检索结果-内蒙古大学图书馆

您好，读者！请登录

内蒙古大学图书馆

首页
概况
党建
资源
服务
科研支持
- 论文收录引用证明
- 科技查新
知识产权
档案馆
帮助

咨询与建议

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

您的常用邮箱：*

您的手机号码：*

问题描述：

当前已输入0个字，您还可以输入200个字

全部搜索
期刊论文
图书
学位论文
标准
纸本馆藏
外文资源发现
数据库导航
超星发现

高级检索

时间限定

出版年份：

文献类型

图书期刊文献学位论文多媒体

馆藏选择

电子馆藏纸本馆藏

核心期刊

全部期刊 SCI 收录期刊 SSCI 收录期刊 EI 收录期刊 CSCD 收录期刊 CSSCI 收录期刊

语言

中文英文

文献类型

期刊文献图书学位论文标准纸本馆藏

帮助

文字说明：

T=题名（书名、题名），A=作者（责任者），K=主题词，P=出版物名称，PU=出版社名称，O=机构（作者单位、学位授予单位、专利申请人），L=中图分类号，C=学科分类号，U=全部字段，Y=年（出版发行年、学位年度、标准发布年）

检索规则说明：

AND代表“并且”；OR代表“或者”；NOT代表“不包含”；(注意必须大写,运算符两边需空一格)

检索范例：

范例一：(K=图书馆学 OR K=情报学) AND A=范并思 AND Y=1982-2016
范例二：P=计算机应用与软件 AND (U=C++ OR U=Basic) NOT K=Visual AND Y=2011-2016

分类表

所选分类

>> <<

限定检索结果

文献类型

535 篇 会议
41 篇 期刊文献

馆藏范围

576 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

381 篇 工学
- 288 篇 信息与通信工程
- 159 篇 计算机科学与技术...
- 113 篇 软件工程
- 91 篇 电子科学与技术（可...
- 52 篇 控制科学与工程
- 51 篇 航空宇航科学与技...
- 45 篇 仪器科学与技术
- 41 篇 光学工程
- 30 篇 机械工程
- 25 篇 生物工程
- 24 篇 电气工程
- 21 篇 土木工程
- 21 篇 化学工程与技术
- 19 篇 建筑学
- 11 篇 测绘科学与技术
- 11 篇 环境科学与工程（可...
- 9 篇 安全科学与工程
- 8 篇 动力工程及工程热...
187 篇 理学
- 111 篇 数学
- 32 篇 物理学
- 31 篇 生物学
- 31 篇 统计学（可授理学、...
- 25 篇 化学
- 12 篇 大气科学
- 9 篇 系统科学
- 8 篇 地质学
49 篇 管理学
- 28 篇 管理科学与工程(可...
- 23 篇 图书情报与档案管...
- 7 篇 工商管理
13 篇 农学
- 8 篇 农业资源与环境
11 篇 军事学
4 篇 医学
2 篇 教育学
1 篇 经济学
1 篇 法学

主题

91 篇 synthetic apertu...
24 篇 radar
23 篇 radar imaging
21 篇 feature extracti...
16 篇 object detection
15 篇 radar polarimetr...
15 篇 target recogniti...
15 篇 field programmab...
14 篇 training
13 篇 remote sensing
11 篇 convolution
11 篇 sar
11 篇 accuracy
10 篇 data processing
10 篇 signal to noise ...
10 篇 radar tracking
10 篇 convolutional ne...
9 篇 target tracking
9 篇 field programmab...
8 篇 imaging

机构

215 篇 beijing key labo...
147 篇 beijing key labo...
72 篇 radar research l...
65 篇 school of inform...
54 篇 beijing institut...
39 篇 advanced technol...
33 篇 beijing key labo...
29 篇 chongqing innova...
23 篇 radar technology...
19 篇 radar research l...
18 篇 hubei province k...
17 篇 beijing key labo...
17 篇 school of electr...
16 篇 school of inform...
13 篇 beijing key labo...
12 篇 beijing key labo...
12 篇 college of compu...
11 篇 zhengzhou academ...
11 篇 shanghai institu...
10 篇 beijing institut...

作者

52 篇 ding zegang
40 篇 chen he
39 篇 he chen
35 篇 chen liang
33 篇 hu cheng
30 篇 wang yanhua
28 篇 liang chen
26 篇 xie yizhuang
26 篇 zegang ding
25 篇 li yang
25 篇 zeng tao
24 篇 cheng hu
24 篇 tao zeng
22 篇 linbo tang
21 篇 tang linbo
20 篇 wang rui
19 篇 teng long
19 篇 tian weiming
19 篇 zhang liang
19 篇 yanhua wang

语言

562 篇 英文
11 篇 其他
3 篇 中文

检索条件"机构=Beijing Key Laboratory of Embedded Real-Time Information Processing Technology"

共 576 条记录，以下是201-210 订阅

全选清除本页清除全部题录导出标记到"检索档案"

详细简洁

排序：

Correlation tracking via robust region proposals

Correlation tracking via robust region proposals

引用

IET International Radar Conference 2018, IRC 2018

作者： Han, Yuqi Nan, Jinghong Zhang, Zengshuo Wang, Jingjing Zhao, Baojun School of Information and Electronics Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China

Recently, correlation filter-based trackers have received extensive researching interest because of their simplicity and superior running speed. However, such trackers perform poorly when the target undergoes occlusion, viewpoint change, or other challenging attributes due to pre-defined sampling strategy. To tackle these issues, the authors propose an adaptive region proposal strategy to facilitate object tracking. To be more specific, a novel tracking monitoring indicator is advocated to forecast tracking failure. Afterwards, detection and scale proposals were incorporated, respectively, to recover from model drift as well as handle aspect ratio variation. The algorithm was tested on several challenging video sequences, which demonstrates that the proposed algorithm outperforms other state-of-the-art trackers. © 2019 Institution of Engineering and technology. All rights reserved.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Spatial-Temporal Segmentation-based Tracking

Spatial-Temporal Segmentation-based Tracking

引用

2019 IEEE International Conference on Signal, information and Data processing, ICSIDP 2019

作者： Han, Yuqi Xiao, Zhongyang Tang, Linbo Beijing Key Laboratory of Embedded Real-time Information Processing Beijing China State Key Laboratory of Automotive Safety and Energy Beijing China

ISBN: (纸本)9781728123455

Visual tracking, a fundamental task in computer vision, has been criticized less well-posed since reliable target information only given at first frame. In this case, most of the existing template-matching-based trackers fail to locate the target when non-rigid deformations or variations occur. To address these issues, we propose a principled way to take advantage of the superpixel labeling and discriminative tracking algorithms. For each frame, a correlation tracker is first adopted to provide the coarse target location. Afterwards, a collaborative segmentation approach is advocated to segment the surrounding region of the target into superpixels. Target appearance and motion trajectory are considered as spatial and temporal constrains and incorporated into superpixel labeling module. The fine-segmentation result, in turn, provides a more accurate target status for template updating. The effectiveness of the proposed algorithm is validated through experimental comparison on widely-used tracking benchmark datasets. © 2019 IEEE.

关键词： Superpixels

来源：评论

学校读者我要写书评

暂无评论

Aircraft detection in Remote Sensing image for Space-borne platform

Aircraft detection in Remote Sensing image for Space-borne p...

引用

2019 IEEE International Conference on Signal, information and Data processing, ICSIDP 2019

作者： Tang, Wei Jun, Baozhao Tang, Linbo Pan, Yu Jin, Dongling Quan, Zhengpiao Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing China

ISBN: (纸本)9781728123455

Aircraft detection in optical remote sensing image is an important research direction in the field of remote sensing. The existing detection methods are difficult to achieve satisfactory results. Traditional detection methods are low robustness, due to manual feature modeling are difficult and subject to background interference;The deep learning target detection method, which improves the detection performance at the cost of complexity improvement, cannot be widely used in space-borne platforms limited resources. In view of the above problems, this paper proposes an aircraft target deep learning detection method of lightweight and multi-scale features. On the basis of the multi-scale target detection framework (SSD), the method firstly uses the dense connection structure and the double convolution channels to form the basic backbone networks with feature reuse and high computational efficiency. To improve the detection performance of the small aircraft target, the basis backbone network connects a residual module and deconvolution to compose the multi-scale feature fusion detection module. Compared with the current classical deep learning object detection methods, the experimental results show that the proposed method has the advantages of maintaining low computational complexity and achieving high detection accuracy. © 2019 IEEE.

关键词： Aircraft detection

来源：评论

学校读者我要写书评

暂无评论

Fusion-based spectral matching method for hyperspectral target detection

Fusion-based spectral matching method for hyperspectral targ...

引用

IET International Radar Conference 2018, IRC 2018

作者： Wenzheng, Wang Baojun, Zhao Linbo, Tang Shichao, Zhou Fan, Feng School of Information and Electronic Beijing Institute of Technology China Beijing Key Laboratory of Embedded Real-time Information Processing Technology China

Target detection using hyperspectral images is one of the most important applications in the field of both civilian and military. Some spectral similarity metric methods have been used in those applications, i.e. spectral angle mapper and spectral gradient angle and so on. However, those methods only consider the similarity between two spectral in one aspect, and many information hidden in the spectrum is ignored. Then, here, the authors propose a new fusion-based spectral-matching method for hyperspectral target-detection task. The proposed Euclidean-angle matching method combines the magnitude and the shape difference together to determine the similarity between the test and target spectra samples. Authors' results indicate that the proposed fusion-based method exhibits a better performance over the other spectral-matching methods. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Military applications

来源：评论

学校读者我要写书评

暂无评论

SAR Image Scattering Center Estimation via Atomic Norm Minimization 6

SAR Image Scattering Center Estimation via Atomic Norm Minim...

引用

6th Asia-Pacific Conference on Synthetic Aperture Radar, APSAR 2019

作者： Xi, Ying Liu, Haibo Wang, Yanhua Li, Yang Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China

ISBN: (纸本)9781728129129

Scatter center matching is one of the choke points of radar automatic target recognition (RATR) based on Synthetic Aperture Radar (SAR). Accuracy of scattering center extraction is an important factor affecting target recognition performance. This paper proposes a SAR images scattering center estimation method based on atomic norm minimization (ANM) model. This method models the scattering center estimation problem as a two-dimensional line spectrum estimation problem, then the estimation is performed in a continuous domain via ANM. The experimental results indicate the superiorities of the proposed method in terms of accuracy and robustness. © 2019 IEEE.

关键词： Synthetic aperture radar

来源：评论

学校读者我要写书评

暂无评论

Fast and robust adaptive beamforming method based on complex-valued RBF neural network

Fast and robust adaptive beamforming method based on complex...

引用

IET International Radar Conference 2018, IRC 2018

作者： Li, Yuqing Yang, Xiaopeng Liu, Feifeng Beijing Key Laboratory of Embedded Real-time Information Processing Technology School of Information and Electronics Beijing Institute of Technology Beijing100081 China

The adaptive beamforming is one of the key techniques for array signal processing. However, the matrix inversion operation in the existing methods will cost a large amount of computational complexity, which results in poor real-time processing ability. In order to reduce the amount of computational cost, a fast and robust adaptive beamforming method based on complex-valued radial basis function (CRBF) neural network is proposed. In the proposed method, the CRBF neural network is established, thus the direct matrix inversion is avoided by the nonlinear mapping processing from the array covariance matrix to the adaptive weight vector, and therefore the calculation speed of adaptive weight vectors is increased. Based on the simulation results, the proposed method is verified that the speed of adaptive beamforming is increased compared with sample matrix inversion (SMI) algorithm method and an improved performance is achieved compared with that of conventional real-valued RBF neural network beamformer. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Beamforming

来源：评论

学校读者我要写书评

暂无评论

Three-dimensional optimal focusing imaging algorithm for wall-penetrating radar

Three-dimensional optimal focusing imaging algorithm for wal...

引用

IET International Radar Conference 2018, IRC 2018

作者： Li, Yinchuan Zhao, Yikun Xiang, Yin Ding, Zegang Liu, Haibo Liu, Quanhua Beijing Key Laboratory of Embedded Real-time Information Processing Technology School of Information and Electronics Beijing Institute of Technology Beijing100081 China

Wall-penetrating radar (WPR) is the device to image very small objects buried inside the wall. The small objects usually a few millimetres, whereas the bandwidth of WPR limits its resolution up to several centimetres. As a result, the target imaging result of the traditional method is 'defocused' in the range direction. To solve this problem, a near-field three-dimensional imaging algorithm based on matching filtering with multiple reference signals and selection processing is proposed. Instead of obtaining a focused image via coherent superposition processing, a focused image is determined by selecting an image from a plurality imaging results of different reference depths according to the minimum entropy criterion. The proposed algorithm is validated by imaging the computer simulation technology simulation data and the measured data. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Radar imaging

来源：评论

学校读者我要写书评

暂无评论

Distorted SAR target recognition with virtual SVM and AP-HOG feature

Distorted SAR target recognition with virtual SVM and AP-HOG...

引用

IET International Radar Conference (IET IRC 2020)

作者： D. Yao Z. Liu F. Li Y. Li School of Information and Electronics Beijing Institute of Technology Radar Research Laboratory Beijing 100081 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing 100081 China

Synthetic aperture radar (SAR) plays an indispensable role in national defence and civil fields due to its unique advantages. Although many methods have been proposed, the recognition to SAR images still faces many difficulties, especially to distorted images. This paper first improves the morphological methods attribute profiles (APs) which is widely employed with optical, and hyperspectral images in particular. We combine the APs method with histogram of oriented gradients (HOG) feature (called AP-HOG) to make it suitable for SAR images. Then, the SVM model is used to identify the labelled samples (i.e., Support Vectors) that are closest to the separating hyperplane with maximum margin. We generate different degrees of distorted images based on support vectors, and use the distorted image and the support vector as a new training set for retraining to improve the recognition effect of the distorted image. Finally, we perform experimental verification based on the MSTAR dataset, which proves the effectiveness of the proposed method.

关键词： feature extraction synthetic aperture radar support vector machines radar imaging image recognition object detection image classification

来源：评论

学校读者我要写书评

暂无评论

Smart noise jamming suppression method based on fast fractional filtering

Smart noise jamming suppression method based on fast fractio...

引用

IET International Radar Conference 2018, IRC 2018

作者： Han, Bowen Yang, Xiaopeng Wu, Xuchen Li, Shuai Beijing Key Laboratory of Embedded Real-time Information Processing Technology School of Information and Electronics Beijing Institute of Technology Beijing100081 China

Smart noise jamming based on digital radio frequency memory (DRFM) has both oppressive jamming effect and deceptive jamming effect, and it is difficult to be effectively suppressed by traditional anti-jamming methods. In order to solve this problem, an anti-jamming method for linear frequency modulation (LFM) pulse compression radar system is proposed. The proposed method is combined with fractional Fourier transform (FrFT) and fractional domain filtering strategy. Here, first, the FrFT transform is performed on the received signal, and the optimal transform angle is calculated by calculating the fractional fourth-order origin moment. Then, the narrow-band filter is performed in the fractional domain to extract the target signal and suppress the jamming signal. Finally, fractional Fourier inverse transform is done to restore the target signal. The simulation results show that the method not only can effectively suppress smart noise jamming but also can accurately detect the position of the target. © 2019 Institution of Engineering and technology. All rights reserved.

关键词： Inverse problems

来源：评论

学校读者我要写书评

暂无评论

SAR target recognition combining class information and classification hyperplane

SAR target recognition combining class information and class...

引用

IET International Radar Conference (IET IRC 2020)

Automatic target recognition (ATR) in synthetic aperture radar (SAR) images plays an important role. An important step in SAR target recognition is to transform the raw data to a feature space with the property of within-class compactness and between-class separation. However, due to the problem of target aspect angle sensitivity in SAR images, the difference of some targets belonging to the same class in different poses is greater than the difference of targets belonging to different class in similar poses, which often causes misjudgement. To solve this problem, we first use the twin support vector machine (TWSVM) to obtain the classification hyperplane of each class. Then, based on the obtained hyperplane, the projection matrix is constructed by fusing the prior class information of the target. In this way, the sample can be close to the classification hyperplane of the same class and distant from the classification hyperplane of a different class. This algorithm can improve TWSVM's ability to classify SAR images. Experiments are conducted on the moving and stationary target acquisition and recognition (MSTAR) database. The results verify the effectiveness of the proposed algorithm.

关键词： support vector machines image recognition pattern classification radar target recognition feature extraction image classification radar imaging synthetic aperture radar

来源：评论

学校读者我要写书评

暂无评论

没有更多数据了...

全选清除本页清除全部题录导出标记到“检索档案”

共58页 << < 17 18 19 20 21 22 23 24 25 26 > >>

检索报告对象比较合并检索0

隐藏清空

合并搜索

回到顶部

执行限定条件

内容：

评分：

请选择保存的检索档案：

请选择收藏分类：

订阅名称：

通借通还

温馨提示：

图书名称：

借书校区：

取书校区：

手机号码：

邮箱地址：

一卡通帐号：

电话和邮箱必须正确填写，我们会与您联系确认。

联系人：

所在院系：

联系邮箱：

联系电话：

内蒙古自治区呼和浩特市赛罕区大学西街235号邮编: 010021

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：