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Effect of electron thermal conductivity on resonant plasmonic detection in the metal/black-AsP/graphene FET terahertz hot-electron bolometers

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arXiv 2023年

作者： Ryzhii, V. Tang, C. Otsuji, T. Ryzhii, M. Mitin, V. Shur, M.S. Research Institute of Electrical Communication Tohoku University Sendai980-8577 Japan Department of Computer Science and Engineering University of Aizu Aizu-Wakamatsu965-8580 Japan Department of Electrical Engineering University at Buffalo SUNY BuffaloNY14260 United States Department of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute TroyNY12180 United States

We analyze the two-dimensional electron gas (2DEG) heating by the incident terahertz (THz) radiation in the field-effect transistor (FET) structures with the graphene channels (GCs) and the black-phosphorus and black-arsenic gate barrier layers (BLs). Such GC-FETs can operate as bolometric THz detectors using the thermionic emission of the hot electrons from the GC via the BL into the gate. Due to the excitation of plasmonic oscillations in the GC by the THz signals, the GC-FET detector response can be pronouncedly resonant, leading to elevated values of the detector responsivity. The lateral thermal conductivity of the 2DEG can markedly affect the GC-FET responsivity, in particular, its spectral characteristics. This effect should be considered for the optimization of the GC-FET detectors. © 2023, CC BY.

关键词： Graphene

Risk-Aware Safe Control for Decentralized Multi-Agent systems via Dynamic Responsibility Allocation

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Risk-Aware Safe Control for Decentralized Multi-Agent System...

IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Yiwei Lyu Wenhao Luo John M. Dolan Department of Electrical and Computer Engineering Carnegie Mellon University Pittsburgh PA USA Department of Computer Science University of North Carolina at Charlotte Charlotte NC USA The Robotics Institute Carnegie Mellon University Pittsburgh PA USA

Decentralized control schemes are increasingly favored in various domains that involve multi-agent systems due to the need for computational efficiency as well as general applicability to large-scale systems. However, in the absence of an explicit global coordinator, it is hard for distributed agents to determine how to efficiently interact with others. In this paper, we present a risk-aware decentralized control framework that provides guidance on how much relative responsibility share (a percentage) an individual agent should take to avoid collisions with others while moving efficiently without direct communications. We propose a novel Control Barrier Function (CBF)-inspired risk measurement to characterize the aggregate risk agents face from potential collisions under motion uncertainty. We use this measurement to allocate responsibility shares among agents dynamically and develop risk-aware decentralized safe controllers. In this way, we are able to leverage the flexibility of robots with lower risk to improve the motion flexibility for those with higher risk, thus achieving improved collective safety. We demonstrate the validity and efficiency of our proposed approach through two examples: ramp merging in autonomous driving and a multi-agent position-swapping game.

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DEAL: Decoupled Classifier with Adaptive Linear Modulation for Group Robust Early Diagnosis of MCI to AD Conversion

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arXiv 2024年

作者： Lee, Donggyu Park, Juhyeon Moon, Taesup The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of The Interdisciplinary Program in Artificial Intelligence Seoul National University Seoul08826 Korea Republic of The Department of Electrical and Computer Engineering Automation and Systems Research Institute Institute of New Media and Communications Interdisciplinary Program in Artificial Intelligence Artificial Intelligent Institute Seoul National University Seoul08826 Korea Republic of

While deep learning-based Alzheimer's disease (AD) diagnosis has recently made significant advancements, particularly in predicting the conversion of mild cognitive impairment (MCI) to AD based on MRI images, there remains a critical gap in research regarding the group robustness of the diagnosis. Although numerous studies pointed out that deep learning-based classifiers may exhibit poor performance in certain groups by relying on unimportant attributes, this issue has been largely overlooked in the early diagnosis of MCI to AD conversion. In this paper, we present the first comprehensive investigation of the group robustness in the early diagnosis of MCI to AD conversion using MRI images, focusing on disparities in accuracy between groups, specifically sMCI and pMCI individuals divided by age. Our experiments reveal that standard classifiers consistently underperform for certain groups across different architectures, highlighting the need for more tailored approaches. To address this, we propose a novel method, dubbed DEAL (DEcoupled classifier with Adaptive Linear modulation), comprising two key components: (1) a linear modulation of features from the penultimate layer, incorporating easily obtainable age and cognitive indicative tabular features, and (2) a decoupled classifier that provides more tailored decision boundaries for each group, further improving performance. Through extensive experiments and evaluations across different architectures, we demonstrate the efficacy of DEAL in improving the group robustness of the MCI to AD conversion prediction. © 2024, CC BY-NC-SA.

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Design and Implementation of Low-Cost Smart Monitoring System for Micro-Grid PV Applications Using LoRa Technology

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Design and Implementation of Low-Cost Smart Monitoring Syste...

Recent Innovation in Smart and Sustainable Technology (ICRISST), International Conference on

作者： Ghassan Mohammed Saadoon Ahmad Ghandour Mohannad Jabbar Mnati Adnan Hussein Ali Bayan Mahdi Sabbar Marwa Mahdi Kareem Faculty Of Engineering Department Communication Systems Department Islamic University of Lebanon Beirut Lebanon Department of Electrical Technology Institute of Technology Baghdad Middle Technical University Baghdad Iraq Medical Instrumentation Engineering Techniques Department College of Engineering and Engineering Techniques Al-Mustaqbal University Babylon Iraq Computer Systems Techniques Department Technical Management Institute Baghdad Middle Technical University Baghdad Iraq

ISBN: (数字)9798350309140

ISBN: (纸本)9798350309157

In this paper, an innovative smart monitoring system has been developed with a low cost for micro-grid photovoltaic systems using LoRa technology. This research addresses traditional monitoring solutions' limitations and explores the advantages of using LoRa technology in micro-grid PV systems. The literature review comprehensively highlights all aspects of micro-grid PV systems and underscores the significance of monitoring for performance enhancement and troubleshooting. The methodology section discusses the proposed system architecture, sensor selection, data collection, LoRa communication use, and data visualization. In the results sections, a real-life view of the effectiveness of a low-cost smart metering system is illustrated concerning issues such as the accuracy of data, transmission range, and energy consumption rate. Examples from real life are used to illustrate how the system improves performance, simplifies maintenance tasks, and helps identify problems in micro-grid PV systems. This research provides insights into exploiting LoRa technology to realize low-cost but efficient smart monitoring solutions in renewable energy microgrids.

关键词： Technological innovation Renewable energy sources LoRa systems architecture Microgrids Voltage Real-time systems Solar system Monitoring Systematic literature review

Fault-tolerant Mobile Service Offloading in Mobile Edge Computing

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IEEE Transactions on Automation Science and engineering 2025年

作者： Long, Tingyan Xia, Yunni Zhou, Mengchu Li, Jianqi Ma, Yong Ai-Turki, Yusuf Guizhou University School of Computer Science and Technology Guiyang550025 China Chongqing University College of Computer Science Chongqing400030 China New Jersey Institute of Technology Department of Electrical and Computer Engineering NewarkNJ07102 United States Global energy interconnection research institute Co. Ltd Beijing102209 China Jiangxi Normal University School of Computer and Information Engineering Nanchang China King Abdulaziz University K. A. Care Energy Research and Innovation Center The Center of Research Excellence in Renewable Energy and Power Systems Department of Electrical and Computer Engineering Faculty of Engineering Jeddah21589 Saudi Arabia

Mobile edge computing (MEC) is an evolving paradigm for rendering services through network-accessible resources deployed over Internet of Things (IoT) nodes at the edge. Nevertheless, an MEC environment usually employs thousands of physical machines connected via hundreds of switches/routers that communicate and coordinate to deliver computing service. In such complicated systems, faults caused by software, human errors, and hardware are often unavoidable. The edge of network presents a dynamic environment with great quantities of terminals, high mobility of mobile devices, heterogeneous applications, and intermittent traffic. In such an environment, MEC can suffer from unbalanced resource provisioning and interruptions of faults occurring at different levels, which further causes task faults and affects service quality. To address this challenge, this work proposes a novel fault-tolerant offloading method for handling faults by leveraging a reinforcement-learning-based service offloading decision model. The model synthesizes a Dueling Deep Q Network (DQN)-based algorithm for deciding user offloading behaviors and an adaptive checkpointing method for improving task execution reliability. For the purpose of model validation and comparison, extensive simulations are conducted. Numerical results clearly demonstrate that the proposed method is highly effective and outperforms existing methods. © 2004-2012 IEEE.

关键词： Mobile applications

Automatic Diagnosis of Pectus Excavatum from CT Images Using a Joint CNN-LSTM Model 9

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Automatic Diagnosis of Pectus Excavatum from CT Images Using...

9th International Conference on Control, Decision and Information Technologies, CoDIT 2023

作者： Liao, Yizhi Zhou, Haiyu Xie, Longhan Cai, Siqi School of Computing and Information Systems University of Melbourne Melbourne Australia Guangdong Lung Cancer Institute Guangdong Provincial People's Hospital Division of Thoracic Surgery Guangzhou China Shien-Ming Wu School of Intelligent Engineering South China University of Technology Guangzhou China National University of Singapore Department of Electrical and Computer Engineering Singapore

ISBN: (纸本)9798350311402

Pectus excavatum (PE) is one of the most common congenital sternal deformities. Accurate preoperative diagnosis of PE is of great significance for subsequent correction and improvement of the patient's quality of life. However, current diagnostic methods rely on the calculation of some PE indices, which is a heavy workload for physical therapists and suffers from measurement errors. To address this issue, we propose an end-to-end automatic assessment of PE, which features a cascaded structure of CNN and LSTM. Specifically, the high-level feature representations of CT images are extracted by the pretrained CNN and then processed through the LSTM layers for classification. In addition, we build up a medical image dataset for PE diagnosis by collecting chest CT images of 42 subjects. Results on this dataset show that the proposed CNN-LSTM framework achieves a relatively high accuracy of 90.20%, which provides a new perspective for the automatic diagnosis of PE in clinics. © 2023 IEEE.

关键词： computerized tomography

Hot-electron resonant terahertz bolometric detection in the graphene/black-AsP field-effect transistors with a floating gate

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arXiv 2023年

We evaluate the terahertz (THz) detectors based on field effect transistor (FET) with the graphene channel GC and a floating metal gate (MG) separated from the GC by a black-phosphorus (b-P) or black-arsenic (b-As) barrier layer (BL). The operation of these GC-FETs is associated with the heating of the two- dimensional electron gas in the GC by impinging THz radiation leading to thermionic emission of the hot electrons from the GC to the MG. This results in the variation of the floating gate potential, which affects the source-drain current. At the THz radiation frequencies close to the plasmonic resonance frequencies in the gated GC, the variation of the source-drain current and, hence, the detector responsivity can be resonantly large. © 2023, CC BY.

关键词： Field effect transistors

Asynchronous Distributed Optimization via ADMM with Efficient Communication

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arXiv 2023年

作者： Rikos, Apostolos I. Jiang, Wei Charalambous, Themistoklis Johansson, Karl H. Department of Electrical and Computer Engineering Division of Systems Engineering Boston University BostonMA02215 United States Hong Kong Hong Kong Department of Electrical and Computer Engineering School of Engineering University of Cyprus Nicosia1678 Cyprus Department of Electrical Engineering and Automation School of Electrical Engineering Aalto University Espoo Finland Division of Decision and Control Systems KTH Royal Institute of Technology StockholmSE-100 44 Sweden Digital Futures United Kingdom

In this paper, we focus on an asynchronous distributed optimization problem. In our problem, each node is endowed with a convex local cost function, and is able to communicate with its neighbors over a directed communication network. Furthermore, we assume that the communication channels between nodes have limited bandwidth, and each node suffers from processing delays. We present a distributed algorithm which combines the Alternating Direction Method of Multipliers (ADMM) strategy with a finite time quantized averaging algorithm. In our proposed algorithm, nodes exchange quantized valued messages and operate in an asynchronous fashion. More specifically, during every iteration of our algorithm each node (i) solves a local convex optimization problem (for the one of its primal variables), and (ii) utilizes a finite-time quantized averaging algorithm to obtain the value of the second primal variable (since the cost function for the second primal variable is not decomposable). We show that our algorithm converges to the optimal solution at a rate of O(1/k) (where k is the number of time steps) for the case where the local cost function of every node is convex and not-necessarily differentiable. Finally, we demonstrate the operational advantages of our algorithm against other algorithms from the literature. © 2023, CC BY.

关键词： Cost functions

Resonant plasmonic detection of terahertz radiation in field-effect transistors with the graphene channel and the black-AsxP1−x gate layer

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arXiv 2023年

We propose the terahertz (THz) detectors based on field-effect transistors (FETs) with the graphene channel (GC) and the black-Arsenic (b-As) black-Phosphorus (b-P), or black-Arsenic-Phosphorus (b-AsxP1−x) gate barrier layer. The operation of the GC-FET detectors is associated with the carrier heating in the GC by the THz electric field resonantly excited by incoming radiation leading to an increase in the rectified current between the channel and the gate over the b-AsxP1−x energy barrier layer (BLs). The specific feature of the GC-FETs under consideration is relatively low energy BLs and the possibility to optimize the device characteristics by choosing the barriers containing a necessary number of the b-AsxP1−x atomic layers and a proper gate voltage. The excitation of the plasma oscillations in the GC-FETs leads to the resonant reinforcement of the carrier heating and the enhancement of the detector responsivity. The room temperature responsivity can exceed the values of 103 A/W. The speed of the GC-FET detector's response to the modulated THz radiation is determined by the processes of carrier heating. As shown, the modulation frequency can be in the range of several GHz at room temperatures. © 2023, CC BY.

关键词： Graphene