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

6,286 篇 会议
65 册 图书
32 篇 期刊文献

馆藏范围

6,383 篇 电子文献
1 种 纸本馆藏

日期分布

学科分类号

3,474 篇 工学
- 2,814 篇 计算机科学与技术...
- 2,071 篇 软件工程
- 1,067 篇 信息与通信工程
- 406 篇 电气工程
- 349 篇 生物工程
- 315 篇 控制科学与工程
- 264 篇 生物医学工程（可授...
- 261 篇 电子科学与技术（可...
- 258 篇 光学工程
- 210 篇 机械工程
- 160 篇 化学工程与技术
- 141 篇 仪器科学与技术
- 115 篇 安全科学与工程
- 115 篇 网络空间安全
- 83 篇 交通运输工程
- 71 篇 动力工程及工程热...
1,734 篇 理学
- 1,177 篇 数学
- 441 篇 物理学
- 368 篇 生物学
- 232 篇 统计学（可授理学、...
- 174 篇 化学
- 141 篇 系统科学
888 篇 管理学
- 576 篇 图书情报与档案管...
- 383 篇 管理科学与工程(可...
- 145 篇 工商管理
309 篇 医学
- 289 篇 临床医学
- 170 篇 基础医学(可授医学...
- 129 篇 药学(可授医学、理...
213 篇 法学
- 160 篇 社会学
74 篇 文学
- 66 篇 新闻传播学
62 篇 教育学
51 篇 农学
37 篇 经济学
18 篇 军事学
11 篇 艺术学
2 篇 哲学

主题

454 篇 streaming media
413 篇 feature extracti...
327 篇 media
325 篇 discrete wavelet...
294 篇 wavelet transfor...
225 篇 social networkin...
223 篇 computational mo...
219 篇 sentiment analys...
213 篇 accuracy
201 篇 deep learning
175 篇 information proc...
172 篇 training
160 篇 image processing
158 篇 wavelet analysis
150 篇 visualization
149 篇 real-time system...
148 篇 natural language...
141 篇 signal processin...
138 篇 robustness
135 篇 data mining

机构

325 篇 school of comput...
111 篇 school of inform...
43 篇 school of inform...
42 篇 univ elect sci &...
40 篇 university of el...
38 篇 university of el...
29 篇 university of el...
27 篇 advanced cryptog...
26 篇 school of cybers...
25 篇 school of comput...
22 篇 school of comput...
20 篇 logist engn univ...
18 篇 department of co...
18 篇 institute of dig...
14 篇 chengdu universi...
14 篇 college of compu...
13 篇 sugon industrial...
13 篇 hubei province k...
12 篇 school of comput...
11 篇 school of comput...

作者

60 篇 li jianping
41 篇 jianping li
35 篇 jian ping li
34 篇 li jian-ping
32 篇 jian-ping li
29 篇 amin ul haq
29 篇 li jian ping
21 篇 huang yuanyuan
18 篇 wang zhou
18 篇 wen gao
15 篇 zhou wang
15 篇 di liu
15 篇 kuang ping
14 篇 bless lord y. ag...
14 篇 yujun yang
14 篇 yuanyuan huang
13 篇 ping kuang
13 篇 liu wei
13 篇 haq amin ul
13 篇 gao wen

语言

6,243 篇 英文
120 篇 其他
43 篇 中文
1 篇 土耳其文

检索条件"任意字段=International Computer Conference on Wavelet Active Media Technology and Information Processing"

共 6383 条记录，以下是61-70 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：

An Improved Digital Signal processing for Signal Enhancement and Interference Mitigation

An Improved Digital Signal Processing for Signal Enhancement...

引用

Automation and Computation (AUTOCOM), international conference on

作者： Intekhab Alam Paramjit Baxi Barnali Sahoo Mohan Garg Roopashree R Thaj Mary Delsy T Maharishi School of Engineering & Technology Maharishi University of Information Technology Uttar Pradesh India Centre of Research Impact and Outcome Chitkara University Rajpura Punjab India Department of Computer Science and Engineering Siksha ‘O’ Anusandhan (Deemed to be University) Bhubaneswar Odisha India Chitkara Centre for Research and Development Chitkara University Baddi Himachal Pradesh India School of Sciences Chemistry and Biochemistry JAIN (Deemed to be University) Bangalore Karnataka India Department of Electronics and Communication Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331542375

ISBN: (纸本)9798331542382

Digital Signal processing has become a fundamental tool in signal processing applications, such as speech and image processing, due to its ability to manipulate and examine signals in a digital form skillfully. A primary challenge in signal processing involves the presence of unwanted noise and interference, which can negatively impact the precision and quality of the processed signal. The proposed advanced DSP approach aims to improve signal enhancement and interference mitigation techniques using sophisticated algorithms and methods. The proposed state-of-the-art DSP approach includes noise reduction algorithms, such as adaptive filtering and spectral subtraction, to eliminate unwanted noise from the signal. Additionally, cutting-edge signal processing techniques, such as wavelet transforms and time-frequency analysis, are used for signal enhancement and demising. The proposed approach employs adaptive equalization techniques to mitigate interference from other signals and substantially improve the overall signal quality. The enhanced DSP approach is evaluated through simulation and real-world experiments, demonstrating its effectiveness in improving signal quality and reducing interference considerably. The proposed approach has the potential to significantly improve the accuracy and reliability of various signal processing applications considerably, making it a valuable tool in fields such as telecommunications, audio and video processing, and medical signal analysis.

关键词： Wireless communication wavelet transforms Time-frequency analysis Accuracy Prevention and mitigation Signal processing algorithms Interference Digital signal processing wavelet analysis Telecommunications

来源：评论

学校读者我要写书评

暂无评论

SpecViT: A Custom Vision-Transformer based Approach for Audio Deepfake Detection

SpecViT: A Custom Vision-Transformer based Approach for Audi...

引用

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

作者： Sharmistha Modak Arnab Kumar Das Ruchira Naskar Department of Information Technology Indian Institute of Engineering Science and Technology Shibpur India

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Degrees of hyper-realism already attained by present-day deepfake technology poses one of the biggest social threats of today. Deepfakes may involve multiple forms of media including audio, video and images. While most of the literature deals with threats posed by visual synthetic media, the endeavor to uncover audio deepfakes is still evolving; it demands more extensive investigation. Our attempt towards audio deepfake detection in this article, involves investigating spectral patterns present in audio spectrograms, captured appropriately by a two-attention vision transformer model (SpecViT2A), finally exploited for discriminating synthetic audios from pristine ones. We employed our model for successful identification of audio as well as multimodal deepfakes, yielding best accuracy over 99%, F1-score 0.9911, and EER as low as 3.5 on ASVSpoof 2021.

关键词： Deepfakes computer vision Visualization Accuracy media Transformers Robustness Fraud Speech processing Spectrogram

来源：评论

学校读者我要写书评

暂无评论

Secure Audio Embedding in Images with Image Transforms and Decompositions

Secure Audio Embedding in Images with Image Transforms and D...

引用

international conference on Wireless Communications, Signal processing and Networking (WiSPNET)

作者： Shyam Tripathi Kritika Agrahari K. Ashwini Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798331524227

ISBN: (纸本)9798331524234

information technology sees unparalleled growth. The demand for stronger data security relating to some sensitive class of multimedia data is growing. This article compares creative methods of embedding audio into images using various image transforms and decomposition methods namely DWT, LP, and PCA. This approach ensures augmented security, robustness, and imperceptibility, and addresses known pitfalls in standard interleaving techniques. With DWT performing hierarchical multi-resolution analysis, LP capturing multi-scale representation, and PCA enabling dimensionality reduction, the embedding process is optimized to preserve both audio and image quality. Rigorous experimentation under attack scenarios ensures successful embedding and secure transmission. Objective evaluations like SNR, PESQ, BER, and LSD affirm robustness and perceptual transparency. The work demonstrates notable improvement, making it a viable solution for secure multimedia communication.

关键词： Measurement Wireless communication Bit error rate Transforms wavelet analysis Robustness Discrete wavelet transforms Principal component analysis Signal to noise ratio Testing

来源：评论

学校读者我要写书评

暂无评论

Performance Measure of Deep Learning for Brian Tumor Image Analysis Using Domain information

Performance Measure of Deep Learning for Brian Tumor Image A...

引用

Machine Learning and Autonomous Systems (ICMLAS), international conference on

作者： S. Anjana S.Jacophine Susmi P M Siva Raja Department of Computer Science & Engineering Amrita College of Engineering and Technology Nagercoil Tamil Nadu India Department of Information Technology University College of Engineering Tindivanam Tamil Nadu

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

The limited amount of medical datasets continues to be a major bottleneck in medical image processing, despite the remarkable success deep learning models like CNNs have had in this field. In an effort to tackle this issue, scientists have begun searching outside of the currently accessible medical datasets for additional information. Conventional methods often use transfer learning to extract information from natural photos. More recent efforts use the domain expertise of physicians to build networks that imitate their diagnostic patterns, mirror their training processes, or concentrate on the characteristics or regions that physicians find particularly important. The present state of research on incorporating medical domain knowledge into deep learning models for a range of tasks, including illness diagnosis, lesion, organ, and anomaly detection, as well as lesion and organ segmentation, systematically classify various medical domain knowledge types used for each task along with matching integration techniques. They also offer prospective research directions and difficulties that are now being faced.

关键词： Deep learning Training wavelet transforms Solid modeling Transfer learning Stochastic processes Medical services wavelet analysis Lesions Medical diagnostic imaging

来源：评论

学校读者我要写书评

暂无评论

MuLAD: Multimodal Aggression Detection from Social media Memes Exploiting Visual and Textual Features 27th

MuLAD: Multimodal Aggression Detection from Social Media Me...

引用

27th international conference on Pattern Recognition, ICPR 2024

作者： Hasan, Md. Maruf Ahsan, Shawly Hoque, Mohammed Moshiul Dewan, M. Ali Akber Chittagong University of Engineering and Technology Chittagong4349 Bangladesh School of Computing and Information Systems Faculty of Science and Technology Athabasca University AthabascaABT9S 3A3 Canada

ISBN: (纸本)9783031781186

Aggression detection from memes is challenging due to their region-specific interpretation and multimodal nature. Detecting or classifying aggressive memes is complicated in low-resource languages (including Bengali) because benchmark datasets and primary language processing software are needed. This paper proposes an innovative meme classification technique that harnesses deep learning (DL) approaches to leverage memes’ visual and textual features in Bengali. Various DL frameworks, such as VGG16, VGG19, ResNet50, CNN, BiLSTM, and BiLSTM+CNN, extract visual and textual features from memes. A novel corpus named the Bengali Meme Dataset (AMemD) is also introduced, comprising a substantial amount of multimodal data, including text and image components. Experimental results on AMemD demonstrate the effectiveness of the proposed approach. The CNN combined with VGG16 obtained the highest f1-score of 0.738 among all multimodal techniques tested. This pioneering research offers valuable insights into the complex task of aggression detection from memes in Bengali and provides a foundation for future studies in this area. The dataset is available at https://***/Maruf089/Multimodal-Aggression-Detection. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Deep learning

来源：评论

学校读者我要写书评

暂无评论

YOLOv8 for Personal Device Screen Detection to Preserve Data Privacy: Enhancing Security Measures in Real-Time Monitoring Systems

YOLOv8 for Personal Device Screen Detection to Preserve Data...

引用

international ECTI Northern Section conference on Electrical, Electronics, computer and Telecommunications Engineering (ECTI-NCON)

作者： Apichaya Nimkoompai Yuenyong Nilsiam Siranee Nuchitprasitchai Puwadol Sirikongtham Thai-Nichi Institute of Technology Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut’s University of Technology North Bangkok Bangkok Thailand Department of Information Technology Faculty of Information Technology and Digital Innovation King Mongkut’s University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

In public and semi-public environments, personal device usage often exposes sensitive information, presenting a challenge to user privacy when such data is captured by surveillance systems. This study investigates the application of YOLOv8, an advanced object detection model, to identify and obscure personal electronic device screens, particularly mobile phones, in CCTV footage. Utilizing real-time blurring with a Gaussian kernel, this approach ensures that sensitive information displayed on these devices remains unreadable in video footage. The study aims to address the following research questions: (1) Each version of YOLOv8 varies in its effectiveness for detecting and identifying mobile device screens? (2) What level of Gaussian blurring is sufficient to obscure sensitive information without compromising processing efficiency? Experimental results demonstrate that YOLOv8m consistently excels, particularly in precision and mAP50, making it the ideal choice for applications where high accuracy is essential. Meanwhile, YOLOv8n, as the smallest model, delivers promising outcomes in terms of processing speed. Additionally, a 35x35 Gaussian kernel achieves an optimal balance between privacy protection and computational efficiency. These findings support the feasibility of integrating YOLOv8-based real-time blurring into surveillance systems, providing a robust solution for privacy protection in high-risk surveillance areas.

关键词： Privacy Accuracy Surveillance Object detection Streaming media Real-time systems Mobile handsets Object recognition Protection Kernel

来源：评论

学校读者我要写书评

暂无评论

SFFCE-CD: Spatial And Frequency Feature Cross Enhancement For Change Detection

SFFCE-CD: Spatial And Frequency Feature Cross Enhancement Fo...

引用

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

作者： Yan Xing Jiali Hu Binbin Jiang Qingyi Zhao Longxi Feng Rui Huang College of Safety Science and Engineering Civil Aviation University of China Tianjin China College of Computer Science and Technology Civil Aviation University of China Tianjin China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Most existing change detection (CD) methods focus on spatial domain modeling, while ignore the rich information of frequency domain. In this paper, we enhance the feature representative ability with spatial and frequency feature cross enhancement. Specifically, we propose a Change Feature Extract Module (CFEM) to obtain high-quality change features from the bi-temporal images. These features are then merged together and refined through three parallel branches: the Local branch uses multiscale max-pooling operations to generate multiscale feature; the wavelet Transform Decomposer (WTD) branch decomposes feature into low-frequency and high-frequency signals with Haar wavelet transform; the Global branch adopts Mamba to capture the long-range dependencies. To bridge the semantic gap between frequency and spatial features, we design Dual-Representation Aggregation Module (DRAM) to promote the combination of features from different representation domains in a flow-based manner and dual cross attention. Extensive experiments demonstrate our method outperforms 11 SOTA CD methods on three remote sensing CD datasets.

关键词： wavelet transforms Frequency-domain analysis Semantics Random access memory Signal processing Feature extraction Spatial databases Sensors Speech processing Remote sensing

来源：评论

学校读者我要写书评

暂无评论

Image-Mixed Glitch Sounds

Image-Mixed Glitch Sounds

引用

international ECTI Northern Section conference on Electrical, Electronics, computer and Telecommunications Engineering (ECTI-NCON)

作者： Noriki Amano Department of Information Science and Data Science Notre Dame Seishin University Okayama Japan

ISBN: (数字)9798331543273

ISBN: (纸本)9798331543280

Glitch art is the art that uses errors (glitches) in images, videos, and sounds that are intentionally created by destroying digital data or physically manipulating electronic devices. This type of glitch art is interesting in that it expresses unpredictable, accidental beauty. We are exploring the methods and effects of creating glitch art, and as part of this research, we attempted to create glitch sounds by mixing sound and images. Specifically, we simultaneously loaded image and sound data into the image processing software Photoshop to create multi-layered data. This generates glitches in the sound data. Our goal is not to generate glitch sounds using a random method, but rather to generate glitch sounds using a method based on legitimate manipulation of images, which are on a different level from sound, and to make the glitch sound visually enjoyable. We have confirmed that this goal can be achieved using the method used in this research. This research is unique in that it simultaneously realizes the audibility and visualization of glitches, and creates a new type of art that brings about a shift in aesthetic sensibilities.

关键词： Art Image processing Digital images Data visualization Nonhomogeneous media Software Timbre Videos Digital information

来源：评论

学校读者我要写书评

暂无评论

Triple Path Enhanced Neural Architecture Search for Multimodal Fake News Detection

Triple Path Enhanced Neural Architecture Search for Multimod...

引用

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

作者： Bo Xu Qiujie Xie Jiahui Zhou Linlin Zong School of Computer Science and Technology Dalian University of Technology School of Computer Science Fudan University School of Software Dalian University of Technology

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Multimodal fake news detection has become one of the most crucial issues on social media platforms. Although existing methods have achieved advanced performance, two main challenges persist: (1) Under-performed multimodal news information fusion due to model architecture solidification, and (2) weak generalization ability on partial-modality contained fake news. To meet these challenges, we propose a novel and flexible triple path enhanced neural architecture search model MUSE. MUSE includes two dynamic paths for detecting partial-modality contained fake news and a static path for exploiting potential multimodal correlations. Experimental results show that MUSE achieves stable performance improvement over the baselines.

关键词： Solid modeling Correlation Social networking (online) Signal processing Data models Acoustics Neural architecture search Fake news Speech processing

来源：评论

学校读者我要写书评

暂无评论

Beyond Spatial: A wavelet Fusion-Based Deep Learning CAD for Skin Cancer Diagnosis 4th

Beyond Spatial: A Wavelet Fusion-Based Deep Learning CAD for...

引用

4th international conference on Intelligent Systems and Pattern Recognition, ISPR 2024

作者： Attallah, Omneya Department of Electronics and Communications Engineering College of Engineering and Technology Arab Academy for Science Technology and Maritime Transport Alexandria21937 Egypt Wearables Biosensing and Biosignal Processing Laboratory Arab Academy for Science Technology and Maritime Transport Alexandria21937 Egypt

ISBN: (纸本)9783031821554

Skin cancer (SC) comprises approximately 33% of all reported cancer cases globally. Thus, rapid and accurate diagnosis is crucial for effective treatment and better patient outcomes. computer-aided diagnostic (CAD) systems are necessary for assisting and improving traditional diagnostic approaches, however, most of them count on only spatial information and/or individual deep-learning models to realize a diagnosis. Furthermore, current CADs retrieve deep features with substantial sizes, resulting in increased training complexity and speed. This article presents a CAD framework to classify multiple subcategories of SC based on multiple Convolutional Neural Networks (CNNs) with distinct configurations. The study explores various combinations of CNNs to select the best mixture of CNNs that impact performance. Additionally, it incorporates spatial and time-frequency demonstrations to successfully diagnose SC through the utilisation of wavelet analysis. In addition, wavelet analysis is employed to reduce the large dimensionality of deep features, which is further minimised using principal component analysis. The results indicate that the suggested CAD accomplished a rate of accuracy of 0.956 using only 159 features. It surpasses the CADs currently available. Therefore, it can serve as a powerful tool for identifying SC and could be employed to enhance the precision of medical diagnostics. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Convolutional neural networks

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 << < 3 4 5 6 7 8 9 10 11 12 > >>

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：