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

127 篇 会议

馆藏范围

127 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

70 篇 工学
- 45 篇 计算机科学与技术...
- 26 篇 信息与通信工程
- 26 篇 软件工程
- 17 篇 光学工程
- 10 篇 电气工程
- 9 篇 电子科学与技术（可...
- 7 篇 材料科学与工程（可...
- 7 篇 生物医学工程（可授...
- 6 篇 控制科学与工程
- 5 篇 机械工程
- 5 篇 化学工程与技术
- 4 篇 仪器科学与技术
- 4 篇 交通运输工程
- 4 篇 安全科学与工程
- 4 篇 网络空间安全
- 3 篇 土木工程
- 3 篇 石油与天然气工程
- 3 篇 航空宇航科学与技...
- 3 篇 生物工程
- 2 篇 动力工程及工程热...
35 篇 理学
- 20 篇 数学
- 14 篇 物理学
- 5 篇 化学
- 5 篇 生物学
- 3 篇 统计学（可授理学、...
13 篇 管理学
- 12 篇 管理科学与工程(可...
- 3 篇 图书情报与档案管...
7 篇 医学
- 6 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...

主题

19 篇 accuracy
18 篇 convolutional ne...
13 篇 training
12 篇 deep learning
11 篇 image processing
11 篇 signal processin...
9 篇 image segmentati...
9 篇 signal processin...
8 篇 image reconstruc...
7 篇 image enhancemen...
7 篇 real-time system...
7 篇 psnr
7 篇 feature extracti...
7 篇 image recognitio...
7 篇 image quality
6 篇 image color anal...
5 篇 support vector m...
5 篇 visualization
5 篇 machine learning...
5 篇 measurement

机构

2 篇 j.c. bose univer...
2 篇 shenyang aerospa...
2 篇 advanced semicon...
2 篇 school of comput...
2 篇 dr. d. y. patil ...
2 篇 school of comput...
2 篇 beijing - dublin...
2 篇 college of trans...
2 篇 school of electr...
2 篇 cyrinn science &...
2 篇 college of mecha...
2 篇 school of comput...
2 篇 college of compu...
2 篇 k. r. mangalam u...
2 篇 school of electr...
1 篇 department of co...
1 篇 electrical and c...
1 篇 international sc...
1 篇 gitam univ dept ...
1 篇 king mongkuts un...

作者

2 篇 yan jun
2 篇 wang changxiang
2 篇 anuradha
2 篇 ma jinshan
1 篇 teodora bakardji...
1 篇 jiang zhihao
1 篇 zhang tao
1 篇 jiayin min
1 篇 k sevitha
1 篇 kapatos c.
1 篇 bhal arpita
1 篇 c surya
1 篇 chit siang soh
1 篇 valaparla david
1 篇 mitali monalisha
1 篇 ishwari kakade
1 篇 ran an
1 篇 jia zhenhong
1 篇 gole nilesh
1 篇 mansour hajji

语言

120 篇 英文
7 篇 其他

检索条件"任意字段=4th International Conference on Advanced Algorithms and Signal Image Processing, AASIP 2024"

共 127 条记录，以下是81-90 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：

Classification of Plant Leaf Diseases Based on EfficientNet

Classification of Plant Leaf Diseases Based on EfficientNet

引用

signal processing, Communications and Engineering Management conference (ISPCEM), international

作者： Yi Fan Xing Zhang School of Computer Science and Technology Huaiyin Normal University Huaian China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

With the in-depth research of deep learning technology, convolutional neural networks have been widely applied in the field of image recognition. However, traditional diagnostic methods for plant leaf diseases often rely on expert experience and time-consuming manual detection, which is inefficient and limited in accuracy. Based on the above background, this paper mainly studies the plant leaf disease classification method based on EfficientNet. Firstly, the EfficientNet algorithm is reviewed, followed by a detailed description of the process of the plant leaf disease classification algorithm and then an analysis of the dataset used. In the simulation analysis, this paper explores the impact of different parameters on the performance of EfficientNet and demonstrates its effectiveness in plant leaf disease classification. the proposed method has efficient and accurate results, significant research significance, and application prospects in the fields of agricultural automation and intelligent diagnosis.

关键词： Training Accuracy image recognition signal processing algorithms Manuals signal processing Classification algorithms Optimization image reconstruction Diseases

来源：评论

学校读者我要写书评

暂无评论

advanced image processing Techniques for the Detection and Monitoring of TV Datacenter System

Advanced Image Processing Techniques for the Detection and M...

引用

international conference on Electrical, Control and Instrumentation Engineering (ICECIE)

作者： Mohamed Fakhreddine Teodora Bakardjieva Galina Momcheva Varna Free University Varna Bulgaria

ISBN: (纸本)9781665480772

TV Datacenter is the primary storage facility where servers for data storage are located. TV Datacenters have exploded in popularity over the last decade, becoming the epicenter of the technical landscape. As the size and ability of the centers grow, the complexity in managing them grows as well. the information and functionality grid are in danger of imploding, with potentially catastrophic consequences. As a result, solutions that can keep pace with the growth of data delivery, as well as Datacenter storage sizes, are needed. the main objective behind this research study is to propose a complete and advanced system for accommodating the enlargement of Datacenters, for enhancing data delivery in Datacenters, and for detection of errors and problems in TV Datacenters. Moreover, the system will also perform advanced and robust monitoring of TV Datacenters. the significance of the study relies on the algorithms that are proposed since these algorithms are completely novel as well as they are highly performant. In addition, these algorithms incorporate new technologies of image processing. the results obtained using the algorithms showed a detection rate obtained of 83.33% which can be considered to be an indicator of the high performance of the proposed detection system. Moreover, according to the results, the minimum time of replacement is zero seconds meaning that the replacement was done automatically after the detection of the corruption in the video. the results also showed that for all videos, the detected number of frames were all successfully replaced. the frame replacement showed 100% efficiency for every video.

关键词： Visualization TV Automation Films signal processing algorithms Media Motion pictures

来源：评论

学校读者我要写书评

暂无评论

Automatic ship detection in SAR image based on Multi-scale Faster R-CNN 4

Automatic ship detection in SAR Image based on Multi-scale F...

引用

2020 4th international Workshop on advanced algorithms and Control Engineering, IWAACE 2020

作者： Zhou, Yiqing Cai, Zemin Zhu, Yuting Yan, Jingwen Guangdong Provincial Key Laboratory of Digital Signal and Image Processing Shantou Guangdong515063 China College of Engineering Shantou University Shantou Shantou515063 China

Automatic ship detection of synthetic aperture radar (SAR) images has been widely used in maritime surveillance. SAR images have the characteristics of all-weather, all-day detection. therefore, many object detection methods ranging from traditional to deep learning techniques have been proposed. However, the objects in large-scale remote sensing images are relatively small, and objects are often appeared at different scales. What's more, the current ship detection methods are insensitive to small-scale vessels. To solve these problems, a novel multi-scale ship detection method based on a Multi-scale Faster R-CNN network in SAR images is proposed in this paper. Firstly, a multi-scale network is used to decompose the SAR images into a pyramid structure and extract the features. then, the region proposal network (RPN) is performed using the feature map for each layer to get the proposals that contains ship targets. Finally, these proposals are fed to the classification and regression network to obtain the final detection results. Multi-scale Faster R-CNN achieves the mean average precision(mAP) score 0.986 on the dataset of SAR-Ship-Dataset, which indicates that the proposed method has high detection accuracy and low missing rate. © 2020 IOP Publishing Ltd. All rights reserved.

关键词： Synthetic aperture radar

来源：评论

学校读者我要写书评

暂无评论

Research on Smoke image Recognition: Multi-Scale Dilated Convolutional Neural Network and Entropy Function threshold

Research on Smoke Image Recognition: Multi-Scale Dilated Con...

引用

signal processing, Communications and Engineering Management conference (ISPCEM), international

作者： Yongmei Xiao Xing Wang Officers College of PAP Chengdu China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

For the application of artificial intelligence in image-based smoke detection in emergency management, this paper proposes an image recognition algorithm based on a multi-scale dilated convolutional neural network and entropy threshold function to prevent the loss of small-scale features, low detection rate, and poor real-time performance of smoke image recognition. First, the smoke features are determined according to a multi-scale analysis model, and multi-scale information such as texture, color, and shape of the smoke image is obtained. Second, we propose a dilated convolutional neural network to solve the feature loss. third, according to the characteristics of the entropy function, we build a model that comprehensively considers smoke features and recognition thresholds, which more accurately describes the image recognition process. Ultimately, this research develops a smoke detection system using the suggested algorithm and demonstrates its efficacy through simulation and experimental validation.

关键词： image recognition Shape image color analysis Engineering management signal processing algorithms signal processing Feature extraction Entropy Real-time systems Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

ISUKAT: Capture, Measure, and Know Your Perfect Shoe Size Using image Recognition 4

ISUKAT: Capture, Measure, and Know Your Perfect Shoe Size Us...

引用

4th international conference on Intelligent Cybernetics Technology and Applications, ICICyTA 2024

作者： Francisco, Andrea Elaine Jumamoy, Jowan Gavriel J. Lim, Bryle Elys N. Centeno, Criselle J. Sison, Ariel Antwaun Rolando C. Marcelo, Erwin D. Dioses, Raymund M. College of Information Systems and Technology Management Pamantasan ng Lungsod ng Maynila Manila Philippines

ISBN: (纸本)9798331506490

iSUKAT represents a groundbreaking web-based application that harnesses advanced image recognition technology, specifically Roboflow 3.0 Instance Segmentation with a COCO-seg checkpoint, to revolutionize foot measurement and shoe selection. By employing sophisticated machine learning algorithms, the system accurately captures and analyzes foot dimensions, identifying foot types and shoe brands with high precision. the application directly addresses the critical issue of improper shoe sizing, which impacts 63-72% of consumers and leads to significant discomfort and potential foot health problems. Utilizing a comprehensive evaluation methodology based on ISO 25010 quality standards, the research assessed the system's performance through quantitative metrics including Mean Average Precision for foot detection and segmentation, instance segmentation accuracy, and real-time image processing inference time. the empirical results were notably impressive, with the system achieving excellent ratings across key performance domains: Functional Appropriateness scored 3.35/4.00, Usability reached 3.26/4.00, and Reliability was evaluated at 3.31/4.00, resulting in an overall system acceptance of 3.21/4.00, which was classified as satisfactory. the application strategically addresses e-commerce shoe shopping challenges by providing personalized recommendations based on precise foot measurements. However, the research also revealed nuanced performance insights, with only 11.6% of users strongly agreeing on measurement precision, while 40.5% expressed concerns about measurement accuracy. Despite these challenges, the study ultimately underscores iSUKAT's transformative potential in digital footwear selection, presenting an innovative solution that seamlessly integrates advanced image recognition, machine learning, and user-centric design to dramatically enhance the online shoe shopping experience. © 2024 IEEE.

关键词： Medical problems

来源：评论

学校读者我要写书评

暂无评论

Research on Film and Television Multimedia Fusion Analysis Technology

Research on Film and Television Multimedia Fusion Analysis T...

引用

signal processing, Communications and Engineering Management conference (ISPCEM), international

作者： Sun Pengfei Hu Yue Shanghai Xingjian College Shanghai City China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

With the development of science and technology and the emergence of complex film and television multimedia, the traditional single media analysis method can no longer meet the needs of automatically obtaining high-level media semantic content. this paper mainly uses the computer to automatically analyze and extract the abstract semantic content of film and television multimedia data. At the same time, it conducts in-depth research on various related technologies of film and television multimedia integration, discusses the analysis methods of film and television multimedia integration, focuses on the research of the scene detection algorithm combining sound and image, and establishes the architecture and model of the multimedia integrated analysis system. the research of this paper has certain reference significance for the analysis of multimedia content of film and television.

关键词： Analytical models image segmentation TV Semantics Computer architecture Media signal processing Multimedia communication Detection algorithms Lenses

来源：评论

学校读者我要写书评

暂无评论

Unified Dictionary Training Approach for High-Resolution image Enhancement

Unified Dictionary Training Approach for High-Resolution Ima...

引用

Artificial Intelligence and signal processing (AISP), INDIA, international conference on

作者： Kannuru Srinadh Valaparla David Goruputi Lakshmi Karlapudi Ramakrishna Department of ECE NIT Rourkela Odisha India Department of ECE Anurag Engineering College(A) Kodada Telangana India Department of Mathematics NIT Rourkela Odisha India

ISBN: (数字)9798350350654

ISBN: (纸本)9798350350661

this research presents a new unified dictionary training super-resolution (UDTSR) approach to single and multi-image super-resolutions that uses a bilevel optimization frame-work and patchwise sparse recovery. By employing interconnected dictionaries to bridge the two spaces of image patches, we ensure that a sparse representation of a low-resolution (LR) image patch can accurately reconstruct its corresponding high-resolution (HR) patch. To facilitate efficient stochastic gradient descent, implicit differentiation calculates the gradient. Further-more, by using a neural network model for rapid sparse inference and selective processing of visually essential areas, we can almost tenfold improve the performance of real-world applications. Also, by discovering the fundamental relationships between different data modalities, our approach overcomes the difficulty of dealing with panchromatic and multispectral images. For example, using shared and individual sparse representations, we describe a data model that can detect similarities and differences in multimodal signals. Single image super-resolution (SISR) and multi-frame super-resolution (MFSR) are advanced separately, with minimal research on their ideal combination. We propose a novel UDTSR analysis using an iterative shrinkage and thresholding algorithm. Our simulations of many combinations of SISR and MFSR, such as x2, x3, and x4, confirm our theory quantitatively and qualitatively.

关键词： Training Dictionaries Sparse approximation Superresolution Stochastic processes signal processing algorithms Noise measurement signal resolution Optimization Noise level

来源：评论

学校读者我要写书评

暂无评论

Alzheimer's disease MRI image recognition method based on deep learning

Alzheimer's disease MRI image recognition method based on de...

引用

signal processing, Communications and Engineering Management conference (ISPCEM), international

作者： Huang Jiahao College of Mechanical and Electronic Engineering Nanjing Forestry University Nanjing China

ISBN: (数字)9798331528676

ISBN: (纸本)9798331528683

Globally, the incidence of Alzheimer's disease is increasing. Although there are many diagnostic methods, traditional methods often rely on professional knowledge and consume much time. therefore, there is an urgent need to introduce automated technology to improve diagnostic efficiency and accuracy. this study used three deep learning models, namely convolutional neural network (CNN), ResNet, and VGGNet. It optimized them according to data characteristics for Alzheimer's MRI image recognition, providing a reference for diagnosing Alzheimer's disease. this study used the dataset from Dataset Alzheimer, based on the data preprocessing of Alzheimer's Disease Neuroimaging Initiative (ADNI). the model was trained after the dataset was processed by data augmentation, pixel value scaling and MOTE oversampling. the experimental results indicated that the VGGNet model attained the highest overall accuracy at 76%, and the CNN and ResNet models reached accuracies of 75% and 74%, respectively. Nonetheless, the model's proficiency in reliably identifying categories with limited samples necessitates enhancement. Furthermore, further analysis is necessary to assess the model's generalisation potential and its relevance in clinical environments. Future research may explore more advanced model architectures, incorporating attention processes and generative adversarial networks (GANs), to enhance the model's recognition capabilities and its suitability for therapeutic applications.

关键词： Deep learning Accuracy image recognition Magnetic resonance imaging Transfer learning signal processing algorithms signal processing Convolutional neural networks Reliability Alzheimer's disease

来源：评论

学校读者我要写书评

暂无评论

Preprocessing of CT Scans for Lung Cancer Detection

Preprocessing of CT Scans for Lung Cancer Detection

引用

Intelligent Technologies (CONIT), international conference on

作者： Akanksha Singh Surekha Kamath Instrumentation and Control Engineering Manipal Institute of Technology Manipal India

ISBN: (数字)9798350349900

ISBN: (纸本)9798350349917

Lung cancer, a pervasive global health concern, requires effective early detection and classification methodologies to improve patient outcomes. this project addresses this challenge by developing a comprehensive framework utilizing medical imaging data for the timely classification of lung cancer with better results of diagnosis. Leveraging advanced processing techniques and DL algorithms, the project aims to make the lung cancer detection system more efficient. By preprocessing DICOM image datasets of lung scans and employing techniques such as noise reduction, contrast enhancement, and feature extraction, the project seeks to improve image quality and extract relevant features.

关键词： image quality image segmentation Histograms Accuracy Computed tomography Noise reduction Noise

来源：评论

学校读者我要写书评

暂无评论

Design Mechanism of Tennis Teaching Assistant System Based on Virtual Reality Technology

Design Mechanism of Tennis Teaching Assistant System Based o...

引用

Electronic Communications, Internet of things and Big Data (ICEIB), international conference on

作者： Liu Tao Li Gaosheng Wuhan Technology and Business University Wuhan China

ISBN: (数字)9798350360721

ISBN: (纸本)9798350360738

A tennis teaching assistant system based on virtual reality (VR) technology was designed to revolutionize the way tennis is taught and learned. By integrating VR technology into tennis training, this system provides an immersive and realistic simulation of a tennis court, allowing players to practice their skills in a virtual environment. the system utilizes advanced image processing techniques and motion object detection algorithms to track player movements and provide real-time feedback. Additionally, the system includes modeling and roaming tools, such as 3DS Max and Vega, to create and navigate the virtual tennis court. Overall, the system offers a unique and effective approach to tennis education, helping players at all levels improve their skills and enhance their understanding of the game.

关键词： Training three-dimensional displays Tracking Navigation image processing Virtual environments 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13页 << < 4 5 6 7 8 9 10 11 12 13 > >>

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：