检索结果-内蒙古大学图书馆

Harmonized-Multinational qEEG norms (HarMNqEEG)

学校读者我要写书评

暂无评论

NEUROIMAGE 2022年 256卷 119190-119190页

作者： Li, Min Wang, Ying Lopez-Naranjo, Carlos Hu, Shiang Reyes, Ronaldo Cesar Garcia Paz-Linares, Deirel Areces-Gonzalez, Ariosky Hamid, Aini Ismafairus Abd Evans, Alan C. Savostyanov, Alexander N. Calzada-Reyes, Ana Villringer, Arno Tobon-Quintero, Carlos A. Garcia-Agustin, Daysi Yao, Dezhong Dong, Li Aubert-Vazquez, Eduardo Reza, Faruque Razzaq, Fuleah Abdul Omar, Hazim Abdullah, Jafri Malin Galler, Janina R. Ochoa-Gomez, John F. Prichep, Leslie S. Galan-Garcia, Lidice Morales-Chacon, Lilia Valdes-Sosa, Mitchell J. Trondle, Marius Zulkifly, Mohd Faizal Mohd Rahman, Muhammad Riddha Bin Abdul Milakhina, Natalya S. Langer, Nicolas Rudych, Pavel Koenig, Thomas Virues-Alba, Trinidad A. Lei, Xu Bringas-Vega, Maria L. Bosch-Bayard, Jorge F. Valdes-Sosa, Pedro Antonio [a]The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation School of Life Science and Technology University of Electronic Science and Technology of China Chengdu China [b]Cuban Center for Neurocience La Habana Cuba [c]McGill Centre for Integrative Neuroscience Ludmer Centre for Neuroinformatics and Mental Health Montreal Neurological Institute Canada [d]Department of Neurosciences School of Medical Sciences Universiti Sains Malaysia Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [e]Brain and Behaviour Cluster School of Medical Sciences Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [f]Hospital Universiti Sains Malaysia Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [g]Humanitarian Institute Novosibirsk State University Novosibirsk 630090 Russia [h]Laboratory of Psychophysiology of Individual Differences Federal State Budgetary Scientific Institution Scientific Research Institute of Neurosciences and Medicine Novosibirsk 630117 Russia [i]Laboratory of Psychological Genetics at the Institute of Cytology and Genetics Siberian Branch of the Russian Academy of Sciences Novosibirsk 630090 Russia [j]University of Pinar del Río “Hermanos Saiz Montes de Oca” Pinar del Río Cuba [k]Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany [l]Department of Cognitive Neurology University Hospital Leipzig Leipzig Germany [m]Center for Stroke Research Charité-Universitätsmedizin Berlin Berlin Germany [n]Grupo Neuropsicología y Conducta - GRUNECO Faculty of Medicine Universidad de Antioquia Colombia [o]Research Department Institución Prestadora de Servicios de Salud IPS Universitaria Colombia [p]The Cuban center aging longevity and health Havana Cuba [q]Research Unit of NeuroInformation Chinese Academy of Medical Sciences Chengdu 2019RU035 China [r]School of Electrical Engineering Zhengzhou University Zhengzhou 4500

This paper extends frequency domain quantitative electroencephalography (qEEG) methods pursuing higher sensitivity to detect Brain Developmental Disorders. Prior qEEG work lacked integration of cross-spectral information omitting important functional connectivity descriptors. Lack of geographical diversity precluded accounting for site-specific variance, increasing qEEG nuisance variance. We ameliorate these weaknesses. (i) Create lifespan Riemannian multinational qEEG norms for cross-spectral tensors. These norms result from the HarMNqEEG project fostered by the Global Brain Consortium. We calculate the norms with data from 9 countries, 12 devices, and 14 studies, including 1564 subjects. Instead of raw data, only anonymized metadata and EEG cross spectral tensors were shared. After visual and automatic quality control, developmental equations for the mean and standard deviation of qEEG traditional and Riemannian DPs were calculated using additive mixed-effects models. We demonstrate qEEG "batch effects " and provide methods to calculate harmonized z-scores. (ii) We also show that harmonized Riemannian norms produce z-scores with increased diagnostic accuracy predicting brain dysfunction produced by malnutrition in the first year of life and detecting COVID induced brain dysfunction. (iii) We offer open code and data to calculate different individual z-scores from the HarMNqEEG dataset. These results contribute to developing bias-free, low-cost neuroimaging technologies applicable in various health settings.

关键词： Quantitative EEG EEG cross-spectrum Riemannian geometry Batch effects Z-score Harmonization Malnutrition Covid induced brain dysfunction Developmental Brain Chart

OpEn: Code Generation for Embedded Nonconvex Optimization *

学校读者我要写书评

暂无评论

IFAC-PapersOnLine 2020年第2期53卷 6548-6554页

作者： Pantelis Sopasakis Emil Fresk Panagiotis Patrinos Queen’s University Belfast School of Electronics Electrical Engineering and Computer Science (EEECS) and Centre for Intelligent and Autonomous Manufacturing Systems (i-AMS) Ashby Building Stranmillis Road BT9 5AG Belfast Northern Ireland WideFind AB Aurorum 1C 977 75 Luleå Sweden KU Leuven Department of Electrical Engineering (ESAT) STADIUS Center for Dynamical Systems Signal Processing and Data Analytics & Optimization in Engineering (OPTEC) Kasteelpark Arenberg 10 3001 Leuven Belgium

We present Optimization Engine (OpEn): an open-source code generation framework for real-time embedded nonconvex optimization, which implements a novel numerical method. OpEn combines the proximal averaged Newton-type method for optimal control (PANOC) with the penalty and augmented Lagrangian methods to compute approximate stationary points of nonconvex problems. The proposed method involves very simple algebraic operations such as vector products, has a low memory footprint and exhibits very good convergence properties that allow the solution of nonconvex problems on embedded devices. OpEn’s core solver is written is Rust — a modern, high-performance, memory-safe and thread-safe systems programming language — while users can call it from Python, MATLAB, C, C++, ROS or over a TCP socket.

关键词： Embedded numerical optimization nonconvex optimization problems code generation model predictive control moving horizon estimation Rust Robot Operating System

Locally decodable index codes

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Natarajan, Lakshmi Krishnan, Prasad Lalitha, V. Dau, Hoang Department of Electrical Engineering Indian Institute of Technology Hyderabad India Signal Processing & Communications Research Center International Institute of Information Technology Hyderabad India School of Science RMIT University Australia

An index code for broadcast channel with receiver side information is locally decodable if each receiver can decode its demand by observing only a subset of the transmitted codeword symbols instead of the entire codeword. Local decodability in index coding is known to reduce receiver complexity, improve user privacy and decrease decoding error probability in wireless fading channels. Conventional index coding solutions assume that the receivers observe the entire codeword, and as a result, for these codes the number of codeword symbols queried by a user per decoded message symbol, which we refer to as locality, could be large. In this paper, we pose the index coding problem as that of minimizing the broadcast rate for a given value of locality (or vice versa) and designing codes that achieve the optimal trade-off between locality and rate. We identify the optimal broadcast rate corresponding to the minimum possible value of locality for all single unicast problems. We present new structural properties of index codes which allow us to characterize the optimal trade-off achieved by: vector linear codes when the side information graph is a directed cycle;and scalar linear codes when the minrank of the side information graph is one less than the order of the problem. We also identify the optimal trade-off among all codes, including non-linear codes, when the side information graph is a directed 3-cycle. Finally, we present techniques to design locally decodable index codes for arbitrary single unicast problems and arbitrary values of locality. Copyright © 2019, The Authors. All rights reserved.

关键词： Decoding

A Level Set Approach to Online Sensing and Trajectory Optimization with Time Delays ⁎

学校读者我要写书评

暂无评论

IFAC-PapersOnLine 2019年第8期52卷 301-306页

作者： Matthew R. Kirchner Image and Signal Processing Branch Research Directorate Naval Air Warfare Center Weapons Division China Lake CA 93555 USA Electrical and Computer Engineering Department University of California Santa Barbara CA 93106 USA

Presented is a method to compute certain classes of Hamilton-Jacobi equations that result from optimal control and trajectory generation problems with time delays. Many robotic control and trajectory problems have limited information of the operating environment a priori and must continually perform online trajectory optimization in real time after collecting measurements. The sensing and optimization can induce a significant time delay, and must be accounted for when computing the trajectory. This paper utilizes the generalized Hopf formula, which avoids the exponential dimensional scaling typical of other numerical methods for computing solutions to the Hamilton-Jacobi equation. We present as an example a robot that incrementally predicts a communication channel from measurements as it travels. As part of this example, we introduce a seemingly new generalization of a non-parametric formulation of robotic communication channel estimation. New communication measurements are used to improve the channel estimate and online trajectory optimization with time-delay compensation is performed.

关键词： Time Delay Systems Hamilton-Jacobi Equation Generalized Hopf Formula Viscosity Solution Optimal Control Communication Seeking Robotics

Reachability as a unifying framework for computing helicopter safe operating conditions and autonomous emergency landing

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Kirchner, Matthew R. Ball, Eddie Hoffler, Jacques Gaublomme, Don Image and Signal Processing Branch Code D5J1000 Naval Air Warfare Center Weapons Division China LakeCA United States Electrical and Computer Engineering Department University of California Santa BarbaraCA93106 United States Flight Vehicle Modeling and Simulation Branch Naval Air Warfare Center Aircraft Division Patuxent RiverMD United States

We present a numeric method to compute the safe operating flight conditions for a helicopter such that we can ensure a safe landing in the event of a partial or total engine failure. The unsafe operating region is the complement of the backwards reachable tube, which can be found as the sub-zero level set of the viscosity solution of a Hamilton-Jacobi (HJ) equation. Traditionally, numerical methods used to solve the HJ equation rely on a discrete grid of the solution space and exhibit exponential scaling with dimension, which is problematic for the high-fidelity dynamics models required for accurate helicopter modeling. We avoid the use of spatial grids by formulating a trajectory optimization problem, where the solution at each initial condition can be computed in a computationally efficient manner. The proposed method is shown to compute an autonomous landing trajectory from any operating condition, even in non-cruise flight conditions. Copyright © 2020, The Authors. All rights reserved.

关键词： Numerical methods

Image Captioning with Sparse Recurrent Neural Network

学校读者我要写书评

暂无评论

arXiv 2019年

作者： Tan, Jia Huei Chan, Chee Seng Chuah, Joon Huang Center of Image and Signal Processing Faculty of Computer Science and Technology University of Malaya Kuala Lumpur50603 Malaysia Department of Electrical Engineering Faculty of Engineering University of Malaya Kuala Lumpur50603 Malaysia

Recurrent Neural Network (RNN) has been widely used to tackle a wide variety of language generation problems and are capable of attaining state-of-the-art (SOTA) performance. However despite its impressive results, the large number of parameters in the RNN model makes deployment in mobile and embedded devices infeasible. Driven by this problem, many works have proposed a number of pruning methods to reduce the sizes of the RNN model. In this work, we propose an end-to-end pruning method for image captioning models equipped with visual attention. Our proposed method is able to achieve sparsity levels up to 97.5% without significant performance loss relative to the baseline (∼ 2% loss at 40× compression after fine-tuning). Our method is also simple to use and tune, facilitating faster development times for neural network practitioners. We perform extensive experiments on the popular MS-COCO dataset in order to empirically validate the efficacy of our proposed method. Copyright © 2019, The Authors. All rights reserved.

关键词： Recurrent neural networks

Secure Edge Caching Placement and Delivery for Ultra-Reliable and Low-Latency Vehicular Networks

学校读者我要写书评

暂无评论

Secure Edge Caching Placement and Delivery for Ultra-Reliabl...

IEEE Globecom Workshops

作者： Haojun Yang Kuan Zhang Kan Zheng Yi Qian Intelligent Computing and Communications (IC) Lab Wireless Signal Processing and Networks (WSPN) Lab Key Laboratory of Universal Wireless Communications Ministry of Education Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering University of Nebraska-Lincoln Omaha NE USA

ISBN: (数字)9781728109602

ISBN: (纸本)9781728109619

To meet the urgent requirements of ultra-reliable and low-latency communications (URLLCs) in vehicular networks, vehicular edge caching (VEC) becomes a promising solution to reduce backhaul costs and mitigate network congestion. However, the data requested by vehicular users may be maliciously tampered by attackers during the caching delivery phase. In this paper, we investigate the problem of secure caching placement and delivery for a VEC URLLC system. Specifically, we first analyze the lower bound of spectral efficiency for URLLCs and security metric, i.e., vulnerability. Then, an optimization problem of minimizing the maximum vulnerability among all vehicular users is formulated based on the analysis. Subsequently, we propose a secure caching placement and delivery algorithm to obtain the solutions of our formulated problem by reasonable complexity. Simulation results show that the proposed algorithm can significantly improve the performance of security for the VEC URLLC system.

关键词： Security Optimization Roads Reliability Fading channels signal to noise ratio Antennas

Livelayer: A semi-automatic software program for segmentation of layers and diabetic macular edema in optical coherence tomography images

学校读者我要写书评

暂无评论

arXiv 2020年

作者： Montazerin, Mansooreh Sajjadifar, Zahra Pour, Elias Khalili Riazi-Esfahani, Hamid Mahmoudi, Tahereh Rabbani, Hossein Movahedian, Hossein Dehghani, Alireza Akhlaghi, Mohammadreza Kafieh, Rahele Department of Electrical and Computer Engineering Isfahan University of Technology Isfahan Iran Retina Service Farabi Eye Hospital Tehran University of Medical Sciences Tehran Iran Department of Biomedical Systems and Medical Physics Tehran University of Medical Sciences Tehran Iran Medical Image and Signal Processing Research Center School of Advanced Technologies in Medicine Isfahan University of Medical Sciences Isfahan Iran Department of Ophthalmology Isfahan University of Medical Sciences Isfahan Iran

Given the capacity of Optical Coherence Tomography (OCT) imaging to display symptoms of a wide variety of eye diseases and neurological disorders, the need for OCT image segmentation and the corresponding data interpretation is latterly felt more than ever before. In this paper, we wish to address this need by designing a semi-automatic software program for applying reliable segmentation of 8 different macular layers as well as outlining retinal pathologies such as diabetic macular edema. The software accommodates a novel graph-based semi-automatic method, called "Livelayer" which is designed for straightforward segmentation of retinal layers and fluids. This method is chiefly based on Dijkstra's Shortest Path (SPF) algorithm and the Live-wire function together with some preprocessing operations on the to-be-segmented images. The software is indeed suitable for obtaining detailed segmentation of layers, exact localization of clear or unclear fluid objects and the ground truth, demanding far less endeavor in comparison to a common manual segmentation method. It is also valuable as a tool for calculating the irregularity index in deformed OCT images. The amount of time (seconds) that Livelayer required for segmentation of ILM, IPL-INL, OPL-ONL was much less than that for the manual segmentation, 5s for the ILM (minimum) and 15.57s for the OPL-ONL (maximum). The unsigned errors (pixels) between the semi-automatically labeled and gold standard data was on average 2.7, 1.9, 2.1 for ILM, IPL-INL, OPL-ONL, respectively. The Bland-Altman plots indicated perfect concordance between the Livelayer and the manual algorithm and that they could be used interchangeably. The repeatability error was around one pixel for the OPL-ONL and © 2020, CC BY.

关键词： Optical tomography

Why is the Winner the Best?

学校读者我要写书评

暂无评论

Why is the Winner the Best?

Conference on computer Vision and Pattern Recognition (CVPR)

作者： M. Eisenmann A. Reinke V. Weru M. D. Tizabi F. Isensee T. J. Adler S. Ali V. Andrearczyk M. Aubreville U. Baid S. Bakas N. Balu S. Bano J. Bernal S. Bodenstedt A. Casella V. Cheplygina M. Daum M. De Bruijne A. Depeursinge R. Dorent J. Egger D. G. Ellis S. Engelhardt M. Ganz N. Ghatwary G. Girard P. Godau A. Gupta L. Hansen K. Harada M. Heinrich N. Heller A. Hering A. Huaulmé P. Jannin A. E. Kavur O. Kodym M. Kozubek J. Li H. Li J. Ma C. Martín-Isla B. Menze A. Noble V. Oreiller N. Padoy S. Pati K. Payette T. Rädsch J. Rafael-Patiño V. Singh Bawa S. Speidel C. H. Sudre K. Van Wijnen M. Wagner D. Wei A. Yamlahi M. H. Yap C. Yuan M. Zenk A. Zia D. Zimmerer D. Aydogan B. Bhattarai L. Bloch R. Brüngel J. Cho C. Choi Q. Dou I. Ezhov C. M. Friedrich C. Fuller R. R. Gaire A. Galdran Á. García Faura M. Grammatikopoulou S. Hong M. Jahanifar I. Jang A. Kadkhodamohammadi I. Kang F. Kofler S. Kondo H. Kuijf M. Li M. Luu T. Martinčič P. Morais M. A. Naser B. Oliveira D. Owen S. Pang J. Park S. Park S. Płotka E. Puybareau N. Rajpoot K. Ryu N. Saeed A. Shephard P. Shi D. Štepec R. Subedi G. Tochon H. R. Torres H. Urien J. L. Vilaça K. A. Wahid H. Wang J. Wang L. Wang X. Wang B. Wiestler M. Wodzinski F. Xia J. Xie Z. Xiong S. Yang Y. Yang Z. Zhao K. Maier-Hein P. F. Jäger A. Kopp-Schneider L. Maier-Hein Division of Intelligent Medical Systems German Cancer Research Center (DKFZ) Heidelberg Germany Helmholtz Imaging German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Division of Biostatistics German Cancer Research Center (DKFZ) Heidelberg Germany Division of Medical Image Computing German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Engineering and Physical Sciences School of Computing University of Leeds Leeds UK Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Sierre Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Lausanne Switzerland Technische Hochschule Ingolstadt Ingolstadt Germany Center for Artificial Intelligence and Data Science for Integrated Diagnostics (AI2D) and Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA Department of Radiology University of Washington Seattle WA USA Department of Computer Science Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS) University College London London UK Universitat Autònoma de Barcelona & Computer Vision Center Barcelona Spain Division of Translational Surgical Oncology National Center for Tumor Diseases (NCT/UCC) Dresden Dresden Germany Department of Advanced Robotics Istituto Italiano di Tecnologia Italy Department of Electronics Information and Bioengineering Politecnico di Milano Milan Italy IT University of Copenhagen Copenhagen Denmark Department of General Visceral and Transplantation Surgery Heidelberg University Hospital Heidelberg Germany Department of Radiology and Nuc

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multicenter study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and post-processing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

关键词：