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Dielectric Function of VO2determined by Spectroscopic Ellipsometry 6

Dielectric Function of VO2determined by Spectroscopic Ellips...

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6th Annual IEEE Research and Applications of Photonics in Defense Conference, RAPID 2023

作者： Louisos, Dustin McLamb, Micheal Ginn, James Warren, Andrew P. Boreman, Glenn D. Mencagli, Mario Junior Willis, Andrew Hofmann, Tino Touma, Jimmy E. University of North Carolina at Charlotte Dept. of Physics and Optical Science CharlotteNC28025 United States Plasmonics Inc 12605 Challenger Pkwy STE 150 OrlandoFL32826 United States University of North Carolina at Charlotte Electrical and Computer Engineering Dept. CharlotteNC28025 United States Air Force Research Laboratory Eglin AFBFL32542 United States

ISBN: (纸本)9781665423526

The transition metal oxide VO2 exhibits a metal-insulator transition and is of interest for dynamic metamaterial applications. Here the complex dielectric function of VO2, de-posited via magnetron sputtering, is determined below and above the metal-insulator transition temperature using spectroscopic ellipsometry over a wide soectral range. © 2023 IEEE.

关键词： Phase change materials

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Tailoring Nonlinearities in Epsilon-Near-Zero Materials via Structure and Excitation

Tailoring Nonlinearities in Epsilon-Near-Zero Materials via ...

引用

Novel Optical Materials and Applications, NOMA 2022

作者： Ball, A. Wu, J. Fomra, D. Secondo, R. Saha, S. Sojib, M. Khurgin, J.B. Lezec, H.J. Kinsey, N. Dept. of Electrical and Computer Engineering Virginia Commonwealth University RichmondVA23284 United States Air Force Research Laboratory Materials and Manufacturing Directorate 2179 12th St. Wright-Patterson Air Force Base OH45433 United States Dept. of Electrical and Computer Engineering Johns Hopkins University BaltimoreMD21218 United States National Institutes of Standards and Technology GaithersburgMD20899 United States

Epsilon-near-zero (ENZ) materials have demonstrated enhanced nonlinear optical interactions and efforts to push performance are underway. Beginning with the origin of nonlinearities in ENZ, we highlight avenues for im...

ISBN: (纸本)9781557528209

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Early Detection and Classification of Diabetic Retinopathy through Analysis of Retinal Medical Images Using Deep Learning

Early Detection and Classification of Diabetic Retinopathy t...

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International Symposium on Signal, Image, Video and Communications (ISIVC)

作者： Youssef Siyah Khalid Minaoui Fatima Zahra El Biach Samir Saoudi Laboratory for research in computer science and telecommunications (LRIT) Faculty of Sciences in Rabat Mohammed V University in Rabat Morocco dept. Mathematical and Electrical Engineering STICC Laboratory UMR CNRS 6285 IMT-Atlantique in Brest France

ISBN: (数字)9798350385267

ISBN: (纸本)9798350385274

In this paper, we propose a model based on deep learning (DL) frameworks for the detection and classification of diabetic retinopathy (DR), along with the assessment of its severity. DL algorithms have been applied to fundus photographs, which are images of the back of the eye, to automatically detect and classify signs of DR. These algorithms can analyze retinal images and identify anomalies, including microaneurysms, hemorrhages, exudates, as well as fluid accumulation in the macula, all of which serve as indicators of the presence of diabetic retinopathy. We utilized the APTOS 2019 dataset sourced from Kaggle, comprising high-resolution retinal images processed with Gaussian filters for diabetic retinopathy detection. Experiments were conducted on six different models, including Visual Geometry Group 16 (VGG16), Visual Geometry Group 19 (VGG19), Residual Network 50 (ResNet50), Inception Version 3 (InceptionV3), basic Convolutional Neural Networks (CNN), and Mobile Network Version 2 (MobileNetV2). The results obtained demonstrate high accuracy, suggesting that this model could contribute to expediting and enhancing the diabetic retinopathy diagnostic process. By automating the detection of anomalies in retinal images, the system proposes an intelligent approach that may help alleviate the workload on medical personnel.

关键词： Geometry Deep learning Diabetic retinopathy Visualization Retina Classification algorithms Convolutional neural networks

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Competitive-Driven Learning for Image Ordinal Classification

Competitive-Driven Learning for Image Ordinal Classification

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Electronic Information engineering and computer Communication (EIECC), International Conference on

作者： Chao Zhang Chao Feng Jianmei Cheng Keren Fu Intelligent Policing Key Laboratory of Sichuan Province Sichuan Police College Luzhou China Dept. Electrical Engineering and Computer Science University of Michigan Ann Arbor USA Dept. Computer Science Sichuan University Chengdu China

Image ordinal classification (IOC) assigns a discrete yet ordinal scalar label to an input image, such as age estimation. For IOC, it is non-trivial to effectively incorporate the ordinal information of inputs into the classification task. In this work, we adopt the concept of CutMix, a data augmentation technique that mixes two samples and their labels proportionally during the training, to help the task of ordinal classification. In particular, we study the CutMix from the perspective of metric learning instead of data augmentation, which naturally and simply implements classification learning and difference regression learning. Firstly, we embed two ordinal images into the training procedure by a virtual combination of image and label, and explain why CutMix fits IOC well. Secondly, the ordinal difference between two images is utilized to learn the fine difference in category classification, which is integrated with CutMix properly. Finally, for further improvement, dual pairing and random augmentation are proposed to enlarge the disparity of two objects, so as to exploit more discriminative feature. Comprehensive experiments demonstrate the effectiveness of our newly proposed approach, which outperforms previous methods with a relatively large margin (>2%).

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Using channel correlation to improve decoding - ORBGRAND-AI

Using channel correlation to improve decoding - ORBGRAND-AI

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IEEE Conference on Global Communications (GLOBECOM)

作者： Ken R. Duffy Moritz Grundei Muriel Médard Dept. of ECE & Dept. Mathematics Northeastern University Boston USA Electrical and Computer Engineering Technical University of Munich Munich Germany Research Laboratory for Electronics Massachusetts Institute of Technology Cambridge USA

To meet the Ultra Reliable Low Latency Communication (URLLC) needs of modern applications, there have been significant advances in the development of short error correction codes and corresponding soft detection decoders. A substantial hindrance to delivering low-latency is, however, the reliance on interleaving to break up omnipresent channel correlations to ensure that decoder input matches decoder assumptions. Consequently, even when using short codes, the need to wait to interleave data at the sender and de-interleave at the receiver results in significant latency that acts contrary to the goals of URLLC. Moreover, interleaving provably reduces capacity in channels with correlation, so that potential decoding performance is degraded. Here we introduce a variant of Ordered Reliability Bits Guessing Random Additive Noise Decoding (ORBGRAND), which we call ORBGRAND-Approximate Independence (ORBGRAND-AI), a soft-detection decoder that can decode any moderate redundancy code and overcomes the limitation of existing decoding paradigms by leveraging channel correlations and circumventing the need for interleaving. By leveraging correlation, not only is latency reduced, but error correction performance can be enhanced by multiple dB, while decoding complexity is also reduced, offering one potential solution for the provision of URLLC.

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A Dual-Band Leaky Wave Antenna Design for K-Band and W-Band

A Dual-Band Leaky Wave Antenna Design for K-Band and W-Band

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IEEE Research and Applications of Photonics In Defense Conference (RAPID)

作者： Andrew Willis Jincheng Zhang Dustin Louisos Tino Hofmann Jimmy E. Touma Electrical and Computer Engineering Dept. University of North Carolina at Charlotte Charlotte NC USA Dept. of Physics and Optical Science University of North Carolina at Charlotte Charlotte NC USA Air Force Research Laboratory Eglin AFB FL USA

A bi-directional dual-band meta-surface antenna designed to operate at 20 GHz in the longitudinal direction and 80 GHz in the transverse direction is described and simulation results are presented.

ISBN: (数字)9798350373097

ISBN: (纸本)9798350373103

A bi-directional dual-band meta-surface antenna designed to operate at 20 GHz in the longitudinal direction and 80 GHz in the transverse direction is described and simulation results are presented.

关键词： Antenna measurements Surface waves Dual band Resonant frequency Directive antennas Leaky wave antennas Dielectric resonator antennas

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Fusion of Parameterized and Physics-oriented Statistical Surrogate Models for EM Coupling on Wires in Complex Electronic Enclosures

Fusion of Parameterized and Physics-oriented Statistical Sur...

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IEEE International Symposium on Electromagnetic Compatibility (EMC)

作者： Shen Lin Sangrui Luo Yang Shao Zhen Peng Bisrat D. Addissie Zachary B. Drikas Dept. Electrical and Computer Engineering University of Illinois at Urbana-Champaign Urbana IL USA Tactical Electronic Warfare Division U.S. Naval Research Laboratory Washington DC USA

ISBN: (数字)9798350360394

ISBN: (纸本)9798350360400

In this paper, we explore a novel approach in the domain of statistical electromagnetics for high-frequency coupling on wires installed in complex electronic enclosures. The study involves the integration of two distinct types of statistical surrogate models: parameterized models, which are based on predefined parameter spaces for internal wire/cable components, and physics-oriented statistical models, which leverage statistical representations of cavity eigenfunctions and eigenvalues. The fusion of parameterized and physics-oriented statistical surrogate models allows for a comprehensive and versatile statistical analysis, accommodating the complex details and variability present in real-world electronic systems. The proposed work has been validated numerically, including the commercial software and traditional Monte Carlo simulation.

关键词： Couplings Uncertain systems Uncertainty Wires Green products Stochastic processes Voltage Software Numerical models Electromagnetics

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Precision DDoS Detection through Gaussian Noise-Augmented Neural Networks

Precision DDoS Detection through Gaussian Noise-Augmented Ne...

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International Conference on the Network of the Future (NOF)

作者： Ali Alfatemi Diogo Oliveira Mohamed Rahouti Abdelatif Hafid Nasir Ghani Computer & Information Science Dept. Fordham University Bronx NY USA College of IST Penn State University Monaca PA USA Computer & Information Science Dept. Fordham University New York NY USA ESISA Analytica Laboratory ESISA Fez Morocco Electrical Engineering Dept. University of South Florida Tampa FL USA

ISBN: (数字)9798350377767

ISBN: (纸本)9798350377774

The detection of Distributed Denial of Service (DDoS) attacks is a critical challenge in network security, requiring effective and efficient solutions to safeguard data and services. This paper addresses this problem by introducing a neural network model specifically designed for DDoS attack detection. The model employs a streamlined architecture to ensure rapid processing times and high performance. A key innovation is the integration of Gaussian noise, which enhances the robustness and generalization capabilities of the model. Extensive experiments validate the effectiveness and resilience of this approach, demonstrating its practicality for real-world network security applications. The findings highlight the significant role of noise regularization in improving the reliability of neural network models for detecting cyber threats.

关键词： Technological innovation Gaussian noise Neural networks Machine learning Network security Denial-of-service attack Robustness Noise measurement computer crime Resilience

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Ultrasensitive and ultra selective liquid biopsy: Nanophotonics-driven digital resolution biomolecule sensing combined with target molecule recycling 21

Ultrasensitive and ultra selective liquid biopsy: Nanophoton...

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Optical Biopsy XXI: Toward Real-Time Spectroscopic Imaging and Diagnosis 2023

作者： Cunningham, Brian T. Dept. of Electrical and Computer Engineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Dept. of Bioengineering University of Illinois at Urbana-Champaign UrbanaIL61801 United States Woese Institute for Genomic Biology UrbanaIL61801 United States Holonyak Micro and Nanotechnology Laboratory UrbanaIL61801 United States Cancer Center at Illinois UrbanaIL61801 United States

ISBN: (纸本)9781510658516

By combining biosensor microscopy that provides high contrast for detecting individual biomolecules with novel biochemistry methods that can effectively turn each target molecule into many digitally-counted sensing events, it is possible to achieve attomolar-scale limits of detection for cancer-specific nucleic acid target molecules (miRNA and ctDNA) while simultaneously obtaining thousands-To-one selectivity against single base variants. The presentation will describe the use of photonic metamaterials and associated detection instruments to amplify optical absorption and fluorescence emission. The biodetection technology platforms are used to perform assays using nucleic acid strand displacement reactions and CRISPR/Cas chemistry to sense target biomarkers from complex media. The "amplify-Then-digitize"approach represents a new and powerful paradigm for molecular diagnostics, compared to the "digitize-Then amplify"approach utilized in methods such as droplet digital PCR (ddPCR). Overall, we seek simple, rapid, room temperature, single-step assay methods that can be operate with small, inexpensive, and robust detection systems for applications in point of care diagnostics, laboratory-based diagnostics, and life science research applications. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Biosensors

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Fault Detection in Cascaded H-Bridge Inverters using Spectrogram Analysis and Convolutional Neural Networks

Fault Detection in Cascaded H-Bridge Inverters using Spectro...

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International Conference on Optimization of electrical and Electronic Equipment, OPTIM

作者： Samuela Rokocakau Giulia Tresca Giansalvo Cirrincione Pericle Zanchetta Rahul Kumar Maurizio Cirrincione Lucia Frosini Electrical Computer and Biomedical Engineering Dept. The University of Pavia Pavia Italy Laboratory of Novel Technologies University of Picardie Jules Verne Amiens France Electrical and Electronic Engineering Dept. University of Nottingham UK School of Information Technology Engineering Mathematics and Physics The University of the South Pacific Suva Fiji

Ensuring reliability and safety in multilevel power converter topologies requires effective fault diagnostics, especially considering the greater number of components involved compared to traditional converters. Recent research has recognized the potential of Artificial Neural Networks (ANN) in enhancing diagnostic strategies. In this context, this study proposes an innovative approach for detecting devices faults in multilevel converters using Convolutional Neural Networks (CNN) through 2D image-based classification. The simulation results demonstrate the efficacy of this method, enabling more dependable and efficient multilevel power electronic systems.

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