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

6,104 篇 会议
64 篇 期刊文献
51 册 图书
1 篇 学位论文

馆藏范围

6,220 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

3,624 篇 工学
- 2,545 篇 计算机科学与技术...
- 1,802 篇 软件工程
- 1,417 篇 信息与通信工程
- 989 篇 电气工程
- 422 篇 控制科学与工程
- 401 篇 电子科学与技术（可...
- 371 篇 光学工程
- 349 篇 生物工程
- 289 篇 生物医学工程（可授...
- 286 篇 仪器科学与技术
- 233 篇 机械工程
- 148 篇 安全科学与工程
- 138 篇 交通运输工程
- 115 篇 化学工程与技术
- 106 篇 材料科学与工程（可...
- 101 篇 网络空间安全
- 99 篇 动力工程及工程热...
- 83 篇 建筑学
1,492 篇 理学
- 691 篇 数学
- 543 篇 物理学
- 374 篇 生物学
- 232 篇 系统科学
- 215 篇 统计学（可授理学、...
- 116 篇 化学
537 篇 管理学
- 324 篇 管理科学与工程(可...
- 256 篇 图书情报与档案管...
- 129 篇 工商管理
346 篇 医学
- 327 篇 临床医学
- 164 篇 基础医学(可授医学...
- 110 篇 药学(可授医学、理...
98 篇 法学
60 篇 农学
48 篇 经济学
40 篇 教育学
27 篇 军事学
16 篇 文学
5 篇 艺术学

主题

326 篇 feature extracti...
305 篇 internet of thin...
261 篇 real-time system...
195 篇 internet
189 篇 image segmentati...
170 篇 signal processin...
167 篇 image processing
157 篇 accuracy
146 篇 deep learning
146 篇 image edge detec...
139 篇 signal processin...
130 篇 cameras
128 篇 image coding
128 篇 training
117 篇 image color anal...
107 篇 visualization
106 篇 image retrieval
99 篇 computational mo...
97 篇 robustness
89 篇 signal to noise ...

机构

11 篇 school of comput...
9 篇 faculty of infor...
8 篇 school of cyber ...
8 篇 school of electr...
8 篇 beijing universi...
8 篇 state key labora...
7 篇 school of inform...
7 篇 school of inform...
7 篇 college of elect...
7 篇 institute of sig...
7 篇 harbin institute...
7 篇 college of infor...
7 篇 school of electr...
6 篇 university of sc...
6 篇 school of inform...
6 篇 school of electr...
6 篇 chaoyang univ te...
6 篇 university of el...
6 篇 wuhan university...
6 篇 department of in...

作者

13 篇 yoshitaka sakura...
13 篇 tsuruta setsuo
13 篇 sakurai yoshitak...
13 篇 fan zhun
12 篇 setsuo tsuruta
11 篇 li li
11 篇 wang wei
10 篇 yetongnon kokou
9 篇 dipanda albert
9 篇 zhang yan
9 篇 lee chin-feng
8 篇 zhang lei
8 篇 jing wang
8 篇 wang hao
7 篇 zhu guijie
7 篇 kinshuk
7 篇 jeon gwanggil
7 篇 ahmad tohari
7 篇 shen jau-ji
7 篇 wang xin

语言

5,661 篇 英文
539 篇 其他
38 篇 中文
1 篇 土耳其文

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

共 6220 条记录，以下是801-810 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：

2nd international conference on Science, Engineering Management and Information technology, SEMIT 2023

2nd International Conference on Science, Engineering Managem...

引用

2nd international conference on Science, Engineering Management and Information technology, SEMIT 2023

ISBN: (纸本)9783031722837

The proceedings contain 46 papers. The special focus in this conference is on Science, Engineering Management and Information technology. The topics include: Verification and Validation of Knowledge Engineering systems: A Life Cycle Framework;The Critical Factors of Success of Gamification in Digital Banking Services: Using Analytic Hierarchy Process (AHP) Approach;e-Shopping Sites Preference Analysis with Multi-criteria Decision-Making Methods;advancing Anemia Diagnosis: Harnessing Machine Learning Methods for Accurate Detection;monkeypox Detection with K-mer Using Machine Learning Algorithms;cloud Computing Model for Handling Medical Big Data: A Mobile Hospital Pervasive Healthcare Application;Harnessing Advanced AI Techniques: An In-Depth Analysis of Machine Learning Models for Improved Diabetes Prediction;image Processing in Toxicology: A Systematic Review;augmented Reality Immersive World with Hologram Special Effect in Early Childhood Education;improving the Visual Ergonomics of Computerised Workplaces Through the Use of Specialised Eye-Rest Software;a Diagnosis Model based on Federated Learning for Lung Cancer Classification;Arrhythmia Detection from ECG Traces images Using Transfer Learning Approach;enhancing Traffic Flow Prediction in Urban Areas Through Deep Learning and Probe Information: A Comparative Study;an Approach to Multi-agent Deep Q-Network Optimization of signal Control in Multi-intersection Road Environments to Enhance Urban Traffic Flow;blockchain-Driven Smart Contracts: An Overview of Application Areas and Gap Identification in Construction Management Literature;stochastic Optimization Methodology for Production Planning with Uncertain Demand and Lead Time based on the Digital Twin;role of Top Management Commitment and Information technology Investment in Digital Transformation;antecedents of Mobile Banking Apps Adoption Among Consumers in Ghana.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Oct image Blind Despeckling based on Gradient Guided Filter with Speckle Statistical Prior 48

Oct Image Blind Despeckling Based on Gradient Guided Filter ...

引用

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

作者： Li, Sanqian Xiong, Muxing Yang, Bing Zhang, Xiaoqing Higashita, Risa Liu, Jiang Southern University of Science and Technology Research Institute of Trustworthy Autonomous Systems Department of Computer Science and Engineering Shenzhen China Tomey Corporation Japan

ISBN: (纸本)9781728163277

Optical coherence tomography (OCT) imaging technique has been widely used for ocular disease diagnosis. However, speckles occur in OCT images due to the property of coherent imaging, inevitably affecting the visual quality and clinical analysis. To alleviate this problem, we propose a novel gradient-guided speckle image filtering method (GGSF) with structure enhancement for directly removing speckles in OCT images. Specifically, the multiplicative characteristic of speckle noise is incorporated into the guided filtering processing for modeling raw OCT images. To avoid getting trapped in image distortions, we further employ gradient regularization to integrate the structure prior information into the guided speckle image filtering procedure. Additionally, we introduce the statistical property of speckle noise obeying a gamma distribution into the least square method solver for the resulting non-convex GGSF model. Experimental results on the AS-OCT dataset demonstrate the effectiveness of GGSF for OCT image despeckling compared with competitive methods. Furthermore, we validate the benefits of GGSF for subsequent clinical analysis with the CM-OCT dataset. © 2023 IEEE.

关键词： Optical tomography

来源：评论

学校读者我要写书评

暂无评论

Communication Synchronization and Anti-Interference technology based on Artificial Intelligence

Communication Synchronization and Anti-Interference Technolo...

引用

Integrated Circuits and Communication systems (ICICACS), IEEE international conference on

作者： Yongdong Li Product & System Rohde-Schwarz Guangzhou China

With the development of the internet of Things and the Industrial internet, more and more devices require stable and reliable wireless connections. When traditional communication synchronization methods encounter complex electromagnetic environments with various interferences and noises, their performance will be limited and they will be difficult to adapt to and accurately process signal characteristics. Therefore, this article introduces the convolutional neural network algorithm based on artificial intelligence for optimization and improvement. This article focuses on exploring the methods of using CNN for signal feature extraction, and proposes the design and optimization of adaptive filters and anti-interference encoding and decoding techniques. Finally, through two sets of simulation experiments, the following conclusions were drawn: compared with traditional methods, the overall average improvement in synchronization accuracy index of CNN is 8.3%, while the signal error rate index is comprehensively reduced by 17.35%. This end-to-end learning and optimization strategy makes CNN signal recovery more effective in complex environments and improves the overall performance and reliability of communication systems.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Mangrove Species Classification in Qi’ao Island based on Gaofen-2 image and UAV LiDAR 1

引用

international conference on Communications, signal Processing, and systems, CSPS 2023

作者： Sun, Yuchao Wei, Zheng Gao, Yang Ren, Hongkai Chen, Qidong Dong, Di Hu, Ping South China Sea Institute of Planning and Environmental Research State Oceanic Administration Guangzhou510300 China Key Laboratory of Marine Environmental Survey Technology and Application Ministry of Natural Resources Guangzhou510300 China Zhuhai519082 China

ISBN: (数字)9789819975051

ISBN: (纸本)9789819975396

Mangrove species classification is of great significance to the study of mangrove community structure and biodiversity. Most researches use foreign high-resolution remote sensing images or UAV images for mangrove species classification. In order to improve classification accuracy, LiDAR and hyper-spectral data are often used to assist classification. In this paper, based on the Gaofen-2 image and the CHM data obtained by the UAV Lidar, the mangroves in Qi'ao Island, Zhuhai are classified among species by using the random forest classification method. The classified species include 5 types of true mangroves, 3 types of semi mangroves, Phragmites australis and non-vegetation. The results show that the use of Gaofen-2 image can only effectively distinguish the Sonneratia apetala, Acrostichum aureum and non-vegetation, the accuracy of distinguishing other mangrove species is not ideal;After Gaofen-2 image fusion of CHM data, the classification accuracy of each mangrove species has been significantly improved, with the overall classification accuracy reaching 91.44%, which verifies the effectiveness of Gaofen-2 image fusion of external data in mangrove species classification research. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： image fusion

来源：评论

学校读者我要写书评

暂无评论

Multi-step stereo matching algorithm based on image fragment and confidence propagation 7

Multi-step stereo matching algorithm based on image fragment...

引用

7th international conference on Communication, image and signal Processing, CCISP 2022

作者： Li, Yingjiang Deng, Yaping Zhong, Yuzhong Chongqing University of Technology Department of Internet of Things Engineering Chongqing China Computer College Sichuan University Chengdu China

ISBN: (数字)9781665459594

ISBN: (纸本)9781665459594

This paper presents a multi-step stereo matching algorithm that can be applied to multiple scenes. To adapt to different application scenarios, the algorithm divides the stereo matching process into three steps: point, fragment, and plane. First, the texture points of an image are extracted and the stereo matching (point disparity) of these points is performed using the improved self-aware matching measure (SAMM) algorithm. Then, according to the edge information of the image, a smooth region is divided into different fragments in the horizontal direction. The disparity estimation of the smooth region (segment disparity) is obtained through the confidence propagation of disparity values of texture points in the fragments. Finally, based on the similarity of plane disparity, a disparity map is generated using the disparity refining algorithm (plane disparity), and a final high-precision disparity value is obtained. The experimental results show that the proposed algorithm has high operational efficiency and accurate disparity estimation. Moreover, the algorithm may be adapted for more application scenarios. © 2022 IEEE.

关键词： image enhancement

来源：评论

学校读者我要写书评

暂无评论

2nd international conference on Science, Engineering Management and Information technology, SEMIT 2023

2nd International Conference on Science, Engineering Managem...

引用

2nd international conference on Science, Engineering Management and Information technology, SEMIT 2023

ISBN: (纸本)9783031722868

关键词：

来源：评论

学校读者我要写书评

暂无评论

Communication Interference signal Recognition based on Complex Number Convolutional Neural Network 6

Communication Interference Signal Recognition Based on Compl...

引用

2022 6th international conference on Electrical, Mechanical and Computer Engineering, ICEMCE 2022

作者： Chen, Yazhong Shan, Jingmiao Zhang, Chuanhao Department of Electrical Information Shandong University of Science and Technology Shandong Jinan250031 China

Modern wireless communication systems are facing an increasingly complex electromagnetic environment, which is affected by a variety of noise and interference signals. In the communication process, if the communication party can effectively identify the type of interference signal, it can take corresponding anti-interference measures to avoid or suppress interference to the maximum extent and reduce the damage of interference to communication quality. In recent years, with the continuous development of deep learning and its outstanding performance in image and speech processing, it has been verified that deep learning has extremely strong nonlinear mapping and data expression capabilities, inspiring researchers to apply deep learning to the field of communication anti-jamming, and improving the antijamming capability of the system. In this paper, an algorithm of interference signal recognition based on a complex convolution neural network is proposed. It also introduces the network architecture. Next, six typical interference signals at each level are identified, and the results are analyzed. The last chapter of the paper summarizes the full text. The experimental results show that the recognition ability of the interference signal recognition algorithm proposed in this paper is better than that of the traditional convolutional neural network. © Published under licence by IOP Publishing Ltd.

关键词： signal interference

来源：评论

学校读者我要写书评

暂无评论

Gini Index based Sparse Reconstruction for Radio Tomographic Imaging 6

Gini Index Based Sparse Reconstruction for Radio Tomographic...

引用

6th IEEE international conference on Electronic Information and Communication technology, ICEICT 2023

作者： Liu, Tong Liang, Jianchu Wan, Kai Liang, Zhuoqian Liu, Jun Huizhou University Department of Electronics Engineering Huizhou China College of Information Science and Technology Jinan University Guangzhou China College of Computer Science and Artificial Intelligence Wenzhou University Wenzhou China

ISBN: (纸本)9798350399059

Radio tomographic imaging (RTI) is a computational imaging technique that estimates the distribution of shadowing loss caused by obstructions in a monitored area using received signal strength (RSS) measurements between radio frequency (RF) network nodes. However, finding a solution to RTI under noise interference is generally an ill-posed inverse problem, and additional measures are required to reduce noise and achieve the desired image properties. The sparse distribution of targets in the monitored areas is intuitively an important prior for accurate image reconstruction. In this article, we propose a Gini index (GI)-based sparse reconstruction approach for RTI that fully leverages sparsity for high-quality imaging. based on the linear cumulative shadowing model, RTI is formulated using the classical GI from economics as the sparsity measure to construct the optimal objective function. The GI-based objective function is quasi-convex, and RTI is solved by the simultaneous perturbation stochastic approximation (SPSA) algorithm. Experimental studies in three different environments are conducted to validate the superior performance of the proposed approach with respect to commonly used regularization and sparsity measures. © 2023 IEEE.

关键词： Stochastic systems

来源：评论

学校读者我要写书评

暂无评论

Smoke Segmentation Improvement based on Fast Segment Anything Model with YOLOv11 for a Wildfire Monitoring System 10

Smoke Segmentation Improvement Based on Fast Segment Anythin...

引用

10th international conference on internet of Things, Big Data and Security, IoTBDS 2025

作者： Bunpleng, Puchit Thunyatada, Puthtipong Aksornsuwan, Bhutharit Tungpimolrut, Kanokvate Murata, Ken T. Sirindhorn International Institute of Technology Thammasat University Pathum Thani Thailand NECTEC National Science and Technology Development Agency Pathum Thani Thailand National Institute of Information and Communications Technology Tokyo Japan

ISBN: (纸本)9789897587504

Forests and wildlife are crucial parts of our ecosystem. Wildfires occurring in dry and hot regions represent a significant threat to these areas, particularly in ASEAN countries during the dry season. While human observers are often employed to detect wildfires, their scarcity and limited availability highlight the need for automated solutions. This study explores the use of machine learning, specifically computer vision, to enhance wildfire detection by segmenting smoke, an approach which potentially gives information regarding the size and the direction of the spread of the smoke, aiding mitigation efforts. We extend prior work by proposing a model to predict the errors and performance of segmentation masks without access to the ground truth, with the aim of facilitating iterative self-improvement of segmentation models. The FireSpot dataset is used to fine-tune a YOLOv11 model to predict bounding boxes of smoke successfully;subsequently, the outputs of this model are used as a prompt to refine a FastSAM model designed to segment the image into a proposed mask containing the smoke. The proposed mask and the corresponding original image are then used to train a machine learning model where the targets are metrics regarding the error rates of the masks. The results show that a gradient boosting model achieves good prediction performance in predicting some error metrics like the IoU (denoted TPP in this paper) between the proposed and actual segmentation masks with an MSE of 0.03 and R2 of 0.46, as well as the proportion of false positives over the union of the proposed and actual masks (denoted FPP in our paper) with an MSE of 0.0002 and R2 of 0.95, while a pre-trained deep learning model fails to learn the distribution, achieving considerably lower performance for IoU with an MSE of 0.05 and R2 of 0.06 and FPP with an MSE of 0.0002 and R2 of -1.15. These findings open the way to future work where the results of the error prediction model can be used as feedback to

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

Research on Vehicle Parking Aid System based on Parking image Enhancement 10th

Research on Vehicle Parking Aid System Based on Parking Imag...

引用

10th international conference on Communications, signal Processing, and systems

作者： Ye, Danxian Yin, Xin Dong, Menghan Tianjin Normal Univ Tianjin Key Lab Wireless Mobile Commun & Power Tr Tianjin 300387 Peoples R China

ISBN: (纸本)9789811903908;9789811903892

At present, the visual parking assistance system in intelligent driving generally has the problems of unclear parking image quality and high hardware cost. In order to reduce the difficulty of parking and improve the ability to adapt to the environment, this paper proposes a vehicle assistance system based on parking image enhancement. Firstly, Retinex algorithm is used to balance the image illumination information and enhance the color saturation, so that it can adapt to more complex environmental conditions;secondly, Ackerman steering theorem is used to draw the dynamic parking aid line, and the coordinate transformation technology is used to output it to the vehicle screen. The adaptability and effectiveness of the developed system are verified by the relevant experimental research.

关键词： image enhancement Retinex algorithm Parking system Intelligent driving

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 << < 77 78 79 80 81 82 83 84 85 86 > >>

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：