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Tunable superconducting josephson dielectric metamaterial

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arXiv 2019年

作者： Trepanier, M. Zhang, Daimeng Filippenko, L.V. Koshelets, V.P. Anlage, Steven M. Center for Nanophysics and Advanced Materials Department of Physics University of Maryland College ParkMD20742-4111 United States Department of Electrical and Computer Engineering University of Maryland College ParkMD20742-3285 United States Laboratory of Superconducting Devices for Signal Detection and Processing Kotelnikov Institute of Radio Engineering and Electronics Moscow125009 Russia

We demonstrate a low-dissipation dielectric metamaterial with tunable properties based on the Josephson effect. Superconducting wires loaded with regularly spaced Josephson junctions (critical current Ic ≈ 0.25 µA) spanning a K-band waveguide and aligned with the microwave electric fields create a superconducting dielectric metamaterial. Applied dc current tunes the cutoff frequency and effective permittivity of this unique electric metamaterial. The results are in agreement with an analytical model for microwave transmission through the artificial dielectric medium. Copyright © 2019, The Authors. All rights reserved.

关键词： Metamaterials

Bifocal technique applied to dual transmitarray antennas 12

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Bifocal technique applied to dual transmitarray antennas

12th European Conference on Antennas and Propagation, EuCAP 2018

作者： Martinez-De-Rioja, Eduardo Encinar, José A. Pino, Antonio Gonzalez-Valdes, Borja Hum, Sean V. Tienda, Carolina Toso, Giovanni Information Processing and Telecomm. Center Universidad Politécnica de Madrid Spain Department of Signal Theory and Communications Universidad de Vigo Spain Dept. of Electrical and Computer Engineering University of Toronto Canada Antenna Group Airbus Defence and Space Stevenage United Kingdom Electromagnetics Division European Space Agency Noordwijk Netherlands

ISBN: (纸本)9781785617911

The bifocal technique has been applied to dual transmitarray antennas in order to improve the performance of multi-beam satellite antennas in Ka-band. The use of a transmitarray design allows centered and rotationally symmetrical geometries to be used without blockage. Different dual transmitarray configurations have been studied for operation in transmission in Ka-band (20 GHz), and the bifocal technique has been evaluated for the generation of closely-spaced beams. The results of the simulations show that it is possible to achieve a high degree of beam compression, but at the cost of a reduction in the antenna radiation efficiency. © Institution of engineering and *** Rights Reserved.

关键词： Satellite antennas

Off-line correlator for antenna array over long baselines 8

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Off-line correlator for antenna array over long baselines

8th ESA International Workshop on Tracking, Telemetry and Command Systems for Space Applications, TTC 2019

作者： Ardito, Alessandro Maguire, Paul Marti, Salvador Maturo, Nicola Mercolino, Mattia Quibus, Laurent Vanhoenacker-Janvier, Danielle Abello, Ricard Aroumont, Anbazhagan Baldi, Marco Barbaglio, Francesco Chiaraluce, Franco Comoretto, Gianni Finocchiaro, Stefano Graziani, Alberto Arpsoft S.r.l. Rome Italy Co. Waterford Zelinda Ireland Ltd Ireland European Space Agency European Space Operations Center Darmstadt Germany University of Luxemburg Signal Processing and Satellite Communications Group Luxemburg Luxembourg European Space Agency European Space Research and Technology Center Noordwijk Netherlands Université Catholique de Louvain Electrical Engineering Department Louvain-la-Neuve Belgium Università Politecnica Delle Marche Dipartimento di Ingegneria dell'Informazione Ancona Italy Osservatorio di Arcetri INAF Firenze Italy Aurora Technology B.V. for ESA/ESTEC Noordwijk Netherlands

ISBN: (纸本)9781728136998

This paper illustrates the design, implementation and testing of a prototype of an off-line correlator for large dish antenna array, in the frame of the ESA-funded study 'Prototype of off-line Correlator for Arraying of Large Aperture Antennas' In order to improve the performance of downlink reception, a four 35-m antenna array is a valid alternative to a single large 70-m dish, offering the same performance with remarkable advantages, such as being more fail-safe against the failure of components. The concept is to acquire two or more replicas of the same signal at different Ground Stations and combine them after delay and phase correction, due mainly to media and clock offset. After a first study phase of the best combination and correlation methods, as well as of the requirements driven by the analysis of the error sources of delay and phase misalignment, the tool has been designed, implemented and successfully tested. Together with the correlator, a simulator has also been developed, in order to better test the performance of the combination tool. Tests with simulated, static (generated in ESOC) and real mission data have been carried out. The improvement in the signal-To-Noise Ratio has been demonstrated, and the information bits have been successfully reconstructed after the combination of the deep space signal acquired in Cebreros and Malargüe stations from Exomars and Mars Express spacecraft, endorsing the tool for an off-line use in the near future. © 2019 IEEE.

关键词： Correlation methods

Granular Computing: An Augmented Scheme of Degranulation Through a Modified Partition Matrix

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arXiv 2020年

作者： Xu, Kaijie Pedrycz, Witold Li, Zhiwu Xing, Mengdao School of Electro-Mechanical Engineering Xidian University Xi’an710071 China Department of remote sensing science and technology School of Electronic Engineering Xidian University Xi’an710071 China Department of Electrical and Computer Engineering University of Alberta EdmontonABT6R 2V4 Canada Systems Research Institute Polish Academy of Sciences Warsaw Poland Faculty of Engineering King Abdulaziz University Jeddah21589 Saudi Arabia Institute of Systems Engineering Macau University of Science and Technology Macau999078 China National Laboratory of Radar Signal Processing Xidian University Xi’an710071 China Collaborative Innovation Center of Information Sensing and Understanding Xidian University Xi’an710071 China National Key Laboratory of Microwave Imaging Technology Institute of Electronics Chinese Academy of Sciences Beijing100190 China

As an important technology in artificial intelligence Granular Computing (GrC) has emerged as a new multi-disciplinary paradigm and received much attention in recent years. Information granules forming an abstract and efficient characterization of large volumes of numeric data have been considered as the fundamental constructs of GrC. By generating centroids (prototypes) and partition matrix, fuzzy clustering is a commonly encountered way of information granulation. As a reverse process of granulation, degranulation involves data reconstruction completed on a basis of the granular representatives (decoding information granules into numeric data). Previous studies have shown that there is a relationship between the reconstruction error and the performance of the granulation process. Typically, the lower the degranulation error is, the better performance of granulation process becomes. However, the existing methods of degranulation usually cannot restore the original numeric data, which is one of the important reasons behind the occurrence of the reconstruction error. To enhance the quality of reconstruction (degranulation), in this study, we develop an augmented scheme through modifying the partition matrix. By proposing the augmented scheme, we dwell on a novel collection of granulation-degranulation mechanisms. In the constructed approach, the prototypes can be expressed as the product of the dataset matrix and the partition matrix. Then, in the degranulation process, the reconstructed numeric data can be decomposed into the product of the partition matrix and the matrix of prototypes. Both the granulation and degranulation are regarded as generalized rotation between the data subspace and the prototype subspace with the partition matrix and the fuzzification factor. By modifying the partition matrix, the new partition matrix is constructed through a series of matrix operations. We offer a thorough analysis of the developed scheme. Copyright © 2020, The Authors. Al

关键词： Granulation

Correction: Candidate variants in DNA replication and repair genes in early-onset renal cell carcinoma patients referred for germline testing

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BMC genomics 2023年第1期24卷 388页

作者： Elena V Demidova Ilya G Serebriiskii Ramilia Vlasenkova Simon Kelow Mark D Andrake Tiffiney R Hartman Tatiana Kent James Virtucio Gail L Rosen Richard T Pomerantz Roland L Dunbrack Erica A Golemis Michael J Hall David Y T Chen Mary B Daly Sanjeevani Arora Cancer Prevention and Control Program Fox Chase Cancer Center 333 Cottman Avenue Philadelphia PA 19111 USA. Kazan Federal University Kazan 420008 Russia. Program in Cancer Signaling and Microenvironment Fox Chase Cancer Center Philadelphia PA 19111 USA. Department of Biochemistry and Molecular Biophysics University of Pennsylvania Philadelphia PA 19104 USA. Arcadia University Glenside PA USA. Department of Biochemistry & Molecular Biology Sidney Kimmel Cancer Center Thomas Jeferson University Philadelphia PA 19107 USA. Ecological and Evolutionary Signal-Processing and Informatics Laboratory Department of Electrical and Computer Engineering College of Engineering Drexel University Philadelphia PA 19104 USA. Department of Cancer and Cellular Biology Lewis Katz School of Medicine Philadelphia PA 19140 USA. Department of Clinical Genetics Fox Chase Cancer Center 333 Cottman Avenue Philadelphia PA 19111 USA. Department of Surgical Oncology Fox Chase Cancer Center Philadelphia PA 19111 USA. Cancer Prevention and Control Program Fox Chase Cancer Center 333 Cottman Avenue Philadelphia PA 19111 USA. Mary.Daly@fccc.edu. Department of Clinical Genetics Fox Chase Cancer Center 333 Cottman Avenue Philadelphia PA 19111 USA. Mary.Daly@fccc.edu. Cancer Prevention and Control Program Fox Chase Cancer Center 333 Cottman Avenue Philadelphia PA 19111 USA. Sanjeevani.Arora@fccc.edu. Department of Radiation Oncology Fox Chase Cancer Center Philadelphia PA 19111 USA. Sanjeevani.Arora@fccc.edu.

Publisher Correction: Brain imaging with portable low-field MRI

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Nature Reviews Bioengineering

Nature Reviews Bioengineering 2023年 680页

作者： W. Taylor Kimberly Annabel J. Sorby-Adams Andrew G. Webb Ed X. Wu Rachel Beekman Kevin N. Sheth Ritvij Bowry Steven J. Schiff Adam de Havenon Francis X. Shen Gordon Sze Pamela Schaefer Juan Eugenio Iglesias Matthew S. Rosen Department of Neurology and the Center for Genomic Medicine Massachusetts General Hospital and Harvard Medical School Boston MA USA Department of Radiology Leiden University Medical Center Leiden The Netherlands Laboratory of Biomedical Imaging and Signal Processing Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong SAR China Division of Neurocritical Care and Emergency Neurology Department of Neurology Yale New Haven Hospital and Yale School of Medicine Yale Center for Brain & Mind Health New Haven CT USA Departments of Neurosurgery and Neurology McGovern Medical School University of Texas Health Neurosciences Houston TX USA Department of Neurosurgery Yale School of Medicine New Haven CT USA Division of Vascular Neurology Department of Neurology Yale New Haven Hospital and Yale School of Medicine New Haven CT USA Harvard Medical School Center for Bioethics Harvard law School Boston MA USA Department of Psychiatry Massachusetts General Hospital Boston MA USA Department of Radiology Yale New Haven Hospital and Yale School of Medicine New Haven CT USA Division of Neuroradiology Department of Radiology Massachusetts General Hospital and Harvard Medical School Boston MA USA Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology Massachusetts General Hospital and Harvard Medical School Boston MA USA Centre for Medical Image Computing University College London London UK Computer Science and AI Laboratory Massachusetts Institute of Technology Boston MA USA

Neural signatures of motor skill in the resting brain

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arXiv 2019年

作者： Özdenizci, Ozan Meyer, Timm Wichmann, Felix Peters, Jan Schölkopf, Bernhard Çetin, Müjdat Grosse-Wentrup, Moritz Department of Electrical and Computer Engineering Northeastern University BostonMA United States Signal Processing and Information Systems Laboratory Faculty of Engineering and Natural Sciences Sabancı University Istanbul Turkey Max Planck Institute for Intelligent Systems Tübingen Germany Faculty of Science University of Tübingen Bernstein Center for Computational Neuroscience Tübingen Tübingen Germany FG Intelligente Autonome Systeme Technische Universität Darmstadt Darmstadt Germany Department of Electrical and Computer Engineering University of Rochester RochesterNY United States Research Group Neuroinformatics Faculty of Computer Science University of Vienna Vienna Austria

Stroke-induced disturbances of large-scale cortical networks are known to be associated with the extent of motor deficits. We argue that identifying brain networks representative of motor behavior in the resting brain would provide significant insights for current neurorehabilitation approaches. Particularly, we aim to investigate the global configuration of brain rhythms and their relation to motor skill, instead of learning performance as broadly studied. We empirically approach this problem by conducting a three-dimensional physical space visuomotor learning experiment during electroencephalographic (EEG) data recordings with thirty-seven healthy participants. We demonstrate that across-subjects variations in average movement smoothness as the quantified measure of subjects' motor skills can be predicted from the global configuration of resting-state EEG alpha-rhythms (8-14 Hz) recorded prior to the experiment. Importantly, this neural signature of motor skill was found to be orthogonal to (independent of) task- as well as to learning-related changes in alpha-rhythms, which we interpret as an organizing principle of the brain. We argue that disturbances of such configurations in the brain may contribute to motor deficits in stroke, and that reconfiguring stroke patients' brain rhythms by neurofeedback may enhance post-stroke neurorehabilitation. Copyright © 2019, The Authors. All rights reserved.

关键词： Electroencephalography

An Efficient Successive Cancellation Polar Decoder Based on New Folding Approaches 12

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An Efficient Successive Cancellation Polar Decoder Based on ...

2017 IEEE 12th International Conference on ASIC

作者： Xiao Liang Yechao She Harish Vangala Xiaohu You Chuan Zhang Emanuele Viterbo Lab of Efficient Architectures for Digital-communication and Signal-processing(LEADS) National Mobile Communications Research Laboratory Southeast University Department of Electronic and Computer Engineering HKUST Department of Electrical and Computer Systems Engineering Monash University

ISBN: (纸本)9781509066261;9781509066254

In this paper,an efficient successive cancellation(SC) polar decoder based on new folding approaches is *** main approach of this paper is called k-level decomposition with 2 *** k and p,the derived architecture can have a very low processing complexity with proper combinations of decomposition method and folding *** to state-of-the-art designs,hardware utilization ratio(HUR) of processing elements can be drastically improved with small latency ***,the memory complexity remains ***,decomposition and folding operations can also be applied to a family of hybrid polar *** validate efficiency of these approaches,two folded SC decoders with N = 64 and 1024 respectively are implemented with Altera Stratix V *** two demos require only 68.3% and 39.1%ALMs,compared to the non-decomposed SC decoder.

关键词： Polar code SC decoder k-level decomposition folding technique VLSI implementation

QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

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arXiv 2021年

作者： Mehta, Raghav Filos, Angelos Baid, Ujjwal Sako, Chiharu McKinley, Richard Rebsamen, Michael Dätwyler, Katrin Meier, Raphael Radojewski, Piotr Murugesan, Gowtham Krishnan Nalawade, Sahil Ganesh, Chandan Wagner, Ben Yu, Fang F. Fei, Baowei Madhuranthakam, Ananth J. Maldjian, Joseph A. Daza, Laura Gómez, Catalina Arbeláez, Pablo Dai, Chengliang Wang, Shuo Reynaud, Hadrien Mo, Yuanhan Angelini, Elsa Guo, Yike Bai, Wenjia Banerjee, Subhashis Pei, Linmin Murat, A.K. Rosas-González, Sarahi Zemmoura, Ilyess Tauber, Clovis Vu, Minh H. Nyholm, Tufve Löfstedt, Tommy Ballestar, Laura Mora Vilaplana, Veronica McHugh, Hugh Talou, Gonzalo Maso Wang, Alan Patel, Jay Chang, Ken Hoebel, Katharina Gidwani, Mishka Arun, Nishanth Gupta, Sharut Aggarwal, Mehak Singh, Praveer Gerstner, Elizabeth R. Kalpathy-Cramer, Jayashree Boutry, Nicolas Huard, Alexis Vidyaratne, Lasitha Rahman, Md Monibor Iftekharuddin, Khan M. Chazalon, Joseph Puybareau, Elodie Tochon, Guillaume Ma, Jun Cabezas, Mariano Llado, Xavier Oliver, Arnau Valencia, Liliana Valverde, Sergi Amian, Mehdi Soltaninejad, Mohammadreza Myronenko, Andriy Hatamizadeh, Ali Feng, Xue Dou, Quan Tustison, Nicholas Meyer, Craig Shah, Nisarg A. Talbar, Sanjay Weber, Marc-André Mahajan, Abhishek Jakab, Andras Wiest, Roland Fathallah-Shaykh, Hassan M. Nazeri, Arash Milchenko, Mikhail Marcus, Daniel Kotrotsou, Aikaterini Colen, Rivka Freymann, John Kirby, Justin Davatzikos, Christos Menze, Bjoern Bakas, Spyridon Gal, Yarin Arbel, Tal McGill University MontrealQC Canada Group University of Oxford Oxford United Kingdom University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine The University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University Institute of Diagnostic and Interventional Neuroradiology University of Bern Inselspital Bern University Hospital Bern Switzerland Department of Radiology University of Texas Southwestern Medical Center DallasTX United States Department of Bioengineering University of Texas DallasTX United States Advanced Imaging Research Center University of Texas Southwestern Medical Center DallasTX United States Universidad de los Andes Bogotá Colombia Data Science Institute Imperial College London London United Kingdom NIHR Imperial BRC ITMAT Data Science Group Imperial College London London United Kingdom Department of Brain Sciences Imperial College London London United Kingdom Machine Intelligence Unit Indian Statistical Institute Kolkata India Department of CSE University of Calcutta Kolkata India Department of Information Technology Uppsala University Uppsala Sweden Department of Diagnostic Radiology The University of Pittsburgh Medical Center PittsburghPA United States UMR U1253 iBrain Université de Tours Inserm Tours France Department of Radiation Sciences Umeå University Umeå Sweden Department of Computing Science Umeå University Umeå Sweden Signal Theory and Communications Department Universitat Politècnica de Catalunya Barcelona Tech Barcelona Spain Faculty of Medical and Health Sciences University of Auckland Auckland New Zealand Radiology Department Auckland City Hospital Auckland New Zealand Auckland Bioengineering Institute University of Auckland New Zealand Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://***/RagMeh11/QU-BraTS. © 2021, CC BY.

关键词： Benchmarking