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

16,564 篇 会议
215 篇 期刊文献

馆藏范围

16,779 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

10,867 篇 工学
- 7,725 篇 计算机科学与技术...
- 4,148 篇 电气工程
- 3,543 篇 软件工程
- 3,192 篇 信息与通信工程
- 1,022 篇 控制科学与工程
- 815 篇 电子科学与技术（可...
- 614 篇 生物医学工程（可授...
- 478 篇 机械工程
- 474 篇 仪器科学与技术
- 458 篇 光学工程
- 303 篇 动力工程及工程热...
- 254 篇 生物工程
- 185 篇 交通运输工程
- 167 篇 航空宇航科学与技...
- 158 篇 网络空间安全
- 124 篇 材料科学与工程（可...
- 114 篇 石油与天然气工程
2,782 篇 理学
- 1,609 篇 数学
- 1,138 篇 物理学
- 413 篇 统计学（可授理学、...
- 295 篇 生物学
- 160 篇 系统科学
- 132 篇 化学
1,776 篇 医学
- 1,671 篇 临床医学
- 206 篇 基础医学(可授医学...
- 122 篇 药学(可授医学、理...
- 108 篇 特种医学
877 篇 管理学
- 608 篇 管理科学与工程(可...
- 323 篇 图书情报与档案管...
- 106 篇 工商管理
80 篇 法学
78 篇 农学
59 篇 经济学
38 篇 文学
28 篇 军事学
27 篇 教育学
19 篇 艺术学

主题

3,290 篇 data compression
1,742 篇 image coding
1,250 篇 image compressio...
1,142 篇 video compressio...
913 篇 point cloud comp...
652 篇 three-dimensiona...
566 篇 encoding
532 篇 compression
501 篇 transform coding
491 篇 decoding
434 篇 data mining
413 篇 compression algo...
368 篇 bandwidth
342 篇 video coding
272 篇 training
255 篇 streaming media
248 篇 computer science
247 篇 data models
245 篇 deep learning
240 篇 discrete cosine ...

机构

29 篇 peng cheng lab p...
25 篇 univ chinese aca...
24 篇 tsinghua univers...
23 篇 ieee
23 篇 department of el...
22 篇 univ calif river...
22 篇 department of el...
19 篇 peking univ peop...
19 篇 peking univ inst...
18 篇 university of ch...
17 篇 argonne natl lab...
16 篇 shanghai jiao to...
16 篇 univ sci & techn...
16 篇 harbin institute...
15 篇 signal and data ...
14 篇 penn state univ ...
14 篇 nanyang technolo...
14 篇 peng cheng labor...
14 篇 chinese acad sci...
14 篇 hewlett packard ...

作者

50 篇 w. kinsner
22 篇 liang xin
22 篇 tao dingwen
21 篇 xiong hongkai
18 篇 lukin vladimir
18 篇 pratas diogo
17 篇 wang shiqi
16 篇 klasky scott
15 篇 li li
14 篇 pinho armando j.
13 篇 ma zhan
12 篇 wang ronggang
12 篇 jin sian
12 篇 moffat a
12 篇 zhou wang
12 篇 shapira dana
11 篇 wang jing
11 篇 ye yan
11 篇 wang wei
10 篇 liu dong

语言

16,034 篇 英文
597 篇 其他
116 篇 中文
28 篇 土耳其文
3 篇 俄文
1 篇 西班牙文

检索条件"任意字段=IEEE Data Compression Conference"

共 16779 条记录，以下是1031-1040 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：

2024 ieee Nordic Circuits and Systems conference, NORCAS 2024 - Proceedings

2024 IEEE Nordic Circuits and Systems Conference, NORCAS 202...

引用

10th ieee Nordic Circuits and Systems conference, NORCAS 2024

ISBN: (纸本)9798331517663

The proceedings contain 52 papers. The topics discussed include: implementation of the tagged geometric history length access interval predictor;cycle count estimation of VLIW processors using machine learning;towards modularity of the rust RTIC real-time scheduling framework;AMPER-X: adaptive mixed-precision RISC-V core for embedded applications;highly linear on-chip delay generator for a depth profiling CMOS SPAD Raman sensor;multi-stream FFT architectures for a distributed MIMO large intelligent surfaces testbed;an on-chip digital aging sensor circuit utilizing leakage-current based charge accumulation;hardware-accelerated compression core on RISC-V for online-BCG data reduction;and RV-ProViler: evaluating RISC-V ISA for application-specific requirements.

关键词：

来源：评论

学校读者我要写书评

暂无评论

EnD: Enhanced Dedensification for Graph Compressing and Embedding 22

EnD: Enhanced Dedensification for Graph Compressing and Embe...

引用

22nd ieee International conference on data Mining (ICDM)

作者： Hossain, Tanvir Akbas, Esra Islam, Muhammad Ifte Khairul Georgia State Univ Dept Comp Sci Atalanta GA 30002 USA Oklahoma State Univ Dept Comp Sci Stillwater OK 74075 USA

ISBN: (纸本)9798350346091

Graph representation learning is essential in applying machine learning methods on large-scale networks. Several embedding approaches have shown promising outcomes in recent years. Nonetheless, on massive graphs, it may be time-consuming and space inefficient for direct applications of existing embedding methods. This paper presents a novel graph compression approach based on dedensification called Enhanced Dedensification with degree-based compression (EnD). The principal goal of our system is to assure decent compression of large graphs that eloquently favor their representation learning. For this purpose, we first compress the low-degree nodes and dedensify them to reduce the high-degree nodes' loads. Then, we embed the compressed graph instead of the original graph to decrease the representation learning cost. Our approach is a general meta-strategy that attains time and space efficiency over the original graph by applying the state-of-the-art graph embedding methods: Node2vec, DeepWalk, RiWalk, and xNetMf. Comprehensive experiments on large-scale real-world graphs validate the viability of our method, which shows sound performance on single and multi-label node classification tasks without losing accuracy.

关键词： Graph compression Graph Mining Network Embedding Node Classification GraphML

来源：评论

学校读者我要写书评

暂无评论

Enhanced Image Concealment: Leveraging LSB and Brotli compression 3

Enhanced Image Concealment: Leveraging LSB and Brotli Compre...

引用

3rd International conference for Advancement in Technology, ICONAT 2024

作者： Yacoob, Shaik Sabnis, Vikrant Jain, Jay Kumar Mansarovar Global University MP Bhopal India Mansarovar Global University Faculty of Engineering and Technology Department of Computer Science MP Bhopal India Maulana Azad National Institute of Technology Department of Mathematics Bioinformatics and Computer Applications MP Bhopal India

ISBN: (纸本)9798350354171

Steganography a cryptographic technique enables the concealment of confidential data within multimedia files like images and videos. This method facilitates the exchange of encrypted information through unconventional means, ensuring that only intended recipients can decipher the hidden message. The LSB technique has attracted significant attention in the literature due to its capability to conceal secret message bits within the least significant parts of image pixels. Although LSB has performed well in image steganography, there is some way to go in terms of its development, especially in the ways in which messages are dealt with. This paper presents a new technique of image steganography basing on the LSB method and the BROTLI algorithm. It uses the compression algorithm to compress message text. After that, to scramble the text, one LSB embedding technique is then used to hide the compressed text in the cover image. Applying the methods of data analysis on the benchmark images, the new approach developed outperforms the standard methods that exist at the present stage. This supports the idea of performing BROTLI on data with a view of compressing the data to be embedded in LSBs. © 2024 ieee.

关键词： Steganography

来源：评论

学校读者我要写书评

暂无评论

data-Driven Autonomous Meal Session Detection Using Smartwatches 20

Data-Driven Autonomous Meal Session Detection Using Smartwat...

引用

20th International conference on Body Sensor Networks

作者： Ariza-Zambrano, W. Camilo Cene, Vinicius H. Goncalves, Dayane Oliveira Caetano, Carlos Penatti, Otavio A. B. Samsung R&D Inst Brazil AI R&D Team Campinas Brazil

ISBN: (纸本)9798331530143

Poor dietary habits are frequently associated with diverse health conditions. Although there are apps designed to monitor eating habits, they require manual annotation, usually leading to user disengagement. This paper presents a datadriven solution using smartwatch sensors to automate meal session detection, addressing limitations of wearables such as a laterality unified model, capturing meal sessions with different durations, model compression to enable embedding, and dynamic inference times to reduce energy consumption. We tested two pipeline variations on a dataset of 91 subjects in a free-living scenario for 12 days, resulting in 93% Precision and 82% Recall, indicating the feasibility of free-living meal session detection. We also developed an application running on Samsung smartwatches to assess the solution feasibility with limited resources. This solution can be used as the building block to track eating-related phenomena such as autonomous dietary guidance and realtime non-invasive glucose monitoring, which remain unsolved problems in literature.

关键词： Meal detection Human activity recognition Wearable devices Inertial signal processing

来源：评论

学校读者我要写书评

暂无评论

引用

data compression conference (DCC)

作者： Wang, Jin Shi, Yunhui Xing, Yinsen Ling, Nam Yin, Baocai Beijing Univ Technol Fac Informat Technol Beijing 100124 Peoples R China Santa Clara Univ Dept Comp Sci & Engn Santa Clara CA 95053 USA

ISBN: (纸本)9781665478939

In this paper, we present a deep correlated image set compression scheme based on Distributed Source Coding(DSC) and multi-scale image fusion. As there exists strong correlation among images in a similar image set, we propose to utilize such correlation to generate side information at decoder side for each image in the set. Specifically, a reference structure of the image set is generated by building a minimum spanning tree according to the similarity between two images at encoder. With the reference structure, the side information of each image to be decoded can be generated based on the decoded reference image. And our network learns the correlation between an image and its side information in the training phase. Based on the principle of DSC, the side information can provide additional information such as rich details at decoder side. To make full use of the side information, the initially decoded image and the additional side information are fused at different scales. A decompressed image enhancement network is introduced to reduce the compression artifacts of the decoded images. Extensive experimental results compared with other mainstream methods validate the superior performance of our scheme in both terms of subjective and objective quality.

关键词： Training Image coding Correlation Source coding Buildings Decoding Image enhancement

来源：评论

学校读者我要写书评

暂无评论

Science Autonomy and Planetary Missions: ML and data Science Applied to the ExoMars Mission

Science Autonomy and Planetary Missions: ML and Data Science...

引用

ieee Aerospace conference

作者： Da Poian, Victoria Lyness, Eric Danell, Ryan Theiling, Bethany Brinckerhoff, William Microtel LLC NASA Goddard Space Flight Ctr 8800 Greenbelt Rd Greenbelt MD 20771 USA Danell Consulting 1313 Addison Court Winterville NC 28590 USA NASA Goddard Space Flight Ctr 8800 Greenbelt Rd Greenbelt MD 20771 USA

ISBN: (纸本)9781665490320

Future planetary science instruments will be capable of producing far more data than can be transmitted back to Earth, potentially leaving valuable scientific data on a planet's surface. Instruments will need to carefully identify the subset of total data to be prioritized for return, as transmission of the full data volume, even after compression, will not be feasible. The concept of science autonomy, where instruments collect measurement data, perform selected science data analyses onboard, and then autonomously act upon those analyses through self-adjustment and tuning of instrument parameters, can be used to identify and produce an optimal and compact data set for return, maximizing the value of each bit returned to Earth. Furthermore, the selection of the next operation(s) to be run following preliminary measurements, without requiring ground-in-the-loop communication, increases mission efficiency and enables successful yet shorter duration missions to hazardous planetary environments. This capability allows missions to prioritize the most compelling or time-critical data, yielding a more efficient and productive scientific investigation overall. In this paper, we present our implementation using different machine learning (ML) techniques (i.e., clustering, classification) for analyzing science data from the Mars Organic Molecule Analyzer (MOMA) instrument onboard the ExoMars rover Rosalind Franklin. MOMA is a dual-source (laser desorption and gas chromatography) mass spectrometer that will search for past or present life on the Martian surface and subsurface through analysis of soil samples. We use data collected from the MOMA flight-like engineering model to develop mass-spectrometry-focused ML techniques. This effort, in preparation for operating on Mars, is aimed at 1) helping the ExoMars science and operations team quickly analyze new data and support them in their decision-making process regarding subsequent operations and, 2) getting a better understandi

关键词： Earth (planet)

来源：评论

学校读者我要写书评

暂无评论

A Deep Learning Model for Analyzing Smartwatch Reviews Based on BO-BiLSTM 2

A Deep Learning Model for Analyzing Smartwatch Reviews Based...

引用

2nd ieee International conference on Electrical, Automation and Computer Engineering, ICEACE 2024

作者： Ma, Mingshang Central South University Dundee International Institute Changsha China

ISBN: (数字)9798350368208

ISBN: (纸本)9798350368208

In recent years, with the advancements in deep learning models, the influence of artificial intelligence technology in social life has been increasingly prominent. Smartwatches, as embedded AI devices, are becoming a key gateway to the era of big data. However, the enrichment of smartwatch functionalities and their widespread usage have generated a vast amount of user feedback data. This paper aims to leverage big data technology to mine and analyze online user reviews of Huawei-branded smartwatches, utilizing deep learning-based natural language processing techniques for feature selection and sentiment value prediction to understand user needs and guide product optimization and innovation. Using the *** platform as the data source, we crawled online review texts and conducted initial preprocessing such as text deduplication and mechanical word compression. We then performed topic analysis and feature extraction on the text and calculated sentiment scores for each feature. Finally, we employed the BO-BiLSTM model to explore the sentiment value fluctuation trends of various attribute features. Taking Huawei smartwatches as a case study, the results indicate that the positive sentiment values for "hardware features"are on an upward trend, while the sentiment values for "user experience features"are on a downward trend. These findings provide valuable insights for enterprises to better understand market changes and technological iterations for enhanced development. © 2024 ieee.

关键词： Big data

来源：评论

学校读者我要写书评

暂无评论

A Novel State Connection Strategy for Quantum Computing to Represent and Compress Digital Images 48

A Novel State Connection Strategy for Quantum Computing to R...

引用

48th ieee International conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Haque, Md Ershadul Paul, Manoranjan Ulhaq, Anwar Debnath, Tanmoy Charles Sturt University School of Computing Mathematics and Engineering BathurstNSW2795 Australia

ISBN: (纸本)9781728163277

Quantum image processing draws a lot of attention due to faster data computation and storage compared to classical data processing systems. Converting classical image data into the quantum domain and state label preparation complexity is still a challenging issue. The existing techniques normally connect the pixel values and the state position directly. Recently, the EFRQI (efficient flexible representation of the quantum image) approach uses an auxiliary qubit that connects the pixel-representing qubits to the state position qubits via Toffoli gates to reduce state connection. Due to the twice use of Toffoli gates for each pixel connection still it requires a significant number of bits to connect each pixel value. In this paper, we propose a new SCMFRQI (state connection modification FRQI) approach for further reducing the required bits by modifying the state connection using a reset gate rather than repeating the use of the same Toffoli gate connection as a reset gate. Moreover, unlike other existing methods, we compress images using block-level for further reduction of required qubits. The experimental results confirm that the proposed method outperforms the existing methods in terms of both image representation and compression points of view. © 2023 ieee.

关键词： compression Quantum image representation SCMFRQI State preparation

来源：评论

学校读者我要写书评

暂无评论

SADDLe: Sharpness-Aware Decentralized Deep Learning with Heterogeneous data

SADDLe: Sharpness-Aware Decentralized Deep Learning with Het...

引用

2025 ieee/CVF Winter conference on Applications of Computer Vision, WACV 2025

作者： Choudhary, Sakshi Aketi, Sai Aparna Roy, Kaushik Purdue University West LafayetteIN United States

ISBN: (纸本)9798331510831

Decentralized training enables learning with distributed datasets generated at different locations without relying on a central server. In realistic scenarios, the data distribution across these sparsely connected learning agents can be significantly heterogeneous, leading to local model over-fitting and poor global model generalization. Another challenge is the high communication cost of training models in such a peer-to-peer fashion without any central coordination. In this paper, we jointly tackle these two-fold practical challenges by proposing SADDLe, a set of sharpness-aware de-centralized deep learning algorithms. SADDLe leverages Sharpness-Aware Minimization (SAM) to seek a flatter loss landscape during training, resulting in better model generalization as well as enhanced robustness to communication compression. We present two versions of our approach and demonstrate its effectiveness through extensive experiments on various Computer Vision datasets (CIFAR-10, CIFAR-100, Imagenette, and ImageNet), model architectures, and graph topologies. Our results show that SADDLe leads to 1-20% improvement in test accuracy as compared to existing techniques while incurring a minimal accuracy drop ( 1%) in the presence of up to 4× compression. © 2025 ieee.

关键词： Federated learning

来源：评论

学校读者我要写书评

暂无评论

Learning Pruned Structure and Weights Simultaneously from Scratch: an Attention based Approach

Learning Pruned Structure and Weights Simultaneously from Sc...

引用

2023 ieee International conference on Big data, Bigdata 2023

作者： He, Qisheng Shi, Weisong Dong, Ming Wayne State University Department of Computer Science Detroit United States

ISBN: (纸本)9798350324457

As a deep learning model typically contains millions of trainable weights, there has been a growing demand for a more efficient network structure with reduced storage space and improved run-time efficiency. Pruning is one of the most popular network compression techniques. In this paper, we propose a novel unstructured pruning pipeline, Attention-based Simultaneous sparse structure and Weight Learning (ASWL). In ASWL, an efficient algorithm is proposed to calculate the pruning ratios layer-wisely from attentions, and both weights for the dense network and the sparse network are tracked so that the pruned structure is simultaneously learned from randomly initialized weights. Our experiments on MNIST, Cifar10, and ImageNet show that ASWL achieves superior pruning results in terms of accuracy, pruning ratio and operating efficiency when compared with state-of-the-art network pruning methods. © 2023 ieee.

关键词： Deep Learning Deep Neural Network compression Network Pruning

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 << < 100 101 102 103 104 105 106 107 108 109 > >>

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：