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Earth Observing Systems XXVIII 2023

作者： Venkatesulu, Erica Shaw, Joseph A. Electrical and Computer Engineering Department and Optical Technology Center Montana State University P.O. Box 173515 BozemanMT United States

ISBN: (纸本)9781510665842

Cloud thermodynamic phase is an important parameter in climate models and cloud remote sensing because it controls whether a cloud tends to have a net heating or cooling effect and it must be known to retrieve other cloud parameters. Passive remote sensing of cloud thermodynamic phase using shortwave infrared radiance ratios is a well-known technique, and adding polarization sensitivity to the radiance ratio method can increase accuracy. Ground-based passive polarimetric remote sensing of cloud phase has also been performed in visible and near infrared wavelengths. Prior work has relied on highly sensitive, expensive polarimeters to detect the small change in polarization state between ice and liquid clouds. We explored the use of a low-cost, commercial division-of-focal-plane polarization imager for cloud thermodynamic phase retrievals. We calibrated and deployed a monochrome polarization imager, with both a moderate field-of-view lens and a fisheye lens. The imagers were deployed alongside a verified dual-polarization lidar that provided a truth measurement at the zenith. In this paper, we discuss the relationship between the Stokes S1 parameter measured by the low-cost polarization imager with both lenses and the cloud thermodynamic phase retrieved by a dual-polarization lidar. © 2023 SPIE · 0277-786X ·

关键词： Cameras

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Ultra-Dense LEO Satellite-Aircraft Access and Service Management in Civil Aviation

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China Communications 2025年第1期22卷 277-292页

作者： Wang Yilei Ma Ting Liu Xiaoyu Gao Zhuxuan Zhou Haibo Shen Xuemin School of Electronic Science and Engineering Nanjing UniversityNanjing 210046China School of Electronic and Optical Engineering Nanjing University of Science and TechnologyNanjing 210094China The Department of Electrical and Computer Engineering University of WaterlooWaterloo N2L 3G1Canada

With the deployment of ultra-dense low earth orbit(LEO)satellite constellations,LEO satellite access network(LEO-SAN)is envisioned to achieve global Internet ***,the civil aviation communications have increased dramatically,especially for providing airborne Internet ***,due to dynamic service demands and onboard LEO resources over time and space,it poses huge challenges in satellite-aircraft access and service management in ultra-dense LEO satellite networks(UDLSN).In this paper,we propose a deep reinforcement learning-based approach for ultra-dense LEO satellite-aircraft access and service ***,we develop an airborne Internet architecture based on UDLSN and design a management mechanism including medium earth orbit satellites to guarantee lightweight ***,considering latency-sensitive and latency-tolerant services,we formulate the problem of satellite-aircraft access and service management for civil aviation to ensure service ***,we propose a proximal policy optimization-based access and service management algorithm to solve the formulated *** results demonstrate the convergence and effectiveness of the proposed algorithm with satisfying the service continuity when applying to the UDLSN.

关键词： civil aviation deep reinforcement learning satellite-aircraft access service management ultra-dense LEO satellite network

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High-Speed Visible Light Communication Using Phenothiazine/Dimesitylborane Derivatives as Color Conversion Materials in Semipolar Micro-LED-Based White-Light Systems

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ACS Photonics 2024年第2期11卷 489-497页

作者： Sadhu, Annada Sankar Chen, Li-Yin Pai, Yi-Hua Hsieh, Chung-An Lin, Hao-Wu Chow, Chi-Wai Kuo, Hao-Chung Department of Photonics Institute of Electro-Optical Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Semiconductor Research Center Hon Hai Research Institute Taipei11492 Taiwan College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Department of Materials Science and Engineering National Tsing Hua University Hsinchu30013 Taiwan

Visible light communication (VLC) has emerged as a cutting-edge high-speed communication technology, poised to meet the surging capacity demands of 6G networks. Micro-light-emitting diodes (μLEDs) are considered as the light sources for achieving high-speed VLC, distinguished by their remarkable modulation bandwidths. However, achieving broadband white light emission hinges on the utilization of color-conversion materials with wide emission spectra. The transmission speed of the white-light system is inherently constrained by the characteristics of these color-conversion materials. In this work, we demonstrate CC-MP7 and CC-MP8, two derivatives of phenothiazine/dimesitylborane, as color conversion materials in a semipolar (20-21) micro-LED-based white-light system for high-speed VLC. The color conversion layers possess wide emission spectra, enabling them to achieve excellent color rendering performance when combined with blue micro-LEDs. CCMP7 and CC-MP8 demonstrate rapid photoluminescence decay characteristics, thereby enhancing the modulation bandwidth of the color-conversion layer in the white-light system. The resulting bandwidths achieved by CC-MP7 and CC-MP8 are 210 and 240 MHz, respectively, which represents an approximately 45-fold increase compared to ordinary phosphors. By combining semipolar (20-21) micro-LEDs with CC-MP7 and CC-MP8, the resulting white-light systems exhibit correlated color temperatures of 6860 and 7500 K, CIE coordinates of (0.3009, 0.3577) and (0.2958, 0.3129), and color-rendering indexes of 80 and 85, respectively. Furthermore, both systems offer high bandwidths of 1063 and 1084 MHz with the data rates of 1.72 Gbps and 1.74 Gbps using non-return-to-zero on-off keying (NRZ-OOK) format, respectively, indicating the significant potential of CC-MP7 and CC-MP8 for practical applications in VLC. © 2024 The Authors.

关键词： Bandwidth

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24.4 THz·V fT×BV figure-of-merit AlN/GaN/AlN MISHEMTs with thin AlN buffer layer

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Science China(Information Sciences) 2025年第3期68卷 403-404页

作者： Chaoqun ZHANG Hong ZHOU Sami ALGHAMDI Kun ZHANG Zhihong LIU Yue HAO Jingcheng ZHANG State Key Laboratory of Wide-Bandgap Semiconductor Devices and Integrated Technology Xidian University Guangzhou Wide Bandgap Semiconductor Innovation Center Guangzhou Institute of TechnologyXidian University Electrical and Computer Engineering Department King Abdulaziz University

Gallium nitride-based high-electron-mobility-transistors(HEMTs) have gained widespread interest and become primary candidates for next-generation high-frequency and high-power RF electronics, due to their wide bandgap,high breakdown field, and strong polarization-induced high-density 2-dimensional electron gas(2DEG) at the heterojunction interface.

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Deep Learning Based Side-Channel Attack Detection for Mobile Devices Security in 5G Networks

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1012-1026页

作者： Amjed A.Ahmed Mohammad Kamrul Hasan Ali Alqahtani Shayla Islam Bishwajeet Pandey Leila Rzayeva Huda Saleh Abbas Azana Hafizah Mohd Aman Nayef Alqahtani Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Imam Alkadhim University College Department of Computer Techniques EngineeringBaghdad 10066Iraq Center for Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangi 43600Malaysia Department of Networks and Communications Engineering College of Computer Science and Information SystemsNajran UniversityNajran 61441Saudi Arabia Institute of Computer Science and Digital Innovation UCSI UniversityKuala Lumpur 56000Malaysia Department of Intelligent Systems and Cyber Security Astana IT UniversityAstana 20000Kazakstan Department of Computer Science College of Computer Science and EngineeringTaibah UniversityMadinah 42353Saudi Arabia Department of Electrical Engineering College of EngineeringKing Faisal UniversityAl-Hofuf 31982Saudi Arabia

Mobile devices within Fifth Generation(5G)networks,typically equipped with Android systems,serve as a bridge to connect digital gadgets such as global positioning system,mobile devices,and wireless routers,which are vital in facilitating end-user communication ***,the security of Android systems has been challenged by the sensitive data involved,leading to vulnerabilities in mobile devices used in 5G *** vulnerabilities expose mobile devices to cyber-attacks,primarily resulting from security ***-permission apps in Android can exploit these channels to access sensitive information,including user identities,login credentials,and geolocation *** such attack leverages"zero-permission"sensors like accelerometers and gyroscopes,enabling attackers to gather information about the smartphone's *** underscores the importance of fortifying mobile devices against potential future *** research focuses on a new recurrent neural network prediction model,which has proved highly effective for detecting side-channel attacks in mobile devices in 5G *** conducted state-of-the-art comparative studies to validate our experimental *** results demonstrate that even a small amount of training data can accurately recognize 37.5％of previously unseen user-typed ***,our tap detection mechanism achieves a 92％accuracy rate,a crucial factor for text *** findings have significant practical implications,as they reinforce mobile device security in 5G networks,enhancing user privacy,and data protection.

关键词： Fifth Generation(5G)networks smartphone information leakage Side-Channel Attack(SCA) deep learning

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Evolutionary Optimization Methods for High-Dimensional Expensive Problems:A Survey

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IEEE/CAA Journal of Automatica Sinica 2024年第5期11卷 1092-1105页

作者： MengChu Zhou Meiji Cui Dian Xu Shuwei Zhu Ziyan Zhao Abdullah Abusorrah Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA School of Information and Electronic Engineering Zhejiang Gongshang UniversityHangzhou 310018China School of Intelligent Manufacturing Nanjing University of Science and TechnologyNanjing 210094China Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China School of Artificial Intelligence and Computer Jiangnan UniversityWuxi 214122China School of Information Science and Engineering Northeastern UniversityShenyang 110819China Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer EngineeringFaculty of EngineeringKing Abdulaziz UniversityJeddah 21589Saudi Arabia K.A.CARE Energy Research and Innovation Center King Abdulaziz UniversityJeddah 21589Saudi Arabia

Evolutionary computation is a rapidly evolving field and the related algorithms have been successfully used to solve various real-world optimization *** past decade has also witnessed their fast progress to solve a class of challenging optimization problems called high-dimensional expensive problems(HEPs).The evaluation of their objective fitness requires expensive resource due to their use of time-consuming physical experiments or computer ***,it is hard to traverse the huge search space within reasonable resource as problem dimension *** evolutionary algorithms(EAs)tend to fail to solve HEPs competently because they need to conduct many such expensive evaluations before achieving satisfactory *** reduce such evaluations,many novel surrogate-assisted algorithms emerge to cope with HEPs in recent *** there lacks a thorough review of the state of the art in this specific and important *** paper provides a comprehensive survey of these evolutionary algorithms for *** start with a brief introduction to the research status and the basic concepts of ***,we present surrogate-assisted evolutionary algorithms for HEPs from four main *** also give comparative results of some representative algorithms and application ***,we indicate open challenges and several promising directions to advance the progress in evolutionary optimization algorithms for HEPs.

关键词： computer optimization evolutionary

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A 1.7 kV Full-SiC Reverse-Blocking Module Enabling Efficient Multiport Operation in Soft-Switching Current Source Converters

A 1.7 kV Full-SiC Reverse-Blocking Module Enabling Efficient...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Marellapudi, Aniruddh Mauger, Mickael J. Benzaquen, Joseph Divan, Deepak Georgia Institute of Technology Center for Distributed Energy Department of Electrical & Computer Engineering AtlantaGA United States

ISBN: (纸本)9798350376067

The Soft-Switching Solid-State Transformer (S4T) is a flexible, multiport, and isolated ZVS soft-switching topology with applications in multiport energy routers for data centers, EV fast charging, storage integration, and solar farms. Prior work investigated a commercial 1.7 kV reverse-blocking (RB) phase-leg module based on a series combination of a Si IGBT and a SiC Schottky diode. While soft switching was achieved, significant residual switching losses due to IGBT tail current limited achievable efficiency. This work presents a high-current, full-SiC 1.7 kV RB half-bridge module with each switch composed of a SiC MOSFET and a SiC Schottky diode. Characterization of the full-SiC 1.7 kV RB module in the S4T resulted in total switching losses of 1.3 mJ at 1000 V, 150 A, Tbaseplate=110 °C, a >9.7X reduction from the previous IGBT-based RB structure (800 V, 150 A, Tbaseplate=90 °C). Critically, the full-SiC RB structure enables near-ideal soft-switching between any set of sources and loads in each S4T switching cycle - unlocking scalable, efficient multiport operation. © 2024 IEEE.

关键词： Schottky barrier diodes

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A novel deep learning approach for automated grading of knee osteoarthritis severity

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Multimedia Tools and Applications 2024年 1-20页

作者： Kaur, PrabSimran Kohli, Guneet Singh Bedi, Jatin Wasly, Saud Department of Computer Science and Engineering Thapar Institute of Engineering and Technology Punjab Patiala India Electrical and Computer Engineering Department King Abdulaziz University Jeddah22254 Saudi Arabia Center of Excellence in Intelligent Engineering Systems King Abdulaziz University Jeddah22254 Saudi Arabia

Knee Osteoarthritis (OA) is a prevalent musculoskeletal disorder that affects the knee joint that causes pain, stiffness, and reduced mobility. It is also known as "Degenerative Joint Disease" and is caused by the degeneration of cartilage in the knee joint, leading to bone-on-bone contact and further damage. Knee OA is prevalent in the population, affecting around 22% to 39% of people in India, and there is currently no treatment available that can halt the progression of the disease. Therefore, early diagnosis and management of symptoms are essential to reduce its impact on an individual’s quality of life. To address this issue, have introduced a framework that leverages ConvNeXt architecture, a modernization of ResNets (ResNet-50) architecture towards Hierarchical Transformers (Swin Transformers), to provide accurate identification and classification of knee osteoarthritis. The classification of knee osteoarthritis was done using the Kellgren and Lawrence (KL) graded X-ray images. These images of the damaged knees are preprocessed and augmented, creating a scaled, enhanced, and varied version of the features, thus making the data fitter and more significant for classification. The performance estimation of the proposed strategy is conducted on the Osteoarthritis Initiative (OAI), a research project focused on knee osteoarthritis that works in partnership with NIH and other private industries to develop a public domain dataset that can facilitate research and evaluation. It involves training the prepared data using various hyper-tuned versions of ConvNeXt. The different fine-tuned results of the ConvNeXt models on each KL Grade are evaluated against the other state-of-the-art models and vision transformers. The comparative assessment of widely used performance measures shows that the proposed approach outperforms the conventional models by generating the highest score for all the KL grades. Lastly, an approach is employed to statistically confirm the validity of t

关键词： Joints (anatomy)

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Peer-to-Peer Electricity-Carbon Coupling Trading Market Based on Multi-Agent Reinforcement Learning

Peer-to-Peer Electricity-Carbon Coupling Trading Market Base...

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2024 IEEE Sustainable Power and Energy Conference, iSPEC 2024

作者： Li, Shunfeng Wang, Peng Zhu, Xiuli Li, Ning Yan, Weiwu Shanghai Jiao Tong University Department of Automation Shanghai China College of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai China

ISBN: (纸本)9798350395075

With the the vigorous development of new power system, energy and carbon coupling trading has emerged as an effective way to promote the renewable generation and reduce carbon footprint. It is a challenge to design an effective market framework for joint electricity and carbon trading to achieve better strategies. This paper introduces a peer-to-peer electricity-carbon coupling trading (ECCT) market and develops a carbon-electricity virtual battery (CEVB) model to represent the relationship between dynamic carbon credits and energy consumption. The trading optimization problem is formulated as a partially observable Markov decision process, then multi-agent double soft actor-critic (MADSAC) algorithm is introduced to solve the problem. The algorithm makes the method more exploratory, protects personal privacy and solves the overestimation problems effectively. Result studies demonstrate the effectiveness of the proposed market and algorithm in optimizing total cost and reducing carbon emissions. © 2024 IEEE.

关键词： Electricity-carbon coupling trading market Multi-agent reinforcement learning Peer-to-peer

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Multilayer Satellite Network Collaborative Mobile Edge Caching:A GCN-Based Multi-Agent Approach

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China Communications 2024年第11期21卷 56-74页

作者： Yang Jie He Jingchao Cheng Nan Yin Zhisheng Han Dairu Zhou Conghao Sun Ruijin School of Telecommunications Engineering Xidian UniversityXi’an 710071China School of Electronic and Optical Engineering Nanjing University of Science and TechnologyNanjing 210000China Department of Electrical and Computer Engineering University of WaterlooWaterloo ON N2L 3G1Canada

With the explosive growth of highdefinition video streaming data,a substantial increase in network traffic has *** emergency of mobile edge caching(MEC)can not only alleviate the burden on core network,but also significantly improve user *** with the MEC and satellite networks,the network is empowered popular content ubiquitously and *** the research gap between multilayer satellite networks and MEC,we study the caching placement problem in this ***,we introduce a three-layer distributed network caching management architecture designed for efficient and flexible handling of large-scale *** the constraint on satellite capacity and content propagation delay,the cache placement problem is then formulated and transformed into a markov decision process(MDP),where the content coded caching mechanism is utilized to promote the efficiency of content ***,a new generic metric,content delivery cost,is proposed to elaborate the performance of caching decision in large-scale ***,we introduce a graph convolutional network(GCN)-based multi-agent advantage actor-critic(A2C)algorithm to optimize the caching ***,extensive simulations are conducted to evaluate the proposed algorithm in terms of content delivery cost and transferability.

关键词： cache placement coded caching graph convolutional network(GCN) mobile edge caching(MEC) multilayer satellite network

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