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

20,250 篇 会议
161 篇 期刊文献
80 册 图书
1 篇 学位论文

馆藏范围

20,492 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

12,756 篇 工学
- 8,297 篇 计算机科学与技术...
- 5,001 篇 软件工程
- 4,986 篇 信息与通信工程
- 4,636 篇 电气工程
- 1,447 篇 控制科学与工程
- 1,319 篇 电子科学与技术（可...
- 1,225 篇 光学工程
- 898 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
- 280 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
4,663 篇 理学
- 2,150 篇 数学
- 2,073 篇 物理学
- 874 篇 生物学
- 668 篇 统计学（可授理学、...
- 521 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

1,121 篇 image segmentati...
1,066 篇 image coding
1,059 篇 signal processin...
1,009 篇 image processing
987 篇 internet of thin...
953 篇 feature extracti...
829 篇 image reconstruc...
632 篇 internet
553 篇 signal processin...
497 篇 signal to noise ...
476 篇 deep learning
476 篇 image edge detec...
458 篇 training
424 篇 image color anal...
400 篇 cameras
397 篇 robustness
396 篇 image enhancemen...
306 篇 image resolution
292 篇 image recognitio...
288 篇 accuracy

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
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...
20 篇 college of big d...
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

19,133 篇 英文
1,273 篇 其他
78 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 20492 条记录，以下是1081-1090 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：

Human Segmentation Using Commercial Wi-Fi Device 3

Human Segmentation Using Commercial Wi-Fi Device

引用

3rd international conference on Neural Networks, Information and Communication Engineering, NNICE 2023

作者： Chen, Zhiguo Xiao, Jiaren Luo, Bing The School of Computer and Software Engineering Xihua University Chengdu China

ISBN: (纸本)9798350335972

image segmentation technology is widely used in automatic driving, smart home and other fields. However, due to the problem of privacy of image methods, they face restrictions when used in private occasions. In order to avoiding private problem image-based method, we proposed a human segmentation method based on commercial Wi-Fi devices named Wi-Seg, which is used to extract human silhouette information and generate pixel-level annotations. So that it can be integrated with existing human- computer interaction schemes using image segmentation, and avoiding the privacy problem of image segmentation methods. In this paper, the channel state index (CSI) in the Wi-Fi signal is collected as the original data, and a encoder-decoder deep neural network is designed to extract the human silhouette information from the CSI signal. Compared with the mainstream image segmentation methods, our Wi-Seg is superior to recent work that using Wi-Fi signal to extract human silhouette information, and performs as well as image segmentation methods. © 2023 ieee.

关键词： image segmentation

来源：评论

学校读者我要写书评

暂无评论

DSP-based Industrial Defect Detection for Intelligent Manufacturing 26

DSP-Based Industrial Defect Detection for Intelligent Manufa...

引用

26th international conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Yue, Han Wang, Rucen Gao, Yi Xia, Ailing Zhang, Jianhua Tianjin University of Technology School of Computer Science and Engineering Tianjin China Zhejiang University of Technology College of Computer Science and Technology Hangzhou China

ISBN: (纸本)9798350331684

internet of Things (IoT) based industrial defect detection has attracted more and more attention. As a key component of intelligent manufacturing, defect detection is very important. Although deep learning (DL) can reduce the cost of traditional manual inspection and improve accuracy and efficiency, it requires huge computing resources and cannot be simply deployed on IoT devices. Digital signal processor (DSP) is an important IoT device with the characteristics of small size, strong performance and low energy consumption, and has been widely used in intelligent manufacturing. In order to achieve accurate defect detection on DSP, we proposed a variety of optimization strategies, and then extended the model to run on multi-core using a parallel scheme, and further quantified the implementation of the model. We evaluated it on three datasets, i.e. NEUSDD, MTDD and RSDD. Experimental results show that our method achieves a faster speed than running the same CNN model on a mainstream desktop CPU, with slightly accuracy loss. © 2023 ieee.

关键词： internet of things

来源：评论

学校读者我要写书评

暂无评论

Machine Learning for signal Processing in Communication systems

Machine Learning for Signal Processing in Communication Syst...

引用

2024 international conference on Innovative Computing, Intelligent Communication and Smart Electrical systems, ICSES 2024

作者： Reddy, PoliLokeshwara Shreyas, Chanda Bhargavi, Pamulapati. Raja, G.V. Rani, D. Leela Maheswari, S. Anurag University Department of Electronics and Communication Engineering Telangana Hyderabad500088 India Kkr & Ksr Institute of Technology and Sciences Department of Computer Science Engineering Andhra Pradesh Guntur522017 India Sri Sairam College of Engineering Department of Electronics and Communication Engineering Karnataka Bengaluru562106 India Mohan Babu University Department of Electronics and Communication Engineering Andhra Pradesh Tirupati517102 India Kongu Engineering College Department of Electrical and Electronic Engineering Tamil Nadu Perundurai638060 India

ISBN: (纸本)9798331543617

The traditional styles of signal processing are passing constraints in their capability to handle the different and dynamic character of ultramodern data transfers. This is because communication systems are getting increasingly complicated. The operation of machine literacy presents a potentially useful answer since it enables data-driven, adaptive approaches that have the eventuality to ameliorate signal processing performance. This composition provides a comprehensive examination of a variety of machine literacy ways and their operations in important disciplines such as modulation recognition, noise reduction, channel estimation, and signal discovery. The study demonstrates how machine literacy ways may vastly increase the effectiveness, delicacy, and robustness of signal-processing tasks in communication systems. This is fulfilled through detailed analysis and comparison. With the help of machine literacy, unborn communication systems will be suitable to attain advanced situations of robotization and intelligence, which will pave the way for further developments in areas similar to wireless dispatches, the internet of Effects(IoT), and other areas. In light of these findings, it's clear that machine literacy can fully transfigure assiduity by furnishing new shoes and approaches to the optimization of signal processing in the unborn generation of communication networks. © 2024 ieee.

关键词： Noise abatement

来源：评论

学校读者我要写书评

暂无评论

Smart Healthcare IoT: Deep Learning-Driven Patient Monitoring and Diagnosis 9

Smart Healthcare IoT: Deep Learning-Driven Patient Monitorin...

引用

9th ieee international conference on Smart Structures and systems, ICSSS 2023

作者： Viswadutt, Nudurupati Jaya Vemula, Dileep Kumar Shardunya, Mamidi Paleti, Narasimha Namitha, Kalakunnath Amrita Vishwa Vidyapeetham Department of Electronics and Communication Engineering Amritapuri India Amrita Vishwa Vidyapeetham Amrita School of Computing Department of Computer Science and Engineering Amritapuri India

ISBN: (纸本)9798350384208

In this paper, we introduce an internet of Things (IoT)-based framework customized for use in home clinical settings. This system is designed to remotely monitor patients and detect potential health issues at an early stage. The system's three primary sensors are the MAX30102 for oxygen saturation and heart rate monitoring, the AD8232 ECG sensor module for capturing ECG signal data, and the Non-contact infrared thermometry (NCIT) for precise temperature readings. Individuals' vital health data can be gathered by these devices in unison. After collection, the Message Queuing Telemetry Transport protocol is used to safely and effectively send the data to a centralized server. The categorization of possible illnesses and health abnormalities is performed on the server side using a pre-trained deep learning model built on Artificial Neural Networks (ANN). This model leverages the rich and diverse dataset collected from the sensors to identify early warning signs and detect emerging health issues. By combining IoT technology, advanced sensor capabilities, and artificial intelligence (AI), this system presents a comprehensive solution for proactive healthcare management and early intervention. The Proposed model shines with an impressive accuracy rate of 97.81%, indicating its exceptional ability to accurately forecast outcomes for nearly 98% of the cases. © 2023 ieee.

关键词： internet of things

来源：评论

学校读者我要写书评

暂无评论

Ground Penetrating Radar image De-Noising Method based on Multi-Noise and Self-Supervised Learning 5

Ground Penetrating Radar Image De-Noising Method Based on Mu...

引用

5th international conference on Intelligent Control, Measurement and signal Processing, ICMSP 2023

作者： Huang, Yaoxin Zhou, Wenlong School of Computer and Network Security Chengdu University of Technology Chengdu China

ISBN: (纸本)9798350336030

The quality of ground penetrating radar (GPR) images is usually low due to unwanted noise interference such as antenna coupling, soil reflection, and industrial frequency interference, which often affects subsequent studies. Deep learning methods in the field of ground penetrating radar image denoising mainly include non-blind denoising studies based on a single additive Gaussian white noise, and blind denoising studies based on masks. Due to the presence of multiple composite noises in GPR images, methods based on such a single noise perform poorly in practical denoising work;and blind denoising deep learning methods based on image masking lose feature details of anomalous regions. To solve the above problems, this paper proposes a multi-noise self-supervised denoising model MNSSDN for GPR image denoising, including a noise generator, a denoising generation network and a label pre-denoising module;meanwhile a corresponding loss function is designed to train the whole network. Firstly, the GPR image is input to the noise generator to obtain the image with random horizontal noise, and then passed into the denoising generation network for denoising. Next, integrate them into one fusion image. Finally, the output of the label pre-denoising module is used as the label for backpropagation to constrain the evolution direction of the network. © 2023 ieee.

关键词： image denoising

来源：评论

学校读者我要写书评

暂无评论

FEA-DETR: An Enhanced ConvNet for Detecting Prohibited Objects in X-Ray images Using Frequency and Edge Aware Information

FEA-DETR: An Enhanced ConvNet for Detecting Prohibited Objec...

引用

2025 ieee international conference on Acoustics, Speech, and signal Processing, ICASSP 2025

作者： Hong, Shilong Zhou, Yanzhou Xu, Weichao School of Automation Guangdong University of Technology Guangzhou510006 China Guangzhou Institute of Science and Technology Guangzhou510006 China

ISBN: (纸本)9798350368741

As the demand for transportation safety grows, traditional X-ray detection technologies face significant challenges, particularly item occlusion in complex backgrounds, which hinders the detection of prohibited objects. To address these issues, we propose Frequency and Edge Aware DETR (FEA-DETR), a robust DETR-based framework. The FEA-DETR integrates the Frequency and Edge Aware Attention Module (FEAM), which enhances feature representation by leveraging frequency and edge-aware information to better handle occlusive prohibited object detection. Additionally, we introduce the Multi-Dimensional Feature Hybrid Module (MDFHM), specifically designed for prohibited object detection in X-ray images. Extensive experiments on public datasets demonstrate that our model achieves competitive performance compared to existing state-of-the-art methods. © 2025 ieee.

关键词： attention module multi-dimensional feature hybrid object detection X-ray image

来源：评论

学校读者我要写书评

暂无评论

Specific Target Contour Extraction Method for Color images based on Priori Information-Assisted Dual-Threshold Binary Segmentation 8

Specific Target Contour Extraction Method for Color Images B...

引用

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

作者： Zhu, Jie Yu, Hang Dai, Keren Nanjing University of Science and Technology School of Mechanical Engineering Nanjing China

ISBN: (纸本)9798350397932

To meet the need for attacking against specific targets, the patrol missile should have some target identification capability to facilitate comparison and targeting of the plotted target contour with the on-board data. For the problem of recognition and capture of specific targets, based on computer vision technology, this paper proposes a method for extracting target contours of color images based on priori information assisted dual-threshold binary segmentation with the ability to recognize and extract specific target contours. Through the experiments conducted on this contour extraction method in natural and artificial environments, it is proved that the method has high accuracy and applicability for the contour extraction of specific targets, and can provide an identification method for patrol missiles to attack specific targets. © 2023 ieee.

关键词： image segmentation

来源：评论

学校读者我要写书评

暂无评论

A CNN-based Classifier for Quality image Selection from Sky Cameras 7

A CNN-Based Classifier for Quality Image Selection from Sky ...

引用

7th international conference on signal Processing and Information Security, ICSPIS 2024

作者： Rezk, Mahmoud Tiwari, Varun Kumar Manandhar, Prajowal Krishnan, Vineeth Pomares, Luis Martin Dubai Electricity and Water Authority Research and Development Centre Dubai United Arab Emirates

ISBN: (纸本)9798350368673

This paper presents quality enhancement techniques for all sky images in solar parks that uses sky camera-based systems. This study addresses the real-world challenges associated with deploying these systems, particularly focusing on external factors such as weather conditions, and interference due to soiling and dust accumulated on top of the domes of the sky cameras installed in desert conditions as well as human, birds, or insects that can compromise the quality of sky images used for irradiance estimation. To tackle these issues, a Convolutional Neural Network (CNN)-based classifier capable of identifying and excluding invalid images from the data processing pipeline is proposed. The proposed classifier achieves an accuracy exceeding 98% in detecting invalid images and is more than seven times smaller than traditionally used pre-trained models like VGG-16 and ResNet50, while also being over 16% faster. This research paves the way for more reliable operations in solar parks, leveraging ML-based methodologies for enhanced solar energy management. © 2024 ieee.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Advanced Deep Learning Method for Thyroid Nodule Detection and Evaluation in Ultrasound images 2

Advanced Deep Learning Method for Thyroid Nodule Detection a...

引用

2nd international conference on Intelligent Data Communication Technologies and internet of Things, IDCIoT 2024

作者： Sesha Vidhya, S. Sarkar, Swagata Rashmi, B.R. Suseendra, S. Prakash Kumar, P.S. Srimathi, S. RMK College of Engineering and Technology Department of ECE India Science Sri Sairam Engineering College Sai Leo Nagar West Tambaram Department of Artificial Intelligence and Data Chennai44 India Raja Rajeswari College of Engineering Department of Computer Science and Engineering Kumbalgodu Karnataka560074 India Panimalar Engineering College Department of Information Technology India K S R Institute for Engineering and Technology Department of Information Technology Tiruchengode Namakkal637215 India Saveetha School of Engineering SIMATS Department of Bio Technology Tamilnadu Chennai India

ISBN: (纸本)9798350327533

Thyroid nodule detection in its earliest stages is a difficult endeavor. Thyroid nodule diagnosis requires a lot of time and effort if done manually. The original ultrasound imaging of thyroid nodules is very variable due to differences in equipment and technicians. The issue with nodules is determining whether or not they are cancerous. Thyroid nodule evaluation through ultrasonography is presently advised as the first line of treatment. Ultrasound imaging can be helpful in making a diagnosis;But this relies heavily on the proficiency of the radiologist. There is a great need for faster and more efficient automated techniques of screening ultrasound pictures. This research study presents a deep learning method to detect thyroid nodules quickly and accurately. Some image processing techniques are required for optimal utilization of ultrasound imaging data of thyroid nodules. In this study, authors used deep neural networks InceptionResnet V2 and image augmentation to create a system for autonomous thyroid nodule categorization. For testing, neural networks with four-layer network nodes and the Local Laplacian Filtering technique are used to improve the image quality. The proposed method's reliability is evaluated by using the Thyroid Digital image Database (TDID), a freely available dataset. The comparative findings demonstrate that the proposed method is superior to the current state of advancement in machine learning. based on experiments, it is evident that the proposed technique has an Accuracy 0.99, Precision 0.98, Recall 0.96, Fl-Score 0.98, AVC-ROC 0.99. © 2024 ieee.

关键词： Deep Learning image Augmentation INCEPTIONRESNET Thyroid Nodule Detection Ultrasound Imaging

来源：评论

学校读者我要写书评

暂无评论

3rd international conference on Advanced Computing and Applications, ICACA 2024

3rd International Conference on Advanced Computing and Appli...

引用

3rd international conference on Advanced Computing and Applications, ICACA 2024

ISBN: (纸本)9789819747986

The proceedings contain 47 papers. The special focus in this conference is on Advanced Computing and Applications. The topics include: Optimizing Task Offloading in internet of Medical Things systems: A Hybrid Fog-Cloud Approach with Actor-Critic Decision Making;solving Delivery Allocation for Logistic Network Using Quantum Annealing;The Combined ECC Scheme with Outer 3-Bit BEC and Inner SEC-DAEC Codes for FSO Communication System;analyzing Multisignature Scheme of Bitcoin based on Threshold Cryptography Guidelines;a Public Key Exchange Protocol Using Tropical Determinant for IoT Environment;Certificateless Verifiable Data Integrity Checking Scheme in Fog-CPSs Architecture;noPass—A Novel Passwordless Multi-authentication-based Approach for Secure Login;an Overview of the Discrete Logarithm Problem in Cryptography;QoS-Aware Decentralized Trustworthy Forensic Evidence Management Framework Using the internet of Vehicle Things (IoVT);implementation of Ethical Hacking—The Importance of Protecting User and System Data;Cryptanalysis of Two Correlation-based Digital Watermarking Schemes in DCT Domain;cryptanalysis of a Histogram-based Watermarking Framework;a Hybrid Deep Learning Framework for Text-Independent Automatic Speaker Recognition System;AMBTC-based High Capacity Data Hiding Scheme Exploiting PVD and BRP;The Impact of UX/UI Usability Constructs on Purchase Decisions for Mobile Food Ordering Applications in India;Breast Cancer Detection and Classification Using MF-FRFCM Segmentation and IGOA-ELM Model;audio Encryption Scheme Applying a 2D Cosine Sine Logistic Map;noisy Electronic Tongue signal Prediction for Tea Quality Estimation Using Autoencoder;a Chain Code-based Methodology for Loop Closure Detection in Digital images;A Hybrid Watermarking Scheme Using DWT and Haar Transform for image Authentication;leveraging Transfer Learning for Screening of Geriatric Depression;emotion-based Song Categorization with Support Vector Machines;filtering-based Movie Recommendati

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 << < 105 106 107 108 109 110 111 112 113 114 > >>

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：