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IEEE Conference on Vehicle Power and Propulsion (VPPC)

作者： Zhicong Huang Zhijian Fang Chi-Seng Lam Pui-In Mak Rui P. Martins State Key Laboratory of Analog and Mixed-Signal VLSI University of Macau Macao China School of Automation China University of Geosciences (Wuhan) Wuhan China Institute of Microelectronics University of Macau Macao China Department of Electrical and Computer Engineering University of Macau Macao China Instituto Superior Técnico Universidade de Lisboa Lisbon Portugal

Inductive power transfer (IPT) converter is increasingly popular as a form of wireless grid to vehicle (G2V) interface. Compared with conventional plug-in interface, wireless G2V interface without physical contact is more user- friendly and preferable. An IPT converter usually connects to a vehicle's DC-link providing a voltage bus for on-board DC/DC converters. To maintain the voltage bus without using a voltage regulator, the IPT converter can work at its native load-independent voltage output point. However, high efficiency performance and low output sensitivity to misalignment are always conflicting, which poses difficulties to design of the IPT converter as a practical wireless G2V interface. In this paper, the relationship among compensation parameters, power efficiency and output voltage sensitivity of a series/series- parallel (S/SP) compensated IPT converter is studied. Detailed design methodology is theoretically illustrated and experimentally verified to optimize the power efficiency and restrict the output sensitivity to an acceptable level, thus satisfying requirements of a wireless G2V interface.

关键词： Wireless communication Wireless sensor networks Couplings Voltage control Sensitivity Transfer functions Capacitors

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AIM 2019 challenge on real-world image super-resolution: Methods and results

arXiv

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arXiv 2019年

作者： Lugmayr, Andreas Danelljan, Martin Timofte, Radu Fritsche, Manuel Gu, Shuhang Purohit, Kuldeep Kandula, Praveen Suin, Maitreya Rajagopalan, A.N. Joon, Nam Hyung Won, Yu Seung Kim, Guisik Kwon, Dokyeong Hsu, Chih-Chung Lin, Chia-Hsiang Huang, Yuanfei Sun, Xiaopeng Lu, Wen Li, Jie Gao, Xinbo Bell-Kligler, Sefi Computer Vision Lab ETH Zurich Indian Institute Of Technology Madras India Image Communication & Signal Processing Laboratory Hanyang University Seoul Korea Republic of CVML Chung-Ang University Department Management Information Systems National Pingtung University of Science and Technology Department of Electrical Engineering National Cheng-Kung University Xidian University Weizmann Institute of Science Israel

This paper reviews the AIM 2019 challenge on real world super-resolution. It focuses on the participating methods and final results. The challenge addresses the real world setting, where paired true high and low-resolution images are unavailable. For training, only one set of source input images is therefore provided in the challenge. In Track 1: Source Domain the aim is to super-resolve such images while preserving the low level image characteristics of the source input domain. In Track 2: Target Domain a set of high-quality images is also provided for training, that defines the output domain and desired quality of the superresolved images. To allow for quantitative evaluation, the source input images in both tracks are constructed using artificial, but realistic, image degradations. The challenge is the first of its kind, aiming to advance the state-of-the-art and provide a standard benchmark for this newly emerging task. In total 7 teams competed in the final testing phase, demonstrating new and innovative solutions to the problem. Copyright © 2019, The Authors. All rights reserved.

关键词： Optical resolving power

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POLAR SEA ICE THICKNESS AND MELT POND FRACTION MEASUREMENTS WITH MULTI-FREQUENCY BISTATIC RADAR POLARIMETRIC AND INTERFEROMETRIC REFLECTOMETRY

POLAR SEA ICE THICKNESS AND MELT POND FRACTION MEASUREMENTS ...

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IEEE International Geoscience and Remote Sensing Symposium

作者： Son V. Nghiem Donald K. Perovich Christopher Polashenski Stephen T. Lowe Rashmi Shah Anthony J. Mannucci Adriano Camps Estel Cardellach Leung Tsang Jiyue Zhu Shurun Tan Jet Propulsion Laboratory California Institute of Technology Pasadena California USA Thayer School of Engineering Dartmouth College Hanover New Hampshire USA Cold Regions Research and Engineering Laboratory Hanover New Hampshire USA Department of Signal Theory and Communications Univ. Politècnica de Catalunya Barcelona Spain Instituto de Ciencias del Espacio (ICE-CSIC/IEEC) Barcelona Spain Department of Electrical Eng. and Computer Sci. Univ. of Michigan Ann Arbor Michigan USA

Arctic and Antarctic sea ice covers are in a sharp contrast in terms of characteristics, distributions, and processes with a drastic decrease in the Arctic versus the opposite increase in the Antarctic in a changing climate. In quantifying polar sea ice differences to address the contrasted sea ice behaviors, two key parameters are sea ice thickness and melt pond fraction, which remain challenging to measure extensively in time and in space with a sustainable approach. Here, we present a new paradigm for such measurements using bistatic radar reflectometry, thanks to developments of low-cost receivers to acquire reflected signals from numerous existing transmitter systems operated at multiple frequencies to be replenished and sustained indefinitely into the future. For sea ice thickness measurement to determine ice volume, reflected signals likely come from the bottom ice-water interface avoiding large errors inherent in current altimetry techniques due to uncertainty in free-board height and snow cover. Regarding melt pond faction on sea ice to estimate albedo and insolation, the bistatic reflection can be dominated by melt pond water with permittivity that is one order of magnitude larger compared to that of snow or ice. These are examined by a combination of numerical Kirchhoff (KA) simulator and Numerical Maxwell Model of 3D simulations (NMM3D) to preserve phase and amplitude information and thereby account for both coherent and incoherent effects. Physical insights from the rigorous theory for bistatic radar reflectometry will be valuable to develop future satellite missions to resolve cryospheric science issues concerning the polar sea ice differences.

关键词： Sea ice Reflectometry Sea measurements Thickness measurement Arctic Satellites

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Publisher Correction: Brain imaging with portable low-field MRI

Nature Reviews Bioengineering

引用

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

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058 mm2 13 Gbit/s inductorless analogue equaliser with low-frequency equalisation compensating 15 dB channel loss

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Electronics Letters 2018年第2期54卷 72-74页

作者： Balachandran, Arya Chen, Yong Choi, Pilsoon Boon, Chirn Chye VIRTUS School of Electrical and Electronic Engineering Nanyang Technological University Singapore State-Key Laboratory of Analog and Mixed-Signal University of Macau C-Macao China Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology CambridgeMA United States

An inductorless 13 Gbit/s analogue equaliser with an additional low-frequency equalisation (LFEQ) to counter the low-frequency channel losses is presented. An active feedback topology for the proposed equaliser together with negative capacitance circuit is used to extend the bandwidth to compensate a 15 dB channel loss at Nyquist. The LFEQ improves the measured data jitter of the conventional equaliser from 0.41 to 0.12 UI, for a pseudorandom binary sequence of 231 - 1. The proposed prototype was implemented in 65 nm CMOS, occupying active area of merely 0.058 mm2. It exhibits a power efficiency of 1.07 mW/Gitb/s under a supply voltage of 1.2 V and a BER -12.

关键词： Equalizers

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Linear Degrees of Freedom for K-user MISO Interference Channels With Blind Interference Alignment

Linear Degrees of Freedom for K-user MISO Interference Chann...

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作者： Yang, Heecheol Shin, Wonjae Lee, Jungwoo Wireless Signal Processing Laboratory Department of Electrical and Computer Engineering Seoul National University Seoul08826 Korea Republic of

In this paper, we characterize the degrees of freedom (DoF) for K-user M × 1 multiple-input single-output interference channels with reconfigurable antennas, which have N-preset modes at the receivers, assuming linear coding strategies in the absence of channel state information at the transmitters, i.e., blind interference alignment. Our linear DoF converse builds on the lemma that if a set of transmit symbols is aligned at their common unintended receivers, those symbols must have independent signal subspace at their corresponding receivers. This lemma arises from the inherent feature that channel state's changing patterns of the links towards the same receiver are always identical, assuming that the coherence time of the channel is long enough. We derive an upper bound for the linear sum DoF, and propose an achievable scheme that exactly achieves the linear sum DoF upper bound when both of the n∗/M=R1 and MK/n∗=R2 are integers, where n∗ denotes the optimal number of preset modes out of N preset modes. For the other cases, where either R1 or R2 is not an integer, we only give some guidelines how the interfering signals are aligned at the receivers to achieve the upper bound. As an extension, we also show the linear sum DoF upper bound for downlink/uplink cellular networks. © 2017 IEEE.

关键词： Channel state information

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

arXiv

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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

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

An Efficient Successive Cancellation Polar Decoder Based on ...

引用

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

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Neural Signatures of Motor Skill in the Resting Brain

Neural Signatures of Motor Skill in the Resting Brain

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IEEE International Conference on Systems, Man and Cybernetics

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

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.

关键词： Electroencephalography End effectors Task analysis Integrated circuits Stroke (medical condition) Planning Visualization

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Machine learning for forecasting mid price movement using limit order book data

arXiv

引用

arXiv 2018年

作者： Nousi, Paraskevi Tsantekidis, Avraam Passalis, Nikolaos Ntakaris, Adamantios Kanniainen, Juho Tefas, Anastasios Gabbouj, Moncef Iosifidis, Alexandros Department of Informatics Aristotle University of Thessaloniki Greece Laboratory of Signal Processing Tampere University of Technology Tampere Finland Laboratory of Industrial and Information Management Tampere University of Technology Tampere Finland Department of Engineering Electrical and Computer Engineering Aarhus University Denmark

Forecasting the movements of stock prices is one the most challenging problems in financial markets analysis. In this paper, we use Machine Learning (ML) algorithms for the prediction of future price movements using limit order book data. Two different sets of features are combined and evaluated: handcrafted features based on the raw order book data and features extracted by ML algorithms, resulting in feature vectors with highly variant dimensionalities. Three classifiers are evaluated using combinations of these sets of features on two different evaluation setups and three prediction scenarios. Even though the large scale and high frequency nature of the limit order book poses several challenges, the scope of the conducted experiments and the significance of the experimental results indicate that Machine Learning highly befits this task carving the path towards future research in this field. Copyright © 2018, The Authors. All rights reserved.

关键词： Forecasting

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