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

4,423 篇 会议
232 篇 期刊文献
55 册 图书
10 篇 学位论文
3 篇 资讯

馆藏范围

4,723 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

2,622 篇 工学
- 1,500 篇 计算机科学与技术...
- 885 篇 电气工程
- 773 篇 软件工程
- 463 篇 信息与通信工程
- 331 篇 控制科学与工程
- 292 篇 生物医学工程（可授...
- 223 篇 光学工程
- 210 篇 机械工程
- 162 篇 电子科学与技术（可...
- 143 篇 仪器科学与技术
- 126 篇 生物工程
- 89 篇 材料科学与工程（可...
- 68 篇 测绘科学与技术
- 58 篇 化学工程与技术
- 32 篇 土木工程
- 27 篇 建筑学
- 25 篇 力学（可授工学、理...
1,484 篇 理学
- 925 篇 物理学
- 656 篇 数学
- 431 篇 统计学（可授理学、...
- 228 篇 系统科学
- 139 篇 生物学
- 64 篇 化学
759 篇 医学
- 594 篇 临床医学
- 222 篇 特种医学
- 78 篇 基础医学(可授医学...
- 44 篇 药学(可授医学、理...
214 篇 管理学
- 119 篇 管理科学与工程(可...
- 103 篇 图书情报与档案管...
- 25 篇 工商管理
24 篇 农学
17 篇 法学
13 篇 教育学
12 篇 经济学
10 篇 艺术学
9 篇 文学
2 篇 军事学

主题

548 篇 image processing
461 篇 image segmentati...
390 篇 stochastic proce...
365 篇 statistical anal...
259 篇 feature extracti...
231 篇 bayesian methods
224 篇 image reconstruc...
188 篇 signal processin...
187 篇 image analysis
141 篇 pixel
135 篇 image restoratio...
109 篇 statistics
106 篇 pattern recognit...
105 篇 image edge detec...
103 篇 hidden markov mo...
103 篇 image coding
99 篇 noise reduction
96 篇 testing
96 篇 robustness
95 篇 histograms

机构

10 篇 school of electr...
8 篇 indian statistic...
7 篇 univ penn dept r...
7 篇 department of el...
7 篇 department of el...
7 篇 univ waterloo de...
7 篇 univ michigan de...
6 篇 nanyang technol ...
6 篇 smart computer v...
6 篇 computer vision ...
5 篇 georgia inst tec...
5 篇 microsoft resear...
5 篇 carnegie mellon ...
5 篇 signal and image...
5 篇 peng cheng lab p...
4 篇 google res mount...
4 篇 texas a&m univ.
4 篇 department of sy...
4 篇 ivan kozhedub kh...
4 篇 tokyo metropolit...

作者

9 篇 landman bennett ...
8 篇 vorapoj patanavi...
8 篇 dougherty er
8 篇 h. krim
7 篇 yannick berthoum...
7 篇 a.s. willsky
7 篇 sang hwa lee
6 篇 astola jaakko t.
6 篇 prince jerry l.
6 篇 umapada pal
6 篇 alexander wong
6 篇 udupa jk
6 篇 nam ik cho
6 篇 w. pieczynski
6 篇 fessler jeffrey ...
6 篇 w.c. karl
6 篇 pesquet jean-chr...
6 篇 pal umapada
6 篇 jong-il park
6 篇 pieczynski w

语言

4,631 篇 英文
49 篇 其他
31 篇 中文
13 篇 土耳其文
1 篇 西班牙文
1 篇 法文
1 篇 俄文

检索条件"任意字段=Conference on Statistical and Stochastic Methods for Image Processing"

共 4723 条记录，以下是581-590 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：

Characterisation of Landuse / Landcover Changes and its Comparison in Vijayawada City using Artificial Neural Networks with Minimum Distance to Mean

Characterisation of Landuse / Landcover Changes and its Comp...

引用

Artificial Intelligence and Knowledge Discovery in Concurrent Engineering (ICECONF), International conference on

作者： K.Pavan Venkat Vidhya Lakshmi Sivakumar Department of Civil Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai Tamil Nadu India

Aim: The main objective of this paper is to find the change detection of a Vijayawada city, Andhra Pradesh. Digital image processing is done by performing image classification, the regions separated into 6 different major classes based on its use and by the cover it is found for a period of 20 years from 2001 to 2020 and comparing the algorithms Artificial Neural Networks (ANN) with Minimum Distance to Mean (MDM) classification to predict which gives higher accuracy. Materials and methods: The images of the city are collected from the satellites landsat 7ETM+ (Enhanced Thematic Mapper plus) &landsat 8 for novel supervised classification from the United States Geological Survey (USGS) earth explorer. Those images are preprocessed for image classification and used for novel supervised classification with a total sample size of N= 6 with 3 samples from 2 groups with different years. The pretest power is to be determined with 80% with an alpha value of 0.05 and CI of 95%. Results and discussion: Based on the results from the SPSS (statistical analysis software) we got p=0.04 for overall accuracy which is p<0.05 for single tailed test, it is evident that there is significant difference between the two groups by the independent sample t test. The novel supervised classification is done for the samples and we have achieved the percentage of land cover, those are taken and we also got key outputs as Overall accuracy and kappa coefficient. We have achieved 97.1067±1.61593 as mean and SD for ANN, 88.8733±6.68356 as mean and SD for MDM for overall accuracy. The kappa coefficients are as 0.9399±0.0550 as mean and SD for ANN, 0.7757±0.1447 as mean and SD for MDM. Conclusion: According to the current research findings, even though there is no significant difference between groups we can say that Artificial Neural Networks provide more accurate classified output and we can say that ANN is better among those two algorithms.

关键词： Earth Artificial satellites Change detection algorithms statistical analysis Urban areas Artificial neural networks Software

来源：评论

学校读者我要写书评

暂无评论

Quantifying statistical significance of neural network-based image segmentation by selective inference 22

Quantifying statistical significance of neural network-based...

引用

Proceedings of the 36th International conference on Neural Information processing Systems

作者： Vo Nguyen Le Duy Shogo Iwazaki Ichiro Takeuchi Nagoya Institute of Technology and RIKEN Nagoya Institute of Technology Nagoya University and RIKEN

ISBN: (纸本)9781713871088

Although a vast body of literature relates to image segmentation methods that use deep neural networks (DNNs), less attention has been paid to assessing the statistical reliability of segmentation results. In this study, we interpret the segmentation results as hypotheses driven by DNN (called DNN-driven hypotheses) and propose a method to quantify the reliability of these hypotheses within a statistical hypothesis testing framework. To this end, we introduce a conditional selective inference (SI) framework—a new statistical inference framework for data-driven hypotheses that has recently received considerable attention—to compute exact (non-asymptotic) valid p-values for the segmentation results. To use the conditional SI framework for DNN-based segmentation, we develop a new SI algorithm based on the homotopy method, which enables us to derive the exact (non-asymptotic) sampling distribution of DNN-driven hypothesis. We conduct several experiments to demonstrate the performance of the proposed method.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Novel Approach to Quantum-Resistant Selective Encryption for Agricultural Sensors with Limited Resources

A Novel Approach to Quantum-Resistant Selective Encryption f...

引用

IEEE Annual Computing and Communication Workshop and conference (CCWC)

作者： Aditya Jagatha Akshay Kappala Mahesh Kamepalli Varghese Vaidyan Eric Yocam Yong Wang Gurcan Comert Dakota State University USA DSU USA North Carolina a T University USA

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

Selective Encryption involves extracting critical features from the data and applying highly secure encryption to those features, while the remaining data is stored using less resource-intensive encryption techniques. Discrete Wavelet Transforms (DWT) provides a means to extract these essential features. Previous works on selective encryption using DWT have explored hardware-specific implementations, such as using a General Purpose GPU (GPGPU). However, in the context of agricultural images captured by edge devices with limited computational capabilities, leveraging a GPGPU would introduce additional hardware requirements and restrict application potential. We present a selective encryption methodology utilizing parallel CPU processing to accelerate calculations, addressing these limitations. Given the advancements in quantum computing, there is a need to ensure the employed encryption methods are secure against potential quantum attacks. We implement NIST-proposed standards: ML-KEM-1024 for key encapsulation and ML-DSA for signature verification, ensuring quantum-resistant security. Our approach provides a security analysis and performance evaluation. We demonstrate significant visual degradation of encrypted data, with mean PSNR of 4.7201 decibel (dB) and SSIM of 0.0003, indicating strong resistance to statistical attacks. Performance improvements range from 21.47% to 52.43% compared to full AES-256 encryption across various file sizes. We discuss optimizations for handling different data sizes and compare our approach's security and performance with existing state-of-the-art methods. This MLPQE method offers a balanced solution for securing agricultural images on resource-constrained edge devices while ensuring long-term data protection against emerging quantum threats.

关键词： Performance evaluation Resistance Visualization Quantum computing image edge detection Streaming media Feature extraction Encryption Discrete wavelet transforms Streams

来源：评论

学校读者我要写书评

暂无评论

A Simple image processing Technique for Non-invasive Blood Glucose Monitoring

A Simple Image Processing Technique for Non-invasive Blood G...

引用

International conference on Automation, Signal processing, Instrumentation and Control, i-CASIC 2020

作者： Kulkarni, Sumedha Sanjiv Madhavi, M. Vijaya Sandhya, K.V. Bobby, Christy Department of Pharmaceutics Ramaiah University of Applied Sciences Bangalore India Faculty of Engineering Technology Department of Electronics and Communication Engineering East Point College of Engineering and Technology VTU Bangalore India Faculty of Engineering Technology Department of Electronics and Communication Engineering Ramaiah University of Applied Sciences Bangalore India

ISBN: (纸本)9789811582202

Diabetes Mellitus causes impaired blood glucose levels due to imbalance in insulin synthesis and if not carefully monitored affects multiple systems in the body leading to loss of vision/limbs, failure of vital organs like kidney, pancreas, etc. Continuous glucose monitoring thus is critical in the management of diabetes. The present study aims to develop a non-invasive method, that is safe, affordable, patient friendly, and practical for routine glucose monitoring using saliva. Two colorimetric methods were developed using chromogenic substrates such as potassium iodide (yellow colour - Method 1) and a combination of 4 Amino anti-pyrine and phenol (pink colour - Method 2) for the estimation of glucose in saliva. The present study involves the development of a paper-based biosensor for non-invasive blood glucose monitoring. The different intensities of the colour developed were captured using a mobile camera and subjected to an image processing technique (grey scaling). Further, statistical analysis of various parameters was carried out to predict the validity of the results obtained. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Color

来源：评论

学校读者我要写书评

暂无评论

Feature Modeling and Dimensionality Reduction to Improve ML-Based DDOS Detection Systems in SDN Environment

Feature Modeling and Dimensionality Reduction to Improve ML-...

引用

International conference on Wavelet Active Media Technology and Information processing (ICWAMTIP)

作者： Mohamed Ali Setitra Ilyas Benkhaddra Zine El Abidine Bensalem Mingyu Fan School of Computer Science and Engineering (Cyberspace Security) University of Electronic Science and Technology of China Chengdu China School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China

ISBN: (纸本)9781665493901

Distributed Denial of Service (DDoS) attacks are one of the most significant challenges in network security, especially in the Software-Defined Network (SDN) environment, due to the centralized network management provided by the Control Plane. Considering the insufficiency of traditional detection approaches because of the growth and sophistication of DDoS attacks, exploiting Machine Learning (ML) techniques is in high demand. For this, feature modeling is essential to obtain an effective ML-based DDoS detection system, especially in the pre-processing phase. In this paper, we proposed and implemented a pre-processing model based on deep studying the dataset, going so far as to increase the features number for a better representation and, if necessary, minimize the data dimension by exploring some dimensionality reduction techniques such as Principal Component Analysis (PCA) or t-distributed stochastic Neighbor Embedding (t-SNE). Moreover, to invest even more in our conceptual aspect relating to SDN environments, as specified in the above-cited challenge, we have chosen to implement our proposed model using an open-source SDN dataset created specially in an SDN environment. Then, the statistical characteristics of these correlations are analyzed. In addition, eight ML techniques between supervised and unsupervised models were used in our work to detect DDoS attacks. Finally, we compared our proposed model with other existing approaches. The outcome showed that the detecting reliability is improved, and the method has a good effect on detecting DDoS attacks compared with other methods.

关键词： Dimensionality reduction Measurement Support vector machines Correlation stochastic processes Denial-of-service attack Feature extraction

来源：评论

学校读者我要写书评

暂无评论

Leveraging Deep Learning Models for Enhanced Detection of Early-Stage Lung Cancer in CT Scan images

Leveraging Deep Learning Models for Enhanced Detection of Ea...

引用

Disruptive Technologies (ICDT), International conference on

作者： Bharti Chugh Karnika Dwivedi Monika Lamba Pankaj Bhatt Shardendu kumar Mishra Jonika Lamba Department of Computer Science & Engineering KIET Group of Institutions Ghaziabad Delhi-NCR India School of computer science engineering & technology Bennett University Greater Noida India Department of Computer Science The North Cap University Gurugram India Pharmacy Lloyd Institute of Management and Technology Greater Noida India KIET School of Pharmacy KIET Group of Institutions Ghaziabad Delhi-NCR India School of Management & Commerce K.R Mangalam University Gurugram India

ISBN: (数字)9798331519582

ISBN: (纸本)9798331519599

Patients afflicted with lung cancer have a high mortality rate, making it a deadly condition. It is possible to save patients' lives by detecting this disease early and correctly determining the stage of lung cancer. Medical professionals face challenges when it comes to accurately and quickly diagnosing lung cancer using various image processing, biomarker-based, and machine automation methodologies. This research makes use of CT scan images extracted from lung images acquired at a hospital. Traditional approaches rely on the patient's permission before providing CT scan images to diagnose lung cancer. Lung Cancer Cell Detection using Fusion Neural Network (LCCDC-FNN) is a new approach to early and accurate diagnosis that is proposed in this research article. Deep neural networks are employed to extract the features from the CT scan images. To save patients from this deadly disease, accurate feature extraction is crucial for early detection of malignant cells. This research aims to improve diagnostic accuracy by using an advanced 3D-convolution neural network (3D-CNN) in conjunction with U-Net and Mask R-CNN. Differentiating between benign and malignant tumors is also made possible by the proposed method. The results validate the feasibility of the suggested hybrid deep learning (DL) method for early detection of lung cancer when assessed using conventional statistical methods. The empirical results obtained from the proposed mechanism demonstrate that the proposed improved model provides U-Net, R-CNN and 3D-CNN classifications with 83.4%, 70% and 80.3% accuracy and LCCDC-FNN has accuracy of 98.99%.

关键词： Deep learning Accuracy statistical analysis Computed tomography Lungs Lung cancer Mortality Feature extraction Classification algorithms Performance analysis

来源：评论

学校读者我要写书评

暂无评论

Deep Neural Decision Forest for Acoustic Scene Classification

Deep Neural Decision Forest for Acoustic Scene Classificatio...

引用

European Signal processing conference (EUSIPCO)

作者： Jianyuan Sun Xubo Liu Xinhao Mei Jinzheng Zhao Mark D. Plumbley Volkan Kılıç Wenwu Wang Centre for Vision Speech and Signal Processing (CVSSP) University of Surrey UK College of Computer Science and Technology Qingdao University China Department of Electrical and Electronics Engineering Izmir Katip Celebi University Turkey

ISBN: (数字)9789082797091

ISBN: (纸本)9781665467995

Acoustic scene classification (ASC) aims to classify an audio clip based on the characteristic of the recording environment. In this regard, deep learning based approaches have emerged as a useful tool for ASC problems. Conventional approaches to improving the classification accuracy include integrating auxiliary methods such as attention mechanism, pre-trained models and ensemble multiple sub-networks. However, due to the complexity of audio clips captured from different environments, it is difficult to distinguish their categories without using any auxiliary methods for existing deep learning models using only a single classifier. In this paper, we propose a novel approach for ASC using deep neural decision forest (DNDF). DNDF combines a fixed number of convolutional layers and a decision forest as the final classifier. The decision forest consists of a fixed number of decision tree classifiers, which have been shown to offer better classification performance than a single classifier in some datasets. In particular, the decision forest differs substantially from traditional random forests as it is stochastic, differentiable, and capable of using the back-propagation to update and learn feature representations in neural network. Experimental results on the DCASE2019 and ESC-50 datasets demonstrate that our proposed DNDF method improves the ASC performance in terms of classification accuracy and shows competitive performance as compared with state-of-the-art baselines.

关键词： Deep learning image analysis Neural networks stochastic processes Training data Forestry Acoustics

来源：评论

学校读者我要写书评

暂无评论

EGSDE: unpaired image-to-image translation via energy-guided stochastic differential equations 22

EGSDE: unpaired image-to-image translation via energy-guided...

引用

Proceedings of the 36th International conference on Neural Information processing Systems

作者： Min Zhao Fan Bao Chongxuan Li Jun Zhu Dept. of Comp. Sci. & Tech. BNRist Center THU-Bosch ML Center Tsinghua University China Gaoling School of Artificial Intelligence Renmin University of China Beijing China and Beijing Key Laboratory of Big Data Management and Analysis Methods Beijing China Dept. of Comp. Sci. & Tech. BNRist Center THU-Bosch ML Center Tsinghua University China and Pazhou Laboratory (Huangpu) Guangzhou China

ISBN: (纸本)9781713871088

Score-based diffusion models (SBDMs) have achieved the SOTA FID results in unpaired image-to-image translation (I2I). However, we notice that existing methods totally ignore the training data in the source domain, leading to sub-optimal solutions for unpaired I2I. To this end, we propose energy-guided stochastic differential equations (EGSDE) that employs an energy function pretrained on both the source and target domains to guide the inference process of a pretrained SDE for realistic and faithful unpaired I2I. Building upon two feature extractors, we carefully design the energy function such that it encourages the transferred image to preserve the domain-independent features and discard domain-specific ones. Further, we provide an alternative explanation of the EGSDE as a product of experts, where each of the three experts (corresponding to the SDE and two feature extractors) solely contributes to faithfulness or realism. Empirically, we compare EGSDE to a large family of baselines on three widely-adopted unpaired I2I tasks under four metrics. EGSDE not only consistently outperforms existing SBDMs-based methods in almost all settings but also achieves the SOTA realism results without harming the faithful performance. Furthermore, EGSDE allows for flexible trade-offs between realism and faithfulness and we improve the realism results further (e.g., FID of 51.04 in Cat → Dog and FID of 50.43 in Wild → Dog on AFHQ) by tuning hyper-parameters.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Deep Learning Applications in Microscopy and Holography for near-field Signal processing

Deep Learning Applications in Microscopy and Holography for ...

引用

International conference on Communication Systems and Network Technologies (CSNT)

作者： Milad Mohseni K T Thilagham Aravinda K B Santhosh Kumar Amandeep Nagpal B. T. Geetha Department of computer and engineering Islamic Azad University Tabriz Branch Tabriz Iran Metallurgical Engineering Government College of Engineering. salem Tamilnadu Department of Electronics and Communication Engineering New Horizon College of Engineering Bangalore Institute of Aeronautical Engineering Dundigal Hyderabad Lovely Professional University Phagwara Department of ECE Saveetha School of Engineering SIMATS TamilNadu India

ISBN: (数字)9798350305463

ISBN: (纸本)9798350305470

Focusing on near-field signal processing, we provide a novel and comprehensive strategy for improving picture quality, obtaining super-resolution, and performing quantitative analysis in the disciplines of microscopy and holography. Three deep learning algorithms—Enhanced imageNet (EINet), HoloReconGAN, and QuantSegNet—are combined in this method to maximize their potential. In order to improve images, super-resolve them, and analyze them quantitatively, many algorithms have been developed. In order to better comprehend each method, mathematical equations are provided to describe the main steps involved. With the use of convolutional neural networks, noise is reduced and finer features are brought into focus with the help of the Enhanced imageNet (EINet) method. To accomplish super-resolution and 3D reconstruction from holographic data, HoloReconGAN combines generative adversarial networks (GANs) with variational autoencoders (VAEs). Label-free segmentation and quantitative analysis of structures in microscopy and holography pictures are the focus of QuantSegNet, a semantic segmentation network. Our suggested technique has been shown to outperform six established methods over a broad variety of assessment criteria. image quality, noise suppression, feature recognition, computational efficiency, and resilience are just few of the areas where it shines. It's also faster, uses less memory, is more accurate, easier to use, cheaper, and faster. Applications in several scientific fields might be facilitated, and the area of microscopy and holography as a whole could see significant advancements as a result.

关键词： Deep learning Technological innovation Three-dimensional displays statistical analysis Microscopy Superresolution Noise reduction

来源：评论

学校读者我要写书评

暂无评论

Hardware-in-the-loop End-to-end Optimization of Camera image processing Pipelines

Hardware-in-the-loop End-to-end Optimization of Camera Image...

引用

IEEE/CVF conference on Computer Vision and Pattern Recognition (CVPR)

作者： Mosleh, Ali Sharma, Avinash Onzon, Emmanuel Mannan, Fahim Robidoux, Nicolas Heide, Felix Algolux Montreal PQ Canada Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9781728171685

Commodity imaging systems rely on hardware image signal processing (ISP) pipelines. These low-level pipelines consist of a sequence of processing blocks that, depending on their hyperparameters, reconstruct a color image from RAW sensor measurements. Hardware ISP hyperparameters have a complex interaction with the output image, and therefore with the downstream application ingesting these images. Traditionally, ISPs are manually tuned in isolation by imaging experts without an end-to-end objective. Very recently, ISPs have been optimized with 1st-order methods that require differentiable approximations of the hardware ISP. Departing from such approximations, we present a hardware-in-the-loop method that directly optimizes hardware image processing pipelines for end-to-end domain-specific losses by solving a nonlinear multi-objective optimization problem with a novel 0th-order stochastic solver directly interfaced with the hardware ISP. We validate the proposed method with recent hardware ISPs and 2D object detection, segmentation, and human viewing as end-to-end downstream tasks. For automotive 2D object detection, the proposed method outperforms manual expert tuning by 30% mean average precision (mAP) and recent methods using ISP approximations by 18% mAP.

关键词： Object detection

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473页 << < 55 56 57 58 59 60 61 62 63 64 > >>

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：