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

847 篇 会议
17 篇 期刊文献
1 篇 学位论文

馆藏范围

865 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

629 篇 工学
- 348 篇 电气工程
- 193 篇 计算机科学与技术...
- 185 篇 信息与通信工程
- 121 篇 软件工程
- 88 篇 航空宇航科学与技...
- 87 篇 测绘科学与技术
- 38 篇 电子科学与技术（可...
- 36 篇 仪器科学与技术
- 31 篇 控制科学与工程
- 30 篇 光学工程
- 11 篇 机械工程
- 9 篇 建筑学
- 9 篇 土木工程
- 8 篇 船舶与海洋工程
- 7 篇 生物医学工程（可授...
- 7 篇 安全科学与工程
- 6 篇 水利工程
- 6 篇 化学工程与技术
- 6 篇 生物工程
408 篇 理学
- 327 篇 物理学
- 58 篇 数学
- 19 篇 统计学（可授理学、...
- 18 篇 系统科学
- 11 篇 化学
- 11 篇 海洋科学
- 11 篇 生物学
- 6 篇 地球物理学
115 篇 医学
- 113 篇 临床医学
19 篇 管理学
- 10 篇 图书情报与档案管...
- 9 篇 管理科学与工程(可...
7 篇 军事学
4 篇 法学
3 篇 经济学
3 篇 文学
1 篇 教育学

主题

99 篇 signal processin...
74 篇 sensors
72 篇 target detection
67 篇 target tracking
62 篇 radar
60 篇 detection and tr...
55 篇 signal to noise ...
45 篇 object detection
44 篇 filtering (signa...
41 篇 signal processin...
40 篇 data association
36 篇 data fusion
33 篇 error analysis
31 篇 electronic filte...
30 篇 signal detection
30 篇 feature extracti...
30 篇 radar signal pro...
28 篇 data processing
26 篇 clutter
25 篇 data modeling

机构

19 篇 univ connecticut...
10 篇 cyberrnd inc cul...
7 篇 univ calif san d...
6 篇 numerica corp lo...
6 篇 univ connecticut...
5 篇 key laboratory o...
5 篇 numerica corp lo...
4 篇 qinetiq ltd malv...
4 篇 nanjing research...
4 篇 univ connecticut...
4 篇 raytheon co walt...
4 篇 national key lab...
4 篇 national laborat...
3 篇 beijing key labo...
3 篇 royal institute ...
3 篇 mcmaster univ de...
3 篇 numerica corp ft...
3 篇 sri int menlo pk...
3 篇 radar research l...
3 篇 mcmaster univ ec...

作者

19 篇 kirubarajan t
18 篇 bar-shalom y
17 篇 kirubarajan t.
17 篇 bar-shalom yaako...
14 篇 willett peter
12 篇 blair wd
10 篇 drummond oliver ...
9 篇 tharmarasa r.
8 篇 sinha a
7 篇 drummond oe
7 篇 willett p
6 篇 huang jim
6 篇 daum fred
5 篇 maskell s
5 篇 romeo kevin
5 篇 barshalom y
5 篇 danford scott
5 篇 xing mengdao
4 篇 pattipati kr
4 篇 paffenroth randy

语言

856 篇 英文
4 篇 其他
3 篇 中文
2 篇 土耳其文

检索条件"任意字段=Conference on Signal and Data Processing of Small Targets 2000"

共 865 条记录，以下是21-30 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：

Infrared small target detection based on long-short term frame difference algorithm(LSTFD) 6

Infrared small target detection based on long-short term fra...

引用

6th International conference on Intelligent Control, Measurement and signal processing, ICMSP 2024

作者： Zhu, Bing Shi, Jiahui Yu, Ruixing Gao, Weixin Wang, Siyu Xi'an Shiyou University School of Electronic Engineering Xi'an China Northwestern Polytechnical University School of Astronautics Xi'an China

ISBN: (纸本)9798331516550

According to the characteristic that the position change of small infrared target in sequence images is more obvious than background and noise, an infrared small target detection algorithm based on multi-frame time domain information is proposed, called the Long-Short Term Frame Difference (LSTFD) algorithm. The algorithm first adopts the SURF (Speeded Up Robust Feature) alignment strategy to eliminate the relative motion of background pixels caused by camera shake and achieve global motion compensation. Then, the image sequence is constructed based on the current frame, and the computational results of long-interval difference and short-interval difference are combined to detect the infrared small targets. The experimental results show that the LSTFD algorithm can accurately detect moving targets with high detection accuracy and low false alarm rate under the condition of low signal-to-noise ratio. And the average detection rate can reach over 90% on five sets of experimental data. © 2024 IEEE.

关键词： Motion compensation

来源：评论

学校读者我要写书评

暂无评论

Target Location in Distributed MIMO radar based on Coherent processing 2

Target Location in Distributed MIMO radar based on Coherent ...

引用

2nd IEEE International conference on signal, Information and data processing, ICSIDP 2024

作者： Wei, Fa Yan, Shi Zhu, Shengqi Lan, Lan Li, Ximin Qi, Jiawei Ma, Runlong Xidian University National Key Laboratory of Radar Signal Processing Xi'an710071 China Beijing Institute of Remote Sensing Device Beijing100854 China

ISBN: (纸本)9798331515669

In this paper, the problem of target location is investigated in the distributed multiple-input multiple-output (MIMO) radar based on coherent processing. At the modelling stage, the distributed MIMO radar is divided into three separated subarrays, where both transmit and receive functions are used in subarrays 1 and 3, while only receive function is adopted in subarray 2. Furthermore, a small frequency increment is introduced among different antenna elements in the subarray 1, in the meantime, a phase modulating operation is performed for various antenna elements in the subarray 3. In this respect, the target location performance can be improved significantly by means of the transmit modulation in subarrays 1 and 3. Under the circumstances, the model of target location is established, and a two-step estimation technology is shown, where a small location sector with respect to targets are firstly obtained, and the accurate location are further estimated. Specifically, in order to reduce the computational cost required by the multi-dimensional search procedures, an alternating procedure is developed by a sequence of 1-D searches obtained alternating between the optimization over each variable keeping the other fixed. Numerical results corroborate the effectiveness of the developed methods for the target location with distributed MIMO radar based on coherent processing. © 2024 IEEE.

关键词： Phase modulation

来源：评论

学校读者我要写书评

暂无评论

A Reinforcement Learning Agent Controlled Multi-branch small Object Detection Framework

A Reinforcement Learning Agent Controlled Multi-branch Small...

引用

2025 IEEE International conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Hong, Junkun Long, Yitian Luo, Yueyi Hua, Liujie Long, Jun Qi, Qianqian School of Computer Science and Engineering Central South University Changsha China Data Science Institute Vanderbilt University NashvilleTN United States School of Mathematics and Statistics Central South University Changsha China Big Data Institution Central South University Changsha China

ISBN: (纸本)9798350368741

The past few years have witnessed the immense development of small object detection, which is aimed at detecting size-limited targets in high-resolution images. The prevailing methods focus on extracting fine-grained information by expanding the receptive fields and then generating the potential small object region. However, these solutions inevitably add sophisticated detectors and extra learning components, which incur time-consuming and computation-costing. Meanwhile, we observe that it's suboptimal to extract fine features in such a generic way. To alleviate the issues, we propose a multi-branch small object detection framework with a regular-scale detection branch and a small-scale detection branch. Specifically, we design and pre-train a reinforcement learning agent to control feature extractors in both branches according to the results of small object areas. Moreover, we present a region clipping algorithm to rebuild the small object to regular size, which can be input into a mature detector directly. The extensive experiments on COCO, VisDrone, SODA-D, and our collecting TVDS datasets demonstrate our method outperforms the state-of-the-art methods in several metrics. © 2025 IEEE.

关键词： multi-branch small object detection region clipping region discriminator reinforcement learning agent

来源：评论

学校读者我要写书评

暂无评论

small animal intrusion detection based on RAFT

Small animal intrusion detection based on RAFT

引用

2024 International conference on Image, signal processing, and Pattern Recognition, ISPP 2024

作者： Li, Xiuguang He, Ninghui Sha, Weiyan Zhou, Xiu Ma, Bo Liu, Rui State Grid Ningxia Electric Power Co. Ltd. Electric Power Research Institute Ningxia Hui Autonomous Region Yinchuan City750001 China Beijing Smartchip Microelectronics Technology Company Limited BeiJing City100000 China

ISBN: (纸本)9781510680425

Due to the variety, size and shape of the invasive animals in substation, the general target detection and case segmentation tasks are often faced with the problems of fewer data and lack of *** flow estimation can accurately detect the boundary of the moving object through the change of optical flow during the moving process of the *** can effectively detect boundaries for small animals moving at low ***, RAFT for small animal intrusion detection can not only effectively eliminate the interference of non-invasive objects, but also cancel the range of intrusion targets, and improve the generalization ability of moving object intrusion *** experimental results show that the detection effect is good for small invasive animals with low speeds. © 2024 SPIE.

关键词： Invertebrates

来源：评论

学校读者我要写书评

暂无评论

Sixteenth International conference on signal processing Systems, ICSPS 2024

Sixteenth International Conference on Signal Processing Syst...

引用

16th International conference on signal processing Systems, ICSPS 2024

ISBN: (纸本)9781510689251

The proceedings contain 197 papers. The topics discussed include: variational modal decomposition based on whale optimization algorithm for vital signs detection of FMCW radar;sea surface small target detection using tri-median normalized doppler spectrum feature;a physical cluster-based passive scatterer detection method in ISAC systems;on detection of moving weak targets using MIMO continuous active sonar and Doppler filtering;design of optical testing turntable data acquisition system based on LabVIEW;AOA /TDOA location technology of weak satellite signal based on satellite-ground cooperation;bearing-crossing localization technology for UAV target based on signal characteristics;and separation and localization of multiple bat signals using frequency domain beamforming and dual-array-localization method.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A small target detection algorithm for aerial image with improved YOLOv5 3

A small target detection algorithm for aerial image with imp...

引用

3rd International conference on Electronic Information Engineering, Big data, and Computer Technology, EIBDCT 2024

作者： Xue, Xianli Jiao, Weidong Zhang, Siyuan Qu, Wenkang Tianjin Key Laboratory of Intelligent Signal and Lmage Processing Civil Aviation University of China TIANJIN300300 China

ISBN: (纸本)9781510680449

The problem of complex backgrounds, dramatic changes in the scale of objects and the presence of a large number of dense small targets in the UAV aerial images. In this study, a detection algorithm for small targets is proposed based on the YOLOv5s algorithm. Firstly, a new BottleNeck: C3LK module is designed for the C3 module using depthwise separable convolution to construct a large convolutional kernel on the YOLOv5 backbone network, which improves the ability to capture global information and compensates for the lack of local information for detecting small targets. Secondly, a small target detection layer and two feature fusion layers are added. At the same time, In order to better detect objects of different scales in complex backgrounds, a module for multi-scale feature extraction is designed by combining dilated convolution and attention mechanism. The experimental results show that the proposed algorithm improved 5.4% over YOLOv5s in terms of mAP@0.5 and over YOLOv7-tiny. The method meet the requirement of real-time detection in aerial image small target detection, and improve the detection performance in small target scenes. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Improved pedestrian detection algorithm of Yolov5 in dense scenes

Improved pedestrian detection algorithm of Yolov5 in dense s...

引用

2024 International conference on Image, signal processing, and Pattern Recognition, ISPP 2024

作者： Lin, Chuanwen Liang, Jiaxing School of Artificial Intelligence and Big Data Hefei University Hefei China Institute of Dataspace Hefei China

ISBN: (纸本)9781510680425

Occluded, small-scale pedestrians are easy to occur in dense scenes to solve this problem, an improved Yolov5 is proposed detection algorithm. First of all, feature extraction for small-size pedestrians is insufficient the problem uses SPD-Conv convolution to enhance the complex background small target feature extraction ability. Second, fuse an efficient multi-foot the degree of attention module further enhances the visual area of the target pedestrian feature extraction. Finally, focal eiou loss was used as the loss letter number, so that the regression more attention to high-quality samples, improve the accuracy of the model and robustness. Training and testing were performed on the dataset CrowdHuman in the test, the improved Yolov5 algorithm AP50 can reach 83.6 %, phase for the original algorithm, AP50 and AP50−95 are improved by 2.5 %, respectively. Average accuracy improved by 2.2%. The experimental results validate the proposed method effectiveness in crowded scenarios. © 2024 SPIE.

关键词： Detection and tracking algorithms Object detection Convolution Performance modeling Feature extraction small targets data modeling Evolutionary algorithms Target detection Statistical modeling

来源：评论

学校读者我要写书评

暂无评论

Bioradar signal processing Algorithms based on Machine Learning 2

Bioradar Signal Processing Algorithms based on Machine Learn...

引用

2nd IEEE International conference on Integrated Intelligence and Communication Systems, ICIICS 2024

作者： Guo, Huanyi Wuhan University of Bioengineering Hubei Wuhan China

ISBN: (纸本)9798331504960

Since human physiological signals (e.g., respiration and heart rate) generate very small Doppler shifts, they are highly susceptible to being masked by strong clutter backgrounds. The main research focus of this project is to accurately extract weak targets (i.e., vital signs) from complex environments. The signals received by BioRadar contain vital signs signals and clutter signals. Based on the analysis of the collected data, it is known that vital signals have low signal-to-noise ratio and are characterized by quasi-periodicity and multiple harmonics;whereas clutter can be manifested as Gaussian colored noise. For this reason, this study translates the bio-radar signal processing into the problems of harmonic modeling, clutter suppression, and signal-to-noise ratio enhancement in the presence of Gaussian colored noise. The research objective is to extract the weak vital sign signals of human body from the complex background by machine learning techniques. In order to effectively extract physiological signals such as respiration and heart rate, this paper employ an ultra-wideband bio-radar system and advanced signal processing algorithms, and the experimental results show that the respiratory signal of participant 1 at 0 s is 2.1 mV, the heartbeat signal is 1.0 mV, and the suppressed clutter signal is 0.1 mV, which effectively suppresses the clutter interference with high accuracy and reliability. © 2024 IEEE.

关键词： White noise

来源：评论

学校读者我要写书评

暂无评论

AugTarget data Augmentation for Infrared small Target Detection 48

AugTarget Data Augmentation for Infrared Small Target Detect...

引用

48th IEEE International conference on Acoustics, Speech and signal processing, ICASSP 2023

作者： Chen, Shengjia Zhu, Jiewen Ji, Luping Pan, Hongjun Xu, Yuhao University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu611731 China Sichuan University School of Computer Science and Engineering Chengdu610065 China

ISBN: (纸本)9781728163277

Sample shortage has always been a frequently-faced problem for the machine-learning models in infrared small target detection. As one of main limitations, it is hampering the further promotion of target detection performance. In this paper, we propose a simple and effective data augmentation scheme, AugTarget, to address this shortage issue of small target samples. Our scheme mainly consists of two crucial algorithms: target augmentation and batch augmentation. The former is designed to generate sufficient targets, by random target representation. The latter is devised to diversify training samples. Moreover, the initially-generated image samples of small targets are further enriched by randomly aggregating the feature representation of different images. The experiments on public datasets demonstrate that our AugTarget could bring an obvious improvement to mean intersection over union (mIoU). Cooperated by the augmentation of our AugTarget, the state-of-the-art (SOTA) method, AGPC could even achieve a distinct performance promotion by 3.03%, 2.17% and 2.63% on MDFA, SIRST-Aug and Merged datasets, respectively. In addition, the experimental results on three baseline models also show the universality & adaptivity of AugTarget to different dataset augmentation. Our source codes are available at https://***/UESTC-nnLab/AugTarget. © 2023 IEEE.

关键词： data augmentation Infrared small target detection Target & batch augmentation

来源：评论

学校读者我要写书评

暂无评论

An Ionospheric Pollution Correction Method Based on Parametric Time-Frequency Analysis of PCT 2

An Ionospheric Pollution Correction Method Based on Parametr...

引用

2nd IEEE International conference on signal, Information and data processing, ICSIDP 2024

作者： Zhao, Suqiang Li, Yajun Xiong, Huai Yao, Tianni Xu, Lin Ming, Xinyue East China Normal University Aerospace System Engineering Shanghai School of Communication and Electronic Engineering Shanghai China East China Normal University School of Communication and Electronic Engineering Shanghai China Shanghai Dianji University Micro-nano Satellite Intelligent Research Institute Shanghai China Aerospace System Engineering Shanghai Shanghai China

ISBN: (纸本)9798331515669

The ionosphere serves as the primary propagation medium for High-Frequency Sky-Surface Wave Radar (HFSSWR). However, its non-stationary and time-varying characteristics cause severe broadening of the first-order sea clutter spectrum in the combined sky-emitting and ground-receiving mode, hindering the detection of slow ship targets. To address this issue, this paper presents an ionospheric contamination correction method for HFSSWR based on the Polynomial Chirplet Transform (PCT) algorithm. Unlike the traditional Chirplet Transform (CT), which uses a sinusoidal kernel, this method employs a polynomial kernel for time-frequency analysis of the broadened echo spectrum. It extracts the phase contamination function by estimating the instantaneous frequency, thereby correcting the ionospheric phase path perturbation in the echo spectrum. This approach effectively suppresses sea clutter broadening caused by ionospheric perturbations. Simulation and experimental results demonstrate that, compared to conventional ionospheric decontamination methods, this method achieves superior suppression of broadened sea clutter spectra caused by both small- and large-amplitude ionospheric phase perturbations, improving the signal-to-noise ratio. This method effectively mitigates sea clutter broadening in HFSSWR. © 2024 IEEE.

关键词： signal to noise ratio

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87页 << < 1 2 3 4 5 6 7 8 9 10 > >>

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：