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

777 篇 会议
5 册 图书
1 篇 期刊文献

馆藏范围

783 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

466 篇 工学
- 404 篇 计算机科学与技术...
- 231 篇 软件工程
- 137 篇 信息与通信工程
- 107 篇 电气工程
- 60 篇 生物医学工程（可授...
- 58 篇 生物工程
- 37 篇 控制科学与工程
- 34 篇 电子科学与技术（可...
- 20 篇 光学工程
- 15 篇 化学工程与技术
- 14 篇 机械工程
- 14 篇 仪器科学与技术
- 12 篇 安全科学与工程
- 11 篇 动力工程及工程热...
- 10 篇 交通运输工程
173 篇 理学
- 91 篇 数学
- 65 篇 生物学
- 43 篇 物理学
- 37 篇 统计学（可授理学、...
- 28 篇 系统科学
- 15 篇 化学
98 篇 管理学
- 63 篇 图书情报与档案管...
- 39 篇 管理科学与工程(可...
- 23 篇 工商管理
63 篇 医学
- 54 篇 临床医学
- 40 篇 基础医学(可授医学...
- 31 篇 药学(可授医学、理...
13 篇 法学
- 12 篇 社会学
13 篇 农学
- 13 篇 作物学
12 篇 教育学
- 12 篇 教育学
8 篇 经济学
1 篇 军事学

主题

207 篇 machine learning
93 篇 signal processin...
87 篇 deep learning
64 篇 signal processin...
62 篇 feature extracti...
56 篇 support vector m...
50 篇 training
49 篇 machine learning...
30 篇 neural networks
21 篇 natural language...
20 篇 image processing
16 篇 computational mo...
14 篇 prediction algor...
13 篇 svm
13 篇 sentiment analys...
13 篇 data models
13 篇 convolutional ne...
12 篇 internet of thin...
12 篇 estimation
11 篇 covid-19

机构

6 篇 college of engin...
6 篇 department of co...
4 篇 xidian universit...
3 篇 department of el...
3 篇 university of du...
3 篇 peng cheng labor...
3 篇 uttaranchal inst...
3 篇 department of el...
3 篇 national laborat...
3 篇 graphic era deem...
2 篇 kl business scho...
2 篇 khulna universit...
2 篇 khulna universit...
2 篇 northwest minzu ...
2 篇 shandong univers...
2 篇 central universi...
2 篇 imt cdl ghaziaba...
2 篇 department of co...
2 篇 school of marine...
2 篇 school of comput...

作者

8 篇 wathiq mansoor
4 篇 yassine himeur
3 篇 alavikunhu panth...
3 篇 li zheng
3 篇 abigail copiaco
3 篇 jing wang
3 篇 joel alanya-belt...
3 篇 rashid ali
3 篇 shadi atalla
3 篇 abbes amira
3 篇 lei huang
2 篇 cheng xinzhou
2 篇 sherin m. yousse...
2 篇 liashchynskyi pe...
2 篇 kumar ashwani
2 篇 sarno riyanarto
2 篇 khawla a. alnajj...
2 篇 chang ming-ching
2 篇 liu ze-guo
2 篇 taleb mohammed

语言

753 篇 英文
27 篇 其他
2 篇 西班牙文
2 篇 中文

检索条件"任意字段=5th International Conference on Signal Processing and Machine Learning, SPML 2022"

共 783 条记录，以下是481-490 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：

A Weak signal Detection Method Based on Spatial Spectrum-LSTM Neural Network

A Weak Signal Detection Method Based on Spatial Spectrum-LST...

引用

IEEE international conference on Information Communication and signal processing (ICICSP)

作者： Yaning Dong Chuanzhang Wu Huizhu Zhu Feng Xu Xin Ren Communication and Navigation System Department AVIC Aeronautical Radio Electronics Research Institute Shanghai China Department of Information and Communication Engineering Harbin Engineering University Harbin China

In this paper, we propose a weak signal detection method based on spatial spectrum-long short-term memory (LSTM) neural network to address the problem that the traditional blind detection method of weak signals is not effective in the condition of low signal-to-noise ratios. We firstly exploit the difference between the spatial spectrum transformed signal and noise to determine whether there is a weak signal. then, the LSTM neural network is used for feature learning to classify different samples. It can avoid the influence of the detection threshold on the detection performance of the system. Numerical results show that the detection performance of our method outperforms LSTM neural network, radial basis function neural network, traditional maximum-minimum eigenvalue, and energy detection methods.

关键词： Representation learning Array signal processing Neural networks Radial basis function networks Eigenvalues and eigenfunctions signal detection signal to noise ratio

来源：评论

学校读者我要写书评

暂无评论

the use of Capsule Endoscopic Examination Videos in the Detection of Abnormalities in the Gastrointestinal Tract

The use of Capsule Endoscopic Examination Videos in the Dete...

引用

international conference on Communications, signal processing, and their Applications (ICCSPA)

作者： Sandra Said Sherin Youssef Mazen Nabil Elagamy Department of Computer Engineering College of Engineering and technology Arab Academy for Science and Technology (AASTMT)

ISBN: (纸本)9781665482387

A gastric abnormality involves the stomach and other nearby organs that are involved in digestion. Diagnosing and screening for gastric abnormalities can be time-consuming and challenging as many stomach and digestive disorders have similar symptoms. 60 to 70 million Americans suffer from gastric abnormalities which lead to nearly 250,000 deaths per year according to the ‘National Institute of Diabetes and Digestive and Kidney Diseases’. To overcome the current limitations, our approach uses deep learning (DL) integrated with wireless capsule endoscopy for segmentation of video capsule endoscopy images to detect four different abnormalities in the Gastrointestinal Tract (polyps, Angiectasias, erythema and Lymphangiectasia) and to develop lightweight, low-latency models that can be integrated with low-end endoscopic hardware devices [1]. Deep learning (DL) is a subfield of machine learning (ML) that utilizes layered structure of algorithms inspired by the biological neural network of the human brain. DL can reinforce disease diagnosis, interventions; and documenting procedure findings and quality measures [2]. DL has the potential to revolutionize gastrointestinal endoscopy as it can enhance clinical performance and support assessing lesions more accurately when trained by domain experts [3]. Experiments has been conducted on large benchmark dataset of Kvasir-Capsule dataset achieving high segmentation accuracy, sensitivity and specificity of 98.60%, 100% and 76.99%, respectively. the experimental findings demonstrate that the proposed model has achieved enhanced performance in terms of a trade-off between model complexity, metric performances and model parameters.

关键词： Deep learning Wireless communication Stomach Image segmentation Wireless sensor networks Endoscopes signal processing algorithms

来源：评论

学校读者我要写书评

暂无评论

Impact of machine and Deep learning in Drug Repurposing

Impact of Machine and Deep Learning in Drug Repurposing

引用

international conference on Contemporary Computing and Informatics (IC3I)

作者： Sherine Glory J P Durga Devi P. Ezhumalai Department of CSE SRM Institute of Science and Technology Chennai India. Department of CSE R M D Engineering College Tamilnadu India.

Deep learning has recently received massive attention on many areas of science and technology such as computer games, speech recognition, computer vision, natural language processing, self-driving cars, among others. Conventional Methods of drug discovery is time consuming and costlier. Drug repurposing is considered to be a promising method to identify the new drug target interactions for the existing drugs. To reduce the development costs and time, various approaches have suggested for repurposing of drug. In this paper, we are going to discuss about several machine and deep learning approaches used for drug repurposing.

关键词： Drugs Deep learning Video games Computer vision Costs Speech recognition Natural language processing

来源：评论

学校读者我要写书评

暂无评论

Fast Iteration Shrinkage thresholding Unfolding Network for Acoustic Source Localization

Fast Iteration Shrinkage Thresholding Unfolding Network for ...

引用

IEEE international conference on Information Communication and signal processing (ICICSP)

作者： Fangchao Chen Youhong Xiao Liang Yu Lin Chen College of Power and Energy Engineering Harbin Engineering University Harbin China State Key Laboratory of Mechanical System and Vibration Shanghai Jiao Tong University Shanghai China

Fast and accurate acoustic source localization methods have essential application value in the field of aircraft. Compared with traditional model-based methods, acoustic source localization technology based on deep learning shows a good application prospect. However, the uninterpretability of deep learning limits the further development of this technology. this paper proposes a deep network based on fast iterative shrinkage threshold algorithm unfolding (FISTA-Net), which combines the advantages of model-based and deep learning methods. In FISTA-Net, the iterative algorithm steps are mapped into the deep network, and the model parameters can be adaptively determined through end-to-end learning. the effectiveness of the proposed method is validated by a simulated dataset for training. the results show that FISTA-Net has higher spatial resolution and accuracy in acoustic source localization than the classical deconvolution algorithms.

关键词： Location awareness Deep learning Training Adaptation models Deconvolution signal processing algorithms signal processing

来源：评论

学校读者我要写书评

暂无评论

machine learning for Multi-Action Classification of Lower Limbs for BCI 5

Machine Learning for Multi-Action Classification of Lower Li...

引用

5th international conference on Computing, Electronics and Communications Engineering. iCCECE 2022

作者： Wang, Zilu Li, Jichun Daly, Ian Li, Junhua University of Essex School of Computer Science and Electronic Engineering Colchester United Kingdom Wuyi University Jiangmen China

ISBN: (数字)9781665489805

ISBN: (纸本)9781665489805

Over the past two decades, significant progress has been made in brain-computer interfaces (BCIs), devices which enable direct communications between human brains and external devices. One of the prevalent control paradigms is motor imagery-based BCI (MI-BCI), by which users imagine specific actions to express their intentions. Left-hand and right-hand motor imageries are frequently used in the MI-BCI. If a third class is needed, the imagination of both feet is usually added. However, it is relatively rare to separate feet into left lower limb and right limb in MI-BCI systems. In addition, previous studies have demonstrated that real movements can be distinguished from one another via processing the electroencephalogram (EEG). Similarly, motor imagery (MI) and movement observations (MO) can also be distinguished from one another. However, classification of left lower limb actions and right lower limb actions between MI, Real Movement (RM), and MO actions, has not been thoroughly explored. To address these questions, we performed a comprehensive experiment to collect EEG under six actions (i.e., Left-MI, Right-MI, Left-RM, Right-RM, Left-MO, and Right-MO) and used three models (convolutional neural network [CNN], support vector machine [SVM], and a K-Nearest Neighbours [KNN]) to classify these actions. Our CNN achieved the highest performance (37.77%) in the classification of six actions. Although the performance of SVM (37.21%) and KNN (25.26%) was worse, it is still better than the chance level (16.67%). Our results suggest that it is possible to distinguish between these six lower limb actions. this study has implications for developing multi-class BCI systems and promoting the research of multiple-Action classification. © 2022 IEEE.

关键词： Electroencephalography

来源：评论

学校读者我要写书评

暂无评论

Adversarial Attack on Communication signal Modulation Recognition

Adversarial Attack on Communication Signal Modulation Recogn...

引用

IEEE international conference on Information Communication and signal processing (ICICSP)

作者： Gang Yang XiaoLei Wang LuLu Wang Yi Zhang Yu Han Xin Tan Shang Yong Zhang Artificial Intelligence Research Center National Innovation Institute of Defense Technology Beijing China

Convolutional network models (CNN) are very vulnerable to adversarial samples, which poses a serious challenge to the security of CNN models. Based on the task of CNN's modulation and identification of communication signals, we propose a white-box attack algorithm, the shortest distance attack method (SD-Alg), which can generate extremely small disturbances and greatly reduce the classification performance of the model. Experiments show that our algorithm excels in attack success rate, running time and adversarial perturbation size among the same type of algorithms.

关键词： Deep learning Perturbation methods signal processing algorithms Modulation Pattern classification Classification algorithms Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Intelligent Reflecting Surfaces Aided Millimetre Wave Blockage Prediction For Vehicular Communication 6

Intelligent Reflecting Surfaces Aided Millimetre Wave Blocka...

引用

6th IEEE international Symposium on Telecommunication Technologies, ISTT 2022

作者： Woon, Fu Seong Leow, Chee Yen Universiti Teknologi Malaysia Wireless Communication Centre Faculty of Electrical Engineering Johor Bahru81310 Malaysia

ISBN: (数字)9781665489423

ISBN: (纸本)9781665489423

the ability of millimeter-wave (mmWave) to deliver gigabit throughput has led to its widespread adoption in Fifth Generation (5G) networks. However, mmWave links between Base Station (BS) and users can be easily obstructed by obstacles. In vehicular networks with dynamic environments and mobile users, the mmWave link blockage issue is even more pronounced. In order to preserve the mmWave link in the vehicular network, it is necessary to predict blockages. For blockage prediction, sensor information from Lidar, Radar, and cameras has been considered. Nonetheless, these non-radio frequency methods necessitate the use of additional equipment and signal processing, which raises the implementation cost and complexity. the existing literature also considers the use of BS and user's Radio Frequency (RF) signatures to predict blockage. However, users' mobility has not been taken into account. An Intelligent Reflecting Surface (IRS), on the other hand, has been viewed as a promising method for providing an alternate path by reflecting the mmWave signal between the BS and user in order to improve the reliability of vehicular networks. therefore, this research investigates the IRS-assisted blockage prediction in order to determine the future link status in the vehicular environment with respect to user mobility. the proposed solution employs a number of active elements that are randomly distributed on the IRS to obtain the RF signatures. Furthermore, it utilises machine learning (ML) techniques to learn the pre-blockage wireless signatures, which can predict future blockages. the results indicate that the proposed method can predict blockages between a single IRS and a moving user with a greater than 98 percent accuracy up to one second before they occur. © 2022 IEEE.

关键词： machine learning

来源：评论

学校读者我要写书评

暂无评论

Garment Sewing machine Fault Prediction Algorithm Based on machine learning

Garment Sewing Machine Fault Prediction Algorithm Based on M...

引用

5th international conference on machine learning and Big Data Analytics for IoT Security and Privacy, SPIoT 2024

作者： Wang, Chenming Intenational Fashion Institute Shandong Vocational College of Light Industry Shandong Zibo255300 China

During the garment production process, garment sewing machines frequently malfunction, often causing a series of problems such as low production efficiency, high maintenance costs and poor product quality. this paper uses time series analysis and XGBoost to predict the fault of clothing sewing machines. this paper first collects various data such as vibration signals and motor current during the operation of the sewing machine, removes data noise and outliers, and ensures data quality. then, based on Fourier transform, key features such as the spectral characteristics of the vibration signal and the volatility of the motor current in the sewing machine operation data are extracted, the features are screened and optimized, and the feature subset that is most sensitive to fault prediction is selected. XGBoost is selected as the fault prediction model, and the model is trained and the model parameters are optimized using the extracted feature subsets and operation data. the model is preliminarily evaluated using methods such as cross-validation, and necessary adjustments and optimizations are made to the model based on the evaluation results. Compared with the SVM model, XGBoost shows advantages in both prediction accuracy and prediction time. Its prediction accuracy is always higher than SVM. the average prediction time of XGBoost is about 232.6 milliseconds, but that of SVM is 401.1 milliseconds. this shows that XGBoost has higher efficiency and accuracy in processing garment sewing machine fault prediction tasks, and can provide reliable fault warning information for garment production, helping to timely discover and deal with potential fault problems, thereby improving production efficiency and equipment reliability. © 2025 the Authors. Published by Elsevier B.V.

关键词： Sewing machines

来源：评论

学校读者我要写书评

暂无评论

A Sparse Recovery Method on EPC-MIMO Radar System

A Sparse Recovery Method on EPC-MIMO Radar System

引用

IEEE international conference on Information Communication and signal processing (ICICSP)

作者： Kun Liu Xiongpeng He Guisheng Liao Jingwei Xu Shengqi Zhu Yifan Guo National Laboratory of Radar Signal Processing Xidian University Xi'an China School of Marine Science and technology Northwestern Polytechnical University Xi'an China

Multiple-Input Multiple-Output Radar with Element-Pulse Coding (EPC) is a novel way to address the performance degradation caused by range ambiguity in space-time adaptive processing. In this paper, we use the sparse recovery method to solve the problem that EPC-MIMO has a large demand for independent and identically distributed (IID) samples. On the one hand, we use the Sparse Bayesian learning (SBL) to achieve space-time spectral estimation under the small sample condition, and on the other hand, the use of prior knowledge, reduce the redundancy of the sparse recovery dictionary and improve the computational efficiency of the algorithm. the simulation results demonstrate the effectiveness of the proposed method.

关键词： Degradation Dictionaries Spaceborne radar Simulation Redundancy signal processing algorithms Estimation

来源：评论

学校读者我要写书评

暂无评论

Adaptive ECG Leads Selection for Low-Power ECG Monitoring Systems Using Multi-class Classification

Adaptive ECG Leads Selection for Low-Power ECG Monitoring Sy...

引用

international conference on Communications, signal processing, and their Applications (ICCSPA)

作者： Hebatalla Ouda Hossam S. Hassanein Khalid Elgazzar Queen's University Kingston Ontario Canada Ontario Tech University Oshawa Ontario Canada

ISBN: (纸本)9781665482387

the computer-aided interpretation of ECG signals has become a pivotal tool for physicians in the clinical assessment of cardiovascular diseases during the last decade. therefore, computerized diagnosis systems depend heavily on machine learning and deep learning models to guarantee high classification accuracy. However, a large amount of power is consumed due to the need for heavy computations to handle the classification tasks which act as a barrier to maintain continuous ECG monitoring. Hence, this work targets energy saving in the constrained embedded environment on a Texas Instruments CC2650 Micro-controller Unit (MCU). We provide a new approach to support energy-efficient ECG monitoring in real-time through the adaptive selection of ECG leads after applying multi-class classification on the raw ECG signals. We deploy two different CNN model scenarios on MIT-BIH and CODE-test datasets, and adjust the number of ECG streamed channels to 1,4, and 8, based on the detected cardiac abnormalities, such as arrhythmias and heart blocks. the adaptive selection of ECG channels achieves 77.7% power saving in the normal cardiac condition and up to 55.5% for the heart blocks, sinus bradycardia, and sinus tachycardia.

关键词： Heart Adaptation models Power demand Adaptive systems Electrocardiography signal processing Real-time systems

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79页 << < 45 46 47 48 49 50 51 52 53 54 > >>

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：