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arXiv 2021年

作者： Suman, Sudhir Singh, Gagandeep Sakla, Nicole Gattu, Rishabh Green, Jeremy Phatak, Tej Samaras, Dimitris Prasanna, Prateek Dept. of Electrical Engineering Indian Institute of Technology Bombay India Dept. of Radiology Newark Beth Israel Medical Center NJ United States Dept. of Computer Science Stony Brook University NY United States Dept. of Biomedical Informatics Stony Brook University NY United States

With more than 60,000 deaths annually in the United States, Pulmonary Embolism (PE) is among the most fatal cardiovascular diseases. It is caused by an artery blockage in the lung;confirming its presence is time-consuming and is prone to over-diagnosis. The utilization of automated PE detection systems is critical for diagnostic accuracy and efficiency. In this study we propose a two-stage attention-based CNN-LSTM network for predicting PE, its associated type (chronic, acute) and corresponding location (leftsided, rightsided or central) on computed tomography (CT) examinations. We trained our model on the largest available public Computed Tomography Pulmonary Angiogram PE dataset (RSNA-STR Pulmonary Embolism CT (RSPECT) Dataset, N=7279 CT studies) and tested it on an in-house curated dataset of N=106 studies. Our framework mirrors the radiologic diagnostic process via a multi-slice approach so that the accuracy and pathologic sequela of true pulmonary emboli may be meticulously assessed, enabling physicians to better appraise the morbidity of a PE when present. Our proposed method outperformed a baseline CNN classifier and a single-stage CNN-LSTM network, achieving an AUC of 0.95 on the test set for detecting the presence of PE in the study. Copyright © 2021, The Authors. All rights reserved.

关键词： computerized tomography

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Expert-driven Rule-based Refinement of Semantic Segmentation Maps for Autonomous Vehicles

Expert-driven Rule-based Refinement of Semantic Segmentation...

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IEEE Symposium on Intelligent Vehicle

作者： Eric L. Manibardo Ibai Laña Javier Del Ser Alexander Carballo Kazuya Takeda Tecnalia Basque Research and Technology Alliance (BRTA) Derio Spain University of the Basque Country (UPV/EHU) Bilbao Spain Dept. of Electrical Electronic and Computer Engineering Gifu University Gifu Japan Institute of Innovation for Future Society Nagoya University Nagoya Japan Tier IV Inc Nagoya University Open Innovation Center Nagoya Japan Graduate School of Informatics Nagoya University Nagoya Japan

Semantic segmentation aims at assigning labels to every pixel of a given image. In the context of autonomous vehicles, semantic segmentation models should be trained with data collected from the traffic network through which vehicles are expected to circulate. Road regulation, weather conditions, and other context features may differ between regions, making local semantic segmentation datasets extremely valuable. However, the high ground truth annotation costs represent a hindrance to the development of such models. The upsurge of powerful feature learning architectures leaves room for semantic segmentation models trained on an unsupervised fashion. This observation vertebrates the purpose of this work: to produce coarse segmentation maps for scene understanding without the need of annotated data. We depart from an unsupervised model that yields low-quality results. The proposed methodology establishes a set of guidelines for the enhancement of segmentation maps. Obtained results expose an improvement of the segmentation quality thanks to the application of our devised guidelines, paving the way for the automatic generation of semantic segmentation datasets.

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Topological modes observed in Si photonics SSH integrated circuit

Topological modes observed in Si photonics SSH integrated ci...

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2022 Conference on Lasers and Electro-Optics Pacific Rim, CLEO/PR 2022

作者： Nakamura, R. Nakama, T. Balčytis, A. Ozawa, T. Ota, Y. Iwamoto, S. Ito, H. Baba, T. Dept of Electrical & Computer Eng. Yokohama National Univ. 79-5 Tokiwadai Hodogaya-ku Yokohama240-8501 Japan School of Engineering RMIT University MelbourneVIC3000 Australia Advanced Institute for Materials Research Tohoku Univ. Sendai980-8577 Japan Dept. of Applied Physics and Physico-Informatics Keio Univ. 3-14-1 Hiyoshi Kohoku-ku Yokohama223-8522 Japan Research Center for Advanced Science and Technology The University of Tokyo 4-6-1 Komaba Meguro-ku Tokyo153-8904 Japan

We fabricated a sophisticated Si photonic integrated circuit for investigating topological photonics SSH model. In the selective excitation and observation of SSH coupled microrings, we observed the wavefunctions of e... 详细信息

关键词： Timing circuits

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Topological modes observed in Si photonics SSH integrated circuit

Topological modes observed in Si photonics SSH integrated ci...

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2022 Conference on Lasers and Electro-Optics Pacific Rim, CLEO-PR 2022

作者： Nakamura, R. Nakama, T. Balčytis, A. Ozawa, T. Ota, Y. Iwamoto, S. Ito, H. Baba, T. Dept of Electrical &Computer Eng. Yokohama National Univ. 79-5 Tokiwadai Hodogaya-ku Yokohama240-8501 Japan School of Engineering RMIT University MelbourneVIC3000 Australia Advanced Institute for Materials Research Tohoku Univ. Sendai980-8577 Japan Dept. of Applied Physics and Physico-Informatics Keio Univ. 3-14-1 Hiyoshi Kohoku-ku Yokohama223-8522 Japan Research Center for Advanced Science and Technology The University of Tokyo 4-6-1 Komaba Meguro-ku Tokyo153-8904 Japan

ISBN: (纸本)9798350350012

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Hexagonal Resonator Loaded Star Shaped Polarization Insensitive Compact Broadband Zero Indexed Nano-Meta Absorber for Optical Region Applications: A Numerical Approach

Research Square

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Research Square 2021年

作者： Samsuzzaman, MD. Hossain, Ismail Hoque, Ahasanul Islam, Mohammad Tariqul Almutairi, Ali F. Universiti Kebangsaan Malaysia UKM Selangor Bangi43600 Malaysia Faculty of Computer Science and Engineering Patuakhali Science and Technology University Bangladesh Electrical Engineering Department Kuwait University Kuwait City13060 Kuwait Dept. of Electrical Electronic and Systems Engineering Faculty of Engineering and Built Environment Universiti Kebangsaan Malaysia Malaysia

Broadband response metamaterial absorber (MMA) remains a challenge among researchers. A nanostructured new Zero Indexed Metamaterial (ZIM) absorber is presented in this study, which is constructed with a hexagonal shape resonator for the optical region applications. The design consists of a resonator and dielectric layers made with tungsten and quartz (Fused), respectively. The proposed absorbent exhibits average absorption of more than 0.8972 (89.72%) within the visible wavelength of 450-600 nm and almost perfect absorption 0.99 (99%) at 458.54 nm. Based on computational analysis, the proposed absorber can be characterized as Zero Indexed Metamaterial. The developments of ZIM absorbers have demonstrated plasmonic resonance characteristics and a perfect impedance match. The incidence obliquity in typically the range of 0°–90° both in TE and TM mode with maximum absorbance is more than 0.8972 (~89.72%) and up to 35° angular stability,which is suitable for solar cell applications, like exploiting solar energy. The proposed structure prototype is designed and simulated by a study of microwave technology numerical computer simulation (CST) tools. Finite integration technique (FIT) and finite element method (FEM) is performed to data analysis in CST software, and HFSS also helps validate the numerical data of the proposed ZIM absorber. The proposed MMA design is appropriate for substantial amounts of absorption, wide-angle stability, absolute invisible layers, magnetic resonance imaging (MRI), color images, and thermal imaging applications. © 2021, CC BY.

关键词： Polarization

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Modified Direct Torque and Flux Control of Switched Reluctance Motor Drive with Reduced Source Current Ripple for Vehicular Applications

Modified Direct Torque and Flux Control of Switched Reluctan...

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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： Apparao Dekka Deepak Ronanki Krishna Reddy Pittam Parthiban Perumal Abdul R. Beig Dept. of Electrical Engineering Lakehead University Thunder Bay ON Canada Dept. of Electrical and Computer Engineering University of Ontario Institute of Technology ON Canada Dept. of Electrical and Electronics Engineering National Institute of Technology Karnataka Surathkal India Advanced Power and Energy Center Dept. of Electrical and Computer Engineering Khalifa University Abu Dhabi UAE

ISBN: (数字)9781728148298

ISBN: (纸本)9781728148304

The direct torque and flux control is employed to control the switched reluctance motor drive to minimize the torque ripple. However, the motor draws high current from the source (battery) to develop the required electromagnetic torque during phase commutation, thus reduces the drive efficiency. At the same time, a high ripple (high-frequency components) presents in the source current due to severe energy fluctuations on the motor side, which adversely effects the battery life. To circumvent the above concerns, a new direct torque and flux control scheme is proposed in this paper, which minimizes the source (battery) current ripple and the torque current ripple simultaneously. The feasibility of the proposed control approach is validated through MATLAB simulations under various operating conditions. The simulation results show that the proposed control approach helps to operate the switched reluctance motor drive at the lower switching frequency and also enhances the lifetime of the battery pack in comparison to the existing direct torque and flux control strategies.

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Fast learning of graph neural networks with guaranteed generalizability: One-hidden-layer case 37

Fast learning of graph neural networks with guaranteed gener...

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37th International Conference on Machine Learning, ICML 2020

作者： Zhang, Shuai Wang, Meng Liu, Sijia Chen, Pin-Yu Xiong, Jinjun Dept. of Electrical Computer and Systems Engineering Rensselaer Polytechnic Institute NY United States MIT-IBM Waston AI Lab CambridgeMA United States IBM Thomas J. Watson Research Center Yorktown HeightsNY United States

ISBN: (纸本)9781713821120

Although graph neural networks (GNNs) have made great progress recently on learning from graph-structured data in practice, their theoretical guarantee on generalizability remains elusive in the literature. In this paper, we provide a theoretically-grounded generalizability analysis of GNNs with one hidden layer for both regression and binary classification problems. Under the assumption that there exists a groundtruth GNN model (with zero generalization error), the objective of GNN learning is to estimate the ground-Truth GNN parameters from the training data. To achieve this objective, we propose a learning algorithm that is built on tensor initialization and accelerated gradient descent. We then show that the proposed learning algorithm converges to the ground-Truth GNN model for the regression problem, and to a model sufficiently close to the ground-Truth for the binary classification problem. Moreover, for both cases, the convergence rate of the proposed learning algorithm is proven to be linear and faster than the vanilla gradient descent algorithm. We further explore the relationship between the sample complexity of GNNs and their underlying graph properties. Lastly, we provide numerical experiments to demonstrate the validity of our analysis and the effectiveness of the proposed learning algorithm for GNNs. © 2020 by the Authors All rights reserved.

关键词： Graph neural networks

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Day-Ahead Forecasting of Solar Irradiance & PV Power Output Through Statistical Machine Learning Methods

Day-Ahead Forecasting of Solar Irradiance & PV Power Output ...

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Saudi Arabia Smart Grid Conference (SASG)

作者： Yiannis Kamarianakis Yannis Pantazis Evangelia Kalligiannaki Theodoros D. Katsaounis Konstantinos Kotsovos Issam Gereige Marwan Abdullah Aqil Jamal Athanasios Tzavaras Data Science Group Institute of Applied and Computational Mathematics Foundation for Research and Technology-Hellas Greece Dept. of Mathematics and Applied Mathematics University of Crete Greece Carbon Management Division Saudi Aramco R&D Center Dhahran Saudi Arabia Computer Electrical and Mathematical Sciences and Engineering Division King Abdullah University of Science and Technology (KAUST) Thuwal Saudi Arabia

Energy production from solar photovoltaic (PV) plants is unpredictable, mainly due to the stochastic formation and movement of clouds or aerosol - dust particles which scatter or disperse solar radiation. Accurate forecasts of PV output are essential to Distribution and Transportation System Operators as they assist efficient solar energy trading and management of electricity grids. This work evaluates an autoregressive, computationally-light KNN-regression scheme (TSFKNN) for hourly, day-ahead forecasts of solar irradiance and energy yield of various PV technologies. The model is being tested and validated using data measured in Thuwal, Saudi Arabia. The available measured records span a 60-month period. The developed forecasting models are designed for online systems and provide increased levels of accuracy and low computational cost. Several parametric and nonparametric specifications, coupled with conventional versus outlier-robust estimation procedures are tested, in order to derive an optimal month-specific daily profile (MDP). Current results demonstrate that including intraday variability to the monthly-based irradiance models achieve improved predictive accuracy between 10% and 25% on average.

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A new smart-cropping pipeline for prostate segmentation using deep learning networks

arXiv

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arXiv 2021年

作者： Zaridis, Dimitrios G. Mylona, Eugenia Tachos, Nikolaos S. Marias, Kostas Papanikolaou, Nikolaos Tsiknakis, Manolis Fotiadis, Dimitrios I. The Dept. of Biomedical Research FORTH-IMBB Ioannina Greece The Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering University of Ioannina IoanninaGR 45110 Greece The Institute of Computer Science FORTH Heraklion Greece The Dept. of Electrical and Computer Engineering Hellenic Mediterranean University Greece The Centre for the Unknown Champalimaud Foundation Clinical Computational Imaging Group Lisbon Portugal Crete Heraklion Greece The Dept. of Biomedical Research Institute of Molecular Biology and Biotechnology FORTH Ioannina Greece The Dept. of Materials Science and Engineering Unit of Medical Technology and Intelligent Information Systems University of Ioannina IoanninaGR 45110 Greece

Prostate segmentation from magnetic resonance imaging (MRI) is a challenging task. In recent years, several network architectures have been proposed to automate this process and alleviate the burden of manual annotation. Although the performance of these models has achieved promising results, there is still room for improvement before these models can be used safely and effectively in clinical practice. One of the major challenges in prostate MR image segmentation is the presence of class imbalance in the image labels where the background pixels dominate over the prostate. In the present work we propose a DL-based pipeline for cropping the region around the prostate from MRI images to produce a more balanced distribution of the foreground pixels (prostate) and the background pixels and improve segmentation accuracy. The effect of DL-cropping for improving the segmentation performance compared to standard center-cropping is assessed using five popular DL networks for prostate segmentation, namely U-net, U-net+, Res Unet++, Bridge U-net and Dense U-net. The proposed smart-cropping outperformed the standard center cropping in terms of segmentation accuracy for all the evaluated prostate segmentation networks. In terms of Dice score, the highest improvement was achieved for the Unet+ and ResU-net++ architectures corresponding to 8.9% and 8%, respectively. © 2021, CC BY.

关键词： Image segmentation

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Correlation Analysis with Experimental Procedures and Peak Structures in Xps Spectral Round-Robin Tests on Mno Powder Sample

SSRN

引用

SSRN 2022年

作者： Murakami, Ryo Harada, Yoshitomo Sonobayashi, Yutaka Oji, Hiroshi Makino, Hisao Tanaka, Hiromi Taguchi, Hideyuki Sakamoto, Takanori Morita, Haruka Wakamori, Akihiko Kibe, Naoko Nishida, Shinsuke Nagata, Kenji Shinotsuka, Hiroshi Shouno, Hayaru Yoshikawa, Hideki The University of Electro-Communications Graduate School of Informatics and Engineering Chofu182-8585 Japan National Institute for Materials Science Research and Services Division of Materials Data and Integrated System Ibaraki Tsukuba305-0044 Japan Kyoto University Department of Materials Science and Engineering Kyoto606-8501 Japan Nagoya University Synchrotron radiation Research center Nagoya464-8603 Japan Kochi University of Technology School of Systems Engineering Japan National Institute of Technology Yonago College Department of Electrical and Computer Engineering Japan Nihon Parkerizing Co. Ltd Central Research Laboratories Kanagawa 254-0012 Japan JX Nippon Mining & Metals Corporation Technology Development Center Japan Melco Semiconductor Engineering Corporation Chemical Analysis & Evaluation Engineering Dept. Fukuoka Japan Kobelco Welding TechnoSolutions Co. Technical Solution Dept. Ltd Kanagawa 251-8551 Japan Furukawa Electric Co. Ltd Analysis Technology Center Sustainable Technology Laboratorie Kanagawa 220-0073 Japan

Spectral measurements provide valuable information on the electronic states and crystal structures of materials. In particular, X-ray photoelectron spectroscopy (XPS) can facilitate analysis of the chemical bond state of material surfaces. In the analysis of XPS spectra, the spectrum to be measured is typically compared to a reference spectrum measured on a known single-phase sample. Therefore, establishing a reference spectral database is imperative. In this study, round-robin tests of XPS spectra are performed with the aim of obtaining recommended XPS reference spectra and experimental procedures, including preprocessing. Determining the variations in peak structures between instrument- and sample-derived spectral shapes, and quantitatively discussing them in round-robin tests of XPS spectra are challenging. Therefore, an analysis method that automatically determines the above mentioned variations and extracts the samplederived common spectral structures is developed. This study presents a new approach to the quantitative analysis of round-robin tests of XPS spectra. The required common © 2022, The Authors. All rights reserved.

关键词： X ray photoelectron spectroscopy

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