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Creative Communication and Innovative Technology (ICCIT), IEEE

作者： Gusti Pangestu Yaya Heryadi Lukas Wayan Suparta Yulyani Arifin Computer Science Department BINUS Graduate Program Computer Science. Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program Computer Science Bina Nusantara University Jakarta Indonesia Cognitive Engineering Research (CERG) Faculty of Engineering Universitas Katolik Indonesia Atma Jaya Jakarta Indonesia Department of Electrical Engineering Faculty of Industrial Technology Institut Teknologi Nasional Yogyakarta Yogyakarta Indonesia

Sign language has importance rule to deal with communication process especially with impairments hearing people. Sign language detection also attract lot of researchers to join the challenge of research to detect and recognize the sign language in the field of computer Science. Hence, there is still no any standard approach and method to recognize the meaning in every pose of sign language. This research proposed a mechanism to detect Alphabet American Sign Language by utilizing Convolutional Neural Network (CNN) process. The CNN approach was chosen based on the ability and capability to recognize image. In this research, MNIST dataset is used for traning and testing process. The proposed CNN architecture produced 97% of accuracy that outperform the previous research using the same dataset which made this architecture promising.

关键词： Deep learning computer science Image recognition Gesture recognition computer architecture Auditory system Assistive technologies

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Nonlinear Twistronics for EM Waves

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IEEE Transactions on Antennas and Propagation 2025年

作者： Valagiannopoulos, Constantinos Alu, Andrea National Technical University of Athens School of Electrical and Computer Engineering Athens15780 Greece City University of New York Photonics Initiative Advanced Science Research Center New YorkNY10031 United States City College of The City University of New York Department of Electrical Engineering New YorkNY10031 United States City University of New York Physics Program Graduate Center New YorkNY10016 United States

The electromagnetic (EM) interaction between two anisotropic nonlinear metasurfaces, under arbitrary rotations of their optical axes, is rigorously studied. Multistability conditions with respect to rotation angles between the two surfaces are determined and explored. The examined setup demonstrates opportunities for memory and storage since these in-plane rotations may act as continuous information carriers. The abrupt transitions of the system response, from one solution branch to the other, materialize the concept of twistronics engineering in the classical EM regime, sensitive to small angular changes. These findings can enhance the design of several photonic devices from ultra-sharp sensors and switches to analog information processors and optical memory elements. © 1963-2012 IEEE.

关键词： Optical bistability

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Graph Laplacian Learning with Exponential Family Noise

arXiv

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arXiv 2023年

作者： Shi, Changhao Mishne, Gal The Electrical and Computer Engineering Department UC San Diego CA92093 United States The Halıcıoglu Data Science Institute The Neurosciences Graduate Program UC San Diego CA92093 United States

Graph signal processing (GSP) is a prominent framework for analyzing signals on non-Euclidean domains. The graph Fourier transform (GFT) uses the combinatorial graph Laplacian matrix to reveal the spectral decomposition of signals in the graph frequency domain. However, a common challenge in applying GSP methods is that in many scenarios the underlying graph of a system is unknown. A solution in such cases is to construct the unobserved graph from available data, which is commonly referred to as graph or network inference. Although different graph inference methods exist, these are restricted to learning from either smooth graph signals or simple additive Gaussian noise. Other types of noisy data, such as discrete counts or binary digits, are rather common in real-world applications, yet are underexplored in graph inference. In this paper, we propose a versatile graph inference framework for learning from graph signals corrupted by exponential family noise. Our framework generalizes previous methods from continuous smooth graph signals to various data types. We propose an alternating algorithm that jointly estimates the graph Laplacian and the unobserved smooth representation from the noisy signals. We also extend our approach to a variational form to account for the inherent stochasticity of the latent smooth representation. Finally, since real-world graph signals are frequently non-independent and temporally correlated, we further adapt our original setting to a time-vertex formulation. We demonstrate on synthetic and real-world data that our new algorithms outperform competing Laplacian estimation methods that suffer from noise model mismatch. Copyright © 2023, The Authors. All rights reserved.

关键词： Signal processing

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Terahertz Time-Domain Imaging for Radiotherapy Treatment Monitoring in Pancreatic Ductal Adenocarcinoma

Terahertz Time-Domain Imaging for Radiotherapy Treatment Mon...

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Conference on Lasers and Electro-Optics (CLEO)

作者： Debamitra Chakraborty Bradley N. Mills Jing Cheng Ivan Komissarov Scott Gerber Roman Sobolewski Materials Science Graduate Program University of Rochester Rochester New York Laboratory for Laser Energetics University of Rochester Rochester New York Department of Surgery University of Rochester Medical Center Rochester New York Department of Electrical and Computer Engineering University of Rochester Rochester New York

ISBN: (纸本)9798350369311

Terahertz time-domain imaging was performed of stereotactic body radiotherapy-treated murine pancreatic ductal adenocarcinoma (PDAC) with a high spatial resolution. To generate 2D maps of the tissue samples, the refractive index and absorption coefficient were used as imaging markers extracted with the maximum a-posteriori probability (MAP) estimation algorithm. The results were then compared with the terahertz maps of untreated murine PDAC tissue samples and demonstrated that terahertz imaging can probe physical changes with high sensitivity in the tissue post treatment.

关键词： Maximum a posteriori estimation Terahertz wave imaging Laser theory Sensitivity Refractive index Radiation therapy Time-domain analysis Spatial resolution Probes Monitoring

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Automated Diagnosis of Heart-Lung Diseases in Chest X-ray Images

Automated Diagnosis of Heart-Lung Diseases in Chest X-ray Im...

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International Conference on engineering Technology and their Applications (IICETA)

作者： Mohammad Alslatie Hiam Alquran Wan Azani Mustafa Isam Abu-Qasmieh Ali Mohammad Alqudah Ahmed Alkhayyat King Hussein Medical Center Royal Jordanian Medical Service The Institute of Biomedical Technology Amman Jordan Biomedical Systems and Medical Informatics Engineering Yarmouk University Irbid Jordan Faculty of Electrical Engineering Technology Univerisiti Malaysia Perlis (UniMAP) Arau Perlis Malaysia Department of Electrical and Computer Engineering Biomedical Engineering Program University of Manitoba Canada Faculty of Engineering The Islamic University Najaf Iraq

ISBN: (数字)9781665472159

ISBN: (纸本)9781665472166

The state of the art of artificial intelligence (AI) for various medical imaging applications leads to enhanced accuracy, analysis, visualization, and interpretation of chest Xray (CXR) images for diagnosis. Many diseases are diagnosed based on CXR images. In this paper, two types of abnormalities are diagnosed based on AI techniques. The two classes are atelectasis and cardiomegaly. The acquired images are segmented to localize the chest region and then enhanced using gray-level transformation methods. The enhanced images are passed to two pretrained convolutional neural networks (CNNs): shuffle and mobile net. The transfer learning approach is utilized in this stage. The automated features are extracted from the last fully connected layer. Each CNN deserves to have the two most representative features for the two classes. These four features are passed to support the vector machine classifier. The training accuracy reached 100% and the test accuracy was 96.7%. The proposed method can be extended to be a milestone in the classification of all heart-lung diseases that can be diagnosed using chest X-ray images.

关键词： Image segmentation Visualization Transfer learning Feature extraction Convolutional neural networks Artificial intelligence X-ray imaging

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GranatumX:A Community-engaging, Modularized, and Flexible Webtool for Single-cell Data Analysis

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Genomics, Proteomics & Bioinformatics 2021年第3期19卷 452-460页

作者： David G.Garmire Xun Zhu Aravind Mantravadi Qianhui Huang Breck Yunits Yu Liu Thomas Wolfgruber Olivier Poirion Tianying Zhao Cédric Arisdakessian Stefan Stanojevic Lana X.Garmire Department of Electrical Engineering&Computer Science University of MichiganAnn ArborMI 48109USA Epidemiology Program University of Hawaii Cancer CenterHonoluluHI 96813USA Department of Biostatistics University of MichiganAnn ArborMI 48109USA Department of Computational Medicine and Bioinformatics University of MichiganAnn ArborMI 48105USA Molecular Biosciences and Bioengineering Graduate Program University of Hawaii at ManoaHonoluluHI 96822USA

We present GranatumX,a next-generation software environment for single-cell RNA sequencing(scRNA-seq)data *** is inspired by the interactive webtool *** enables biologists to access the latest scRNA-seq bioinformatics methods in a web-based graphical *** also offers software developers the opportunity to rapidly promote their own tools with others in customizable *** architecture of GranatumX allows for easy inclusion of plugin modules,named Gboxes,which wrap around bioinformatics tools written in various programming languages and on various *** can be run on the cloud or private servers and generate reproducible *** is a community-engaging,flexible,and evolving software ecosystem for scRNA-seq analysis,connecting developers with bench *** is freely accessible at http://***/granatumx/app.

关键词： Single-cell RNA sequencing Analysis Pipeline Webtool Module

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Multi-level Asymmetric Contrastive Learning for Medical Image Segmentation Pre-training

arXiv

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arXiv 2023年

作者： Zeng, Shuang Zhu, Lei Zhang, Xinliang Nnamdi, Micky C. Shi, Wenqi Tamo, J. Ben Chen, Qian He, Hangzhou Jin, Lujia Tian, Zifeng Ren, Qiushi Xie, Zhaoheng Lu, Yanye The Department of Biomedical Engineering Peking University Beijing China Institute of Medical Technology Peking University Health Science Center Peking University Beijing China The National Biomedical Imaging Center Peking University Beijing China The Institute of Biomedical Engineering Peking University Shenzhen Graduate School Shenzhen China school of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States DallasTX United States

Medical image segmentation is a fundamental yet challenging task due to the arduous process of acquiring large volumes of high-quality labeled data from experts. Contrastive learning offers a promising but still problematic solution to this dilemma. Firstly existing medical contrastive learning strategies focus on extracting image-level representation, which ignores abundant multi-level representations. Furthermore they under-utilize the decoder either by random initialization or separate pre-training from the encoder, thereby neglecting the potential collaboration between the encoder and decoder. To address these issues, we propose a novel multi-level asymmetric contrastive learning framework named MACL for volumetric medical image segmentation pre-training. Specifically, we design an asymmetric contrastive learning structure to pre-train encoder and decoder simultaneously to provide better initialization for segmentation models. Moreover, we develop a multi-level contrastive learning strategy that integrates correspondences across feature-level, image-level, and pixel-level representations to ensure the encoder and decoder capture comprehensive details from representations of varying scales and granularities during the pre-training phase. Finally, experiments on 8 medical image datasets indicate our MACL framework outperforms existing 11 contrastive learning strategies. i.e. Our MACL achieves a superior performance with more precise predictions from visualization figures and 1.72%, 7.87%, 2.49% and 1.48% Dice higher than previous best results on ACDC, MMWHS, HVSMR and CHAOS with 10% labeled data, respectively. And our MACL also has a strong generalization ability among 5 variant U-Net backbones. Our code will be released at https://***/stevezs315/MACL. Copyright © 2023, The Authors. All rights reserved.

关键词： Contrastive Learning

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Intrinsic Parameter Fluctuation and Process Variation Effect of Vertically Stacked Silicon Nanosheet Complementary Field-Effect Transistors

Intrinsic Parameter Fluctuation and Process Variation Effect...

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IEEE International Symposium on Quality Electronic Design

作者： Sekhar Reddy Kola Yiming Li Min-Hui Chuang Parallel and Scientific Computing Laboratory Hsinchu Taiwan Electrical Engineering and Computer Science International Graduate Program Hsinchu Taiwan Institute of Communications Engineering Hsinchu Taiwan Institute of Biomedical Engineering Hsinchu Taiwan Institute of Pioneer Semiconductor Innovation Hsinchu Taiwan Institute of Artificial Intelligence Innovation Hsinchu Taiwan Department of Electronics and Electrical Engineering National Yang Ming Chiao Tung University Hsinchu Taiwan Center for mmWave Smart Radar System and Technologies National Yang Ming Chiao Tung University Hsinchu Taiwan

We study the variability of vertically stacked gate-all-around silicon nanosheet (GAA Si NS) complementary field-effect transistors (CFETs). The process variation effect (PVE), the work function fluctuation (WKF), and the random dopant fluctuation (RDF) of CFETs are statistically estimated using an experimentally validated device simulation technique. Among five factors of PVE, the channel thickness (T Nch /T Pch ), the channel width (W ch ), and the gate length (L G ) are significant. Owing to superior GAA channel control and increased effective gate area, both WKF and RDF are suppressed. Notably, the PVE on both N-/P-FETs of GAA Si CFET induce the largest off-state current fluctuations of 80% and 278%, respectively, because the device characteristic is very sensitive to the layer thickness and width of channel.

关键词： Fluctuations Gallium arsenide Logic gates Electric variables Silicon Resource description framework Semiconductor process modeling

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A Flexible Conductive Electrode Using Boronic-Acid Modified Carbon Dots

A Flexible Conductive Electrode Using Boronic-Acid Modified ...

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

作者： Amaal Abdulraqeb Ali Mohammad H. Al-Sayah Amani Al-Othman Hasan Al-Nashash Biomedical Engineering Graduate Program American University of Sharjah Sharjah United Arab Emirates Department of Biology Chemistry and Environmental Sciences College of Arts and Sciences American University of Sharjah Sharjah United Arab Emirates Department of Chemical Engineering American University of Sharjah Sharjah United Arab Emirates Department of Electrical Engineering American University of Sharjah Sharjah United Arab Emirates

Flexible electrodes are becoming a topic of interest for a range of applications including implantation. They can be used for neural signal recording and for electrical stimulation of atrophying muscles. Unlike the traditionally used metal electrodes that are harsh to the body’s tissues, flexible electrodes conduct electricity while preserving the delicate tissues. Polydimethylsiloxane (PDMS), a non-conductive synthetic polymer characterized by its flexibility, low cost, biocompatibility, and durability during implantation, has been explored as a matrix for flexible electrodes. This study reports the synthesis of composite boronic acid-modified carbon dots (BA-CDs)/PDMS electrode materials. The performance of the composite electrode is evaluated electrochemically (for its conductivity and charge storage capacity) and mechanically (Young’s modulus). Furthermore, the effect of increasing the PDMS crosslinking density on the electrode’s performance is studied based on the hypothesis that a higher crosslinking will bring the BA-CDs closer together, thereby facilitating the movement of electrons. Results of this study showed that incorporating 10% BA-CDs dispersed with 16% glycerol in 74% PDMS with a higher crosslinking density resulted in a bulk impedance of 47.7 Ω and a conductivity of 2.68×10 -3 S/cm, both of which surpassed that of the same composition with lower crosslinking. The synthesized flexible electrode material was capable of charge storage although the charge storage capacity (0.00365 mC/cm 2 ) was lower than the safe limit for some tissue activation. Furthermore, the electrode maintained a modulus of elasticity (0.2322 MPa) that is compatible with biological soft *** Relevance— This study reports a conductive electrode that has a flexibility compatible with that of biological tissues for future purposes such as neural signal recording and tissue electrical stimulation (e.g. atrophying muscles). The reported BA-CD/PDMS electrode overcomes the

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Condition Assessment of Underground Cable System Using Fuzzy Logic and Scoring and Weighting Average Methods

Condition Assessment of Underground Cable System Using Fuzzy...

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Conference on Power, Energy and Innovations (ICPEI)

作者： Dineenath Romphoyen Nattapon Panmala Thanapong Suwanasri Cattareeya Suwanasri EPE Program The Sirindhorn International Thai-German Graduate School of Engineering King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand Department of Teacher Training in Electrical Engineering Faculty of Technical Education King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangsue Bangkok Thailand

In this paper, the overall health index of underground cable system is determined using Fuzzy Logic and Scoring and Weighting Average methods. The relevant data of 73 feeders has been collected in the prepared evaluation forms, divided into five major component groups: cable, joint, termination, manhole, and ductbank. In each component, various testing methods and diagnostic techniques are applied, and then the numerical score is determined according to the technical criteria for condition assessment. Subsequently, the obtained overall health index is multiplied with the conditional factor to incorporate the differences in installation type and configuration as well as the operating and environmental conditions. Finally, the overall health index based on these two methods is compared to verify the accuracy of the obtained results to properly plan the preventive and condition-based maintenance and to improve the reliability of the underground cable system.

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