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2023 IMEKO TC4 International Conference on Metrology for Archaeology and Cultural Heritage

作者： Shi, Haolian Jacobi, Leor Locquet, Alexandre Citrin, D.S. School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA20223-0250 United States Georgia Tech-CNRS IRL2958 Georgia Tech-Europe 2 Rue Marconi Metz57070 France Device Spectroscopy Laboratory Institute for Nanotechnology and Advanced Materials Bar-Ilan University Ramat-Gan5290002 Israel

ISBN: (纸本)9781713888345

Iron-gall ink has been widely used ink in the production of documents and artworks since the fifth century C.E. Terahertz imaging is used to read characters written in such ink on stacked sheets of paper. Several contrast mechanisms including maximum/minimum of the reflected terahertz signal, signature frequency, Gaussian mixture model, and short-time Fourier transform are tested to compare the image quality of the buried text. The results show that the short-time Fourier transform mechanism outperforms other contrast mechanism and has the ability to extract clear images of buried text within paper stacks. © 2023 IMEKO TC TC4. All rights reserved.

关键词： Iron

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Online Self-distillation and Self-modeling for 3D Brain Tumor Segmentation

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IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Pang, Yan Li, Yunhao Huang, Teng Liang, Jiaming Wang, Zhen Dong, Changyu Kuang, Dongyang Hu, Ying Chen, Hao Lei, Tim Wang, Qiong The Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China The School of Artificial Intelligence Guangzhou University China The Zhejiang Lab Hangzhou China Sun Yat-sen University China The Department of Computer Science and Engineering The Department of Chemical and Biological Engineering Hong Kong University of Science and Technology China The Department of Electrical Engineering University of Colorado Denver United States

In the specialized domain of brain tumor segmentation, supervised segmentation approaches are hindered by the limited availability of high-quality labeled data, a condition arising from data privacy concerns, significant costs, and ethical issues. In response to this challenge, this paper presents a training framework that adeptly integrates a plug-and-play component, MOD, into current supervised learning models, boosting their efficacy in scenarios with limited data. The MOD consists of an Online Tokenizer and a Dense Predictor, which employs self-distillation and self-modeling on masked patches, promoting swift convergence and efficient representation learning. During the inference phase, the plug-and-play MOD component is excluded, preserving the computational efficiency of the original model without incurring extra processing costs. We substantiated the value of our approach through experiments on leading 3D brain tumor segmentation baselines. Remarkably, models augmented with the MOD consistently showcased superior results, achieving elevated Dice coefficients and HD95 scores on two datasets: BraTS 2021 and MSD 2019 Task-01 Brain Tumor. Code: https://***/aigzhusmart/MOD © 2013 IEEE.

关键词： Self-supervised learning

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A Networked Competitive Multi-Virus SIR Model: Analysis and Observability

arXiv

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arXiv 2022年

作者： Zhang, Ciyuan Gracy, Sebin Başar, Tamer Paré, Philip E. Elmore Family School of Electrical and Computer Engineering Purdue University United States School of Electrical Engineering and Computer Science KTH Royal Institute of Technology Stockholm Sweden Coordinated Science Laboratory University of Illinois at Urbana-Champaign United States

This paper proposes a novel discrete-time multi-virus SIR (susceptible-infectedrecovered) model that captures the spread of competing SIR epidemics over a population network. First, we provide a sufficient condition for the infection level of all the viruses over the networked model to converge to zero in exponential time. Second, we propose an observation model which captures the summation of all the viruses' infection levels in each node, which represents the individuals who are infected by different viruses but share similar symptoms. We present a sufficient condition for the model to be locally observable. We propose a Luenberger observer for the system state estimation and show via simulations that the estimation error of the Luenberger observer converges to zero before the viruses die out. © 2022, CC BY.

关键词： Viruses

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Spatial Attention-Enhanced Encoder-Decoder Network for Accurate Segmentation of the Prostate’s Transition Zone

Spatial Attention-Enhanced Encoder-Decoder Network for Accur...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Dimitrios I. Zaridis Eugenia Mylona Nikolaos S. Tachos Charalampos Kalantzopoulos Kostas Marias Manolis Tsiknakis Dimitrios I. Fotiadis Biomedical Engineering Laboratory School of Electrical & Computer Engineering and Biomedical Research Institute National Technical University of Athens and FORTH Athens & Ioannina Greece Biomedical Research Institute FORTH Ioannina Greece Computational Biomedicine Laboratory FORTH Heraklion Greece Biomedical Research Institute and Unit of Medical Technology and Intelligent Information Systems FORTH University of Ioannina Ioannina Greece

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

Accurate segmentation of the prostate and its substructures consist the most important component for reliable localization and characterization of prostate cancer. In this study a Spatial Attention Residual U-Net (Spatial ResU-Net) deep learning (DL) network is proposed for segmenting the transitional zone of the prostate, by leveraging the learning capacity of spatial attention modules and residual connections. Spatial attention modules efficiently extract features in intra-channel manner and boost the performance of encoder and decoder while residual connections facilitate the information flow within the different network’s levels. The proposed model was compared against 8 state-of-the-art DL segmentation models demonstrating a superior performance. The improvement in terms of Sensitivity, Dice Score, Hausdorff distance and Average surface distance was at least 1%, 1%, 0.05 mm and 0.09 mm, respectively.

关键词： Location awareness Deep learning Accuracy Sensitivity Feature extraction Decoding Reliability Prostate cancer engineering in medicine and biology Testing

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DC Terminal Impedance Modeling and Stability Analysis for VSG-Controlled Grid-Connected Inverter

DC Terminal Impedance Modeling and Stability Analysis for VS...

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DC Technologies and systems (DCTS), IEEE International Conference on

作者： Xinyang Su Minghao Wang Ying Xue Junyu Chen Wenzhuo Shi Zhao Xu Department of Electrical and Electronic Engineering The Hong Kong Polytechnic University Hong Kong SAR China Department of Electrical and Computer Engineering State Key Laboratory of Internet of Things for Smart City The University of Macau Macau SAR China School of Electric Power Engineering South China University of Technology Guangzhou China

ISBN: (数字)9798350379464

ISBN: (纸本)9798350379471

As a kind of grid-forming control method that can effectively provide virtual inertia for the power grid, the virtual synchronous generator (VSG) control has received increasing attention in recent years. However, the VSG control will extract energy from the DC side during a grid frequency drop, which brings a burden on the stability of the DC side of a VSG. To address this problem, this paper establishes a fully analytical small-signal model of the VSG DC terminal impedance, incorporating both the inertia mechanism and the DC side dynamics. The accuracy of the model is validated through comparison with the sweep frequency results of the simulation. Finally, the Nyquist criterion is employed to analyze the DC stability of the VSG, and the simulation results match well with the theoretical predictions.

关键词： Analytical models Simulation Stability criteria Power system dynamics Microgrids Power system stability Synchronous generators Impedance Testing Grid forming

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Cache-Enabled in Cooperative Cognitive Radio Networks for Transmission Performance

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Tsinghua Science and Technology 2020年第1期25卷 1-11页

作者： Jiachen Yang Chaofan Ma Jiabao Man Huifang Xu Gan Zheng Houbing Song School of Electrical and Information Engineering Tianjin UniversityTianjin 300000China Wolfson School of Mechanical Electrical and Manufacturing EngineeringLoughborough UniversityLoughboroughLE11 3TUUK Department of Electrical ComputerSoftwareand Systems EngineeringEmbry-Riddle Aeronautical UniversityDaytona BeachFL 32114USA.

The proliferation of mobile devices that support the acceleration of data services(especially smartphones)has resulted in a dramatic increase in mobile traffic. Mobile data also increased exponentially, already exceeding the throughput of the backhaul. To improve spectrum utilization and increase mobile network traffic, in combination with content caching, we study the cooperation between primary and secondary networks via content caching. We consider that the secondary base station assists the primary user by pre-caching some popular primary ***, the secondary base station can obtain more licensed bandwidth to serve its own user. We mainly focus on the time delay from the backhaul link to the secondary base station. First, in terms of the content caching and the transmission strategies, we provide a cooperation scheme to maximize the secondary user’s effective data transmission rates under the constraint of the primary users target rate. Then, we investigate the impact of the caching allocation and prove that the formulated problem is a concave problem with regard to the caching capacity allocation for any given power allocation. Furthermore, we obtain the joint caching and power allocation by an effective bisection search algorithm. Finally, our results show that the content caching cooperation scheme can achieve significant performance gain for the primary and secondary systems over the traditional two-hop relay cooperation without caching.

关键词： cooperative cognitive radio network content caching power allocation

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Pattern Transformation of Low-Profile Multi-Mode Antenna for A Phased Array with Simultaneous Wide-Angle Scanning and High-Gain Properties

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IEEE Transactions on Antennas and Propagation 2025年

作者： Yan, Qi-Long Liu, Neng-Wu Zhu, Lei Yun, Yu Liu, Ying Xidian University National Key Laboratory of Radar Detection and Sensing Xi'an710071 China University of Macau Faculty of Science and Technology Department of Electrical and Computer Engineering China Shaanxi University of Science & Technology School of electronic information and artificial intelligence Xi'an710021 China

In this article, a novel multi-mode design approach for pattern transformation of low-profile multi-mode microstrip patch antenna (MPA) has been proposed and developed, aiming to simultaneously achieve the wide-angle scanning and high-gain properties for a phased array. Herein, the TM00, TM01, TM20, and TM21 modes of the MPA are employed. Firstly, the working principle about how to move these four modes closely to each other in resonant frequency is deeply studied, the results prove that the shorting pins and slots could be loaded on the radiator for frequency reallocation of these four modes. Secondly, the radiation pattern transformation of the MPA at TM00, TM20, and TM21 modes are deeply investigated, which demonstrates that the non-broadside beam of its TM21 mode could be reshaped into the broadside one, and the omnidirectional pattern of its TM00 mode could be reshaped into the dual beam at yoz-plane. Most importantly, the radiation pattern of its TM20 mode could be rotated into the dual-beam with co-polarized Eθ component at yoz-plane. With these arrangements, the broadside-beam and dual-beam could be excited under same frequency at different modes. Thirdly, based on these two beams with different phases at two ports, the antenna element has successfully gained the improved beam scanning and gain performance simultaneously. Fourthly, the proposed multi-mode array is arranged in a triangular grid. By controlling the phase difference between these ports, the antenna could achieve both wide-beam scanning and high gain. Experimental results show that the antenna element has an impedance bandwidth (|S11| © 1963-2012 IEEE.

关键词： Antenna phased arrays

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Federated Learning for Enhanced ECG Signal Classification with Privacy Awareness

Federated Learning for Enhanced ECG Signal Classification wi...

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Annual International Conference of the IEEE engineering in Medicine and Biology Society (EMBC)

作者： Quoc Bao Phan Linh Nguyen Ngoc Thang Bui Dinh C. Nguyen Lan Zhang Tuy Tan Nguyen School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff AZ USA Department of Radiology Mayo Clinic Rochester MN USA Department of Electrical and Computer Engineering The University of Alabama in Huntsville Huntsville AL USA

ISBN: (数字)9798350371499

ISBN: (纸本)9798350371505

This paper presents a novel approach for classifying electrocardiogram (ECG) signals in healthcare applications using federated learning and stacked convolutional neural networks (CNNs). Our innovative technique leverages the distributed nature of federated learning to collaboratively train a high-performance model while preserving data privacy on local devices. We propose a stacked CNN architecture tailored for ECG data, effectively extracting discriminative features across different temporal scales. The evaluation confirms the strength of our approach, culminating in a final model accuracy of 98.6% after 100 communication rounds, significantly exceeding baseline performance. This promising result paves the way for accurate and privacy-preserving ECG classification in diverse healthcare settings, potentially leading to improved diagnosis and patient monitoring.

关键词： Data privacy Accuracy Protocols Federated learning Scalability Medical services Electrocardiography Feature extraction Robustness Convolutional neural networks

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Comparative Analysis of Interval Reachability for Robust Implicit and Feedforward Neural Networks

Comparative Analysis of Interval Reachability for Robust Imp...

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IEEE Conference on Decision and Control

作者： Alexander Davydov Saber Jafarpour Matthew Abate Francesco Bullo Samuel Coogan Center for Control Dynamical Systems and Computation University of California Santa Barbara School of Electrical and Computer Engineering Georgia Institute of Technology

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

Implicit neural networks (INNs) are a class of learning models that use implicit algebraic equations as layers and have been shown to exhibit several notable benefits over traditional feedforward neural networks (FFNNs). In this paper, we use interval reachability analysis to study robustness of INNs and compare them with FFNNs. We first introduce the notion of tight inclusion function and use it to provide the tightest rectangular over-approximation of the neural network’s input-output map. We also show that tight inclusion functions lead to sharper robustness guarantees than the well-studied robustness measures of Lipschitz constants. Like exact Lipschitz constants, tight inclusions functions are computationally challenging to obtain, and thus we develop a framework based upon mixed monotonicity and contraction theory to estimate the tight inclusion functions for INNs. We show that our approach performs at least as well as, and generally better than, state-ofthe-art interval-bound propagation methods for INNs. Finally, we design a novel optimization problem for training robust INNs and we provide empirical evidence that suitably-trained INNs can be more robust than comparably-trained FFNNs.

关键词： Training Robustness Mathematical models Feedforward neural networks Computational efficiency Reachability analysis Optimization

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A Novel Microwave Sensor for Traditional Chinese Medicine Decoctions Detection

A Novel Microwave Sensor for Traditional Chinese Medicine De...

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IEEE International Workshop on Radio-Frequency Integration Technology (RFIT)

作者： Ming-Feng Xu Jing Wu Hua-Feng Li Jin-Peng He Zhong-Qi Zhang Min Chen Rui-Sen Chen Guan-Long Huang Zi-Yu Pang Kam-Weng Tam School of Electronics and Information Engineering Foshan University Foshan Guangdong China Guangdong Provincial Key Laboratory of Industrial Intelligent Inspection Technology Foshan University Foshan China Department of Electrical and Computer Engineering University of Macau Macau SAR China

ISBN: (数字)9798331541095

ISBN: (纸本)9798331541101

This paper demonstrates a high-sensitivity substrate-integrated waveguide (SIW) microwave sensor for traditional Chinese medicine decoctions detection. The designed microwave sensor is based on elliptical electric-LC(EELC) and interdigital capacitors (IDC). The regent under test (RUT) with different permittivities from 2 to 5 and dimensions of 9 mm $\times 11$ mm $\times 0.8$ mm is placed on the sensitive area of the sensor. According to the simulated result, the resonant frequency of the sensor shifts between 4.06 and 5.63 GHz with the variation of the RUT permittivity. The designed sensor can achieve sensitivity up to 8.28%, which shows a good feasibility in detecting liquid dielectric properties.

关键词： Radio frequency Sensitivity Dielectric constant Capacitors Resonant frequency Metals Microwave sensors Sensors Permittivity Substrates

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