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

20,242 篇 会议
164 篇 期刊文献
80 册 图书
3 篇 学位论文

馆藏范围

20,489 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

12,755 篇 工学
- 8,298 篇 计算机科学与技术...
- 5,001 篇 软件工程
- 4,985 篇 信息与通信工程
- 4,638 篇 电气工程
- 1,447 篇 控制科学与工程
- 1,321 篇 电子科学与技术（可...
- 1,224 篇 光学工程
- 899 篇 生物医学工程（可授...
- 868 篇 机械工程
- 830 篇 仪器科学与技术
- 811 篇 生物工程
- 326 篇 网络空间安全
- 291 篇 交通运输工程
- 284 篇 安全科学与工程
- 278 篇 化学工程与技术
- 247 篇 动力工程及工程热...
- 189 篇 建筑学
- 179 篇 土木工程
4,662 篇 理学
- 2,149 篇 数学
- 2,072 篇 物理学
- 874 篇 生物学
- 668 篇 统计学（可授理学、...
- 522 篇 系统科学
- 330 篇 化学
1,523 篇 医学
- 1,405 篇 临床医学
- 383 篇 基础医学(可授医学...
- 269 篇 药学(可授医学、理...
1,368 篇 管理学
- 824 篇 管理科学与工程(可...
- 615 篇 图书情报与档案管...
- 208 篇 工商管理
204 篇 法学
136 篇 农学
78 篇 经济学
69 篇 军事学
64 篇 教育学
40 篇 文学
24 篇 艺术学
1 篇 哲学
1 篇 历史学

主题

1,120 篇 image segmentati...
1,067 篇 image coding
1,059 篇 signal processin...
1,010 篇 image processing
987 篇 internet of thin...
952 篇 feature extracti...
830 篇 image reconstruc...
631 篇 internet
553 篇 signal processin...
495 篇 signal to noise ...
476 篇 image edge detec...
475 篇 deep learning
456 篇 training
423 篇 image color anal...
400 篇 cameras
397 篇 robustness
395 篇 image enhancemen...
306 篇 image resolution
292 篇 image recognitio...
287 篇 visualization

机构

58 篇 school of inform...
39 篇 university of sc...
37 篇 signal processin...
29 篇 microsoft resear...
27 篇 university of ch...
26 篇 institute of sig...
26 篇 signal processin...
25 篇 center for signa...
24 篇 university of el...
23 篇 school of electr...
21 篇 ieee
21 篇 college of big d...
20 篇 school of cyber ...
20 篇 department of si...
20 篇 school of inform...
20 篇 signal and infor...
20 篇 school of electr...
19 篇 hsch darmstadt d...
19 篇 peng cheng labor...
19 篇 harbin institute...

作者

31 篇 li zhuo
27 篇 zhuo li
25 篇 sridharan sridha
23 篇 yang yang
23 篇 r.m. mersereau
20 篇 jing zhang
20 篇 wei li
20 篇 gabbouj moncef
19 篇 mansouri majdi
18 篇 zhang lei
18 篇 busch christoph
18 篇 m. gabbouj
17 篇 m.j.t. smith
17 篇 bouzrara kais
17 篇 clinton fookes
17 篇 wang wei
17 篇 zhang yan
16 篇 wang lei
16 篇 takahiro ogawa
16 篇 ghassan alregib

语言

19,942 篇 英文
461 篇 其他
75 篇 中文
7 篇 俄文
6 篇 土耳其文
1 篇 西班牙文

检索条件"任意字段=IEEE International Conference on Signal Image Technology and Internet Based Systems"

共 20489 条记录，以下是751-760 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：

Degraded Data Enhancement based on Regional Similarity Fusion 6

Degraded Data Enhancement Based on Regional Similarity Fusio...

引用

6th international conference on Information Communication and signal Processing, ICICSP 2023

作者： Ding, Bosheng Zhang, Ruiheng Xu, Lixin Wang, Haichao Liu, Yumeng Zhao, Yijing Su, Yi School of Mechatronical Engineering Beijing Institute of Technology Beijing China Anhui Dongfeng Electromechanical Technology Co. Ltd Research and Development Department Heifei China Institute of Software Chinese Academy of Sciences Beijing China Institute of Software University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9798350339994

The scattering and absorption effect of dust particles on light leads to low contrast and serious color offset of visible light images obtained in sand-dust weather, which affects the reliability of outdoor visual applications such as traffic safety and monitoring systems. Due to complex scene structure and difficult parameter estimation, the existing sand-dust image enhancement methods can not effectively extract the semantic components of the image, resulting in unreal colors and blurred details of the enhanced image. Therefore, we propose a two-stage sand image enhancement method based on regional similarity fusion. Firstly, the gray distribution of the input dust image is compensated to recover the potential information in the scene, and two sub-images with color balance and high contrast are derived. Then, regional similarity calculation, weight allocation, and image fusion are carried out to generate the final clear image based on the regional similarity fusion strategy. The experimental results show that the proposed method can effectively restore the potential features in the dust scene, and the visible edge number ratio (e-score) and edge gradient ratio (r-score) have increased by 0.24 and 0.47 respectively. © 2023 ieee.

关键词： degraded data image enhancement regional similarity fusion

来源：评论

学校读者我要写书评

暂无评论

An image-Text Association Matching Algorithm based on CRNN 20

An Image-Text Association Matching Algorithm Based on CRNN

引用

20th international Computer conference on Wavelet Active Media technology and Information Processing, ICCWAMTIP 2023

作者： Wei, Wu Rui, Xu Weisha, Zhang Zhongzheng, Fu Xiaofeng, Xu Chengdu University of Information Technology Sichuan Chengdu610225 China University of Electronic Science and Technology of China Sichuan Chengdu611731 China

ISBN: (纸本)9798350318982

The text in the pictures accompanying internet rumors always doesn't match the rumors themselves. In order to improve the efficiency of internet rumor detection, we propose an image-text association matching algorithm based on CRNN. The algorithm first determines the image suffix format and URL, and then uses a model that uses the fusion of CNN, RNN and CTC to identify the image. Among them, CNN performs feature extraction on input fake news images. RNN trains feature sequences and predicts label distributions, CTC converts all obtained label distributions, and finally get the text in the image. Then use the formatting algorithm to get the matching degree between the image and the text to associate both of them. Experiments show that our algorithm can achieve an accuracy of 94 % in matching pictures and press releases, which can effectively solve the problem of online rumor detection. Experiments show that our algorithm can achieve an accuracy of 94 % in matching pictures and press releases, which can effectively solve the internet rumor detection problem. © 2023 ieee.

关键词： image enhancement

来源：评论

学校读者我要写书评

暂无评论

Smart Irrigation Management System 16

Smart Irrigation Management System

引用

16th international conference on signal-image technology and internet-based systems (SITIS)

作者： Sellami, Bassem El Fazziki, Abdelaziz Bennani, Mohamed Taha Ben Yahia, Sadok Univ Tunis El Manar Dept Comp Sci Fac Sci Tunis Tunisia Cadi Ayyad Univ Dept Comp Sci Comp Syst Engn Lab Marrakech Morocco Tallinn Univ Technol Dept Software Sci Tallinn Estonia

ISBN: (纸本)9781665464956

The significant problem in the area of sustainable development is the effective management of water resources. There are a number of approaches for resolving this issue in the literature, particularly in the agricultural sector, which serves as the primary consumer through irrigation. As a result, irrigation management is a crucial and cutting-edge discipline that has been the focus of several research and study projects to address the various activities, behaviors, and conflicts between the various users. This paper introduces an intelligent irrigation system based on smart sensors that can be used sparingly and affordably to monitor farms. By incorporating some connected electronic devices and other useful tools frequently used in the field of IoT, it calculates the water requirement of each farm based on the water loss caused by the process of evapotranspiration. Data gathered from a number of sensors placed across the plantation farm are used to determine the amount of water needed. It may be utilized by farmer organizations whose endowments and irrigation planning are determined according to the requirement and quantity of water available in the rural municipality. This project focuses on smart irrigation based on IoT. Each node in the system functions as an IoT device and incorporates a microprocessor with the integration of sensors, actuators, and valve modules. An irrigation management system simplifies the operation of each node and configuration settings for irrigation allow for the direct monitoring of environmental factors. The Penman model is used to get the daily evapotranspiration baseline and is used to calculate the quantity of water needed for irrigation. The suggested irrigation approach is anticipated to help reduce water usage and distribute it fairly without affecting crop productivity compared to the traditional irrigation system.

关键词： Smart Irrigation internet of Things Planning and scheduling Evapotranspiration Planification

来源：评论

学校读者我要写书评

暂无评论

Optimization of HOG Algorithm for Computer Vision in Industrial Robots 5

Optimization of HOG Algorithm for Computer Vision in Industr...

引用

5th ieee international conference on Computing, Power, and Communication Technologies, IC2PCT 2024

作者： Sawai, Nandkishor Marotrao Mankar, Avinash Rambhau Mahant, Roshan Ingole, Kartik Mahulkar, Yogesh Ramdas Asutkar, Sanjay M. Mechanical Engineering Sandip Institute of Technology and Research Centre Maharashtra Nashik India Guru Nanak Institute of Technology Maharashtra Nagpur India Launch It Corp UrbandaleIA United States K.D.K.College of Engineering Department of Etc Maharashtra Nagpur India Tulsiramji Gaikwad Patil College of Engineering and Technology Nagpur India Tulsiramji Gaikwad Patil College of Engineering and Technology Department of Ece Nagpur India

ISBN: (纸本)9798350383522

Individuals working in the contemporary industrial sector face a diverse range of obstacles, such as the occurrence of costly item misplacement and the following attempt to locate them. In order to tackle this matter, we are now broadening our research in the realm of object detection and recognition, a subject that has advantages for both the industrial and academic spheres. The proposed system utilizes the Matrix Laboratory (MATLAB) software and employs image transmission and receiving algorithms based on the internet of Things (IoT) to carry out item identification based on pictures. In this scenario, two discrete 32-bit Advanced RISC Machine (ARM) controllers are effectively used. The experimental setup involves establishing a connection between a camera module and a controller using a camera. The captured image is then transmitted to the STM32F426zi microcontroller through the Universal Asynchronous Receiver and Transmitter (UART) interface. Subsequently, the image is forwarded to the CC3200 microcontroller via Wi-Fi, with the objective of minimizing transmission delay. Finally, the image is transmitted from the CC3200 to MATLAB using the User Datagram Protocol (UDP). The real-time acquisition and transmission of photos is facilitated by a four-wheeled robot, which is navigated along a black line using an infrared (IR) sensor. © 2024 ieee.

关键词： Advanced RISC Machine (ARM) Infra Red (IR) internet of Things IoT Matrix Laboratory (MATLAB) User Datagram Protocol (UDP)

来源：评论

学校读者我要写书评

暂无评论

FEW-SHOT HYPERSPECTRAL image CLASSIFICATION WITH SPECTRAL-SPATIAL FEATURE FUSION based ON FUZZY BROAD LEARNING SYSTEM 30

FEW-SHOT HYPERSPECTRAL IMAGE CLASSIFICATION WITH SPECTRAL-SP...

引用

30th ieee international conference on image Processing (ICIP)

作者： Hu, Xiaopei Zhao, Guixin Dong, Aimei Lv, Guohua Zhai, Yi Guo, Ying Dong, Xiangjun Qilu Univ Technol Shandong Acad Sci Minist EducShandong Comp Sci Ctr Key Lab Comp Power Network & Informat SecurFac C Jinan Peoples R China Shandong Fundamental Res Ctr Comp Sci Shandong Prov Key Lab Comp Networks Jinan Peoples R China

ISBN: (纸本)9781728198354

In the few-shot hyperspectral image (HSI) classification, most current models don't fully utilize the advantage of spectralspatial feature fusion, resulting in low classification accuracy. Therefore, we propose a few-shot HSI classification model with spectral-spatial feature fusion based on fuzzy broad learning system (FBLS) (FSFBLS). Firstly, we use a Gaussian filter to suppress noise while smoothing spectral features based on spatial information to achieve the first fusion of spectral-spatial features. Secondly, we use FBLS with fuzzy rules to fully model the complex mapping relationship between spectral-spatial features and HSI labels to complete HSI classification. The fuzzy processing can extract rich discriminative features to enhance the recognition of different categories. Finally, the guided filter corrects the misclassified samples of FBLS based on the guided image to achieve the second fusion of spectral-spatial features. Extensive experimental results on three public datasets demonstrate that FSFBLS achieves state-of-the-art classification performance compared to nine popular models.

关键词： Hyperspectral image classification few-shot Gaussian filter fuzzy board learning system guided filter

来源：评论

学校读者我要写书评

暂无评论

Automated Deep Learning Models for Medical image Segmentation and Denoising 17

Automated Deep Learning Models for Medical Image Segmentatio...

引用

17th international conference on signal Processing and Communication systems, ICSPCS 2024

作者： Rajesh, Chilukamari Sadam, Ravichandra Kumar, Sushil Andhra Pradesh Amaravati522237 India National Institute of Technology Department of Computer Science and Engineering Telangana Warangal506004 India National Institute of Technology Department of Computer Engineering Haryana Kurukshetra136119 India

ISBN: (纸本)9798350389630

Medical image segmentation plays a critical role in diagnosing and treating disease;it is a challenging task due to the complexity and variety of medical images. One of the critical factors in enhancing the accuracy and reliability of segmentation results is denoising, which involves removing noise from input images. Deep Neural Networks (DNNs) have shown promising results in medical image segmentation, but the manual design of DNN models is time-consuming and requires domain knowledge. To address this problem, Neural Architecture Search (NAS) provides an automated approach for developing neural network structures, eliminating manual design requirements. This paper introduces two evolutionary DNN models for medical image denoising and segmentation. The denoising model serves as a preprocessing step for segmentation, enabling us to assess its impact on medical image segmentation performance compared to normal images. The proposed approach performed well, with Dice scores of 86.99% and 88.62% on the original images for the Spleen and Heart segmentation datasets, respectively. While it achieved Dice scores of 89.12% and 87.78% on denoised images for the Heart and Spleen segmentation datasets, respectively. The experimental findings show that the architecture derived from the proposed approach outperforms existing models. © 2024 ieee.

关键词： image denoising

来源：评论

学校读者我要写书评

暂无评论

Heating Energy Performance Assessment using KNN Classification method: Investigating Test Size, Neighbor Parameter, and Class Count Effects 8

Heating Energy Performance Assessment using KNN Classificati...

引用

8th ieee international conference on image and signal Processing and their Applications, ISPA 2024

作者： Boudjella, Aissa Boudjella, Manal Yasmine Liu, Chuang Samarkand International University of Ttechnology Dept. of Applied Mathematics Samarkand Uzbekistan Bircham International University Dept. Electrical Engineering Miami United States University of Sciences and Technology USTO Dept. of Physics Oran Algeria Samarkand International University of Technology Dept. of Thermal Engineering Samarkand Uzbekistan

ISBN: (纸本)9798350309249

We analyze the energy requirements for assessing the heating load of building systems using the k-Nearest Neighbor (KNN) method for classification. Unsupervised transformations of the dataset are employed in learning algorithms. Two distinct data representation classes are generated separately from the original dataset based on captured heating load magnitudes. The unsupervised transformations of the dataset yield 4 and 8 classes, *** evaluate performance, various test and training sizes are employed, comparing measurement outcomes and predicting class label descriptions for two dataset representations as the parameter for the nearest neighbor (k) ranges from 1 to 20. Results obtained from the simulation indicate that accuracy is influenced by factors such as test size, the k parameter, and dataset class count. The training and test accuracy rates reach maximum values of 95% and 85%, respectively. Keeping k constant, the test size has a negligible impact on accuracy. The classes exhibit three critical regions: 1) Region I, where accuracy fluctuates with increasing k in the interval [1]-[5], and 2) Region II, where accuracy declines with increasing k in the interval [5]-[10]. In Region III k [10]-[20], accuracy remains relatively constant. While reducing the number of classes may result in a slight improvement in accuracy by simplifying the classification task, the trade-offs and complexities of increasing the number of classes for data representation should be carefully considered. Although this approach can lead to a more flexible design, it may slightly reduce accuracy. © 2024 ieee.

关键词： Learning algorithms

来源：评论

学校读者我要写书评

暂无评论

Improving image Captioning with Control signal of Sentence Quality 48

Improving Image Captioning with Control Signal of Sentence Q...

引用

48th ieee international conference on Acoustics, Speech and signal Processing, ICASSP 2023

作者： Zhu, Zhangzi Wang, Shuai Qu, Hong University of Electronic Science and Technology of China School of Computer Science and Engineering Chengdu China

ISBN: (纸本)9781728163277

In the dataset of image captioning, each image is aligned with several descriptions. Despite the fact that the quality of these descriptions varies, existing captioning models treat them equally in the training process. In this paper, we propose a new control signal of sentence quality, which is taken as an additional input to the captioning model. By integrating the control signal information, captioning models are aware of the quality level of the target sentences and handle them differently. Moreover, we propose a novel reinforcement training method specially designed for the control signal of sentence quality: Quality-oriented Self-Annotated Training (Q-SAT). Extensive experiments on MSCOCO dataset show that without extra information from ground truth captions, models controlled by the highest quality level outperform baseline models on accuracy-based evaluation metrics, which validates the effectiveness of our proposed methods. © 2023 ieee.

关键词： Control signal image Captioning Quality-oriented Self-Annotated Training Sentence Quality

来源：评论

学校读者我要写书评

暂无评论

CVulDetector: Cross-domain Vulnerability Detection based on Transfer Learning 8

CVulDetector: Cross-domain Vulnerability Detection Based on ...

引用

8th international conference on signal and image Processing, ICSIP 2023

作者： Zeng, Guoqiang Zhou, Wen Qin, Zhongyuan Sun, Xin Wang, Wen Southeast University School of Cyber Science and Engineering Nanjing211189 China State Grid Zhejiang Electric Power Co. Ltd. Research Institute Internet Technology Center Hangzhou310063 China State Grid Zhejiang Electric Power Co. Ltd Internet Department Hangzhou310063 China

ISBN: (纸本)9798350397932

Software vulnerabilities pose a significant threat to computer security, and accurate identification of vulnerabilities has become a challenging task. Although previous work on deep learning-based vulnerability detection has achieved some success, it still faces issues such as 1) insufficient feature extraction capability of the models, and 2) unrealistic assumption of identical distribution between the source and target domains. To address these problems, we propose CVulDetector, a cross-domain vulnerability detection scheme based on transfer learning. Our approach incorporates both natural language characteristics and semantic syntax information of programs, enabling a more comprehensive description of program features. By employing two neural network models, we extract vulnerability-rich features and fine-tune the model using limited labeled data from the target domain after pre-training on a wide range of features from the source domain. Experimental evaluation on three CWE datasets shows an F1-score of 90.6%. © 2023 ieee.

关键词： Semantics

来源：评论

学校读者我要写书评

暂无评论

Proceedings - 2024 ieee 10th international conference on Edge Computing and Scalable Cloud, EdgeCom 2024

Proceedings - 2024 IEEE 10th International Conference on Edg...

引用

10th ieee international conference on Edge Computing and Scalable Cloud, EdgeCom 2024

ISBN: (纸本)9798350377132

The proceedings contain 19 papers. The topics discussed include: improving robustness of compressed models with weight sharing through knowledge distillation;multi-tool integration application for math reasoning using large language model;a power fusion data cleaning method based on exponential moving average and cosine similarity algorithms;digital watermarking technology of data element circulation transaction;research on key technologies for enhancing image analysis capability of power transmission and transformation equipment based on large visual models;improving robustness of compressed models with weight sharing through knowledge distillation;a power fusion data cleaning method based on exponential moving average and cosine similarity algorithms;and reinforcement learning minimizes payment costs in internet of vehicles caching.

关键词：

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 << < 72 73 74 75 76 77 78 79 80 81 > >>

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：