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Learning to reconstruct and understand indoor scenes from sparse views

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arXiv 2019年

作者： Yang, Jingyu Xu, Ji Li, Kun Lai, Yu-Kun Yue, Huanjing Lu, Jianzhi Wu, Hao Liu, Yebin School of Electrical and Information Engineering Tianjin University Tianjin300072 China Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin300350 China School of Computer Science and Informatics Cardiff University Wales United Kingdom 3vjia company Department of Automation Tsinghua University Beijing10084 China

This paper proposes a new method for simultaneous 3D reconstruction and semantic segmentation of indoor scenes. Unlike existing methods that require recording a video using a color camera and/or a depth camera, our method only needs a small number of (e.g., 3-5) color images from uncalibrated sparse views as input, which greatly simplifies data acquisition and extends applicable scenarios. Since different views have limited overlaps, our method allows a single image as input to discern the depth and semantic information of the scene. The key issue is how to recover relatively accurate depth from single images and reconstruct a 3D scene by fusing very few depth maps. To address this problem, we first design an iterative deep architecture, Iter-Net, that estimates depth and semantic segmentation alternately, so that they benefit each other. To deal with the little overlap and non-rigid transformation between views, we further propose a joint global and local registration method to reconstruct a 3D scene with semantic information from sparse *** also make available a new indoor synthetic dataset simultaneously providing photorealistic high-resolution RGB images, accurate depth maps and pixel-level semantic labels for thousands of complex layouts, useful for training and evaluation. Experimental results on public datasets and our dataset demonstrate that our method achieves more accurate depth estimation, smaller semantic segmentation errors and better 3D reconstruction results, compared with stateof-the-art methods. Copyright © 2019, The Authors. All rights reserved.

关键词： Cameras

Collision-free kinematics for redundant manipulators in dynamic scenes using optimal reciprocal velocity obstacles

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arXiv 2018年

作者： Zhao, Liangliang Zhao, Jingdong Liu, Hong Manocha, Dinesh State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150001 China Department of Computer Science and Electrical & Computer Engineering University of Maryland at College Park MD United States

We present a novel algorithm for collision-free kinematics of multiple manipulators in a shared workspace with moving obstacles. Our optimization-based approach simultaneously handles collision-free constraints based on reciprocal velocity obstacles and inverse kinematics constraints for high-DOF manipulators. We present an efficient method based on particle swarm optimization that can generate collision-free configurations for each redundant manipulator. Furthermore, our approach can be used to compute safe and oscillation-free trajectories in a few milli-seconds. We highlight the real-time performance of our algorithm on multiple Baxter robots with 14-DOF manipulators operating in a workspace with dynamic obstacles. Videos are available at https://***/view/collision-free-kinematics. Copyright © 2018, The Authors. All rights reserved.

关键词： Inverse problems

Embedded whispering-gallery mode microsphere resonator in a tapered hollow annular core fiber

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Photonics Research 2018年第12期6卷 1124-1129页

作者： JIAWEI WANG XIAOBEI ZHANG MING YAN LEI YANG FENGYU HOU WEN SUN XIAOTONG ZHANG LIBO YUAN HAI XIAO TINGYUN WANG Key Laboratory of Specialty Fiber Optics and Optical Access Networks Joint International Research Laboratory of Specialty Fiber Optics and Advanced Communication Shanghai Institute for Advanced Communication and Data Science Shanghai University The Key Laboratory of In-Fiber Integrated Optics Ministry of Education College of Science Harbin Engineering University School of Electronic Engineering and Automation Guilin University of Electronic Technology Department of Electrical and Computer Engineering Clemson University

We propose and demonstrate a tapered hollow annular core fiber (HACF) coupler for excitation of whisperinggallery modes (WGMs) of an embedded microsphere resonator. The coupler is simply fabricated by fusion splicing of a segment of HACF with the single-mode fiber (SMF), and then improved by tapering the splicing joint to reduce the cone-apex angle. Therefore, the coupling efficiency from the SMF to the HACF is enhanced to excite various WGMs via evanescent field coupling. Normal positive, negative symmetrical Lorentzian and asymmetric Fano line shapes can be obtained by varying the resonator size and location. Another interesting phenomenon is observed that a higher Q-factor mode in a lower Q-factor mode has a contrast as high as 58. Temperature sensing with good stability is also demonstrated. This embedded WGM microsphere resonator in the tapered HACF is expected to promote environmental adaptability in practical applications due to its simplicity and

关键词： Single mode fiber (SMF) cavity Embedded WGM microsphere resonator

Deep Learning based Downlink Channel Prediction for FDD Massive MIMO System

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arXiv 2019年

作者： Yang, Yuwen Gao, Feifei Li, Geoffrey Ye Jian, Mengnan Department of Automation Tsinghua University Beijing100084 China Key Laboratory of Digital TV System of Guangdong Province and Shenzhen City Research Institute of Tsinghua University in Shenzhen Shenzhen518057 China School of Electrical and Computer Engineering Georgia Institute of Technology AtlantaGA United States Department of Algorithm Wireless Product R&D Institute ZTE Corporation Beijing100029 China

In a frequency division duplexing (FDD) massive multiple-input multiple-output (MIMO) system, the acquisition of downlink channel state information (CSI) at base station (BS) is a very challenging task due to the overwhelming overheads required for downlink training and uplink feedback. In this paper, we reveal a deterministic uplink-to-downlink mapping function when the position-to-channel mapping is bijective. Motivated by the universal approximation theorem, we then propose a sparse complex-valued neural network (SCNet) to approximate the uplink-to-downlink mapping function. Different from general deep networks that operate in the real domain, the SCNet is constructed in the complex domain and is able to learn the complex-valued mapping function by off-line training. After training, the SCNet is used to directly predict the downlink CSI based on the estimated uplink CSI without the need of either downlink training or uplink feedback. Numerical results show that the SCNet achieves better performance than general deep networks in terms of prediction accuracy and exhibits remarkable robustness over complicated wireless channels, demonstrating its great potential for practical deployments. Copyright © 2019, The Authors. All rights reserved.

关键词： Channel state information

Naive gabor networks for hyperspectral image classification

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arXiv 2019年

作者： Liu, Chenying Li, Jun He, Lin Plaza, Antonio Li, Shutao Li, Bo Guangdong Provincial Key Laboratory of Urbanization and Geo-simulation School of Geography and Planning Sun Yat-sen University Guangzhou510275 China School of Automation Science and Engineering South China University of Technology Guangzhou510640 China College of Electrical and Information Engineering Hunan University Changsha410082 China Hyperspectral Computing Laboratory Department of Technology of Computers and Communications Escuela Politécnica University of Extremadura CáceresE-10071 Spain Beijing Key Laboratory of Digital Media School of Computer Science and Engineering State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191 China

Recently, many convolutional neural network (CNN) methods have been designed for hyperspectral image (HSI) classification since CNNs are able to produce good representations of data, which greatly benefits from a huge number of parameters. However, solving such a high-dimensional optimization problem often requires a large amount of training samples in order to avoid overfitting. Additionally, it is a typical non-convex problem affected by many local minima and flat regions. To address these problems, in this paper, we introduce naive Gabor Networks or Gabor-Nets which, for the first time in the literature, design and learn CNN kernels strictly in the form of Gabor filters, aiming to reduce the number of involved parameters and constrain the solution space, and hence improve the performances of CNNs. Specifically, we develop an innovative phase-induced Gabor kernel, which is trickily designed to perform the Gabor feature learning via a linear combination of local low-frequency and high-frequency components of data controlled by the kernel phase. With the phase-induced Gabor kernel, the proposed Gabor-Nets gains the ability to automatically adapt to the local harmonic characteristics of the HSI data and thus yields more representative harmonic features. Also, this kernel can fulfill the traditional complex-valued Gabor filtering in a real-valued manner, hence making Gabor-Nets easily perform in a usual CNN thread. We evaluated our newly developed Gabor-Nets on three well-known HSIs, suggesting that our proposed Gabor-Nets can significantly improve the performance of CNNs, particularly with a small training set. Copyright © 2019, The Authors. All rights reserved.

关键词： Spectroscopy

Semi-Global Output Containment Control of Linear Multi-Agent Systems with Actuator Saturation

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Semi-Global Output Containment Control of Linear Multi-Agent...

Chinese Control Conference (CCC)

作者： Liangren Shi Zongli Lin Department of Automation Key Laboratory of System Control Shanghai China Charles L. Brown Department of Electrical and Computer Engineering University of Virginia Charlottesville U.S.A.

In this paper, we study the semi-global output containment control problem of a multi-agent system in the presence of actuator saturation over a directed network. The leader and follower agents are of different dynamics, and the follower agents are affected by disturbances generated by the leader agents through directed paths in the communication topology. The control objective of the output containment control is to drive the output trajectories of the follower agents to converge to the convex hull spanned by the outputs of the leader agents in the presence of disturbances. Low gain feedback containment protocols are constructed based on the output regulation approach and distributed observers. It is shown that semi-global output containment control can be achieved by the proposed protocols, if each follower agent is connected to at least one leader agent through a directed path in the communication topology. Simulation results verify the theoretical conclusions.

关键词： Multi-agent systems Observers Actuators Protocols Topology Trajectory Eigenvalues and eigenfunctions

Automated stroke lesion detection and diagnosis system

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Automated stroke lesion detection and diagnosis system

2017 International MultiConference of Engineers and computer Scientists, IMECS 2017

作者： Saad, N. Mohd Noor, N.S.M. Abdullah, A.R. Muda, Sobri Muda, A.F. Rahman, N. N. S. Abdul Center for Robotics and Industrial Automation Faculty of Electronics and Computer Engineering Universiti Teknikal Malaysia Melaka Malaysia Center for Robotics and Industrial Automation Faculty of Electronics and Computer Engineering Universiti Teknikal Malaysia Melaka Malaysia Center for Robotics and Industrial Automation Faculty of Electrical Engineering Universiti Teknikal Malaysia Melaka Malaysia Radiology Department Fakulti Perubatan dan Sains Kemanusiaan Universiti Putra Malaysia Malaysia Faculty for Electronics and Computer Engineering Universiti Teknikal Malaysia Melaka Malaysia Center for Robotics and Industrial Automation Faculty of Electronics and Computer Engineering Universiti Teknikal Malaysia Melaka Malaysia

ISBN: (纸本)9789881404732

This study proposes a technique for automated detection and diagnosis of stroke lesions based on diffusion-weighted imaging (DWI). The technique consists of several stages which are pre-processing, segmentation, feature extraction, and classification. The proposed analytical framework of this study is based on Fuzzy C-Means (FCM) segmentation, statistical parameters for features extraction and rule-based classification. The three-dimensional (3D) view is developed to enable observing directions of the gained 3D structure along the three axes. The segmentation results have been validated by using Jaccard and Dice indices, false positive rate (FPR), and false negative rate (FNR). The results for Jaccard, Dice, FPR and FNR of acute stroke are 0.7, 0.84, 0.049 and 0.205, respectively. The accuracy for acute stroke is 90% and chronic stroke is 70%, while the sensitivity and the specificity is 84.38% and 83.33%, respectively.

关键词： Extraction

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

A Novel Forwarding Method for Full-Duplex Two-Way Relay Networks

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China Communications 2017年第4期14卷 65-72页

作者： Shuangshuang Han Xiang Cheng Liuqing Yang The State Key Laboratory of Management and Control for Complex Systems Institute of Automation Chinese Academy of SciencesBeijing China and Qingdao Academy of Intelligent Industries The State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Engineering and Computer Science Peking University Department of Electrical and Computer Engineering Colorado State University

An estimate-and-forward(EF) scheme for single-input single-output(SISO) and multiple-input multiple-output(MIMO) full-duplex two-way relay networks is proposed and analyzed. The relay estimates the received signal from two terminal nodes by a minimum mean squared error(MMSE) estimation and forwards a scaled version of the MMSE estimate to the destination. The proposed EF outperforms conventional amplify-and-forward(AF) and decode-and-forward(DF) across all signal-to-noise ratio(SNR) region. Because its computational complexity is high for relays with a large number of antennas(large MIMO) and/or high order constellations, an approximate EF scheme, called list EF, are thus proposed to reduce the computational complexity. The proposed list EF computes a candidate list for the MMSE estimate by using a sphere decoder, and it approaches the performance of the exact EF relay at a negligible performance loss. The proposed forwarding approach also could be used to other relay networks, such as half-duplex, one-way or massive MIMO relay networks.

关键词： estimate-and-forward full-duplex two-way relay wireless communications