Based on the energy distribution about the extracted amplitude and associated frequency, the uncertainty measurement is processed through Rényi entropy analysis method with time-frequency nat-ure. Nonparametric statistical method is used to test the randomness of wind speed, more precisely, whether or not the wind speed time series is independent and iden-tically distribution (i.i.d) based on the output probability. Seasonal characteristics of wind speed are analyzed based on self-similarity in periodogram under scales range generated by wavelet transformation to reasonably divide the original dataset and effectively reflect the seasonal distribution characteristics. Experimental evaluation based on the dataset from National Renewable Energy laboratory (NREL) is given to demonstrate the performance of the proposed approach. © 2020, Tech science Press. All rights reserved.

关键词： Wind speed

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Image and Point-cloud Classification for Jet Analysis in High-Energy Physics: A survey

arXiv

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arXiv 2024年

作者： Kheddar, Hamza Himeur, Yassine Amira, Abbes Soualah, Rachik LSEA Laboratory Electrical Engineering Department University of Medea Algeria College of Engineering and Information Technology University of Dubai Dubai United Arab Emirates Department of Computer Science University of Sharjah United Arab Emirates Institute of Artificial Intelligence De Montfort University Leicester United Kingdom Department of Physics Khalifa University of Science and Technology P.O. Box 127788 Abu Dhabi127788 United Arab Emirates Strada Costiera 11 TriesteI-34151 Italy

Nowadays, there has been a growing trend in the field of high-energy physics (HEP), in both its experimental and phenomenological studies, to incorporate machine learning (ML) and its specialized branch, deep learning (DL). This review paper provides a thorough illustration of these applications using different ML and DL approaches. The first part of the paper examines the basics of various particle physics types and establishes guidelines for assessing particle physics alongside the available learning models. Next, a detailed classification is provided for representing Jets that are reconstructed in high-energy collisions, mainly in proton-proton collisions at well-defined beam energies. This section covers various datasets, preprocessing techniques, and feature extraction and selection methods. The presented techniques can be applied to future hadron-hadron colliders (HHC), such as the high-luminosity LHC (HL-LHC) and the future circular collider - hadron-hadron (FCChh). The authors then explore several AI techniques analyses designed specifically for both image and point-cloud (PC) data in HEP. Additionally, a closer look is taken at the classification associated with Jet tagging in hadron collisions. In this review, various state-of-the-art (SOTA) techniques in ML and DL are examined, with a focus on their implications for HEP demands. More precisely, this discussion addresses various applications in extensive detail, such as Jet tagging, Jet tracking, particle classification, and more. The review concludes with an analysis of the current state of HEP using DL methodologies. It highlights the challenges and potential areas for future research, which are illustrated for each application. © 2024, CC BY.

关键词： Hadrons

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Stabilization of perovskite quantum dots in polymer matrix in thin porous film for display technology 57th

Stabilization of perovskite quantum dots in polymer matrix i...

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57th SID International Symposium, Seminar and Exhibition, Display Week, 2020

作者： Gao, Yiyang Prodanov, Maksym F. Gupta, Swadesh K. Kang, Chengbin Vashchenko, Valerii V. Srivastava, Abhishek K. State Key Laboratory of Advanced Displays and Optoelectronics Technologies Department of Electronics and Computer Engineering Hong Kong University of Science and Technology Hong Kong

A new hybrid post-synthetic and in-Jilm encapsulation method of colloidal perovskite nanoparticles is disclosed. The stabilization of the encapsulated perovskites against oxygen and humidity is tested and discussed. T... 详细信息

关键词： Perovskite

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Organs and Systems Glucose Metabolism Analysis in Different Smoking Groups for Lung Cancer Patients using Total-body PET/CT

Organs and Systems Glucose Metabolism Analysis in Different ...

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IEEE Symposium on Nuclear science (NSS/MIC)

作者： H. Wang Z. Huang Y. Wu Z. Liu W. Li M. Wang G.S.P. Mok Z. Hu Department of Electrical and Computer Engineering Faculty of Science and Technology University of Macau Macau China Chinese Academy of Sciences Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Shenzhen China Department of Medical Imaging Henan Provincial People’s Hospital & People’s Hospital of Zhengzhou University Zhengzhou China University of Macau Institute of Collaborative Innovation Macau China

ISBN: (数字)9798350388152

ISBN: (纸本)9798350388169

Smoking history has been an important indicator in lung cancer (LC) screening as it damages lung function. However, whether smoking is harmful to other organs and what abnormalities may occur among various organs have not yet been studied since total-body imaging is not feasible for previous scanners. With the emergence of the total-body PET/CT, we may explore the correlation between abnormal glucose metabolism among various organs and systems for smoking. This work investigates if the smoking intensity plays a role in the metabolic abnormalities among different organs and systems in different smoking groups for LC patients. ${ }^{18} \mathrm{~F}$-FDG PET and CT images from 60 anonymized subjects with 40 LC patients and 20 healthy controls (HCs) are retrospectively recruited and scanned from a uEXPLORER PET/CT. Among LC patients, their smoking intensities were surveyed (non-smokers: light smokers: heavy smokers $=21: 4: 15$). For each scan, 17 regionsof-interests are outlined. To analyze the abnormal metabolism of different smoking groups, we construct individual and group abnormal metabolism networks respectively based on a reference network with HCs. Pearson correlation coefficients between standardized uptake values of each organ pair and system pair are calculated to analyze their connectivities. Results show that more significant abnormal metabolism is observed in smokers as compared to non-smokers. Besides, heavy smokers have a substantially greater proportion of metabolic decline compared with others for lungs, heart, kidneys and esophagus. In addition, the abnormal metabolism connectivity of the lungs-esophagus and lungs-thyroid gland increases significantly along with the increased intensity of smoking, while stronger abnormal metabolism connectivity is also observed between the nervous system and other systems for heavy smokers. The proposed analysis method is feasible for LC patients using total-body FDG PET/CT and has the potential to be applied to other

关键词： Heart Microwave integrated circuits Semiconductor detectors Lung cancer Lung Nervous system Glucose

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