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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Yukio Endo Yu Morishita Ryu Sugimoto Ryo Natsuaki Masanobu Shimada Chiaki Tsutsumi Toru Kouyama Ryosuke Nakamura Digital Architecture Research Center National Institute of Advanced Industrial Science and Technology Tokyo Japan Department of Electrical Engineering and Information Systems The University of Tokyo Tokyo Japan Division of Architectural Civil and Environmental Engineering School of Science and Engineering Tokyo Denki University Saitama Japan

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

In repeat-pass interferometric synthetic aperture radar (In-SAR), the coherence loss derived from temporal decorrelation can be modeled using an exponential decay function. Many researches have investigated the relationships between decay parameters over various land-cover types for C-band SAR satellites. Although ALOS PALSAR provides many L-band SAR images, its revisit time was insufficient for understanding the rapid exponential drop in temporal decorrelation. Due to the lack of an appropriate archived dataset, analyzing the decay parameters for L-band SAR was a challenging task. In this study, we processed four-year interferometric pairs derived from ALOS-2 PALSAR-2 full-aperture ScanSAR images. This dataset has a 14 day minimum temporal interval. We selected Nairobi, Kenya, in the middle of the East African rift, as the study area and investigated how decay functions differ over multiple land-cover types. In addition, we compared decay models for Sentinel-1 interferograms. This study demonstrates that the decay parameters of L-band coherence completely differs across forest types, whereas C-band coherence is low coherence regardless of forest type.

关键词： Grasslands C-band Vegetation mapping Sentinel-1 Coherence Forestry Radar polarimetry

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Neuromorphic Imaging with Super-Resolution

arXiv

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arXiv 2024年

作者： Zhang, Pei Zhu, Shuo Wang, Chutian Zhao, Yaping Lam, Edmund Y. The Department of Electrical and Electronic Engineering The University of Hong Kong Pokfulam Hong Kong ACCESS — AI Chip Center for Emerging Smart Systems Hong Kong Science Park Hong Kong

Neuromorphic imaging is an emerging technique that imitates the human retina to sense variations in dynamic scenes. It responds to pixel-level brightness changes by asynchronous streaming events and boasts microsecond temporal precision over a high dynamic range, yielding blur-free recordings under extreme illumination. Nevertheless, this modality falls short in spatial resolution and leads to a low level of visual richness and clarity. Pursuing hardware upgrades is expensive and might cause compromised performance due to more burdens on computational requirements. Another option is to harness offline, plug-in-play super-resolution solutions. However, existing ones, which demand substantial sample volumes for lengthy training on massive computing resources, are largely restricted by real data availability owing to the current imperfect high-resolution devices, as well as the randomness and variability of motion. To tackle these challenges, we introduce the first self-supervised neuromorphic super-resolution prototype. It can be self-adaptive to per input source from any low-resolution camera to estimate an optimal, high-resolution counterpart of any scale, without the need of side knowledge and prior training. Evaluated on downstream tasks, such a simple yet effective method can obtain competitive results against the state-of-the-arts, significantly promoting flexibility but not sacrificing accuracy. It also delivers enhancements for inferior natural images and optical micrographs acquired under non-ideal imaging conditions, breaking through the limitations that are challenging to overcome with frame-based techniques. In the current landscape where the use of high-resolution cameras for event-based sensing remains an open debate, our solution is a cost-efficient and practical alternative, paving the way for more intelligent imaging systems. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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Threat Modeling for ML-based Topology Prediction in Vehicular Edge Computing Architecture

Threat Modeling for ML-based Topology Prediction in Vehicula...

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IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Hong Hanh Doan Audri Adhyas Paul Harald Zeindlinger Yiheng Zhang Sajjad Khan Davor Svetinovic Information Systems and Operations Management Vienna University of Economics and Business Vienna Austria Center for Cyber-Physical Systems Electrical Engineering and Computer Science Khalifa University Abu Dhabi UAE

The Internet of Vehicles (IoV), a network that interlinks vehicles, infrastructure, and assorted entities, serves as a cornerstone for intelligent transportation systems and the emergence of smart cities. Within this context, edge computing has been identified as a critical solution for providing rapid and reliable data processing. Machine Learning (ML) techniques have become essential to IoV activities such as resource allocation and load balancing across mobile edge servers, typified by decentralized services that range from natural language processing to image recognition. The fusion of ML with edge computing within IoV architecture promises enhanced performance, efficiency, and safety. However, this amalgamation also creates challenges related to data privacy, cybersecurity, malfunctioning edge devices, inconsistent network connectivity, human errors, and malicious insiders. Consequently, this paper focuses on modeling security threats within an ML-based edge computing framework for the IoV. We analyze the system provided by the Linux Foundation Edge Akraino Project's Stable Topology Prediction blueprint by employing a hybrid threat modeling technique. Our strategy leverages STRIDE to elicit threats on distinct system elements like vehicle-to-vehicle communication networks, edge networks, and ML models. Subsequently, these threats are consolidated for a comprehensive view using an attack tree.

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Enhanced Prognostics and Health Management in Automated Driving systems: Using Graph Neural Networks to Recognize Operational Contexts

Enhanced Prognostics and Health Management in Automated Driv...

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Prognostics and System Health Management Conference (PHM)

作者： Peng Su Yeqi Wang Conglei Xiang Erik Wendel Madhav Mishra DeJiu Chen Department of Engineering Design Mechatronics Division KTH Royal Institute of Technology Stockholm Sweden Embedded Systems Master Program of Electrical Engineering and Computer Science (EECS) School KTH Royal Institute of Technology Stockholm Sweden Digital Systems Division RISE Research Institutes of Sweden Gothenburg Sweden

ISBN: (数字)9798350360585

ISBN: (纸本)9798350360592

Prognostics and health management (PHM) is an engineering discipline that aims to maintain system behaviour and function and ensure mission success, safety and effectiveness. Addressing the challenges in prognostics and health management for modern intelligent systems, especially automated driving systems, is complex due to the contextual nature of faults. This complexity necessitates a thorough understanding of spatial, and temporal conditions, and relationships within operational scenarios and life-cycle stages. This paper introduces a framework designed to automatically recognize driving scenarios in automated driving systems using graph neural networks (GNNs). The framework extracts relational data from image frames, constructing graph-based models and transforming unstructured sensory data into structured data with diverse node types and relationships. A specific graph neural network processes the graph model to reveal and detect operational conditions and relationships. The proposed framework is evaluated using the KITTI dataset, demonstrating superior performance compared to conventional feed-forward networks such as MLP, particularly in handling relational data.

关键词： Graph neural networks Data models Safety Complexity theory Data mining Prognostics and health management Intelligent systems

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Safeguarding Healthcare: A Comprehensive Threat Analysis of Clinical Decision Support systems

Safeguarding Healthcare: A Comprehensive Threat Analysis of ...

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IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Aleksandra Ursula Charlotte Hamel Bogdan-Cristian Zarcu András-Gergely Csenteri Tamara Pfliegler Sajjad Khan Davor Svetinovic Information Systems and Operations Management Vienna University of Economics and Business Vienna Austria Center for Cyber-Physical Systems Electrical Engineering and Computer Science Khalifa University Abu Dhabi UAE

Using digital data gathering and analytics in healthcare brings benefits and risks to patients and practitioners. Smart Health Information systems, such as Clinical Decision Support systems (CDSSs), consolidate data from various sources, utilizing artificial intelligence for decision support. However, machine learning models in CDSSs are vulnerable to various attacks, leading to incorrect predictions with severe consequences. This paper systematically investigates security and privacy threats related to CDSSs. First, we leverage the data flow and sequence diagrams to identify the critical use cases that might lead to security or privacy breaches. Second, we identify and classify threats imminent to the CDSSs using Security Cards and STRIDE. Lastly, the persona non-grata who pose a significant threat to the integrity of the CDSSs are identified. Implementing our method can assist teams in addressing security threats to CDSSs by considering their unique vulnerabilities. This research contributes to developing comprehensive security strategies for CDSSs.

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First Characterization of Si, SiC and GaN Power Deivces at Deep Cryogenic Temperatures Down to 0.1 K

First Characterization of Si, SiC and GaN Power Deivces at D...

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International Electron Devices Meeting (IEDM)

作者： Xin Yang Matthew Porter Zineng Yang Zichen Xi Qiang Li Linbo Shao Yuhao Zhang Center for Power Electronics Systems (CPES) Virginia Tech Blacksburg VA USA Bradley Department of Electrical and Computer Engineering Virginia Tech Blacksburg VA USA Center for Quantum Information Science and Engineering (VTQ) Virginia Tech Blacksburg VA USA

ISBN: (数字)9798350365429

ISBN: (纸本)9798350365436

electrical energy conversion at deep cryogenic temperatures $(T < 4.2\ \mathrm{K})$ is highly desirable in space, quantum, healthcare, and power delivery applications. However, it still remains unknown if power semiconductors can function at this $T$ range; moreover, high-voltage or dynamic switching tests have not been reported for GaN and SiC devices below 77 K. Here we characterize the static and dynamic performance of Si IGBT, SiC MOSFET, and GaN HEMT down to 0.1 K for the first time. The test is enabled by employing a dilution refrigerator, which has not been applied for testing power devices in prior reports, connected with a custom circuit setup that integrates the double-pulse test and dynamic on-resistance $(R_{\text{ON}})$ test. The key findings at $T < 1\ \mathrm{K}$ include: 1) all three devices retain the normally-off operation and high breakdown voltage, suggesting the functionality of the embedded p-n junctions, and they all can hard-switch under the gate signal control; 2) GaN HEMT is dynamic $R_{\text{ON}}$ free with $R_{\text{ON}}$ being ~4.5 times lower than that at 295 K; 3) differently, SiC MOSFET becomes non-Ohmic below ~60 K; the turn-on voltages $(V_{\text{ON}})$ of SiC MOSFET and Si IGBT both rise with decreased $T$ , resulting in an elevated conduction loss. The high $V_{\text{ON}}$ and the viability of current conduction in SiC and Si drift layers, despite carrier freeze-out, are explained by the shallow-level impact ionization with donor states. The many firsts reported in this work open the door for developing cryogenic power electronics at temperatures below the current boundaries.

关键词： Insulated gate bipolar transistors MOSFET Silicon carbide Power system dynamics Cryogenics Switches HEMTs Silicon Voltage control Testing

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Cyber Vulnerabilities in Blockchain Electronic Health Records: An In-Depth Threat Analysis

Cyber Vulnerabilities in Blockchain Electronic Health Record...

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IEEE International Symposium on Dependable, Autonomic and Secure Computing (DASC)

作者： Marija Brkić Horia-Stefan Dinu Alida Mirković Alma Sabirović Sajjad Khan Davor Svetinović Information Systems and Operations Management Vienna University of Economics and Business Vienna Austria Center for Cyber-Physical Systems Electrical Engineering and Computer Science Khalifa University Abu Dhabi UAE

The rapid evolution of information systems and the surge in data generation and dissemination have fostered the emergence of Electronic Health Records (EHRs). Specifically, blockchain-enabled EHRs are revolutionizing modern healthcare by centralizing patient data promoting transparency, accessibility, and interoperability. However, amidst increasing cyberattacks on healthcare institutions, the security of these EHRs is a critical concern. Existing research predominantly focuses on the advantages and disadvantages of incorporating blockchain technology into EHRs, leaving a noticeable gap in analyzing specific threats. Our study bridges this gap by leveraging a hybrid threat modeling methodology, integrating attack tree models with an exhaustive review of existing literature on threats to blockchain-based standardized EHRs. This approach has identified and organized 26 unique threats into an attack tree. Each threat is elaborated upon and discussed within the context of the Blockchain EHRs.

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Revealing Non-Hermitian Band Structure of Photonic Floquet Media

Revealing Non-Hermitian Band Structure of Photonic Floquet M...

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Frontiers in Optics, FiO 2022

作者： Park, Jagang Cho, Hyukjoon Lee, Seojoo Lee, Kyungmin Lee, Kanghee Park, Hee Chul Ryu, Jung-Wan Park, Namkyoo Jeon, Sanggeun Min, Bumki Department of Mechanical Engineering Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of Department of Physics Korea Advanced Institute of Science and Technology Daejeon34141 Korea Republic of Center for Theoretical Physics of Complex Systems Institute of Basic Science Daejeon34126 Korea Republic of Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of School of Electrical Engineering Korea University Seoul02841 Korea Republic of

We experimentally reveal the unique non-Hermitian band structures of a photonic Floquet medium emulated in the microwave regime. We observe the presence of the momentum gaps and the subharmonic parametric oscillations...

ISBN: (纸本)9781557528209

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Circumnavigation of UAVs integrating QP-Based CLF and CBF in a Cooperative Path-Following Framework

Circumnavigation of UAVs integrating QP-Based CLF and CBF in...

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IEEE Conference on Decision and Control

作者： Pallov Anand A. Pedro Aguiar P. B. Sujit SYSTEC-ARISE Research Center for Systems and Technologies Faculdade de Engenharia Universidade do Porto Porto Portugal Department of Electrical Engineering and Computer Science Indian Institute of Science Education and Research Bhopal Bhopal India

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Unmanned Aerial Vehicles (UAVs) are increasingly utilized in various applications often requiring coordinated operations of multiple UAVs. Circular formation control has garnered significant attention due to its diverse relevance in many situations. In this paper, we propose a Quadratic programming (QP) based Control Lyapunov Function (CLF) and Control Barrier Function (CBF) framework for circumnavigating stationary and moving targets for single and multiple UAVs while avoiding collision with a known obstacle. By integrating CLF and CBF constraints in a cooperative path-following framework, our approach offers robustness and scalability for complex multi-agent systems, and ensures asymptotic tracking of the target while maintaining closed-loop stability of the error dynamics and safety margins around the obstacle. Through theoretical analysis and simulation studies, we demonstrate the effectiveness and versatility of our proposed framework in challenging environments.

关键词： Target tracking Scalability Simulation Stability analysis Robustness Trajectory Safety Quadratic programming Collision avoidance Vehicle dynamics

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Flexibility Prediction of Aggregated Electric Vehicles and Domestic Hot Water systems in Smart Grids

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engineering 2021年第8期7卷 1101-1114页

作者： Junjie Hu Huayanran Zhou Yihong Zhou Haijing Zhang Lars Nordströmd Guangya Yang State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources(North China Electric Power University) Beijing 102206China Division of Electric Power and Energy Systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of TechnologyStockholm 10044Sweden Center for Electric Power and Energy Department of Electrical Engineering Technical University of DenmarkKgs Lyngby 2800Denmark

With the growth of intermittent renewable energy generation in power grids,there is an increasing demand for controllable resources to be deployed to guarantee power quality and frequency *** flexibility of demand response(DR)resources has become a valuable solution to this ***,existing research indicates that problems on flexibility prediction of DR resources have not been *** study applied the temporal convolution network(TCN)-combined transformer,a deep learning technique to predict the aggregated flexibility of two types of DR resources,that is,electric vehicles(EVs)and domestic hot water system(DHWS).The prediction uses historical power consumption data of these DR resources and DR signals(DSs)to facilitate *** prediction can generate the size and maintenance time of the aggregated *** accuracy of the flexibility prediction results was verified through simulations of case *** simulation results show that under different maintenance times,the size of the flexibility *** proposed DR resource flexibility prediction method demonstrates its application in unlocking the demand-side flexibility to provide a reserve to grids.

关键词： Load flexibility Electric vehicles Domestic hot water system Temporal convolution network-combined transformer Deep learning

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