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

15,795 篇 会议
362 篇 期刊文献
192 册 图书

馆藏范围

16,349 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

8,318 篇 工学
- 5,418 篇 计算机科学与技术...
- 2,987 篇 软件工程
- 2,164 篇 电气工程
- 1,865 篇 信息与通信工程
- 1,127 篇 控制科学与工程
- 878 篇 光学工程
- 647 篇 机械工程
- 639 篇 电子科学与技术（可...
- 538 篇 生物医学工程（可授...
- 537 篇 仪器科学与技术
- 477 篇 生物工程
- 224 篇 测绘科学与技术
- 217 篇 交通运输工程
- 203 篇 材料科学与工程（可...
- 199 篇 航空宇航科学与技...
- 164 篇 化学工程与技术
- 163 篇 网络空间安全
- 141 篇 安全科学与工程
4,006 篇 理学
- 2,389 篇 物理学
- 1,359 篇 数学
- 527 篇 生物学
- 368 篇 统计学（可授理学、...
- 341 篇 系统科学
- 205 篇 化学
1,522 篇 医学
- 1,348 篇 临床医学
- 200 篇 特种医学
- 197 篇 基础医学(可授医学...
1,055 篇 管理学
- 613 篇 管理科学与工程(可...
- 481 篇 图书情报与档案管...
- 140 篇 工商管理
84 篇 法学
66 篇 农学
45 篇 经济学
43 篇 教育学
37 篇 军事学
32 篇 文学
26 篇 艺术学
1 篇 哲学

主题

2,780 篇 image processing
1,550 篇 image segmentati...
1,513 篇 signal processin...
865 篇 feature extracti...
652 篇 image edge detec...
634 篇 cameras
633 篇 signal processin...
604 篇 real-time system...
575 篇 image reconstruc...
517 篇 image coding
498 篇 algorithm design...
399 篇 clustering algor...
397 篇 image color anal...
396 篇 deep learning
379 篇 object detection
367 篇 image enhancemen...
352 篇 accuracy
332 篇 hardware
323 篇 image analysis
312 篇 computer vision

机构

38 篇 xi'an jiaotong u...
32 篇 northwestern pol...
32 篇 school of electr...
31 篇 xi an jiao tong ...
31 篇 school of electr...
26 篇 university of ch...
24 篇 xi an jiao tong ...
23 篇 national laborat...
22 篇 xi an jiao tong ...
21 篇 signal and image...
19 篇 samara national ...
17 篇 xidian universit...
16 篇 school of inform...
16 篇 institute of art...
15 篇 department of el...
15 篇 school of automa...
15 篇 national key lab...
15 篇 school of comput...
14 篇 school of electr...
14 篇 chang'an univers...

作者

39 篇 chen xi
27 篇 xi chen
24 篇 licheng jiao
22 篇 nanning zheng
20 篇 li xi
20 篇 liu ying
18 篇 xuelong li
17 篇 anon
15 篇 xi ning
15 篇 xi li
14 篇 yuan yuan
13 篇 de-shuang huang
13 篇 wei li
13 篇 ying liu
12 篇 jie yang
12 篇 yang xi
12 篇 yanning zhang
11 篇 p. pirsch
11 篇 liu yang
11 篇 mingyi he

语言

16,039 篇 英文
165 篇 其他
133 篇 中文
20 篇 土耳其文
3 篇 葡萄牙文
2 篇 法文
2 篇 俄文
1 篇 西班牙文

检索条件"任意字段=Conference on Image Processing - Algorithms and Systems XI"

共 16349 条记录，以下是201-210 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 Comparative Study of VGG16, MobileNet, and Ensemble Methods on Fundus image-Based Cataract Detection

A Comparative Study of VGG16, MobileNet, and Ensemble Method...

引用

Advancement in Data Science, E-learning and Information systems (ICADEIS), International conference on

作者： Tabina Shafa Nabila Syahda Mochamad Nizar Palefi Ma'ady Berlian Rahmy Lidiawaty Department of Information Systems Telkom University Surabaya Surabaya Indonesia

ISBN: (数字)9798331513320

ISBN: (纸本)9798331513337

Artificial Intelligence (AI) in ophthalmology has been growing, driven by the increasing volume of clinical data that can be utilized for algorithm development. image processing on fundus disease is crucial in providing visual information for cataract detection. Therefore, the application of technology that can leverage fundus images for cataract detection is still a few. In this regard, several previous studies have explored CNN algorithms for automatic cataract screening using fundus images. However, other research groups have proposed or developed new algorithms as alternatives. This research compares two CNN models, VGG16 and MobileNet, while also incorporating additional approaches such as Ensemble Learning and Hybrid Method. VGG16 and MobileNet were chosen because they are known for their high accuracy performance and computational efficiency. Source data obtained from Kaggle as many as 1000 datasets. The results of the study indicate that the model with the Ensemble Learning approach achieved the highest average accuracy, followed by MobileNet, MobileNet + SVM, VGG16, and VGG16 + SVM, with accuracies of 0.9377; 0.9150; 0.9143; 0.9129; and 0.9104, respectively. This research is expected to aid medical professionals in diagnosing cataracts more accurately and guide engineers in selecting the best machine-learning models for fundus image-based cataract detection systems.

关键词： Cataracts Support vector machines Visualization Accuracy Computational modeling Data models Ophthalmology Ensemble learning Medical diagnostic imaging Overfitting

来源：评论

学校读者我要写书评

暂无评论

Enhancing Graph Neural Networks by Structure-wise Graph Data Augmentation

Enhancing Graph Neural Networks by Structure-wise Graph Data...

引用

IEEE International conference on Big Data Security on Cloud (BigDataSecurity), High Performance and Smart Computing (HPSC) and Intelligent Data and Security (IDS)

作者： xiaozhen Cheng Zhenghao Yang Zhaozhan Li Ning Bai Jianxin Zhang Air Defense and Antimissile School Air Force Engineering University China School of Computer Science and Technology Xi’an Jiaotong University China ccdc Changqing General Drilling Company the Fourth Military Representative Office of the Air Force Xi’an China

ISBN: (数字)9798331595104

ISBN: (纸本)9798331595111

Graph neural network is a new neural network model in recent years, whose advantage lies in processing graph structure data. In the era of big data, people can collect a large amount of graph data for training neural network models, but in other scenarios, they can only use limited, missing, and noisy data. Such data will undoubtedly bring negative benefits to the performance of the neural network model. In the field of image processing, there are already many mature methods to deal with noisy pictures or small sample data. However, related research on graph data still needs to be carried out. This paper introduces a graph data augmentation technique based on edge probability prediction. Assume that the collected graph data has edge noise, which means we cannot confirm whether there exists an edge between two nodes that are recorded not connected in the adjacency matrix. The graph autoencoder to represent the probability that there is an edge between each node in the graph. By adding those edges with high probability that do not exist in the adjacency matrix to the graph, the connectivity of the same kind of nodes can be enhanced, thereby improving the final accuracy of the model. In this paper, experiments are carried out on the reference network datasets of papers such as cora, and classic graph neural networks such as gcn and gat are selected as the baseline for comparison. Results shows a significant improvement is achieved.

关键词： Training Accuracy image edge detection Autoencoders Probability Prediction algorithms Generative adversarial networks Data models Graph neural networks Noise measurement

来源：评论

学校读者我要写书评

暂无评论

Research on Audio Feature Extraction and Music Style Automatic Classification Algorithm Based on Deep Learning

Research on Audio Feature Extraction and Music Style Automat...

引用

Digital Analysis and processing, Intelligent Computation (DAPIC), International conference on

作者： Yue Wang Education College of Xi'an FanYi University Xi’an Shaanxi China

ISBN: (数字)9798331536169

ISBN: (纸本)9798331536176

With the increasing demand for accurate classification of music content, the automatic classification technology of music style is particularly important in the fields of personalized music recommendation, copyright management and emotional analysis. This paper discusses the algorithm of audio feature extraction and music style automatic classification based on deep learning, and automatically extracts audio features and realizes accurate music style classification through deep neural network. The study first uses wavelet transform based denoising methods to preprocess audio signals, and then extracts audio features through deep convolutional neural networks (CNN), which can capture key information in music such as melody, rhythm, and harmony. Next, a deep learning model combining long shortterm memory (LSTM) and fully connected layers is used to classify music styles. The experiment uses the data set of GTZAN Music Genre Collection, and the results show that the classification accuracy of CNN+LSTM model is better than the traditional MFCC+SVM model in many music styles. T-SNE algorithm is used to reduce the dimension and visualize the extracted audio features, which verifies that CNN successfully extracts distinctive audio features. This study not only provides new ideas and methods for the field of music style classification, but also promotes the further development of this field.

关键词： Deep learning Wavelet transforms Accuracy Feature extraction Data models Classification algorithms Multiple signal classification Convolutional neural networks Recommender systems Long short term memory

来源：评论

学校读者我要写书评

暂无评论

Overcoming Hardware Limitations in Massive MIMO: A Generative AI Take

Overcoming Hardware Limitations in Massive MIMO: A Generativ...

引用

IEEE conference on Wireless Communications and Networking

作者： Bitan Banerjee Ahmad Nimr Gerhard Fettweis Vodafone Chair for Mobile Communications Systems Technische Universität Dresden Germany

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

Recent transition in mobile communication standards suggests massive multiple-input multiple-output (MIMO) to be an integral part of the foreseeable future. However, as antenna elements increase to hundreds in the fifth-generation (5G) and beyond, traditional signal processing methods become prone to significant hardware impairments compound from multiple chains, leading to a substantial performance degradation. This paper explores the effectiveness of generative artificial intelligence (AI) techniques in addressing these challenges within massive MIMO systems. For this purpose, the conditional generative adversarial network (CGAN), a special class of generative AI algorithms, is employed to enhance the accuracy of channel state information (CSI) estimation in a hardware-impaired transceiver setup. This problem is treated as an image-denoising task, where the noise is introduced by the hardware impairments and LS estimation error. Through simulations conducted across various antenna array sizes, the potential of generative AI to improve CSI estimation accuracy under hardware impairments is demon-strated. This highlights its capacity to address critical signal processing challenges in the next-generation wireless systems.

关键词： Accuracy Generative AI Array signal processing Massive MIMO Generative adversarial networks Throughput Hardware Transceivers Next generation networking Antenna arrays

来源：评论

学校读者我要写书评

暂无评论

Quantum-Enhanced Machine Learning for Precision Breast Cancer Diagnosis

Quantum-Enhanced Machine Learning for Precision Breast Cance...

引用

International Communication systems and Networks and Workshops, COMSNETS

作者： Agniva Banerjee Dept. Electrical Engineering and Computer Science Indian Institute of Science Education and Research Bhopal Bhopal India

ISBN: (数字)9798331531195

ISBN: (纸本)9798331531201

Cancer is the leading cause of premature mortality. Globally, the prevalence of cancer is on the rise, and early-stage diagnosis is crucial for cancer recovery and survival. However, the presence of circulating tumor cells (CTCs) and heterogeneity within the tumor makes it hard to track how cancer changes over time and how resistance to treatment develops. Unfortunately, the cancer diagnosis precision remains inadequate, endangering precious human lives and necessitating costly procedures for accurate treatment. Machine learning (ML) has proven capable of discovering meaningful information and making predictions, particularly with large datasets. Moreover, quantum computers (QCs) inherently possess superposition and parallel computation, features not found in classical computing. Researchers have demonstrated that quantum neural networks (QNNs) achieve a significantly better effective dimension than classical NNs. In this context, this study investigates the application of classical and quantum machine learning (QML) algorithms to classify breast cancer images. Region-based segmentation is used to identify regions of interest and image processing algorithms are performed on segmented breast cancer images to extract meaningful features (e.g., perimeter, area, compactness). An user interface (UI) based software is created to swiftly distinguish benign and malignant instances during image analysis. Although QNNs perform poorly than the classical NNs, there is still a long way to go in developing feature maps and techniques for better performance. In contrast, the classical NNs have gone a long way with well-established algorithms and packages available to us. This research shows that with further development, QML has the potential to enhance cancer diagnosis accuracy and speed, ultimately improving early detection and treatment outcomes.

关键词： image segmentation Accuracy Machine learning algorithms Software algorithms Artificial neural networks Machine learning Feature extraction Breast cancer Software Tumors

来源：评论

学校读者我要写书评

暂无评论

High Resolution Millimeter Wave Imaging Based on FMCW Radar systems at W-Band

High Resolution Millimeter Wave Imaging Based on FMCW Radar ...

引用

IEEE Annual Computing and Communication Workshop and conference (CCWC)

作者： Shahrokh Hamidi MR Nezhad-Ahmadi Department of Electrical and Computer Engineering University of Waterloo Canada MMSENSE Technologies Inc. Waterloo Canada

ISBN: (数字)9798331507695

ISBN: (纸本)9798331507701

In this paper, we present a unique 2D high resolution, compact, low-cost, lightweight, and highly accurate millimeter-wave imagery system capable of operating in all weather conditions. We explain different aspects of the imagery system in detail and describe the process of millimeter-wave image formation thoroughly. To create the array, we utilize the Synthetic Aperture Radar (SAR) concept. The imagery system presented in this paper, can strongly compete with the Lidar systems as the resolution limit is at the same level. Furthermore, in contrast to the Lidar systems, our imagery system can operate in heavy rain as well as dense fog and produce high quality images. Finally, we utilize our wide-band custom-made Frequency Modulated Continuous Wave (FMCW) radar, which operates at W-band with 33 GHz bandwidth, for data collection and present the results.

关键词： image resolution Laser radar Frequency modulation Imaging Signal processing algorithms Millimeter wave radar Radar imaging Millimeter wave communication Synthetic aperture radar Signal resolution

来源：评论

学校读者我要写书评

暂无评论

Colored Point Cloud-based Mesh Registration for Enhancing Inter-frame Coding of Texture Video in V-DMC

Colored Point Cloud-based Mesh Registration for Enhancing In...

引用

International conference on Acoustics, Speech, and Signal processing (ICASSP)

作者： Jianxiang Sun Wenjie Zou Jiapan Zhao Fuzheng Yang School of Telecommunications Engineering Xidian University Xi’an China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The Moving Picture Experts Group (MPEG) is developing a new standard, i.e., Video-based Dynamic Mesh Coding (V-DMC), for dynamic meshes with time-varying topology. The time-varying topology and attributes challenge the V-DMC encoder in exploiting temporal correlations for redundancy minimization. Currently, the inter-frame coding in V-DMC is constrained to tracked sequences having consistent topology, with non-tracked ones predominantly using intra-coding. Texture images, as the dominant bit rate component, are inadequately compressed in Random Access (RA) mode. The precision of the mesh registration algorithm directly influences the intra-frame blocks’ proportion during the inter-frame coding of texture images, thereby affecting its coding efficiency. This paper proposed a colored point cloud-based mesh registration technique to bolster the intra-frame coding efficiency for non-tracked sequences. This method transforms the mesh into a colored point cloud and employs an inter-frame search algorithm for enhanced motion search efficiency. Local deformation near the matching point is then ascertained through directional searches at individual points. Experimental results indicate that the proposed method achieves a geometric gain of 4.1% and a texture gain of 6.47% for non-tracked sequences.

关键词： image coding Signal processing algorithms Transform coding Termination of employment Transforms Signal processing Encoding Topology Speech processing Standards

来源：评论

学校读者我要写书评

暂无评论

Smart Medical Box using Arduino

Smart Medical Box using Arduino

引用

International conference on ent Computing and Control systems (ICICCS)

作者： Sweadha. M M. Selvi Dept. of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu

ISBN: (数字)9798331512088

ISBN: (纸本)9798331512095

The Smart Medical Box is a sophisticated IoT-driven solution designed to enhance medication adherence by utilizing computer vision and machine learning. The system automates the identification and extraction of essential information from medication labels, ensuring precision in dosage reminders and reducing human error. It incorporates a high-resolution camera, an OCR engine, and an Arduino-based microcontroller to enable seamless image processing and real-time analysis. With an intuitive interface and GPS-enabled location services, users can efficiently manage their medication schedules while accessing nearby pharmacies when needed. This research focuses on designing and implementing a scalable and cost-effective system that improves medication compliance and minimizes health risks associated with missed or incorrect dosages. By integrating advanced image processing techniques and AI-driven recognition algorithms, the Smart Medical Box ensures high accuracy in identifying medications. Additionally, features such as automated alerts, real-time monitoring, and pharmacy locator tools enhance the system's usability and accessibility. This innovation is particularly beneficial for elderly individuals and patients with chronic conditions, offering a simplified and automated approach to medication tracking. Performance evaluations of the system indicate a significant improvement in medication adherence, a high recognition accuracy rate, and positive user feedback. Future enhancements, including AI-based OCR, voice assistance, and mobile application connectivity, are expected to further improve functionality and ease of use. This paper presents the conceptualization, development, and testing of the Smart Medical Box, highlighting its potential to revolutionize personal healthcare management by providing an intelligent, efficient, and user-friendly solution.

关键词： Accuracy image recognition Microcontrollers Optical character recognition Medical services Real-time systems Usability Older adults Biomedical imaging Global Positioning System

来源：评论

学校读者我要写书评

暂无评论

Guided View Approach for Lane Detection and Caution Analysis using Computer Vision and Deep Learning Techniques

Guided View Approach for Lane Detection and Caution Analysis...

引用

Artificial Intelligence and Data Engineering (AIDE), International conference on

作者： Manjula Gururaj Rao Vinyas S Gururaj Rao H Priyanka Vishal Aisiri NITTE (Deemed to be University) NMAM Institute of Technology Nitte Karkala Sasken Technologies Limited Bangaluru Computer Science & Engineering PESU Bangaluru

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

In recent years, advancements in computer vision technology have enabled innovative applications in various fields, including transportation safety. One such application is a Lane Detection and Caution Measurement Analyzer system aimed at enhancing road safety. This system uses computer vision techniques to provide real-time analysis of road conditions, helping drivers navigate more safely. The Lane Detection module employs image processing algorithms such as edge detection, Hough transform, and perspective transformation to identify lane boundaries accurately. Additionally, the Caution Measurement Analyzer module utilizes object detection and tracking algorithms to identify potential hazards, such as potholes, and measures the caution required for safe driving. The proposed model integrates CNN, OpenCV, and YOLO, achieving 87% accuracy in object detection and caution measurement, and 94% accuracy in lane boundary detection.

关键词： YOLO Computer vision Accuracy Lane detection image edge detection Transforms Road safety Real-time systems Hazards Vehicles

来源：评论

学校读者我要写书评

暂无评论

Deepfake image and Video Detection Using Deep Learning algorithms

Deepfake Image and Video Detection Using Deep Learning Algor...

引用

Multi-Agent systems for Collaborative Intelligence (ICMSCI), International conference on

作者： Srivanth Srinivasan Nischitha P Akshita Chavan Mohana Artificial Intelligence & Machine Learning Engineering RV College of Engineering® Bengaluru India Computer Science & Engineering (Cyber Security) RV College of Engineering® Bengaluru India

ISBN: (数字)9798331509828

ISBN: (纸本)9798331509835

Deep learning (DL) algorithms are swiftly finding applications in computer vision and natural language processing. Nonetheless, they can also be employed for creating convincing deepfakes, which are challenging to distinguish from reality. The advancements in image and video technology and tools, especially on social media platforms, potentially lead to misuse for malicious purposes like blackmail or defamation. To tackle this issue, several group of researchers tried upon spreading or creating awareness on real or fake data. The proposed approach involves combining Deepfake generation using GANs and Autoencoders with a Deepfake detection method. The aim of this initiative is exclusively to combat disinformation and online fraud for the welfare of the general population. Deepfakes, products of AI, have become increasingly realistic, rendering it nearly difficult to distinguish the content. Auto-encoders with sufficient time can achieve about 92 % accuracy. As the generator improves, the discriminator performance worsens as it struggles to differentiate real or fake data. A perfect generator results in 50% accuracy. With advancements in computational capacity and data availability, the proposed DDM (Deepfake Detection Model) has achieved greater accuracy rate of up to 92.3%.

关键词： Deep learning Deepfakes Accuracy Social networking (online) Streaming media Feature extraction Generators Convolutional neural networks Artificial intelligence Research and development

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 << < 17 18 19 20 21 22 23 24 25 26 > >>

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：