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

8,747 篇 会议
441 篇 期刊文献
19 篇 学位论文
3 篇 科技报告
2 册 图书
1 篇 专利

馆藏范围

9,213 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

5,656 篇 工学
- 2,415 篇 信息与通信工程
- 2,363 篇 电气工程
- 2,229 篇 计算机科学与技术...
- 1,270 篇 软件工程
- 653 篇 控制科学与工程
- 636 篇 电子科学与技术（可...
- 600 篇 仪器科学与技术
- 448 篇 航空宇航科学与技...
- 361 篇 测绘科学与技术
- 237 篇 机械工程
- 229 篇 生物医学工程（可授...
- 214 篇 光学工程
- 164 篇 生物工程
- 92 篇 化学工程与技术
- 90 篇 土木工程
- 86 篇 船舶与海洋工程
- 83 篇 材料科学与工程（可...
- 76 篇 建筑学
- 76 篇 交通运输工程
2,788 篇 理学
- 1,661 篇 物理学
- 778 篇 数学
- 301 篇 统计学（可授理学、...
- 245 篇 系统科学
- 194 篇 生物学
- 167 篇 化学
- 95 篇 地球物理学
817 篇 医学
- 748 篇 临床医学
- 73 篇 基础医学(可授医学...
312 篇 管理学
- 176 篇 管理科学与工程(可...
- 141 篇 图书情报与档案管...
58 篇 军事学
30 篇 经济学
30 篇 农学
28 篇 法学
18 篇 文学
15 篇 教育学
5 篇 艺术学

主题

1,273 篇 signal processin...
583 篇 signal processin...
464 篇 signal to noise ...
351 篇 target tracking
319 篇 object detection
303 篇 feature extracti...
275 篇 training data
230 篇 data mining
220 篇 radar
212 篇 training
202 篇 data processing
199 篇 array signal pro...
192 篇 neural networks
189 篇 sensors
164 篇 signal resolutio...
159 篇 radar tracking
154 篇 speech processin...
153 篇 signal detection
152 篇 data models
151 篇 synthetic apertu...

机构

59 篇 national laborat...
35 篇 school of inform...
25 篇 nanjing research...
25 篇 univ connecticut...
24 篇 duke univ dept e...
24 篇 school of electr...
21 篇 xidian univ natl...
20 篇 national key lab...
20 篇 xidian universit...
20 篇 national lab of ...
17 篇 school of inform...
17 篇 university of el...
17 篇 department of el...
16 篇 national key lab...
16 篇 beijing institut...
15 篇 xidian universit...
14 篇 college of elect...
13 篇 xidian univ natl...
13 篇 university of ch...
12 篇 univ chinese aca...

作者

28 篇 bar-shalom yaako...
23 篇 willett peter
21 篇 kirubarajan t.
19 篇 bar-shalom y
17 篇 jun wang
17 篇 blair wd
14 篇 xu shuwen
14 篇 liu hongwei
14 篇 drummond oliver ...
13 篇 collins leslie m...
13 篇 shui penglang
13 篇 bin yang
12 篇 wang wei
12 篇 torrione peter a...
12 篇 mengdao xing
12 篇 liao guisheng
11 篇 drummond oe
11 篇 abdelhak m. zoub...
10 篇 morton kenneth d...
10 篇 sinha a

语言

8,958 篇 英文
157 篇 中文
60 篇 其他
34 篇 土耳其文
2 篇 法文
2 篇 俄文

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

共 9213 条记录，以下是4731-4740 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：

Goodput Improvement with Low–Latency in data Center Network 4th

Goodput Improvement with Low–Latency in Data Center Network

引用

4th International conference on Machine Intelligence and signal processing, MISP 2022

作者： Ramkumar, M.P. Emil Selvan, G.S.R. Mahalakshmi, M. Jeyarohini, R. Department of Computer Science and Engineering Thiagarajar College of Engineering Tamilnadu Madurai India Department of Electronics and Communication Engineering PSNA College of Engineering and Technology Tamilnadu Dindigul India

ISBN: (纸本)9789819900466

data Center is used for large data storage in various industries and Organizations. The data traversing from source to destination is a process of data Center Network (DCN). In the recent years, many modified TCP protocols were used because TCP is not capable for DCN. One of the modified TCP protocol snamed MPTCP (Multipath Transmission control Protocol) is efficient for DCN. This protocol has multiple paths from data transferring. MPTCP is highly suitable for a large volume of data packets and not efficient for transferring a small volume of data packets. The transmission of the small and large volume of data packets takes equal time, so there is a possibility of latency in time. These MPTCP issues are eliminated by the proposed Enhanced Multipath Transmission Control Protocol (EMPTCP). This protocol further adds a Packet sprinkling method to resolve the issues like low data transmission, timeout, and latency of MPTCP. Packet Sprinkling is a method which focuses on sending a small volume of data packets effectively. MPTCP is also efficient for transmission of the large volume of data packets. The implementation is done in NS2 for evaluating the proposed protocol and the results show that EMTCP is better than MPTCP. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Digital storage

来源：评论

学校读者我要写书评

暂无评论

High-Resolution Neural Network processing of LFM Radar Pulses

High-Resolution Neural Network Processing of LFM Radar Pulse...

引用

International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Jabran Akhtar Norwegian Defence Research Establishment (FFI) Kjeller Norway

In radar applications, the bandwidth of a transmitted pulse determines the range resolution and the ability to disclose densely spaced targets. The processing of radar signals is often carried out through matched filtering (MF) which aims to maximize the signal to noise ratio. This work presents an alternative processing scheme for oversampled radar signals based on small-sized neural networks. The networks are trained with an objective to return MF outcomes corresponding to a higher bandwidth pulse. The article demonstrates how such a neural network design can be constructed and compares against traditional processing and detection.

关键词： Matched filters Filtering Neural networks Radar detection Bandwidth Radar signal processing Speech processing

来源：评论

学校读者我要写书评

暂无评论

9th International conference on Image and signal processing, ICISP 2020

9th International Conference on Image and Signal Processing,...

引用

9th International conference on Image and signal processing, ICISP 2020

ISBN: (纸本)9783030519346

The proceedings contain 40 papers. The special focus in this conference is on Image and signal processing. The topics include: Incep-eegnet: A convnet for motor imagery decoding;fuzzy-based approach for assessing traffic congestion in urban areas;big data and reality mining in healthcare: Promise and potential;a dataset to support sexist content detection in arabic text;multistage deep neural network framework for people detection and localization using fusion of visible and thermal images;diagnosing tuberculosis using deep convolutional neural network;semantic segmentation of diabetic foot ulcer images: Dealing with small dataset in dl approaches;dermoNet: A computer-aided diagnosis system for dermoscopic disease recognition;a new method of image reconstruction for pet using a combined regularization algorithm;towards the tactile discovery of cultural heritage with multi-approach segmentation;visualizing blood flow of palm in different muscle tense state using high-speed video camera;segmentation of microscopic image of colorants using u-net based deep convolutional networks for material appearance design;a deep cnn-lstm framework for fast video coding;microcontrollers on the edge – is esp32 with camera ready for machine learning?;speech enhancement based on deep autoencoder for remote arabic speech recognition;handwriting based gender classification using cold and hinge features;extraction and recognition of bangla texts from natural scene images using cnn;detection of elliptical traffic signs;image-based place recognition using semantic segmentation and inpainting to remove dynamic objects;CNN-svm learning approach based human activity recognition;convolutional neural networks backbones for object detection;graph-based image retrieval: State of the art.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Automatic Modulation Classification Based on the Multi-channel Large-kernel Convolutional Neural Network 23

Automatic Modulation Classification Based on the Multi-chann...

引用

23rd IEEE International conference on Communication Technology, ICCT 2023

作者： Hu, XiaoLei Cai, ZuoRan Zhou, RuoYu School of Physics and Electronic Information YanTai University YanTai China

ISBN: (纸本)9798350325959

Automatic Modulation Classification (AMC) has become increasingly significant in spectrum management, signal detection, and cognitive radio domains. In deep learning, networks such as Transformer and Vits have gained popularity. Inspired by these networks, the Large Kernel Neural Network has been widely adopted to enhance the receptive field and model performance. Although the Large Kernel Neural Network consumes fewer floating-point operations per second (FLOPS) than small kernel networks, it incurs higher memory access costs. In contrast to small kernel neural networks, large kernel neural networks are more suitable for handling large datasets. This paper employs the data Truncation Migration Algorithm to further enhance the extracted features. The RML2016.10A dataset is utilized, and the amplitude and phase information are augmented in the channel dimension to enhance the dataset features and increase the dataset size. However, processing such a large dataset with conventional neural networks takes time. Therefore, a Multi-Channel Large Kernel Convolutional Neural Network (MCLCNN) is introduced, decomposing the network into four parallel branches along the channel dimension, consisting of a square kernel, two orthogonal kernels, and a unit mapping. By utilizing the MCLCNN, processing such a large dataset becomes feasible, achieving approximately 90% accuracy at an SNR of 4dB. © 2023 IEEE.

关键词： Automatic modulation classification data truncation migration Deep learning Large Convolutional Kernel

来源：评论

学校读者我要写书评

暂无评论

Geo-location of mines detected via vehicular-mounted forward-looking sensors

Geo-location of mines detected via vehicular-mounted forward...

引用

conference on Detection and Remediation Technologies for Mines and Minelike targets VI

作者： Cook, G Sherbondy, K Jakkidi, S George Mason Univ Dept Elect & Comp Engn Fairfax VA 22030 USA

ISBN: (纸本)0819440892

This paper is concerned with the problem of target geo-location when using forward-looking vehicular-mounted sensors for landmine detection. Intermediate and downward-looking sensors may also be used, but the geolocation problem is most complex for the forward-looking sensor. A nonlinear state model for the vehicle is developed and a Kalman filter is combined with the available measurements to estimate the vehicle position and attitude. Knowledge of the sensors specifications along with information as to the location and orientation of the sensor on the vehicle combined with knowledge of the vehicle position and attitude make it possible for one to compute the sensor field-of-view or footprint. Given this, one can then analyze sensor frames and for any detected mines, convert their locations from sensor-frame coordinates to ground coordinates. The vehicle model, the Kalman filter equations, the coordinate transformations and other algorithms required for geo-locating any detected mines are presented and implemented in a simulation. Actual data collected at a test site is then used as input to the simulation. The simulation results indicate that for forward-looking sensors mine geo-location can in fact be accomplished. The actual position estimates are shown to have precision (ellipses of 0.9 confidence level) of less than 0.25 meters for the particular data used. Precise geo-location is one of the essential elements for mine neutralization.

关键词： Sensors Mining Land mines Antennas Filtering (signal processing) Target detection Image sensors data modeling Lithium Motion models

来源：评论

学校读者我要写书评

暂无评论

Multi-dimensional super-resolution of off-grid dense multi-targets based on atomic norm minimization

Multi-dimensional super-resolution of off-grid dense multi-t...

引用

signal, Information and data processing (ICSIDP), IEEE International conference on

作者： Tianjing Chen Chaoshen Xiao Yuyang Shao Hui Ma Hongwei Liu National Key Laboratory of Radar Signal Processing Xidian University Xi’an China

ISBN: (数字)9798331515669

ISBN: (纸本)9798331515676

Sparse recovery algorithm can achieve super-resolution processing of targets by handling the received signals and dictionary matrix. However, when dealing with dense multitargets that require multi-dimensional super-resolution, the dictionary matrix significantly impacts the recovery results and can lead to grid mismatch issues. Moreover, most existing target recovery methods focus on two-dimensional super-resolution and are prone to off-grid situations. To address these challenges, this paper combines Atomic Norm Minimization (ANM) with the Sparse Bayesian Learning (SBL) algorithm to propose a multidimensional target super-resolution algorithm optimized based on atomic norm minimization theory. The simulation results show that this method effectively achieves multi-dimensional super-resolution of off-grid dense multi-targets.

关键词： Dictionaries Simulation Superresolution Minimization data processing Bayes methods Sparse matrices

来源：评论

学校读者我要写书评

暂无评论

RadarCNN: Learning-based Indoor Object Classification from IQ Imaging Radar data

RadarCNN: Learning-based Indoor Object Classification from I...

引用

IEEE Radar conference (RadarConf)

作者： Haegele, Stefan Seguel, Fabian Salihu, Driton Zakour, Marsil Steinbach, Eckehard Tech Univ Munich Sch Computat Informat & Technol Chair Media Technol Munich Inst Robot & Machine Intelligence Munich Germany

ISBN: (纸本)9798350329216;9798350329209

Radar sensors operating in the mmWave frequency range face challenges when used as indoor perception and imaging devices, primarily due to noise and multipath signal distortions. These distortions often impair the sensors' ability to accurately perceive and image the indoor environment. Nevertheless, this sensor offers distinct advantages over camera and LiDAR sensors. This encompasses the estimation of object reflectivity, known as radar cross-section (RCS), and the ability to penetrate through objects that are thin or have low reflectivity. This results in a 'through-the-wall' sensing capability. Due to the aforementioned disadvantages, most research in the field of imaging radar tends to exclude indoor areas. We introduce a machine learning-based mmWave MIMO FMCW imaging radar object classifier designed to identify small, hand-sized objects in indoor settings, utilizing only radar IQ samples as input. This system achieves 97-99% accuracy on our test set and maintains approximately 50% accuracy even under challenging conditions, such as increased background noise and occlusion of sample objects, without the need for adjusting training or pre-processing. This demonstrates the robustness of our approach and offers insights into what needs to be improved in the future to achieve generalization and very high accuracy even in the presence of significant indoor perturbations.

关键词： mmWave radar imaging radar classification signal processing machine learning deep learning indoor environments digital twin

来源：评论

学校读者我要写书评

暂无评论

The Group Assignment Problem

The Group Assignment Problem

引用

signal and data processing of small targets 2003

作者： Poore, Aubrey B. Gadaleta, Sabino Numerica Corporation PO Box 251246 Fort Collins CO 80527-1246

Multiple frame data association, whether it is based on multiple hypothesis tracking or multi-dimensional assignment problems, has established itself as the method of choice for difficult tracking problems, principally due to the ability to hold difficult data association decisions in abeyance until additional information is available. Over the last twenty years, these methods have focused on one-to-one assignments, many-to-one, and many-to-many assignments. Group tracking, on the other hand, introduces new complexity into the association process, especially if some soft decision making capability is desired. Thus, the goal of this work is to combine multiple grouping hypotheses for each frame of data (tracks or measurements) with matching these hypotheses across multiple frames of data using one-to-one, many-to-one, or many-to-many assignments to determine the correct hypothesis on each frame of data and connectivity across the frames. The resulting formulation is sufficiently general to cover four broad classes of problems in multiple target tracking, namely (a) group cluster tracking, (b) pixel (clump) IR cluster tracking, (c) the merged measurement problem, and (d) MHT for track-to-track fusion. What is more, the cluster assignment problem for either two or multiple dimensions represents a generalized data association problem in the sense that it reduces to the classical assignment problems when there are no overlapping groups or clusters. The formulation of the assignment problem for resolved object tracking and candidate group methods for use in multiple frame group tracking are briefly reviewed. Then, three different formulations of the group assignment problem are developed.

关键词： data reduction

来源：评论

学校读者我要写书评

暂无评论

Wind Vector Inversion of ADS-B data Based on Improved PSO-PF Algorithm

Wind Vector Inversion of ADS-B Data Based on Improved PSO-PF...

引用

International conference on Guidance, Navigation and Control, ICGNC 2022

作者： Chen, Wantong Wu, Duo Ren, Shiyu Diao, Tianru Tianjin Key Laboratory of Intelligent Signal and Image Processing Civil Aviation University of China Tianjin300300 China School of Electronic Information and Automation Civil Aviation University of China Tianjin300300 China

ISBN: (纸本)9789811966125

At present, inversing wind vector from aircraft trajectory captured by automatic dependent surveillance-broadcast (ADS-B) is one of the main solutions to obtain wind field in aviation meteorology. However, some algorithms in this scheme still have some shortcomings, especially the poor performance when the aircraft turns at a small angle. In this paper, the geometric structure of ADS-B data is used to improve particle swarm optimized particle filter algorithm (PSO-PF) to inverse wind vectors in small area of single aircraft data and large area of multi-aircraft data respectively. The mean absolute error of wind estimation under different turning angles is evaluated. Experimental results show that the algorithm is feasible, and it still has a good inversion effect when the aircraft turns at a small angle. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Aircraft

来源：评论

学校读者我要写书评

暂无评论

targets Number Detection for Wideband Radar Based on Two-Dimensional Sparse Structure Model

Targets Number Detection for Wideband Radar Based on Two-Dim...

引用

IEEE International conference on signal processing, Communications and Computing

作者： Yichang Chen Le Zhou Yuanpeng Zhang Yanwu Zhu Wantian Wang Xin Xiong Air Force Early Warning Academy

ISBN: (纸本)9781728117096

A targets number detection method for conventional wide-band radar is proposed in this paper. Firstly, the echo signals received by radar at different slow-time are viewed as multiple measurement vectors (MMV) model. Based on the MMV model, the target echo sparse 2D spectrum in range-azimuth domain is reconstructed by simultaneous orthogonal matching pursuit (S-OMP) algorithm. Then, the detection threshold is determined according to the signal-to-noise ratio, and the binary spectrum of the echo data is obtained under this threshold. Finally, the target sorties are identified in the spectrogram by using the method of area labeling. Experimental results based on both simulated and real data demonstrated that the proposed method utilizes the two-dimensional information of the target and can improve the accuracy of sorties recognition. In addition, the proposed method and can realize the target sortie recognition from under-sampled echo data.

关键词： targets number detection Wideband radar Multiple measurement vectors Sparse structure Simultaneous orthogonal matching pursuit wideband radar targets two-dimensional spectra ECHO signal signal to noise ratios Spectrogram Structural Models

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500页 << < 470 471 472 473 474 475 476 477 478 479 > >>

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：