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High-Gain Metasurface in Polyimide On-Chip Antenna Based on CRLH-TL for Sub-Terahertz Integrated Circuits (vol 10, 4298, 2020)

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SCIENTIFIC REPORTS 2020年第1期10卷 1-9页

作者： Alibakhshikenari, Mohammad Virdee, Bal S. See, Chan H. Abd-Alhameed, Raed A. Falcone, Francisco Limiti, Ernesto Electronic Engineering Department University of Rome 'Tor Vergata' Via del Politecnico 1 00133 Rome Italy London Metropolitan University Center for Communications Technology & Mathematics School of Computing & Digital Media N7 8DB London UK School of Engineering & the Built Environment Edinburgh Napier University 10 Colinton Road EH10 5DT Edinburgh UK School of Engineering University of Bolton Deane Road BL3 5AB Bolton UK School of Electrical Engineering & Computer Science University of Bradford BD7 1DP Bradford UK Electrical and Electronic Engineering Department Public University of Navarre 31006 Pamplona Spain

This paper presents a novel on-chip antenna using standard CMOS-technology based on metasurface implemented on two-layers polyimide substrates with a thickness of 500 μm. The aluminium ground-plane with thickness of 3 μm is sandwiched between the two-layers. Concentric dielectric-rings are etched in the ground-plane under the radiation patches implemented on the top-layer. The radiation patches comprise concentric metal-rings that are arranged in a 3 × 3 matrix. The antennas are excited by coupling electromagnetic energy through the gaps of the concentric dielectric-rings in the ground-plane using a microstrip feedline created on the bottom polyimide-layer. The open-ended feedline is split in three-branches that are aligned under the radiation elements to couple the maximum energy. In this structure, the concentric metal-rings essentially act as series left-handed capacitances CL that extend the effective aperture area of the antenna without affecting its dimensions, and the concentric dielectric rings etched in the ground-plane act as shunt left-handed inductors LL, which suppress the surface-waves and reduce the substrates losses that leads to improved bandwidth and radiation properties. The overall structure behaves like a metasurface that is shown to exhibit a very large bandwidth of 0.350–0.385 THz with an average radiation gain and efficiency of 8.15dBi and 65.71%, respectively. It has dimensions of 6 × 6 × 1 mm3 that makes it suitable for on-chip implementation. [ABSTRACT FROM AUTHOR]

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DOMINO: Domain-aware Model Calibration in Medical Image Segmentation

arXiv

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arXiv 2022年

作者： Stolte, Skylar E. Volle, Kyle Indahlastari, Aprinda Albizu, Alejandro Woods, Adam J. Brink, Kevin Hale, Matthew Fang, Ruogu United States Department of Mechanical and Aerospace Engineering Herbert Wertheim College of Engineering UF United States Center for Cognitive Aging and Memory McKnight Brain Institute UF United States Department of Clinical and Health Psychology College of Public Health and Health Professions UF United States Department of Neuroscience College of Medicine UF United States United States Air Force Research Laboratory Eglin Air Force Base FL United States Department of Electrical and Computer Engineering Herbert Wertheim College of Engineering UF United States

Model calibration measures the agreement between the predicted probability estimates and the true correctness likelihood. Proper model calibration is vital for high-risk applications. Unfortunately, modern deep neural networks are poorly calibrated, compromising trustworthiness and reliability. Medical image segmentation particularly suffers from this due to the natural uncertainty of tissue boundaries. This is exasperated by their loss functions, which favor overconfidence in the majority classes. We address these challenges with DOMINO, a domain-aware model calibration method that leverages the semantic confusability and hierarchical similarity between class labels. Our experiments demonstrate that our DOMINO-calibrated deep neural networks outperform non-calibrated models and state-of-the-art morphometric methods in head image segmentation. Our results show that our method can consistently achieve better calibration, higher accuracy, and faster inference times than these methods, especially on rarer classes. This performance is attributed to our domain-aware regularization to inform semantic model calibration. These findings show the importance of semantic ties between class labels in building confidence in deep learning models. The framework has the potential to improve the trustworthiness and reliability of generic medical image segmentation models. The code for this article is available at: https://***/lab-smile/DOMINO. Copyright © 2022, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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Schgt-Im: Influence Maximization for Heterogeneous Networks Based on a Self-Supervised Clustered Heterogeneous Graph Transformer

SSRN

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SSRN 2022年

作者： Li, Ying Li, Linlin Liu, Xiangyu Liu, Yijun Li, Qianqian Key Laboratory of Symbolic Computation and Knowledge Engineering College of Computer Science and Technology Jilin University Jilin Province Changchun130013 China The Faculty of Science and Engineering University of Nottingham NottinghamNG7 2RD United Kingdom Institutes of Science and Development Chinese Academy of Sciences Beijing100190 China School of Public Policy and Management University of Chinese Academy of Sciences Beijing100190 China

Influence maximization (IM) has drawn considerable attention in recent years. Most existing IM methods focus on homogeneous networks, and do not consider the heterogeneity and the attributes of different types of nodes in heterogeneous networks. However, heterogeneous networks are ubiquitous in real-world and encompass rich semantics and complex structural information. Additionally, the clustering characteristics embedded in a network have a critical and significant impact on the process of information diffusion, which has not been considered in IM models based on heterogeneous networks. To address the challenges posed by the heterogeneity and clustering structure in heterogeneous networks, we propose a novel deep learning framework based on a self-supervised clustered heterogeneous graph transformer for IM in heterogeneous networks, named SCHGT-IM. SCHGT-IM aggregates the heterogeneity and clustering information in heterogeneous networks and includes a clustered cascade (CC) model as an information diffusion model to improve the realism of simulations. We evaluate the performance of SCHGT-IM in comparison with that of state-of-the-art IM models on three academic heterogeneous networks extracted from DBLP dataset. The experimental influence spread results show that SCHGT-IM is superior to five state-of-the-art algorithms and performs effectively in terms of selecting influential seed nodes of different types from heterogeneous networks. © 2022, The Authors. All rights reserved.

关键词： Heterogeneous networks

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GSLB: The Graph Structure Learning Benchmark

arXiv

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arXiv 2023年

作者： Li, Zhixun Wang, Liang Sun, Xin Luo, Yifan Zhu, Yanqiao Chen, Dingshuo Luo, Yingtao Zhou, Xiangxin Liu, Qiang Wu, Shu Yu, Jeffrey Xu Department of Systems Engineering and Engineering Management The Chinese University of Hong Kong Hong Kong Center for Research on Intelligent Perception Computing State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences China School of Artificial Intelligence University of Chinese Academy of Sciences China Department of Automation University of Science and Technology of China China School of Cyberspace Security Beijing University of Posts and Telecommunications China Department of Computer Science University of California Los Angeles United States Heinz College of Information Systems and Public Policy Machine Learning Department School of Computer Science Carnegie Mellon University United States

Graph Structure Learning (GSL) has recently garnered considerable attention due to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the computation graph structure simultaneously. Despite the proliferation of GSL methods developed in recent years, there is no standard experimental setting or fair comparison for performance evaluation, which creates a great obstacle to understanding the progress in this field. To fill this gap, we systematically analyze the performance of GSL in different scenarios and develop a comprehensive Graph Structure Learning Benchmark (GSLB) curated from 20 diverse graph datasets and 16 distinct GSL algorithms. Specifically, GSLB systematically investigates the characteristics of GSL in terms of three dimensions: effectiveness, robustness, and complexity. We comprehensively evaluate state-of-the-art GSL algorithms in node- and graph-level tasks, and analyze their performance in robust learning and model complexity. Further, to facilitate reproducible research, we have developed an easy-to-use library for training, evaluating, and visualizing different GSL methods. Empirical results of our extensive experiments demonstrate the ability of GSL and reveal its potential benefits on various downstream tasks, offering insights and opportunities for future research. The code of GSLB is available at: https://***/GSL-Benchmark/GSLB. © 2023, CC BY.

关键词： Benchmarking

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Time variation of high-risk groups for liver function deteriorations within fluctuating long-term liver function after hepatic radiotherapy in patients with hepatocellular carcinoma

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European Journal of Medical Research 2024年第1期29卷 104-104页

作者： Tsai, Yu-Lun Yu, Pei-Chieh Nien, Hsin-Hua Lu, Tzu-Pin Institute of Epidemiology and Preventive Medicine College of Public Health National Taiwan University Taipei Taiwan Department of Radiation Oncology Cathay General Hospital Taipei Taiwan School of Medicine China Medical University Taichung Taiwan School of Medicine Fu Jen Catholic University New Taipei City Taiwan Institute of Biomedical Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Bioinformatics and Biostatistics Core Center of Genomic and Precision Medicine National Taiwan University Taipei Taiwan Institute of Health Data Analytics and Statistics College of Public Health National Taiwan University Taipei Taiwan

PURPOSE: The purpose of this study is to find essential risk factors associated with liver function (LF) deteriorations within fluctuating long-term LF and their time-varying effects in patients with hepatocellular carcinoma (HCC) receiving hepatic radiotherapy and to identify high-risk groups for adverse LF deteriorations and their changes over time in facilitating the prevention of hepatic decompensation and the improvement of survival. MATERIALS AND METHODS: A total of 133 HCC patients treated by hepatic radiotherapy were enrolled. A study design was conducted to convert posttreatment long-term LF with fluctuating levels over time to recurrent LF events using defined upgrades in a grading scale. The hazard ratios (HR) of pretreatment biochemical, demographic, clinical, and dosimetric factors in developing posttreatment LF events were estimated using the Cox model. Methodologies of the counting process approach, robust variance estimation, goodness-of-fit testing based on the Schoenfeld residuals, and time-dependent covariates in survival analysis were employed to handle the correlation within subjects and evaluate the time-varying effects during long-term follow-up. RESULTS: Baseline LF score before radiotherapy and gender were significant factors. Initial HR in developing LF events was 1.17 (95% CI 1.11-1.23;P < 0.001) for each increase of baseline LF score and kept almost constant over time (HR, 1.00;95% CI 1.00-1.01;P = 0.065). However, no difference was observed regarding initial hazards for gender (HR, 1.00;95% CI 0.64-1.56;P = 0.994), but the hazard for women got higher monthly over time compared with men (HR, 1.04;95% CI 1.01-1.07;P = 0.006). CONCLUSIONS: High-risk groups for adverse LF deteriorations after hepatic radiotherapy may change over time. Patients with poor baseline LF are vulnerable from the beginning. Women require prevention strategies and careful monitoring for deteriorations at a later stage. © 2024. The Author(s).

关键词： Hepatic radiotherapy Hepatocellular carcinoma High-risk group Long-term liver function Time variation

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MoTransFrame:Model Transfer Framework for CNNs on Low-Resource Edge Computing Node

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computers, Materials & Continua 2020年第12期65卷 2321-2334页

作者： Panyu Liu Huilin Ren Xiaojun Shi Yangyang Li Zhiping Cai Fang Liu Huacheng Zeng National University of Defense Technology Changsha410073China Training and Administration Department the Central Military CommissionBeijing100851China Department of Science and Technology China Electronics Technology Group CorporationBeijing100846China National Engineering Laboratory for Public Safety Risk Perception and Control by Big Data Beijing100041China School of Design Hunan UniversityChangsha410082China Department of Electrical and Computer Engineering University of LouisvilleLouisvilleKY 40292USA

Deep learning technology has been widely used in computer vision,speech recognition,natural language processing,and other related *** deep learning algorithm has high precision and high ***,the lack of resources in the edge terminal equipment makes it difficult to run deep learning algorithms that require more memory and computing *** this paper,we propose MoTransFrame,a general model processing framework for deep learning *** of designing a model compression algorithm with a high compression ratio,MoTransFrame can transplant popular convolutional neural networks models to resources-starved edge devices promptly and *** the integration method,Deep learning models can be converted into portable projects for Arduino,a typical edge device with limited *** experiments show that MoTransFrame has good adaptability in edge devices with limited *** is more flexible than other model transplantation *** can keep a small loss of model accuracy when the number of parameters is compressed by tens of *** the same time,the computational resources needed in the reasoning process are less than what the edge node could handle.

关键词： Edge computing convolutional neural network model transformation model compression

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Label-Free Detection of Respiratory Virus Co-Infections: Integrating Surface-Enhanced Raman Scattering with Deep Learning

Label-Free Detection of Respiratory Virus Co-Infections: Int...

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Photonic Diagnosis, Monitoring, Prevention, and Treatment of Infections and Inflammatory Diseases 2025

作者： Yang, Yanjun Cui, Jiaheng Kumar, Amit Luo, Dan Murray, Jackelyn Jones, Les Chen, Xianyan Hülck, Sebastian Tripp, Ralph A. Zhao, Yiping Department of Physics and Astronomy Franklin College of Arts and Sciences The University of Georgia AthensGA30602 United States School of Electrical and Computer Engineering College of Engineering The University of Georgia AthensGA30602 United States Department of Statistics Franklin College of Arts and Sciences The University of Georgia AthensGA30602 United States Department of Infectious Diseases College of Veterinary Medicine The University of Georgia AthensGA30602 United States Department of Epidemiology & Biostatistics College of Public Health The University of Georgia AthensGA30602 United States Tec5USA Inc. PlainviewNY11803 United States

ISBN: (纸本)9781510683440

Concurrent or sequential infections by multiple respiratory viruses pose significant challenges for diagnosis, treatment, and disease management, necessitating accurate and rapid identification methods. Here, we introduce a label-free diagnostic platform that combines surface-enhanced Raman scattering (SERS) with deep learning to enable rapid, quantitative detection of respiratory virus co-infections. Utilizing highly sensitive silica-coated silver nanorod array substrates, we collected over 1.2 million SERS spectra from 11 viruses, nine two-virus mixtures, and four three-virus mixtures at varying concentrations in saliva. A novel deep learning model, MultiplexCR, was developed to simultaneously identify virus species and quantify their concentrations from the SERS spectra. The model achieved an exceptional classification accuracy of 98.6% for virus co-infections and a mean absolute error of 0.028 for concentration regression. In blind testing, MultiplexCR consistently demonstrated high accuracy and precise concentration predictions. This SERS-MultiplexCR platform performs the entire detection process within 15 minutes, highlighting its potential for rapid, point-of-care diagnostics in infection detection, as well as broader applications in food safety and environmental monitoring. © 2025 SPIE.

关键词： Nanorods

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Cardiac strain imaging with dynamically skipped frames: A simulation study

Cardiac strain imaging with dynamically skipped frames: A si...

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2020 IEEE International Ultrasonics Symposium, IUS 2020

作者： Mukaddim, Rashid Al Varghese, Tomy University of Wisconsin - Madison Department of Electrical and Computer Engineering MadisonWI53706 United States Department of Medical Physics School of Medicine and Public Health University of Wisconsin - Madison MadisonWI-53706 United States

ISBN: (纸本)9781728154480

Accurate lateral displacement estimation is a critical requirement to reliably use cardiac strain imaging for myocardial health or carotid strain imaging to assess atherosclerosis. Estimation of small lateral displacements are challenging even with 2-D kernels. We investigate the feasibility of using a dynamic frame skip (DFS) approach to improve lateral strain estimation for cardiac and carotid strain imaging. DFS automatically determines the optimal frame skip using axial strain information estimated during displacement tracking. We incorporated DFS into a hierarchical Bayesian regularization based displacement tracking method for cardiac and carotid strain imaging. A finite element analysis (FEA) model of canine cardiac deformation was utilized to quantify estimation performance with DFS and compare against conventional inter-frame displacement (CFS) approach. DFS displacement and strain temporal curves showed improved qualitative agreement with FEA ground truth compared to CFS results. Consequently, DFS had lower strain total temporal relative (TTR) strain errors (TTRaxial= 18.37 %, TTRlateral= 29.91 %, TTRradial = 17.32 %, and TTRlongitudinal = 14.51 %) compared to CFS (TTRaxial= 22.39 %, TTRlateral = 30.88 %, TTRradial = 22.35 %, and TTRlongitudinal= 15.87 %). These results show that DFS can potentially improve cardiac strain estimation quality. © 2020 IEEE.

关键词： Heart

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Invariant neuromorphic representations of tactile stimuli improve robustness of a real-time texture classification system

arXiv

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arXiv 2024年

作者： Iskarous, Mark M. Chaudhry, Zan Li, Fangjie Bello, Samuel Sankar, Sriramana Slepyan, Ariel Chugh, Natasha Hunt, Christopher L. Greene, Rebecca J. Thakor, Nitish V. Department of Biomedical Engineering Johns Hopkins School of Medicine BaltimoreMD21205 United States Department of Organismal Biology and Anatomy University of Chicago ChicagoIL60637 United States National Heart Lung and Blood Institute National Institutes of Health BethesdaMD20814 United States Department of Biomedical Engineering Vanderbilt University NashvilleTN37325 United States Department of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD21218 United States Department of Public Health Johns Hopkins University BaltimoreMD21218 United States Texas College of Osteopathic Medicine The University of North Texas Health Science Center Fort WorthTX76107 United States Infinite Biomedical Technologies LLC. BaltimoreMD21202 United States

Humans have an exquisite sense of touch which robotic and prosthetic systems aim to recreate. We developed algorithms to create neuron-like (neuromorphic) spiking representations of texture that are invariant to the scanning speed and contact force applied in the sensing process. The spiking representations are based on mimicking activity from mechanoreceptors in human skin and further processing up to the brain. The neuromorphic encoding process transforms analog sensor readings into speed and force invariant spiking representations in three sequential stages: the force invariance module (in the analog domain), the spiking activity encoding module (transforms from analog to spiking domain), and the speed invariance module (in the spiking domain). The algorithms were tested on a tactile texture dataset collected in 15 speed-force conditions. An offline texture classification system built on the invariant representations has higher classification accuracy, improved computational efficiency, and increased capability to identify textures explored in novel speed-force conditions. The speed invariance algorithm was adapted to a real-time human-operated texture classification system. Similarly, the invariant representations improved classification accuracy, computational efficiency, and capability to identify textures explored in novel conditions. The invariant representation is even more crucial in this context due to human imprecision which seems to the classification system as a novel condition. These results demonstrate that invariant neuromorphic representations enable better performing neurorobotic tactile sensing systems. Furthermore, because the neuromorphic representations are based on biological processing, this work can be used in the future as the basis for naturalistic sensory feedback for upper limb amputees. © 2024, CC BY-NC-ND.

关键词： Sensory feedback

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Sponge City As A Civil engineering Solution To Climate Change That Improves Urban Flood Resilience

Sponge City As A Civil Engineering Solution To Climate Chang...

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E-Health and Bioengineering Conference (EHB)

作者： Yu-Tong Shao Kevin Sheng-Kai Ma Department of Bioenvironmental Systems Engineering National Taiwan University Taipei Taiwan Center for Global Health Perelman School of Medicin University of Pennsylvania Philadelphia PA USA Department of Epidemiology Harvard T.H. Chan School of Public Health Boston MA USA Graduate Institute of Biomedical Electronics and Bioinformatics College of Electrical Engineering and Computer Science National Taiwan University Taipei Taiwan

ISBN: (数字)9781665485579

ISBN: (纸本)9781665485586

Climate change and urbanization drive the demand for water management to make cities more water resilient. Climate change brings heavy precipitation and causes urban flooding, with conventional construction of cities no longer suitable given the rising risk of flooding. Characterized by constructing cities as large sponges that absorb and treat water, the idea of sponge cities is a solution to stormwater control and drainage water management, which can be achieved by creating water absorbing areas, increasing permeable surface, and integrating stormwater and drainage systems. To assess flood resilience, evaluation indicators have been introduced to measure the effectiveness of sponge cities. A case study was also provided in this review to demonstrate how the concept of sponge cities was put into practice.

关键词： Climate change Precipitation Urban areas Water quality Floods Surface treatment Civil engineering

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