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arXiv 2019年

作者： Zhang, Xinlin Guo, Di Huang, Yiman Chen, Ying Wang, Liansheng Huang, Feng Qu, Xiaobo Department of Electronic Science Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance School of Electronic Science and Engineering National Model Microelectronics College Xiamen University Xiamen361005 China School of Computer and Information Engineering Fujian Provincial University Key Laboratory of Internet of Things Application Technology Xiamen University of Technology Xiamen361024 China Department of Computer Science School of Information Science and Engineering Xiamen University Xiamen361005 China Neusoft Medical System Shanghai200241 China

Parallel magnetic resonance imaging has served as an effective and widely adopted technique for accelerating scans. The advent of sparse sampling offers aggressive acceleration, allowing flexible sampling and better reconstruction. Nevertheless, faithfully reconstructing the image from limited data still poses a challenging task. Recent low-rank reconstruction methods exhibit superiority in providing a high-quality image. However, none of them employ the routinely acquired calibration data for improving image quality in parallel magnetic resonance imaging. In this work, an image reconstruction approach named STDLR-SPIRiT was proposed to explore the simultaneous two-directional low-rankness (STDLR) in the k-space data and to mine the data correlation from multiple receiver coils with the iterative self-consistent parallel imaging reconstruction (SPIRiT). The reconstruction problem was then solved with a singular value decomposition-free numerical algorithm. Experimental results of phantom and brain imaging data show that the proposed method outperforms the state-of-the-art methods in terms of suppressing artifacts and achieving the lowest error. Moreover, the proposed method exhibits robust reconstruction even when the auto-calibration signals are limited in parallel imaging. Overall the proposed method can be exploited to achieve better image quality for accelerated parallel magnetic resonance imaging. Copyright © 2019, The Authors. All rights reserved.

关键词： Magnetic resonance imaging

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A Fast HDL Model for Full-Custom FPGA Verification 12

A Fast HDL Model for Full-Custom FPGA Verification

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2017 IEEE 12th International Conference on ASIC

作者： Kaixuan Zhang Zhihua Feng Hao Zhou State Key Laboratory of ASIC & System Fudan University Beijing Institute of Computer Technology and Application

ISBN: (纸本)9781509066261;9781509066254

Verification occupies an important role in design of full-custom Field Programmable Gate Array(FPGA).In general,a full-custom FPGA is manually designed in the transistor ***,it is normally too slow for conventional Electronic Design Automation(EDA) software to run the full-chip transistor-level simulation and verification *** order to solve this problem,we propose a methodology to build a fast Hardware Description Language(HDL) model for FPGA,which can then be simulated by EDA tools in the multi-core mode efficiently and *** build an automatic platform based on the methodology to generate the HDL model and a verification platform to realize a fast and convenient functional verification for a full-custom *** an experiment,an internally developed twenty-million-transistor FDP FPGA [1] is used to verify the efficiency of the *** our experiment,it takes 3.65 h to verify the model of the internally developed twenty-million-transistor FPGA with 32-core simulation.

关键词： A Fast HDL Model for Full-Custom FPGA Verification HDL Model

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Convergence Analysis of a Fast Non-negative Latent Factor Model

Convergence Analysis of a Fast Non-negative Latent Factor Mo...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Yue Zhou Zhigang Liu Xiaojiang Yu Yajuan Wu Chongqing Engineering Research Center of Big Data Application for Smart Cities and Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing China Computer School of China West Normal University Nanchong Sichuan China

A fast non-negative latent factor (FNLF) model adopts a single latent factor-dependent, non-negative, multiplicative and momentum-incorporated update (SLF-NM 2 U) algorithm, which can ensure fast convergence on a high-dimensional and sparse (HiDS) matrix according to empirical studies in prior researches. However, it is crucial to investigate the theoretical proof regarding the reason why incorporation of a generalized momentum method into an SLF-NM 2 U algorithm can ensure the fast convergence of an FNLF model, which has not been addressed in previous work. Therefore, this paper aims to unveil how a generalized momentum method improves the convergence rate of an FNLF model in the discrete time case by combining physical analysis. The FNLF model is superior to the NLF model in terms of the convergence rate and the prediction accuracy of missing data. This conclusion is obtained by empirically research on the HiDS matrix in industrial applications, which also provides empirical basis for theoretical proof.

关键词： Convergence Analytical models Sparse matrices Data models Computational modeling Acceleration Friction

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Sunspot Umbral Oscillation Analysis Using Wavelet Transform 29

Sunspot Umbral Oscillation Analysis Using Wavelet Transform

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第29届中国控制与决策会议

作者： Zeyuan Du Song Feng Yunnan Key Laboratory of Computer Technology Application/Faculty of Information Engineering and Automation Kunming University of Science and Technology

ISBN: (纸本)9781509046584

Umbral oscillation is one kind of basic phenomena appearing in *** measurement the oscillation period is quiet important for understanding the structure and evolution of sunspot.A long line of work indicates that there are two kinds of typical oscillations in sunspot umbral area:3-minute oscillations and 5-minute *** high quality observational sunspot data set obtained with the HINODE/Solar Optical Telescope in the Ca II H line on 20 August 2013 was employed to study the oscillation period of the chromosphere above a *** this paper,we used two kinds of wavelet transform methods to analyze sunspot umbral *** method based on continuous wavelet transform(CWT) was applied to analyze umbral oscillation period and frequency changing over *** other method based on cross wavelet transform(XWT) was used to analyze phase relationship between the two time *** outcomes demonstrate the viability and efficiency of the wavelet transform as a analyzing tool by accurately calculating period and studying phase difference.

关键词： Sunspot Umbral oscillation period Phase relationship Continuous wavelet transform Cross wavelet transform

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A method of calculating the mean life-time of solar granules 29

A method of calculating the mean life-time of solar granules

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第29届中国控制与决策会议

作者： Jianshu Wang Song Feng Yunnan Key Laboratory of Computer Technology Application Faculty of Information Engineering and Automation Kunming University of Science and Technology

ISBN: (纸本)9781509046584

Solar granulation is a kind of morphological characteristics of solar photosphere caused by convective *** of the mechanism of convective motion,when we calculated the mean life-time of solar granules,we can get the time of convective *** are many methods based on the classification of solar granules and tracking solar granules to calculate the mean life-time of solar *** a result of the existence time of solar granules is very short and the evolution is more complex,identifying the fragmentation and merging of solar granules will get us into *** this paper,we present a power spectrum analysis based on periodic analysis method to calculate the mean life-time of solar ***,we use the κ-ω filter to remove the 5-minute *** removing the 5-minute oscillations can we more accurately calculate the mean life-time of solar ***,we use gradient transformation to remove the direct current component(DC component) and reduce spectral energy leakage with the Hamming ***,using the function of Periodogram calculates the mean life-time of solar *** order to verify the accuracy of the method,we set three data to calculate the mean life-time of solar *** final results of calculating the mean life-time of solar granules are 9.24 min,9.04 min and 10.67 min *** our method is effective and accurate compared with the results of other literature.

关键词： Solar granules k-ω filter Power spectrum The mean life-time

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On linear precoding of unitary space-time modulation for spatial-temporal correlated flat fading channel 1st

On linear precoding of unitary space-time modulation for spa...

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1st International Conference on Smart Vehicular technology, Transportation, Communication and applications, VTCA 2017

作者： Yu, Caihong Jia, Kejun Yang, Zheng Fujian Provincial Key Laboratory of Digital Equipment Fujian University of Technology FuzhouFujian350000 China School of Computer and Communication Lanzhou University of Technology LanzouGansu730050 China Fujian Provincial Engineering Technology Research Center of Photoelectric Sensing Application Fujian Normal University FuzhouFujian350000 China

ISBN: (纸本)9783319707297

In this paper, we present a precoding scheme of unitary space-time modulation (USTM) system considering that the fading channel is spatial-temporal correlated, which is a typical wireless communication scenario for high speed railway. The design of the precoder uses transmit correlation matrix of the channel, aiming at minimizing the mean-square error of channel estimation, where the receiver utilizes all of channel correlation information to the non-coherent detection method. Simulation results show that the proposed scheme can significantly outperform the non-coherent receiver without precoding. © Springer International Publishing AG 2018.

关键词： Channel estimation

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From digitized whole-slide histology images to biomarker discovery: A protocol for handcrafted feature analysis in brain cancer pathology

Brain-X

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Brain-X 2025年第2期3卷

作者： Lu, Xuanjun Ying, Yawen Chen, Jing Chen, Zhiyang Wu, Yuxin Prasanna, Prateek Chen, Xin Jing, Mingli Liu, Zaiyi Lu, Cheng School of Electronic Engineering Xi'an Shiyou University Shaanxi Xi'an China Department of Medical Research Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences) Southern Medical University Guangdong Guangzhou China Department of Radiology Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences) Southern Medical University Guangdong Guangzhou China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Guangzhou China School of Clinical Dentistry University of Sheffield Sheffield United Kingdom Wallace H. Coulter Department of Biomedical Engineering Georgia Institute of Technology and Emory University Atlanta GA United States Department of Computer Science & Informatics Emory University Atlanta GA United States Department of Biomedical Informatics Stony Brook University Stony Brook NY United States Department of Radiology School of Medicine Guangzhou First People's Hospital South China University of Technology Guangdong Guangzhou China

Hematoxylin and eosin (H&E)-stained histopathological slides contain abundant information about cellular and tissue morphology and have been the cornerstone of tumor diagnosis for decades. In recent years, advancements in digital pathology have made whole-slide images (WSIs) widely applicable for diagnosis, prognosis, and prediction in brain cancer. However, there remains a lack of systematic tools and standardized protocols for using handcrafted features in brain cancer histological analysis. In this study, we present a protocol for handcrafted feature analysis in brain cancer pathology (PHBCP) to systematically extract, analyze, model, and visualize handcrafted features from WSIs. The protocol enabled the discovery of biomarkers from WSIs through a series of well-defined steps. The PHBCP comprises seven main steps: (1) problem definition, (2) data quality control, (3) image preprocessing, (4) feature extraction, (5) feature filtering, (6) modeling, and (7) performance analysis. As an exemplary application, we collected pathological data of 589 patients from two cohorts and applied the PHBCP to predict the 2-year survival of glioblastoma multiforme (GBM) patients. Among the 72 models combining nine feature selection methods and eight machine learning classifiers, the optimal model combination achieved discriminative performance with an average area under the curve (AUC) of 0.615 over 100 iterations under five-fold cross-validation. In the external validation cohort, the optimal model combination achieved a generalization performance with an AUC of 0.594. We provide an open-source code repository (GitHub website: https://***/XuanjunLu/PHBCP) to facilitate effective collaboration between medical and technical experts, thereby advancing the field of computational pathology in brain cancer. © 2025 The Author(s). Brain-X published by John Wiley & Sons Australia, Ltd on behalf of Ainuohui Medical technology.

关键词： brain cancer pathology handcrafted features protocol whole-slide images

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Gowl: A fast ontology-mediated query answering

Gowl: A fast ontology-mediated query answering

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2018 ISWC Posters and Demonstrations, Industry and Blue Sky Ideas Tracks, ISWC-P and D-Industry-BlueSky 2018

作者： Meng, Chenhong Zhang, Xiaowang Xiao, Guohui Feng, Zhiyong Qi, Guilin School of Computer Science and Technology Tianjin University Tianjin300350 China School of Computer Software Tianjin University Tianjin300350 China Faculty of Computer Science Free University of Bozen-Bolzano BolzanoI-39100 Italy School of Computer Science and Engineering Southeast University Nanjing211189 China Tianjin Key Laboratory of Cognitive Computing and Application Tianjin300350 China

This poster shows an ontology-mediated query answering system (gOWL) based on pure materialization to avoid query rewriting online. gOWL shows that answering queries over a partial materialization of the chase is complete for a large fragment of practical queries with bounded depth. From a system engineering perspective, the materialization approach allows us to design a modular architecture to integrate off-the-shelf efficient SPARQL query engines. The poster will be based on DBpedia and UOBM datasets. We will compare the performance of gOWL with PAGOdA, Ontop, and Pellet (with speedup up to three orders of magnitude). © 2018 CEUR-WS. All rights reserved.

关键词： Ontology

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RESIDUAL SHUFFLING CONVOLUTIONAL NEURAL NETWORKS for DEEP SEMANTIC IMAGE SEGMENTATION USING MULTI-MODAL DATA

RESIDUAL SHUFFLING CONVOLUTIONAL NEURAL NETWORKS for DEEP SE...

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2018 ISPRS TC II Mid-term Symposium "Towards Photogrammetry 2020"

作者： Chen, K. Weinmann, M. Gao, X. Yan, M. Hinz, S. Jutzi, B. Weinmann, M. Key Laboratory of Spatial Information Processing and Application System Technology Institute of Electronics Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Institute of Computer Science II University of Bonn Bonn Germany Institute of Photogrammetry and Remote Sensing Karlsruhe Institute of Technology Karlsruhe Germany

In this paper, we address the deep semantic segmentation of aerial imagery based on multi-modal data. Given multi-modal data composed of true orthophotos and the corresponding Digital Surface Models (DSMs), we extract a variety of hand-crafted radiometric and geometric features which are provided separately and in different combinations as input to a modern deep learning framework. The latter is represented by a Residual Shuffling Convolutional Neural Network (RSCNN) combining the characteristics of a Residual Network with the advantages of atrous convolution and a shuffling operator to achieve a dense semantic labeling. Via performance evaluation on a benchmark dataset, we analyze the value of different feature sets for the semantic segmentation task. The derived results reveal that the use of radiometric features yields better classification results than the use of geometric features for the considered dataset. Furthermore, the consideration of data on both modalities leads to an improvement of the classification results. However, the derived results also indicate that the use of all defined features is less favorable than the use of selected features. Consequently, data representations derived via feature extraction and feature selection techniques still provide a gain if used as the basis for deep semantic segmentation. © 2018 Copernicus GmbH. All rights reserved.

关键词： Aerial Imagery CNN Deep Learning Multi-Modal Data Residual Network Semantic Segmentation

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User Identity Authentication and Identification Based on Multi-Factor Behavior Features

User Identity Authentication and Identification Based on Mul...

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IEEE Globecom Workshops

作者： Xing Ding Changgen Peng Hongfa Ding Maoni Wang Hui Yang Qinyong Yu College of Computer Science & Technology State Key Laboratory of Public Big Data Guizhou University Guiyang China College of Information Guizhou University of Financial and Economics Guiyang China National Engineering Laboratory Big Data Application on Improving Government Governance Capabilities Guiyang China CETC Big Data Research Institute Co. Ltd Guiyang China

ISBN: (数字)9781728109602

ISBN: (纸本)9781728109619

Behavior-based identity authentication has been of research interests due to its low cost and the fact that such authentication factors cannot easily copied nor stolen. However, single behavior feature authentication usuall cannot gaurantee sufficient accuracy. In this paper, a multi-factor behavior features identity authentication method is proposed by combining factors of mouse, keystroke and address. We apply support vector machine by using the information divergence of features as the weight of the linear kernel function to solve quantification's problem of features influence degree on authentication. The tailored support vector machine is applied to multi-factor behavior features to identity authentication. Our experimental results show that the improved support vector machine is better than both the linear and Gaussian kernel support vector machines for identity authentication in accuracy. The proppsed method can be applied to large volume users' identity authentication cases where their behavior features are likely to be similar.

关键词： Authentication Mice IP networks Feature extraction Support vector machines Hidden Markov models Presses

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