检索结果-内蒙古大学图书馆

Attacking Intel UEFI by Using Cache Poisoning

Journal of Physics: Conference Series 2019年第4期1187卷

作者： Dong Wang Wei Yu Dong State Key Laboratory of Mathematical Engineering And Advanced Computing Zhengzhou China

The Unified Extensible Firmware Interface (UEFI) is a software interface between an operating system and platform firmware designed to replace a traditional BIOS. In this paper, we evaluated the security mechanisms used to protected SPI Flash, and then analyzed the attack surface presented by those security mechanisms. Intel provides several registers in its chipset relevant to locking down the SPI Flash chip that contains the UEFI in order to prevent arbitrary writes. Since these registers implement their functions through the system management mode, the main attack surface is concentrated in the system management mode. In this paper, we propose an attack vector for the system management mode, which uses the method of cache poisoning to attack the system management mode and destroy the protection mechanism of SPI Flash. This method can overcome the limitations for the traditional attacks. Experimental results proved that this kind of attack can arbitrarily write to the UEFI.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Rethinking loss design for large-scale 3D shape retrieval

arXiv

引用

arXiv 2019年

作者： Li, Zhaoqun Xu, Cheng Leng, Biao School of Computer Science and Engineering Beihang University Beijing100191 Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing100191

Learning discriminative shape representations is a crucial issue for large-scale 3D shape retrieval. In this paper, we propose the Collaborative Inner Product Loss (CIP Loss) to obtain ideal shape embedding that discriminative among different categories and clustered within the same class. Utilizing simple inner product operation, CIP loss explicitly enforces the features of the same class to be clustered in a linear subspace, while inter-class subspaces are constrained to be at least orthogonal. Compared to previous metric loss functions, CIP loss could provide more clear geometric interpretation for the embedding than Euclidean margin, and is easy to implement without normalization operation referring to cosine margin. Moreover, our proposed loss term can combine with other commonly used loss functions and can be easily plugged into existing off-the-shelf architectures. Extensive experiments conducted on the two public 3D object retrieval datasets, ModelNet and ShapeNetCore 55, demonstrate the effectiveness of our proposal, and our method has achieved state-of-the-art results on both datasets. Copyright © 2019, The Authors. All rights reserved.

关键词： Embeddings

来源：评论

学校读者我要写书评

暂无评论

A Static Backward Taint Data Analysis Method for Detecting Web Application Vulnerabilities 9

A Static Backward Taint Data Analysis Method for Detecting W...

引用

2017 9th IEEE International Conference on Communication Software and Networks (ICCSN 2017)

作者： Xuexiong Yan Hengtai Ma Qingxian Wang Institute of Software Chinese Academy of Sciences State Key Laboratory of Mathematical Engineering and Advanced Computing Institute of Software Chinese Academy of Sciences State Key Laboratory of Mathematical Engineering and Advanced Computing

ISBN: (纸本)9781509038237;9781509038220

This paper addresses detecting taint-style Thisvulnerabilities in PHP code. It extends classical taint-style model with an element called "cleans", which is used to specify sanitation routines. Based on the new model, a static backward taint data analysis method is proposed to detecting taint-style vulnerabilities. This method includes four key steps, first of which is collecting sinks and constructing contexts, the second is backward tracing variables during a basic block, the third is tracing variables between blocks, and the last is tracing variables crossing function call. A tool called POSE implements this method and testing results show that the method is valid for detecting taint-style web application vulnerabilities.

关键词： taint data analysis PHP web application vulnerabilities

来源：评论

学校读者我要写书评

暂无评论

Does or did the supernova remnant Cassiopeia A operate as a PeVatron?

arXiv

引用

arXiv 2023年

作者： Cao, Zhen Aharonian, F. An, Q. Axikegu Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Cai, J.T. Cao, Q. Cao, W.Y. Cao, Zhe Chang, J. Chang, J.F. Chen, A.M. Chen, E.S. Chen, Liang Chen, Lin Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, Y. Cheng, N. Cheng, Y.D. Cui, M.Y. Cui, S.W. Cui, X.H. Cui, Y.D. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu della Volpe, D. Dong, X.Q. Duan, K.K. Fan, J.H. Fan, Y.Z. Fang, J. Fang, K. Feng, C.F. Feng, L. Feng, S.H. Feng, X.T. Feng, Y.L. Gabici, S. Gao, B. Gao, C.D. Gao, L.Q. Gao, Q. Gao, W. Gao, W.K. Ge, M.M. Geng, L.S. Giacinti, G. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. He, H.H. He, H.N. He, J.Y. He, X.B. He, Y. Heller, M. Hor, Y.K. Hou, B.W. Hou, C. Hou, X. Hu, H.B. Hu, Q. Hu, S.C. Huang, D.H. Huang, T.Q. Huang, W.J. Huang, X.T. Huang, X.Y. Huang, Y. Huang, Z.C. Ji, X.L. Jia, H.Y. Jia, K. Jiang, K. Jiang, X.W. Jiang, Z.J. Jin, M. Kang, M.M. Ke, T. Kuleshov, D. Kurinov, K. Li, B.B. Li, Cheng Li, Cong Li, D. Li, F. Li, H.B. Li, H.C. Li, H.Y. Li, J. Li, Jian Li, Jie Li, K. Li, W.L. Li, X.R. Li, Xin Li, Y.Z. Li, Zhe Li, Zhuo Liang, E.W. Liang, Y.F. Lin, S.J. Liu, B. Liu, C. Liu, D. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, J.Y. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Lu, R. Luo, Q. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Min, Z. Mitthumsiri, W. Mu, H.J. Nan, Y.C. Neronov, A. Ou, Z.W. Pang, B.Y. Pattarakijwanich, P. Pei, Z.Y. Qi, M.Y. Qi, Y.Q. Qiao, B.Q. Qin, J.J. Ruffolo, D. Sáiz, A. Semikoz, D. Shao, C.Y. Shao, L. Shchegolev, O. Sheng, X.D. Shu, F.W. Song, H.C. Stenkin, Yu.V. Stepanov, V. Su, Y. Sun, Q.N. Sun, X.N. Sun, Z.B. Tam, P.H.T. Tang, Q.W. Tang, Z.B. Tian, W.W. Key Laboratory of Particle Astrophysics & Experimental Physics Division & Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China Tianfu Cosmic Ray Research Center Sichuan Chengdu610000 China Dublin Institute for Advanced Studies 31 Fitzwilliam Place 2 Dublin Ireland Max-Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany State Key Laboratory of Particle Detection and Electronics China University of Science and Technology of China Anhui Hefei230026 China School of Physical Science and Technology School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China Hebei Normal University Hebei Shijiazhuang050024 China Key Laboratory of Dark Matter and Space Astronomy Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Tsung-Dao Lee Institute School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China Key Laboratory of Cosmic Rays Tibet University Ministry of Education Tibet Lhasa850000 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Sun Yat-sen University Guangdong Zhuhai519000 China Sun Yat-sen University Guangdong Guangzhou510275 China Sun Yat-sen University Guangdong Zhuhai519000 China School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Département de Physique Nucléaire et Corpusculaire Faculté de Sciences Université de Genève 24 Quai Ernest Ansermet Geneva1211 Switzerland Institute of Frontier and Interdis

For decades, supernova remnants (SNRs) have been considered the prime sources of Galactic Cosmic rays (CRs). But whether SNRs can accelerate CR protons to PeV energies and thus dominate CR flux up to the knee is currently under intensive theoretical and phenomenological debate. The direct test of the ability of SNRs to operate as CR PeVatrons can be provided by ultrahigh-energy (UHE;Eγ ≥ 100 TeV) γ-rays. In this context, the historical SNR Cassiopeia A (Cas A) is considered one of the most promising target for UHE observations. This paper presents the observation of Cas A and its vicinity by the LHAASO KM2A detector. The exceptional sensitivity of LHAASO KM2A in the UHE band, combined with the young age of Cas A, enabled us to derive stringent model-independent limits on the energy budget of UHE protons and nuclei accelerated by Cas A at any epoch after the explosion. The results challenge the prevailing paradigm that Cas A-type SNRs are major suppliers of PeV CRs in the Milky Way. © 2023, CC BY.

关键词： Gamma rays

来源：评论

学校读者我要写书评

暂无评论

Measurement of ultra-high-energy diffuse gamma-ray emission of the Galactic plane from 10 TeV to 1 PeV with LHAASO-KM2A

arXiv

引用

arXiv 2023年

作者： Cao, Zhen Aharonian, F. An, Q. Axikegu Bai, Y.X. Bao, Y.W. Bastieri, D. Bi, X.J. Bi, Y.J. Cai, J.T. Cao, Q. Cao, W.Y. Cao, Zhe Chang, J. Chang, J.F. Chen, A.M. Chen, E.S. Chen, Liang Chen, Lin Chen, Long Chen, M.J. Chen, M.L. Chen, Q.H. Chen, S.H. Chen, S.Z. Chen, T.L. Chen, Y. Cheng, N. Cheng, Y.D. Cui, M.Y. Cui, S.W. Cui, X.H. Cui, Y.D. Dai, B.Z. Dai, H.L. Dai, Z.G. Danzengluobu della Volpe, D. Dong, X.Q. Duan, K.K. Fan, J.H. Fan, Y.Z. Fang, J. Fang, K. Feng, C.F. Feng, L. Feng, S.H. Feng, X.T. Feng, Y.L. Gabici, S. Gao, B. Gao, C.D. Gao, L.Q. Gao, Q. Gao, W. Gao, W.K. Ge, M.M. Geng, L.S. Giacinti, G. Gong, G.H. Gou, Q.B. Gu, M.H. Guo, F.L. Guo, X.L. Guo, Y.Q. Guo, Y.Y. Han, Y.A. He, H.H. He, H.N. He, J.Y. He, X.B. He, Y. Heller, M. Hor, Y.K. Hou, B.W. Hou, C. Hou, X. Hu, H.B. Hu, Q. Hu, S.C. Huang, D.H. Huang, T.Q. Huang, W.J. Huang, X.T. Huang, X.Y. Huang, Y. Huang, Z.C. Ji, X.L. Jia, H.Y. Jia, K. Jiang, K. Jiang, X.W. Jiang, Z.J. Jin, M. Kang, M.M. Ke, T. Kuleshov, D. Kurinov, K. Li, B.B. Li, Cheng Li, Cong Li, D. Li, F. Li, H.B. Li, H.C. Li, H.Y. Li, J. Li, Jian Li, Jie Li, K. Li, W.L. Li, W.L. Li, X.R. Li, Xin Li, Y.Z. Li, Zhe Li, Zhuo Liang, E.W. Liang, Y.F. Lin, S.J. Liu, B. Liu, C. Liu, D. Liu, H. Liu, H.D. Liu, J. Liu, J.L. Liu, J.Y. Liu, M.Y. Liu, R.Y. Liu, S.M. Liu, W. Liu, Y. Liu, Y.N. Lu, R. Luo, Q. Lv, H.K. Ma, B.Q. Ma, L.L. Ma, X.H. Mao, J.R. Min, Z. Mitthumsiri, W. Mu, H.J. Nan, Y.C. Neronov, A. Ou, Z.W. Pang, B.Y. Pattarakijwanich, P. Pei, Z.Y. Qi, M.Y. Qi, Y.Q. Qiao, B.Q. Qin, J.J. Ruffolo, D. Sáiz, A. Semikoz, D. Shao, C.Y. Shao, L. Shchegolev, O. Sheng, X.D. Shu, F.W. Song, H.C. Stenkin, Yu V. Stepanov, V. Su, Y. Sun, Q.N. Sun, X.N. Sun, Z.B. Tam, P.H.T. Tang, Q.W. Tang, Z.B. Tian, W.W. Wang, C. Key Laboratory of Particle Astrophyics & Experimental Physics Division & Computing Center Institute of High Energy Physics Chinese Academy of Sciences Beijing100049 China University of Chinese Academy of Sciences Beijing100049 China Tianfu Cosmic Ray Research Center Sichuan Chengdu610000 China Dublin Institute for Advanced Studies 31 Fitzwilliam Place Dublin 2 Ireland Max-Planck-Institut for Nuclear Physics P.O. Box 103980 Heidelberg69029 Germany State Key Laboratory of Particle Detection and Electronics China University of Science and Technology of China Anhui Hefei230026 China School of Physical Science and Technology School of Information Science and Technology Southwest Jiaotong University Sichuan Chengdu610031 China School of Astronomy and Space Science Nanjing University Jiangsu Nanjing210023 China Center for Astrophysics Guangzhou University Guangdong Guangzhou510006 China Hebei Normal University Hebei Shijiazhuang050024 China Key Laboratory of Dark Matter and Space Astronomy Key Laboratory of Radio Astronomy Purple Mountain Observatory Chinese Academy of Sciences Jiangsu Nanjing210023 China Tsung-Dao Lee Institute School of Physics and Astronomy Shanghai Jiao Tong University Shanghai200240 China Key Laboratory for Research in Galaxies and Cosmology Shanghai Astronomical Observatory Chinese Academy of Sciences Shanghai200030 China Key Laboratory of Cosmic Rays [Tibet University Ministry of Education Tibet Lhasa850000 China National Astronomical Observatories Chinese Academy of Sciences Beijing100101 China Sun Yat-sen University 519000 Zhuhai Guangdong Guangzhou510275 China School of Physics and Astronomy Yunnan University Yunnan Kunming650091 China Département de Physique Nucléaire et Corpusculaire Faculté de Sciences Université de Genève 24 Quai Ernest Ansermet Geneva1211 Switzerland Institute of Frontier and Interdisciplinary Science Shandong University Shandong Qingdao266237 China APC Universit’e Paris Cit’e C

The diffuse Galactic γ-ray emission, mainly produced via interactions between cosmic rays and the interstellar medium and/or radiation field, is a very important probe of the distribution, propagation, and interaction of cosmic rays in the Milky Way. In this work we report the measurements of diffuse γ-rays from the Galactic plane between 10 TeV and 1 PeV energies, with the square kilometer array of the Large High Altitude Air Shower Observatory (LHAASO). Diffuse emissions from the inner (15◦ ◦, |b| ◦) and outer (125◦ ◦, |b| ◦) Galactic plane are detected with 29.1σ and 12.7σ significance, respectively. The outer Galactic plane diffuse emission is detected for the first time in the very- to ultra-high-energy domain (E > 10 TeV). The energy spectrum in the inner Galaxy regions can be described by a power-law function with an index of −2.99 ± 0.04, which is different from the curved spectrum as expected from hadronic interactions between locally measured cosmic rays and the line-of-sight integrated gas content. Furthermore, the measured flux is higher by a factor of ∼ 3 than the prediction. A similar spectrum with an index of −2.99 ± 0.07 is found in the outer Galaxy region, and the absolute flux for 10 E 60 TeV is again higher than the prediction for hadronic cosmic ray interactions. The latitude distributions of the diffuse emission are consistent with the gas distribution, while the longitude distributions show clear deviation from the gas distribution. The LHAASO measurements imply that either additional emission sources exist or cosmic ray intensities have spatial variations. Copyright © 2023, The Authors. All rights reserved.

关键词： Cosmic rays

来源：评论

学校读者我要写书评

暂无评论

Residual Networks for Light Field Image Super-Resolution

Residual Networks for Light Field Image Super-Resolution

引用

IEEE/CVF Conference on Computer Vision and Pattern Recognition

作者： Shuo Zhang Youfang Lin Hao Sheng Beijing Key Lab of Traffic Data Analysis and Mining School of Computer and Information Technology Beijing Jiaotong University Key Laboratory of Intelligent Passenger Service of Civil Aviation CAAC State Key Laboratory of Software Development Environment School of Computer Science and Engineering Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University

Light field cameras are considered to have many potential applications since angular and spatial information is captured simultaneously. However, the limited spatial resolution has brought lots of difficulties in developing related applications and becomes the main bottleneck of light field cameras. In this paper, a learning-based method using residual convolutional networks is proposed to reconstruct light fields with higher spatial resolution. The view images in one light field are first grouped into different image stacks with consistent sub-pixel offsets and fed into different network branches to implicitly learn inherent corresponding relations. The residual information in different spatial directions is then calculated from each branch and further integrated to supplement high-frequency details for the view image. Finally, a flexible solution is proposed to super-resolve entire light field images with various angular resolutions. Experimental results on synthetic and real-world datasets demonstrate that the proposed method outperforms other state-of-the-art methods by a large margin in both visual and numerical evaluations. Furthermore, the proposed method shows good performances in preserving the inherent epipolar property in light field images.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Dynamic Kinetic Resolution of Helical Polycyclic Arenes Directed by Non-Covalent Enantioselective Interactions at Inorganic Chiral Surfaces Deposited via Substrate Rotation

SSRN

引用

SSRN 2025年

作者： Qin, Ping Liu, Junjun Zhang, Miao Yang, Lin Zhong, Xiaoyan Xia, Guangjie Shen, Chengshuo Qiu, Huibin Huang, Zhifeng Department of Biology Hong Kong Baptist University Kowloon Tong Kowloon Hong Kong School of Materials and Environmental Engineering Shenzhen Polytechnic University Shenzhen518055 China TRACE EM Unit Department of Materials Science and Engineering City University of Hong Kong Kowloon Hong Kong Shenzhen518048 China City University of Hong Kong Shenzhen Research Institute Shenzhen518057 China Department of Chemistry The Chinese University of Hong Kong Shatin Hong Kong School of Physical Sciences Great Bay University Guangdong Dongguan523000 China Center for Intelligent Computing Great Bay Institute for Advanced Study Guangdong Dongguan523000 China School of Chemistry and Chemical Engineering Zhejiang Sci-Tech University Zhejiang Hangzhou310018 China School of Chemistry and Chemical Engineering Zhangjiang Institute of Advanced Study Frontiers Science Center for Transformative Molecules State Key Laboratory of Metal Matrix Composites Shanghai Jiao Tong University Shanghai200240 China Shenzhen Research Institute The Chinese University of Hong Kong No.10 2nd Yuexing Road Nanshan Guangdong Province Shenzhen518057 China

Manipulating symmetry breaking of a pair of enantiomers is one of fundamentally important topics in modern chemistry, as practically desired to be applied to generate enantiomerically enriched molecules through asymmetric kinetic resolution or enantioselective synthesis. It is challenging to direct dynamic kinetic resolution (DKR) when enantiomers have small interconversion barriers, such as helical polycyclic arenes. In this work, reliably controlling the DKR of nitrogen (N)-doped helical polycyclic arenes is demonstrated at inorganic surfaces composed of chiral topographies at the atomic scale. Through forming the non-covalent N-inorganic bonds, helical polycyclic arenes enantiospecifically adsorb at the atomically nominal left-handed and right-handed inorganic surfaces (resulting from the deposition of various inorganic materials on a substrate rotated in clockwise and counterclockwise) to break symmetry and then generate the homochiral P- and M-configuration supramolecular aggregates, respectively. The homochiral aggregation can be ascribed to layer-by-layer homochiral π-π stacking on the adsorbed configuration-determined helicenes, i.e., chirality amplification following the directed symmetry breaking. Such symmetry-breaking manipulation can be generally adapted to inorganic materials exhibiting high surface energy, such as silica, titanium oxides, iron oxides, tin oxides and gold. This work introduces a general principle to direct DKR of enantiomers with small interconversion barriers, which provides an insight into understanding the mysterious origins of prebiotic homochirality on Earth. © 2025, The Authors. All rights reserved.

关键词： Enantioselectivity

来源：评论

学校读者我要写书评

暂无评论

Author Correction: Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer

引用

Nature communications 2021年第1期12卷 4370页

作者： Xueyi Zheng Zhao Yao Yini Huang Yanyan Yu Yun Wang Yubo Liu Rushuang Mao Fei Li Yang Xiao Yuanyuan Wang Yixin Hu Jinhua Yu Jianhua Zhou Department of Ultrasound Sun Yat-Sen University Cancer Center State Key Laboratory of Oncology in South China Collaborative Innovation Center for Cancer Medicine Guangzhou China. Department of Electronic Engineering Fudan University Shanghai China. Paul C. Lauterbur Research Center for Biomedical Imaging Institute of Biomedical and Health Engineering Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China. The key laboratory of medical imaging computing and computer assisted intervention of Shanghai Shanghai China. Department of Electronic Engineering Fudan University Shanghai China. jhyu@***. The key laboratory of medical imaging computing and computer assisted intervention of Shanghai Shanghai China. jhyu@***. Department of Ultrasound Sun Yat-Sen University Cancer Center State Key Laboratory of Oncology in South China Collaborative Innovation Center for Cancer Medicine Guangzhou China. zhoujh@***.

来源：评论

学校读者我要写书评

暂无评论

Enhancing 2D Representation via Adjacent Views for 3D Shape Retrieval

Enhancing 2D Representation via Adjacent Views for 3D Shape ...

引用

International Conference on Computer Vision (ICCV)

作者： Cheng Xu Zhaoqun Li Qiang Qiu Biao Leng Jingfei Jiang School of Computer Science & Engineering Beihang University Duke University Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University State Key Laboratory of Virtual Reality Technology and Systems Beihang University National Laboratory for Parallel and Distributed Processing National University of Defense Technology

ISBN: (数字)9781728148038

ISBN: (纸本)9781728148045

Multi-view shape descriptors obtained from various 2D images are commonly adopted in 3D shape retrieval. One major challenge is that significant shape information are discarded during 2D view rendering through projection. In this paper, we propose a convolutional neural network based method, CenterNet, to enhance each individual 2D view using its neighboring ones. By exploiting cross-view correlations, CenterNet learns how adjacent views can be maximally incorporated for an enhanced 2D representation to effectively describe shapes. We observe that a very small amount of, e.g., six, enhanced 2D views, are already sufficient for a panoramic shape description. Thus, by simply aggregating features from six enhanced 2D views, we arrive at a highly compact yet discriminative shape descriptor. The proposed shape descriptor significantly outperforms state-of-the-art 3D shape retrieval methods on the ModelNet and ShapeNetCore55 benchmarks, and also exhibits robustness against object occlusion.

关键词： Shape Three-dimensional displays Correlation Two dimensional displays Feature extraction Solid modeling Computational modeling

来源：评论

学校读者我要写书评

暂无评论

Design of Gesture Recognition System Based on Multi-Channel Myoelectricity Correlation

Design of Gesture Recognition System Based on Multi-Channel ...

引用

2019 IEEE Global Communications Conference (GLOBECOM)

作者： Di Wu Huiyong Li Xuefeng Liu Jianwei Niu State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University Beijing China Beijing Advanced Innovation Center for Big Data and Brain Computing (BDBC) Beihang University Hangzhou Innovation Research Institute Beihang University

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Gesture recognition systems based on myoelectric signal have raised more and more attention from researchers. Traditional gesture recognition methods are susceptible to multiple types of noise and require a large number of features, which increase overhead and decrease recognition efficiency. Fully utilizing the characteristics of the signal to recognize gestures is a big challenge. This paper proposes an improved empirical mode decomposition method (XB-EMD) based on autocorrelation function to denoise myoelectric signal. In addition, a novel deep neural network (CRNet) which combines the CNN and RNN layers together is trained for classifying the gestures based on denoised myoelectric signal. Experimental results show that the proposed gesture recognition system can improve recognition effectiveness at 97.4\% with 10 typical gestures, and 72.55\% with 16 complicated gestures.

关键词： Correlation Gesture recognition Feature extraction Empirical mode decomposition Recurrent neural networks Sensors

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：