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

94 篇 会议
22 篇 期刊文献

馆藏范围

116 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

91 篇 工学
- 53 篇 计算机科学与技术...
- 51 篇 软件工程
- 28 篇 信息与通信工程
- 20 篇 控制科学与工程
- 16 篇 电子科学与技术（可...
- 14 篇 光学工程
- 9 篇 仪器科学与技术
- 9 篇 电气工程
- 9 篇 生物工程
- 7 篇 机械工程
- 7 篇 生物医学工程（可授...
- 6 篇 化学工程与技术
- 5 篇 交通运输工程
- 5 篇 航空宇航科学与技...
- 4 篇 力学（可授工学、理...
- 4 篇 材料科学与工程（可...
- 4 篇 环境科学与工程（可...
- 3 篇 土木工程
- 3 篇 轻工技术与工程
69 篇 理学
- 51 篇 数学
- 26 篇 物理学
- 14 篇 统计学（可授理学、...
- 9 篇 生物学
- 6 篇 化学
20 篇 管理学
- 11 篇 管理科学与工程(可...
- 7 篇 图书情报与档案管...
9 篇 医学
- 5 篇 临床医学
- 3 篇 基础医学(可授医学...
- 3 篇 公共卫生与预防医...
- 3 篇 药学(可授医学、理...
4 篇 农学
2 篇 经济学
1 篇 教育学
1 篇 文学
1 篇 军事学

主题

6 篇 feature extracti...
4 篇 support vector m...
4 篇 visualization
4 篇 databases
4 篇 image reconstruc...
3 篇 speckle
3 篇 motion planning
3 篇 iterative method...
3 篇 computational mo...
3 篇 synthetic apertu...
2 篇 image fusion
2 篇 image enhancemen...
2 篇 hamming distance
2 篇 hidden markov mo...
2 篇 three-dimensiona...
2 篇 image segmentati...
2 篇 image
2 篇 face recognition
2 篇 spatial resoluti...
2 篇 computed tomogra...

机构

7 篇 key lab. of inte...
7 篇 school of inform...
7 篇 image processing...
4 篇 jiangsu province...
3 篇 college of life ...
3 篇 guangxi colleges...
3 篇 shanghai jiao to...
3 篇 guangxi colleges...
3 篇 center for image...
3 篇 lab. of image in...
3 篇 party committee ...
3 篇 school of inform...
3 篇 institute for pa...
2 篇 lab. of image in...
2 篇 dept. of mechani...
2 篇 institute of ima...
2 篇 inst. of intelli...
2 篇 school of scienc...
2 篇 science and tech...
2 篇 key lab. of adva...

作者

8 篇 hassan ghassemia...
8 篇 jiao licheng
6 篇 xuelong hu
5 篇 ghassemian hassa...
4 篇 cai chao
4 篇 ding mingyue
4 篇 yang yang
4 篇 yang xiaokang
4 篇 gao jun
3 篇 wang lei
3 篇 zhang xiaohua
3 篇 zhan-jiang ji
3 篇 maryam imani
3 篇 liu fang
3 篇 jiao li-cheng
3 篇 wei shi
3 篇 lu ziyun
3 篇 zhong hua
3 篇 xu yi
3 篇 shen hong-bin

语言

99 篇 英文
10 篇 中文
7 篇 其他

检索条件"机构=Lab. of Image Information Processing"

共 116 条记录，以下是1-10 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：

Unsupervised Hyperspectral image Classification: Spatial and Spectral Feature Fusion with Masked Autoencoders 20

Unsupervised Hyperspectral Image Classification: Spatial and...

引用

20th CSI International Symposium on Artificial Intelligence and Signal processing, AISP 2024

作者： Mirsharji, Seyed Ali Ghassemian, Hassan Tarbiat Modares University Image Processing and Information Analysis Lab. Faculty of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798350383942

In this study, we present an innovative unsupervised hyperspectral image classification method using a dual-branch architecture that merges spatial and spectral feature extraction. Our unique approach employs masked autoencoders, significantly outperforming traditional methods with an impressive overall accuracy of 97.1%. The paper details the model's performance evaluation, offers visual insights into its classification capabilities, and compares it with existing techniques, demonstrating its effectiveness and potential for advancing remote sensing applications. © 2024 IEEE.

关键词： image classification

来源：评论

学校读者我要写书评

暂无评论

Unsupervised Hyperspectral image Classification: Spatial and Spectral Feature Fusion with Masked Autoencoders

Unsupervised Hyperspectral Image Classification: Spatial and...

引用

International Symposium on Artificial Intelligence and Signal processing (AISP)

作者： Seyed Ali Mirsharji Hassan Ghassemian Image Processing and Information Analysis Lab. Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran

In this study, we present an innovative unsupervised hyperspectral image classification method using a dual-branch architecture that merges spatial and spectral feature extraction. Our unique approach employs masked autoencoders, significantly outperforming traditional methods with an impressive overall accuracy of 97.1%. The paper details the model’s performance evaluation, offers visual insights into its classification capabilities, and compares it with existing techniques, demonstrating its effectiveness and potential for advancing remote sensing applications.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Research on auto focusing algorithm of microscopic imaging system

Research on auto focusing algorithm of microscopic imaging s...

引用

2022 IEEE International Conference on Advances in Electrical Engineering and Computer Applications, AEECA 2022

作者： Lv, Meini Yang, Qiuhui Ma, Wenjing Huang, Zeqiong College of Wuzhou Wuzhou China Wuzhou University Guangxi Colleges and Universities Key Lab. of Image Processing and Intelligent Information System Wuzhou China

ISBN: (数字)9781665480901

ISBN: (纸本)9781665480901

In the intelligent microscopic imaging system, the focusing evaluation function is one of the important core links in the automatic focusing system. In order to solve the problem that the focusing curve loses the characteristics of the ideal curve caused by illumination change, an automatic focusing algorithm based on Robert and wavelet transform is proposed in this paper. The algorithm extracts the maximum value of the high-frequency component wavelet coefficients decomposed by the wavelet transform, and uses the extracted wavelet coefficients to weight the Robert evaluation value coefficients, increases the sensitivity factor of the algorithm, and makes an experimental analysis with five traditional focusing functions under the condition of different illumination intensity to verify the feasibility of the algorithm. The experimental results show that the algorithm can overcome the change of illumination, has strong anti noise, sensitivity and stability, and is suitable for the fine focusing process in the microscopic imaging system. © 2022 IEEE.

关键词： Imaging systems

来源：评论

学校读者我要写书评

暂无评论

Discussion on application of FLIC-Fluent coupled simulation technology on medium-scale and large-scale municipal solid waste incinerators 6

Discussion on application of FLIC-Fluent coupled simulation ...

引用

6th International Conference on Intelligent Computing, Communication, and Devices, ICCD 2023

作者： Zhang, Hongze Guangxi Key Laboratory of Machine Vision and Intelligent Control Guangxi Wuzhou China Guangxi Colleges and Universities Key Lab. of Image Processing and Intelligent Information System Guangxi Wuzhou China School of Electronics and Information Engineering Wuzhou University Guangxi Wuzhou China

ISBN: (数字)9781510666269

ISBN: (纸本)9781510666252

Today, the world economy is in the stage of rapid development, followed by the rising quantity, the decreasing moisture content, and the increasing heat value of Municipal Solid Waste (MSW). Since waste incineration has the advantages of excellent volume reduction effect, high resource utilization efficiency, and less secondary pollution to the environment, waste incineration disposal technology has received extensive attention worldwide. With the continuous increase of the urban population, the amount of sewage sludge is growing year by year. The process of the sludge incineration system is highly complicated, and the cost of construction and operation is high. The waste incinerator can process not only MSW but also sewage sludge. Therefore, the co-incineration of sewage sludge with MSW in the existing waste incinerator is a safe and effective way to dispose of sewage sludge. In this paper, the simulation results of Computational Fluid Dynamics (CFD) are analyzed for medium-scale and large-scale waste incinerators, characterized by high moisture content, high ash content, low heat value of sewage sludge, and incomplete combustion and high pollutant emissions in waste incinerators. With FLIC-Fluent coupled simulation method, the critical information such as temperature field, velocity field, concentration field of principal components, and the pollutant emissions can be predicted. Based on the CFD simulation results, the performance and structure of waste incinerators can be optimized, and the new products can be designed and developed. © COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only.

关键词： Waste incineration

来源：评论

学校读者我要写书评

暂无评论

IRSEnet: Differentially Private image Generation with Multi-Scale Feature Extraction and Residual Channel Attention 13

IRSEnet: Differentially Private Image Generation with Multi-...

引用

13th International Conference on Intelligent Control and information processing, ICICIP 2025

作者： Li, Jiahao Wang, Zhongshuai Ghazali, Kamarul Hawari Bin Yan, Suqing Lan, Rushi Sun, Xiyan Luo, Xiaonan Guangxi Key Lab. of Image and Graphic Intelligent Processing Guilin University of Electronic Technology Guilin541004 China Centre for Advanced Industrial Technology University of Malaysia Pahang Al-Sultan Abdullah Pahang Pekan26600 Malaysia Int. Joint Research Lab. of Spatio-temporal Information and Intelligent Location Services Guilin University of Electronic Technology Guilin541004 China

ISBN: (纸本)9798331516147

Privacy-preserving image generation is particularly crucial in fields like healthcare, where data are both sensitive and limited. However, effective privacy preservation often compromises the visual quality and utility of the generated images due to privacy budget constraints. To address this issue, in this paper, We propose a novel network architecture, IRSEnet, which combines multi-scale feature extraction technology and residual channel attention mechanisms, aiming to enhance the visual quality of generated images and improve the performance of downstream classification tasks under differential privacy. The differential privacy mechanism ensures the security of sensitive data during training, while the multi-scale feature extraction module enhances feature extraction capabilities through parallel convolutional layers at multiple scales. Additionally, the channel attention module dynamically adjusts channel weights to focus on the most discriminative features. Experimental results demonstrate that this model significantly improves the utility of generated images and the accuracy of downstream classification tasks while preserving privacy. Future work will explore the application of this approach on larger datasets and across more diverse tasks. © 2025 IEEE.

关键词： Differential privacy

来源：评论

学校读者我要写书评

暂无评论

Research on key technologies of overlay multiple access based on deep learning

Research on key technologies of overlay multiple access base...

引用

2021 International Conference on Computer, Communication, Control, Automation and Robotics, CCCAR 2021

作者： Huang, Ronglan Guangxi Colleges and Universities Key Lab. of Image Processing and Intelligent Information System Wuzhou University Guangxi Wuzhou543002 China

NOMA (Non-Orthogonal Multiple Access), as one of the candidate technologies of 5G, can improve the spectrum efficiency and system capacity, and has attracted wide attention. The essence of NOMA is multi-user overlay transmission in power domain, and multiple users will schedule on the same sub-band. The fundamental principle and key technologies of NOMA technology, such as power multiplexing superposition coding technology and SIC (Successful Interference Cancellation) technology, are described. The BER performance of NOMA communication system based on power domain in downlink is studied, and the appropriate power allocation ratio range is obtained. From the overall point of view of the system, users are grouped with the goal of maximizing the channel gain difference of all matched users, and then the problem of maximizing the weighted sum rate is transformed into the problem of minimizing the weighted sum mean square error by using the deep learning algorithm. By updating each optimization parameter iteratively in turn, it converges to the local optimum of the objective function. This method can improve the weighting and rate performance of the system with fewer iterations. © Published under licence by IOP Publishing Ltd.

关键词： Mean square error

来源：评论

学校读者我要写书评

暂无评论

Selection of Cancer Biomarkers from Microarray Gene Expression Data Utilizing the Bi-Objective Optimization Method

Selection of Cancer Biomarkers from Microarray Gene Expressi...

引用

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Jiang, Hanjing Lin, Jianqing Zhu, Huachao Huang, Yabing Huazhong University of Science and Technology Key Lab. of Image Information Processing and Intelligent Control of Education Ministry of China Institute of Artificial Intelligence School of Artificial Intelligence and Automation Wuhan430074 China Wuhan University School of Computer Science Wuhan430064 China Renmin Hospital of Wuhan University Department of Pathology Wuhan430060 China

ISBN: (纸本)9798350337488

Cancer-associated biomarker genes play an indispensable role in the intricate tapestry of cancer development and manifestation. The expression of biomarkers in different types of tumor cells has beneficial implications for shedding light on the development of various cancers, guiding clinical diagnosis, and treatment. Microarray technology enables the expression levels of thousands of genes in samples to be sequenced simultaneously. However, sparse and high-dimensional microarray data present a formidable challenge in identifying biomarker genes. This study presents EREF-NSGA2, a novel method for cancer biomarker selection from microarray data, employing a hybrid gene selection approach. Firstly, the combination of the wrapper and embedded gene selection methods is proposed to filter the microarray data, which efficiently decreases the search space of the algorithm. After that, the improved NSGA-II algorithm is used to search the genes subset obtained from the previous step to reach the optimal subset of cancer biomarker genes. The proposed EREF-NSGA2 is compared with other reported methods on six cancer benchmark gene expression datasets. A detailed biological analysis is performed to analyze the relationship between the selected genes and the cancer data sets they belong to. To summarize, EREF-NSGA2 proves its effectiveness in selecting a feature subset comprising the fewest genes while maintaining the highest classification accuracy. © 2023 IEEE.

关键词： Bi-objective optimization Cancer biomarker Gene selection NSGA-II

来源：评论

学校读者我要写书评

暂无评论

SliceProp: A Slice-Wise Bidirectional Propagation Model for Interactive 3D Medical image Segmentation 1

SliceProp: A Slice-Wise Bidirectional Propagation Model for ...

引用

1st IEEE International Conference on Medical Artificial Intelligence, MedAI 2023

作者： Xu, Xin Lu, Wenjing Lei, Jiahao Qiu, Peng Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Ninth People's Hospital Shanghai Jiao Tong University School of Medicine Department of Vascular Surgery China Shanghai Jiao Tong University Institute of Image Processing and Pattern Recognition Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China

ISBN: (纸本)9798350358780

Interactive medical image segmentation methods have become increasingly popular in recent years. These methods combine manual lab.ling and automatic segmentation, reducing the workload of annotation while maintaining high accuracy. However, most current interactive segmentation frameworks are limited to 2D image data, and are not suitable for 3D image data due to the large size and high complexity of 3D data, as well as the challenges posed by information asymmetry and sparse annotation. In this paper, we propose SliceProp, an interactive segmentation framework that implements slice-wise lab.l Bidirectional Propagation (LBP) for 3D medical image segmentation. SliceProp extends the interactive 2D image segmentation algorithm to 3D image segmentation, and can handle 3D data with large size and high complexity. Moreover, equipped with a Backtracking Feedback Check (BFC) module, SliceProp effectively addresses the issues of information asymmetry and spatial sparse annotation in 3D medical image segmentation. Additionally, we adopt an uncertainty-based criterion to pri-oritize the slices to be refined interactively, which enhances the efficiency of the interaction process by enabling the model to focus on the regions with the most unreliable predictions. SliceProp is evaluated on two datasets and achieves promising results compared to state-of-the-art methods. © 2023 IEEE.

关键词： Medical imaging

来源：评论

学校读者我要写书评

暂无评论

Isoform Function Prediction Based on Heterogeneous Graph Attention Networks

Isoform Function Prediction Based on Heterogeneous Graph Att...

引用

2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Guo, Kuo Li, Yifan Chen, Hao Shen, Hong-Bin Yang, Yang Shanghai Jiao Tong University Key Lab. of Shanghai Education Commission for Intelligent Interaction and Cognitive Engineering Department of Computer Science and Engineering Shanghai200240 China Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China Institute of Image Processing and Pattern Recognition Shanghai200240 China Carnegie Mellon University School of Computer Science Computational Biology Department PittsburghPA15213 United States

ISBN: (纸本)9798350337488

Isoforms refer to different mRNA molecules transcribed from the same gene, which can be translated into proteins with varying structures and functions. Predicting the functions of isoforms is an essential topic in bioinformatics as it can provide valuable insights into the intricate mechanisms of gene regulation and biological processes. Conventionally, gene function lab.ls are standardized in Gene Ontology (GO) terms. However, traditional methods for predicting isoform function are largely limited by the absence of isoform-specific lab.ls, sparse annotations, and the vast number of GO terms. To address these issues, we propose HANIso, a deep learning-based method for isoform function prediction. HANIso leverages a pretrained protein language model to extract features from protein sequences. It also integrates heterogeneous information, such as isoform sequence features, GO annotations, and isoform interaction data, using a Heterogeneous Graph Attention Network (HAN). This allows the model to learn the importance of different sources of information and their semantic relationships through the attention mechanism. Our method can predict function lab.ls at both the gene level and isoform level. We conduct experiments on two species datasets, and the results demonstrate that our method outperforms existing methods on both AUROC and AUPRC. HANIso has the potential to overcome the limitations of traditional methods and provide a more accurate and comprehensive understanding of isoform function. © 2023 IEEE.

关键词： alternative splicing gene ontology heterogeneous graph attention network isoform function prediction protein language model

来源：评论

学校读者我要写书评

暂无评论

A New Composite Multimodality image Fusion Method Based on Shearlet Transform and Retina Inspired Model 1

A New Composite Multimodality Image Fusion Method Based on S...

引用

1st International Conference on Machine Vision and image processing, MVIP 2020

作者： Sayadi, Mohammadmahdi Ghassemian, Hassan Naimi, Reza Imani, Maryam Tarbiat Modares University Image Processing and Information Analysis Lab. Tehran Iran

ISBN: (纸本)9781728168326

Medical imaging is a very important element in disease diagnosis. MRI image has structural information, while PET image has functional information. However, there is no medical imagery device that has both structural and functional information simultaneously. Thus, the image fusion technique is used. This work concentrates on PET and MRI fusion. It is based on the combination of retina-inspired model and Non-Subsampled shearlet transform. In the first step, the high-frequency component is obtained by applying the shearlet transform to the MRI image, which produces sub-images in several scales and directions, and by adding up these images together a single edge image is reconstructed. In the second step, the PET image is transferred from RGB color space into IHS color space. Then the low-frequency component is produced by applying a Gaussian low pass filter to the luminance channel of the PET image. By adding up low frequency component and high-frequency component together and transferring the result from IHS color space to RGB color space the fused image is obtained. © 2020 IEEE.

关键词： image fusion

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12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：