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

489 篇 会议
33 篇 期刊文献

馆藏范围

522 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

351 篇 工学
- 273 篇 信息与通信工程
- 137 篇 计算机科学与技术...
- 96 篇 软件工程
- 86 篇 电子科学与技术（可...
- 50 篇 航空宇航科学与技...
- 49 篇 控制科学与工程
- 41 篇 仪器科学与技术
- 38 篇 光学工程
- 27 篇 机械工程
- 20 篇 电气工程
- 20 篇 生物工程
- 19 篇 土木工程
- 17 篇 建筑学
- 16 篇 化学工程与技术
- 10 篇 环境科学与工程（可...
- 9 篇 测绘科学与技术
- 8 篇 安全科学与工程
- 6 篇 动力工程及工程热...
166 篇 理学
- 103 篇 数学
- 27 篇 统计学（可授理学、...
- 26 篇 生物学
- 24 篇 物理学
- 18 篇 化学
- 12 篇 大气科学
- 8 篇 地质学
- 8 篇 系统科学
41 篇 管理学
- 22 篇 管理科学与工程(可...
- 20 篇 图书情报与档案管...
13 篇 农学
- 8 篇 农业资源与环境
- 5 篇 作物学
11 篇 军事学
1 篇 经济学
1 篇 法学
1 篇 教育学

主题

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

机构

215 篇 beijing key labo...
147 篇 beijing key labo...
72 篇 radar research l...
61 篇 school of inform...
50 篇 beijing institut...
36 篇 advanced technol...
33 篇 beijing key labo...
25 篇 chongqing innova...
21 篇 radar technology...
19 篇 radar research l...
17 篇 beijing key labo...
17 篇 school of electr...
15 篇 school of inform...
13 篇 beijing key labo...
12 篇 beijing key labo...
11 篇 zhengzhou academ...
11 篇 shanghai institu...
10 篇 beijing institut...
9 篇 the advanced tec...
8 篇 school of inform...

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
20 篇 linbo tang
20 篇 wang rui
19 篇 zhang liang
19 篇 yanhua wang
19 篇 tang linbo
18 篇 zhao baojun
17 篇 teng long

语言

511 篇 英文
8 篇 其他
3 篇 中文

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

共 522 条记录，以下是61-70 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：

SAR-DARTS: A CONVOLUTIONAL NEURAL ARCHITECTURE SEARCH FOR SAR SCENE CLASSIFICATION

SAR-DARTS: A CONVOLUTIONAL NEURAL ARCHITECTURE SEARCH FOR SA...

引用

IET International Radar Conference 2023, IRC 2023

作者： Zhou, Tian Xu, Yinsheng Li, Qihai Qi, Baogui Chen, He Chen, Liang Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing100081 China Yangtze Delta Region Academy of Beijing Institute of Technology Zhejiang Jiaxing314019 China Shanghai Institute of Satellite Engineering Shanghai200240 China

ISBN: (纸本)9781839539954

In recent years, the collaborative service architecture of cloud computing and edge computing to reduce the demand for computing, storage and other capabilities of artificial intelligence methods has developed rapidly, but their application in synthetic aperture radar (SAR) image scene classification is still immature. In this paper, we propose a neural architecture search and pruning model for SAR scene classification. By introducing network morphisms and path regularization method, we can reduce the unfair competition between cells on the basis of expanding the search space. The model approach is applied to the FUSAR-Ship and OpenSARShip datasets for performance evaluation. The proposed method can achieve higher accuracy with fewer parameters. © The Institution of Engineering & technology 2023.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

A MODEL COMPRESSION METHOD BASED ON MULTI-TEACHER DISTILLATION FOR SAR SCENE CLASSIFICATION

A MODEL COMPRESSION METHOD BASED ON MULTI-TEACHER DISTILLATI...

引用

IET International Radar Conference 2023, IRC 2023

作者： Li, Qihai Zhang, Jiawei Zhou, Tian Qi, Baogui Chen, He Chen, Liang Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing100081 China Yangtze Delta Region Academy of Beijing Institute of Technology Zhejiang Jiaxing314019 China Shanghai Institute of Satellite Engineering Shanghai200240 China

ISBN: (纸本)9781839539954

In recent years, with the advancement of deep learning, convolutional neural networks tailored for processing image content have gained increasing attention within the remote sensing imagery domain. However, deep convolutional neural networks that exhibit exceptional performance often encompass a substantial number of parameters, thereby necessitating heightened computational resources and time for network training and prediction. Given the constraints posed by resource limitations in the realm of remote sensing, deploying such high-performance networks onto these devices becomes a challenging endeavor. To address this set of challenges, this paper introduces a methodology grounded in target differential probability for multi-teacher knowledge distillation. This approach effectively reduces the model's complexity while preserving the network models' commendable performance. By conducting a statistical analysis of the model's output probability distribution and computing the differential probabilities between teacher and student networks, we regulate the output proportions of teachers within the multi-teacher model. As a result, a high-performing student network is cultivated. In aggregate, experimental validation on two distinct datasets substantiates the efficacy of this approach in facilitating efficient knowledge transfer, consequently yielding heightened performance outcomes. © The Institution of Engineering & technology 2023.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Research on a Method of realizing High Performance Ultra-Long Pulse Compression Processor Based on FPGA

Research on a Method of Realizing High Performance Ultra-Lon...

引用

IET International Radar Conference 2023, IRC 2023

作者： Wu, HongHao Qiao, TingTing Chen, He Xie, YiZhuang Li, YongRui Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing100081 China Yangtze Delta Region Academy of Beijing Institute of Technology Zhejiang Jiaxing314019 China

ISBN: (纸本)9781839539954

Pulse compression plays a vital role in radar signal processing. With the rapid development of synthetic aperture radar (SAR) imaging technology, the demand for spaceborne SAR imaging processing is becoming more and more urgent, especially the need to support the real-time application of ultra-long pulse compression accelerators. However, limited on-chip resources and memory access bandwidth have always been key bottlenecks restricting the design of ultra-long pulse compression processing. FPGA is widely regarded as a processor that is both highly parallelized and has excellent computing speed. Therefore, this research is committed to proposing a new processor design method based on FPGA to cope with ultra-long point pulse compression processing. First, to be able to effectively handle ultra-long sequence pulse compression up to 1M points, we propose an architecture with careful optimization. Secondly, in order to improve processing efficiency and reduce resource overhead, we propose a fixed-point data fitting implementation strategy. Finally, this study also explores an on-chip storage optimization method for reference function spectrum for linear frequency modulation (LFM) signals, which not only effectively reduces the cost of on-chip resources, but also saves storage transmission bandwidth. These innovative designs and optimization strategies will provide strong support for further development in the field of ultra-long-point pulse compression processing. © The Institution of Engineering & technology 2023.

关键词： Bandwidth compression

来源：评论

学校读者我要写书评

暂无评论

A Quantitative Estimation Method of Migrating Insects Using Weather Radar by High Resolution Radar Joint Observation

A Quantitative Estimation Method of Migrating Insects Using ...

引用

IET International Radar Conference 2023, IRC 2023

作者： Li, Jiayi Cui, Kai Hu, Cheng Wang, Rui Mao, Huafeng Radar Research Laboratory School of Information and Electronics Beijing Institute of Technology Beijing100081 China Key Laboratory of Embedded Real-time Information Processing Technology Radar Research Laboratory School of Information and Electronic Beijing Institute of Technology Beijing100081 China School of Computer Sciences Beijing Institute of Technology Beijing100081 China Advanced Technology Research Institute Beijing Institute of Technology Jinan250300 China

ISBN: (纸本)9781839539954

The main migration animals in China are insects, and a large number of large-scale seasonal migration insects have a great impact on ecology. The establishment of a comprehensive early warning system for migratory insects necessitates the implementation of large-scale macro monitoring. Among the array of methodologies available, weather radar stands out as an efficacious tool for facilitating extensive macro-level monitoring. Nonetheless, several unresolved challenges persist in accurately quantifying the density of vast insect populations on a large scale. This article introduces an innovative method for insect quantification through the collaborative use of weather radar and high-resolution phased array radar for insect target observation and joint data analysis. This novel method harnesses the collective observational data derived from both weather radar and high-resolution radar systems, harmonizing the temporal and spatial dimensions of these datasets. By establishing a nuanced mapping correlation between insect density and weather radar reflectivity, this method enables the determination of insect density within specific geographical regions. In the final part of this article, joint observation experiments were conducted to examine the experimental data collected from operating weather radar and specialized phased array radar systems. A total of 244 days of data were analysed, and the proposed method was rigorously validated. © The Institution of Engineering & technology 2023.

关键词： Radar reflection

来源：评论

学校读者我要写书评

暂无评论

Dual-Level Contrast Enhancement Method for Infrared Small Target Detection

Dual-Level Contrast Enhancement Method for Infrared Small Ta...

引用

Signal, information and Data processing (ICSIDP), IEEE International Conference on

作者： Haotian Sun Wenzheng Wang Yuhan Xu Qiuyu Jin Zipeng Zhang Beijing Key Laboratory of Embedded Real-Time Information Processing Technology Beijing Institute of Technology Beijing China School of Information and Electronics Beijing Institute of Technology Beijing China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

In the field of target detection, infrared small target detection(ISTD) is a critical area of research, playing a vital role in reconnaissance, guidance, and early warning systems. Existing spatial Filtering algorithms perform well in clean backgrounds; however, as these filters require manual design based on prior knowledge, they are prone to interference from complex scenes (e.g., clouds, buildings), leading to target omission. Small targets typically exhibit blurry features and weak energy, making direct detection susceptible to interference from high-intensity areas, which may result in missed detections. To tackle these challenges, this paper presents a two-tier contrast improvement method (DLCE). First, a gradient filter group is used to enhance the signal-to-noise ratio (SNR) of the target, and then, built upon the concept of local contrast measurement (LCM), a novel intensity calculation method is developed. The new contrast window design effectively enhances the target’s SNR. Compared with existing algorithms, like MPCM (Multi-Scale Patch-based Contrast Measurement) on publicly available datasets, the proposed method more effectively enhances the target’s radiative intensity while suppressing background noise. The effectiveness of the proposed algorithm is confirmed by the simulation results.

关键词： Filters Simulation Object detection Interference Reconnaissance Robustness Windows Noise measurement Signal to noise ratio Visual perception

来源：评论

学校读者我要写书评

暂无评论

An Improved Method for Rockfall Detection and Tracking Based on Video Stream

An Improved Method for Rockfall Detection and Tracking Based...

引用

IET International Radar Conference 2023, IRC 2023

作者： Wang, Longyue Wang, Songge Xie, Xin Deng, Yunkai Tian, Weiming Radar Research Lab School of Information and Electronics Beijing Institute of Technology Beijing China Chongqing Innovation Center Beijing Institute of Technology Chongqing China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing China Advanced Technology Research Institute Beijing Institute of Technology Jinan China

ISBN: (纸本)9781839539954

Rockfall events occur frequently in mountainous areas. To address the problems of missed detection, false detection, and trajectory interruption when using the deep learning-based online multiple object tracking methods to detect rockfalls, this paper proposes a rockfall detection and tracking method based on video streams. In the detection stage, three-frame difference method is utilized to obtain the moving targets from the video streams, and they are combined with the detection results of the rock detector obtained by the offline-trained YOLOX model. In the tracking stage, data association is firstly performed based on the rockfall detection results. For the existing trajectories that are not matched at the current moment, re-matching is performed by combining the moving object detection results to achieve accurate tracking of rockfalls. Simulations and field experiments prove that the detection method proposed in this paper can effectively separate the rockfalls in the video, and the detected rockfalls have high precision. Besides, it significantly improves the accuracy of rockfall tracking, effectively suppressing phenomena such as trajectory interruption during tracking. © The Institution of Engineering & technology 2023.

关键词： Rock bursts

来源：评论

学校读者我要写书评

暂无评论

AN MULTILAYER FUSION STRATEGY BASED ON IMPROVED YOLOV5 FOR SHIP DETECTION IN SAR IMAGES

AN MULTILAYER FUSION STRATEGY BASED ON IMPROVED YOLOV5 FOR S...

引用

IET International Radar Conference 2023, IRC 2023

作者： Chen, Fan Shi, Hao Zhao, Liangbo Mao, Yongfei Pan, Hongxin Chen, Liang Radar Research Lab Beijing Institute of Technology Beijing China General Department of Remote Sensing Satellites China Academy of Space Technology China Beijing Institute of Technology Chongqing Innovation Center Chongqing China Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing China

ISBN: (纸本)9781839539954

Since entering the era of deep learning, the single-stage detection algorithm represented by YOLO has achieved some progress in the detection of ship targets by synthetic aperture radar (SAR). However, the accuracy of single-stage detection is lower than that of two-stage detection, especially for small target detection. To this end, this paper proposes an improved YOLOv5 detection method based on convolutional bolck attention module CBAM) and WPAN. At the same time, the focal efficient intersection over union (Focal EIoU) is introduced to optimize the calculation of bounding box regression loss. The experiment is verified on SAR ship detection dataset(SSDD). The results show that the proposed improved YOLOv5 algorithm can improve the false alarm and missing detection problems in multi-scale target detection, and improve the detection accuracy consequently. © The Institution of Engineering & technology 2023.

关键词： Synthetic aperture radar

来源：评论

学校读者我要写书评

暂无评论

DESIGN AND IMPLEMENTATION OF A CONFIGURABLE PARALLEL FFT PROCESSOR IN ONBOARD SAR IMAGING SYSTEM BASED ON FPGA

DESIGN AND IMPLEMENTATION OF A CONFIGURABLE PARALLEL FFT PRO...

引用

IET International Radar Conference 2023, IRC 2023

作者： Xu, Ming Zhang, Jiawei Li, Yongrui Yin, Yifei Zhang, Ao Dong, Heng Chen, Liang Shi, Hao Beijing Key Laboratory of Embedded Real-time Information Processing Technology Beijing Institute of Technology Beijing100081 China Shanghai Institute of Satellite Engineering Shanghai201109 China Sichuan TianFu New Area Beijing Institute of Technology Innovation Equipment Research Institute Chengdu610299 China

ISBN: (纸本)9781839539954

Spaceborne Synthetic Aperture Radar (SAR) plays an important role in the field of emergency disaster reduction. In order to meet the stringent timeliness requirements in onboard SAR data processing, this paper proposes a configurable four-channel parallel FFT processor, which serves as the core of SAR imaging processing. This processor is capable of supporting up to 128K-point FFT operations. Additionally, it supports radix-2 time decimation FFT concatenation processing, radix-4 time decimation FFT concatenation processing, radix-2 frequency decimation FFT concatenation processing, and radix-4 frequency decimation FFT concatenation processing, all of which can be flexibly configured based on data structure. This processor is implemented on the Xilinx Virtex-7 XC7VX690T FPGA and can achieve a maximum FFT processing capability of 128K points at a frequency of 300MHz. The evaluation and analysis results demonstrate that the processor reaches a higher processing rate, effectively meeting the demanding requirements for efficient spaceborne processing. © The Institution of Engineering & technology 2023.

关键词： Synthetic aperture radar

来源：评论

学校读者我要写书评

暂无评论

Dual-Band HRRP Fusion Recognition via Wavelet Decomposition embedded Autoencoder 2

Dual-Band HRRP Fusion Recognition via Wavelet Decomposition ...

引用

2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： Wang, Weijia Qi, Zhuchang Wang, Lue Yang, Wei Zhang, Liang Wang, Yanhua Xi'an Institute of Modern Control Technology Xi'an710065 China Beijing Institute of Technology Zhengzhou Academy of Intelligent Technology Zhengzhou450000 China Radar Technology Research Institute Beijing Institute of Technology Beijing100081 China Beijing Key Laboratory of Embedded Real-Time Information: Processing Technology Beijing100081 China Beijing Institute of Technology Chongqing Innovation Center Chongqing401120 China

ISBN: (纸本)9798331515669

High-resolution range profile (HRRP) is a crucial technique for radar target recognition. Due to the coupling of azimuthal scattering information and aspect sensitivity, fine target recognition using HRRP remains challenging. One way to mitigate HRRP aspect sensitivity is through enhancing the dimensionality of radar information acquisition. Therefore, this paper proposes a Dual-Band HRRP Fusion Recognition Model via Wavelet Decomposition embedded Autoencoder (DF-WD-AENet). The method we proposed mainly comprises two modules. Firstly, the autoencoder model based on wavelet decomposition (WD-AEM) achieves constraint on feature information, making it better suited for dual-band fusion. Next is the dual-band information fusion module (DFM), in which we achieved the interaction and fusion of feature information through convolutional block attention module (CBAM) and omni-dimensional dynamic convolution (ODConv), and used a fusion loss function to train the model. Experiment shows that the recognition rate of our method is 7.73% higher than that of single-band, proving the effectiveness of the proposed method. © 2024 IEEE.

关键词： Wavelet decomposition

来源：评论

学校读者我要写书评

暂无评论

Dual-Band HRRP Recognition via Wavelet Packet Decomposition and Redundancy Reduction Model 2

Dual-Band HRRP Recognition via Wavelet Packet Decomposition ...

引用

2nd IEEE International Conference on Signal, information and Data processing, ICSIDP 2024

作者： Yang, Wei Qi, Zhuchang Wu, Heke Li, Yang Zhang, Liang Wang, Yanhua Radar Technology Research Institute Beijing Institute of Technology Beijing100081 China Zhengzhou Academy of Intelligent Technology Beijing Institute of Technology Zhengzhou450000 China Xi'an Institute of Modern Control Technology Xi'an710065 China Beijing Key Laboratory of Embedded Real-time Information: Processing Technology Beijing100081 China Chongqing Innovation Center Beijing Institute of Technology Chongqing401120 China

ISBN: (纸本)9798331515669

High resolution range profiles (HRRP) is widely used in radar automatic target recognition. Due to aspect sensitivity, HRRP of different targets can be similar under certain aspects, making it difficult for recognition via traditional methods. One way to mitigate it is to increase the frequency dimension, utilizing the differences of target frequency response to extract inter-class discriminative information. Therefore, we propose a HRRP recognition model based on redundancy reduction criterion and wavelet packet decomposition. Specifically, we use deep wavelet packet decomposition to perform multi-level decomposition of dual-band HRRP and adaptively extract discriminative features of sub-wavelets;we use redundancy reduction layer to cluster intra-class features and expand inter-class features, thereby reducing feature heterogeneity. Furthermore, we propose a deep discriminative correlation analysis for joint analysis and transformation of dual-band features to fuse complementary information. Experimental results show that proposed method has excellent recognition performance. Additionally, analysis study also verify the effectiveness of each submodule. © 2024 IEEE.

关键词： Wavelet decomposition

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53页 << < 3 4 5 6 7 8 9 10 11 12 > >>

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：