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

170 篇 会议
62 篇 期刊文献
1 册 图书

馆藏范围

233 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

106 篇 工学
- 67 篇 计算机科学与技术...
- 51 篇 软件工程
- 45 篇 信息与通信工程
- 24 篇 生物工程
- 19 篇 生物医学工程（可授...
- 13 篇 电气工程
- 13 篇 电子科学与技术（可...
- 9 篇 控制科学与工程
- 7 篇 化学工程与技术
- 6 篇 仪器科学与技术
- 5 篇 光学工程
- 4 篇 机械工程
- 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
70 篇 理学
- 34 篇 数学
- 26 篇 生物学
- 19 篇 物理学
- 13 篇 统计学（可授理学、...
- 7 篇 化学
- 6 篇 系统科学
39 篇 管理学
- 24 篇 图书情报与档案管...
- 15 篇 管理科学与工程(可...
- 10 篇 工商管理
22 篇 医学
- 18 篇 临床医学
- 15 篇 基础医学(可授医学...
- 11 篇 药学(可授医学、理...
- 5 篇 公共卫生与预防医...
7 篇 经济学
- 7 篇 应用经济学
7 篇 法学
- 7 篇 社会学
3 篇 农学
2 篇 文学
1 篇 教育学

主题

20 篇 feature extracti...
15 篇 support vector m...
13 篇 deep learning
9 篇 speech
8 篇 education
7 篇 programming prof...
7 篇 neural networks
7 篇 databases
6 篇 intrusion detect...
6 篇 gabor filters
6 篇 long short-term ...
6 篇 image segmentati...
6 篇 electrocardiogra...
6 篇 machine learning
6 篇 estimation
6 篇 training
5 篇 noise reduction
5 篇 task analysis
5 篇 generative adver...
5 篇 computer archite...

机构

31 篇 center for compu...
29 篇 center for compu...
15 篇 center for compu...
8 篇 lakhshya cyber s...
7 篇 center for compu...
6 篇 division of biom...
6 篇 center for excel...
6 篇 amrita vishwa vi...
6 篇 department of el...
5 篇 center for excel...
5 篇 center for compu...
5 篇 wyoming gis cent...
5 篇 center for compu...
4 篇 department of el...
4 篇 center for compu...
4 篇 center for compu...
4 篇 amrita vishwa vi...
4 篇 center for compu...
3 篇 department of ph...
3 篇 center for compu...

作者

49 篇 soman k.p.
30 篇 k.p. soman
15 篇 sowmya v.
13 篇 neethu mohan
12 篇 d. govind
11 篇 k p soman
10 篇 vinayakumar r.
9 篇 sachin kumar s.
9 篇 v sowmya
9 篇 sachin kumar s
8 篇 r vinayakumar
8 篇 prabaharan poorn...
8 篇 pradeep menon
8 篇 kp soman
7 篇 r. ramanathan
7 篇 simran k.
7 篇 shanmugha sundar...
7 篇 soman k p
7 篇 anand kumar m.
6 篇 k. p. soman

语言

232 篇 英文
1 篇 其他

检索条件"机构=Center for Computational Engineering and Networking"

共 233 条记录，以下是51-60 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：

Effect of annotation and loss function on epiphyte identification using conditional generative adversarial network 1

Effect of annotation and loss function on epiphyte identific...

引用

1st IEEE International Conference on Advances in Electrical, Computing, Communications and Sustainable Technologies, ICAECT 2021

作者： Aswin, S. Sajithvariyar, V.V. Sivanpillai, Ramesh Sowmya, V. Brown, Gregory K. Shashank, A. Soman, K.P. Center for Computational Engineering and Networking Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India Wyoming GIS Center University of Wyoming LaramieWY82071 United States Department of Botany University of Wyoming LaramieWY82071 United States

ISBN: (纸本)9781728157900

The deep neural networks are capable of processing large amounts of data for image processing applications like classification, segmentation and identification etc. Conditional- Generative Adversarial Network (C-GAN) is an example of Deep Learning (DL)based image processing algorithm that is used widely for identifying targets in images. In a Deep Learning based image processing application the quality of the input image plays an important role. The quality and variations within the dataset helps the neural network filters to derive the best features for the classification and identification of the target in the image. Many deep learning approaches will have a preprocessing pipeline which improves the quality of the input training data. The objective of this study was to evaluate the effect of preprocessing methods to improve C-GAN's ability to detect epiphytes in images acquired by Unmanned Aerial Vehicles (UAVs). These methods include 1) trimming the training images to include mostly the target plant, 2) generating annotation images with a thresholding technique, and 3) incorporating binary cross entropy loss function. Results obtained from this study shows that the percent occupancy of input images and annotation method plays an important role in identifying the target plant. © 2021 IEEE

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Analysis of four generator architectures of C-gan, loss function, and annotation method for epiphyte identification

Analysis of four generator architectures of C-gan, loss func...

引用

American Society for Photogrammetry and Remote Sensing, ASPRS 2021 Annual Conference

作者： Sajithvariyar, V.V. Aswin, S. Sowmya, V. Soman, K.P. Sivanpillai, R. Brown, G.K. Center for Computational Engineering and Networking Amrita School of Engineering Amrita Vishwa Vidyapeetham TN Coimbatore641 112 India Wyoming GIS Center University of Wyoming LaramieWY82072 United States Department of Botany University of Wyoming LaramieWY82072 United States

The deep learning (DL) models require timely updates to continue their reliability and robustness in prediction, classification, and segmentation tasks. When the deep learning models are tested with a limited test set, the model will not reveal the drawbacks. Every deep learning baseline model needs timely updates by incorporating more data, change in architecture, and hyper parameter tuning. This work focuses on updating the Conditional Generative Adversarial Network (C-GAN) based epiphyte identification deep learning model by incorporating 4 different generator architectures of GAN and two different loss functions. The four generator architectures used in this task are Resnet-6. Resnet-9, Resnet-50 and Resnet-101. A new annotation method called background removed annotation was tested to analyse the improvement in the epiphyte identification protocol. All the results obtained from the model by changing the above parameters are reported using two common evaluation metrics. Based on the parameter tuning experiment, Resnet-6, and Resnet-9, with binary cross-entropy (BCE) as the loss function, attained higher scores also Resnet-6 with MSE as loss function performed well. The new annotation by removing the background had minimal effect on identifying the epiphytes. © 2021 International Society for Photogrammetry and Remote Sensing. All rights reserved.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Implementation of Hack ALU using Quantum Dot Cellular Automata

Implementation of Hack ALU using Quantum Dot Cellular Automa...

引用

Microelectronics, Signals & Systems (ICMSS), International Conference on

作者： A. Aadharsh Aadhithya J. Akshaya K. Ghaayathri Devi B. Nithesh G. Jyothish Lal K.P. Soman Coimbatore Amrita Vishwa Vidyapeetham Center for Computational Engineering and Networking (CEN) Amrita School of Engineering India

ISBN: (纸本)9781665448864

Quantum Dot Cellular Automata (QCA) technology is one of the emerging next-generation nano-scale technologies, to subdue the limitations of existing CMOS technologies. As researchers continue to work hard to find an alternative to CMOS technology, QCA provides a solution for a faster computer with a smaller size and low power consumption. This paper implements an operation-rich “HACK ALU”, capable of performing many operations. Hack AL U is a versatile design that implements 64 op-erations with just 6 control bits along with novel implementations of adders and multiplexers. This ALU has been designed using a bottom-up approach by beginning the design by constructing the basic gates and progressing to the construction of adders, and multiplexers. The scope of the research is to provide an efficient ALU design that can be integrated in the CPU design. The present work has been implemented and tested on the QCA designer software. Experimental evaluation shows that logical and arithmetic operations are consistent in the proposed design.

关键词： Multiplexing Wiring Computer hacking Quantum dots Automata CMOS technology Software

来源：评论

学校读者我要写书评

暂无评论

Analysis of Stop Consonants and Vowels in Indian Languages: A Multifractal Approach

Analysis of Stop Consonants and Vowels in Indian Languages: ...

引用

Microelectronics, Signals & Systems (ICMSS), International Conference on

作者： S. Roshan Tushar O. Jayashree U. Asmitha G. Jyothish Lal K.P. Soman Center for Computational Engineering and Networking (CEN) Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India

ISBN: (纸本)9781665448864

The work presented in this paper analyse the stop consonants and vowels in Indian language from a novel perspective. In the context of many Indian languages, the combination of these sounds, consonant-vowel (CV), forms an important subset of alphabets. These sounds are characterized by their place of articulation and manner of articulation. Most of the previous works in the analysis of manner of articulation, for a given place of articulation, focused on the excitation source features and events such as voicing onset time, based on linear source-filter theory. In this study, we solve the problem from a nonlinear perspective by analysing the fractal nature of isolated vowels and stop consonants. Experimental result shows that the multifractal approach is very effective in identifying the fluctuations in the speech sounds. The result also shows that the spectral width computed from multifractal analysis distinguishes the four categories of manner of articulation in stop sounds.

关键词： Fluctuations Production Fractals

来源：评论

学校读者我要写书评

暂无评论

Exploiting Graph Matrix Duality for Efficient Graph Data Processing

Exploiting Graph Matrix Duality for Efficient Graph Data Pro...

引用

Recent Advances and Innovations in engineering (ICRAIE)

作者： Vinith Rejathalal Aadharsh Aadhithya A Soman K. P Prabhaharan Poornachandran Amrita Vishwa Vidyapeetham Centre for Internet Studies and Artificial Intelligence Amritapuri Kerala India Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham Center for Computational Engineering and Networking (CEN) India

Linear algebra is the backbone of modern applied data analytics. Not only does it provide the theoretical substratum for the development of analytical algorithms it also lends the technological support for extremely efficient implementations. The linear algebra ecosystem consists of remarkably fast hardware support with equally efficient software solutions leveraging the hardware capacity. Graphs are an ubiquitous modelling framework for the representation and analysis of connected data.A flipside from practical perspective is that conventional graph algorithms are challenging as they are hard to parallelize and often make too much use of non-linear data structures which do not have hardware support. In this context, this paper presents an invitation to an alternate way to look at graph processing in terms of algebraic constructs, formulations and technology. The paper presents the theoretical underpinnings of the new view and also some interesting progresses at the frontier level in this direction. Above all, an intended objective is to present a compelling invite to the academia and research community to adopt this approach in their academic and research activities.

关键词： Technological innovation Data analysis Software algorithms Ecosystems Linear algebra Data structures Hardware

来源：评论

学校读者我要写书评

暂无评论

Retraction Note: Deep learning architectures for land cover classification using red and near-infrared satellite images

引用

Multimedia Tools and Applications 2024年第34期83卷 81899-81899页

作者： Unnikrishnan, Anju Sowmya, V. Soman, K. P. Center for Computational Engineering and Networking (CEN) Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India

来源：评论

学校读者我要写书评

暂无评论

Prospects of NFMD for Power System Frequency and Amplitude Estimation

Prospects of NFMD for Power System Frequency and Amplitude E...

引用

Recent Advances and Innovations in engineering (ICRAIE)

作者： Aadharsh Aadhithya A Neethu Mohan Sachin Kumar S Soman K. P Prabaharan Poornachandran Center for Computational Engineering and Networking (CEN) Amrita School of Engineering Coimbatore Amrita Vishwa Vidyapeetham India Centre for Internet Studies and Artificial Intelligence Amrita Vishwa Vidyapeetham Amritapuri Kerala India

Power quality monitoring and parameter estimation are essential for the proper functioning of modern power grids. Various techniques have been proposed to estimate the characteristics of power signals and to gain insight into their dynamics. Non-stationary Fourier mode decomposition (NFMD) is a new interpretable time-frequency analysis framework for non-stationary and nonlinear signals. This work investigates the prospects of NFMD in the estimation of power-system characteristics such as fundamental frequency, amplitude and presence of disturbance components. Usage of NFMD as a noise removal technique is also explored in the current work. Results show that NFMD is robust to noisy disturbances and can perform well in noisy environments. The proposed methodology can be used for real-time analysis and interpretation of large volume of data from advanced smart metering techniques such as microphasor measurement units in microgrids.

关键词： Time-frequency analysis Technological innovation Power system dynamics Power quality Noise reduction Estimation Frequency estimation

来源：评论

学校读者我要写书评

暂无评论

Design and Optimization of CNN for Lane Detection 11

Design and Optimization of CNN for Lane Detection

引用

11th International Conference on Computing, Communication and networking Technologies, ICCCNT 2020

作者： Aravind, H. Sivraj, P. Ramachandran, K.I. Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Electrical and Electronics Engineering Coimbatore India Center for Computational Engineering and Networking Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (数字)9781728168517

ISBN: (纸本)9781728168517

The large push for smart transportation and self-driving cars has brought out an array of research problems and a pertinent one among them is lane detection. With Convolutional Neural Network (CNN) coming up as the leading algorithm for this purpose, a lot of research has gone into the optimization of its performance. This paper proposes the application of Dynamic Mode Decomposition (DMD) algorithm for reducing the complexity of CNN for lane detection. The work involves implementation of a CNN model for lane detection and optimization of its inference time by reducing the number of layers while maintaining accuracy of the model with the help of DMD. The model implementation was done using TensorFlow library and the optimized model achieved accuracy levels similar to that of the baseline model. Both models were tested and it was found that the proposed model performed faster than the baseline;due to the lesser number of computations it performs as a result of reduction in its layers. This work shows that DMD as a background foreground separation tool, can be used to optimize the layers in a deep neural network trained to identify sparse details in images. © 2020 IEEE.

关键词： Dynamic mode decomposition

来源：评论

学校读者我要写书评

暂无评论

Driver Inattention Monitoring System Based on the Orientation of the Face Using Convolutional Neural Network 11

Driver Inattention Monitoring System Based on the Orientatio...

引用

11th International Conference on Computing, Communication and networking Technologies, ICCCNT 2020

作者： Manjula, P.M. Adarsh, S. Ramachandran, K.I. Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Electronics and Communication Engineering Coimbatore India Center for Computational Engineering and Networking Amrita School of Engineering Amrita Vishwa Vidyapeetham Coimbatore India

ISBN: (纸本)9781728168517

Driving inattentively is one of the prime reasons for vehicle accidents worldwide and has significant implications for road safety. A prompt alert to the inattentive driver can mitigate many accidents and save numerous lives, and reduce the cost of damages caused by accidents. To achieve this, a proposal of a nonintrusive and noninvasive driver inattention monitoring and alerting system in real time has been put forward. A mobile camera mounted on the windshield captures the video of the driver. Viola-Jones algorithm detects the face in each frame of the video and the Kanade-Lucas-Tomasi (KLT) algorithm tracks the detected face from one frame to another frame. The driver is classified as inattentive or attentive using Convolutional Neural Network (CNN). The transfer learning of the AlexNet Convolutional Neural Network architecture is adopted for the classification. The accuracy, precision, sensitivity, F1 score, and specificity of the system proposed in this paper are 98.24%, 100%, 96.47%, 98.21% and 100%, respectively. © 2020 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Comparison of image enhancement techniques for rapid processing of post flood images

Comparison of image enhancement techniques for rapid process...

引用

American Society for Photogrammetry and Remote Sensing, ASPRS 2020 Annual Conference

作者： Harichandana, M. Sowmya, V. Sajithvariyar, V.V. Sivanpillai, Ramesh Center for Computational Engineering and Networking Amrita School of Engineering Amrita Vishwa Vidyapeetham CoimbatoreTN641 112 India Wyoming GIS Center University of Wyoming LaramieWY82072 United States

Satellite images are widely used for assessing the areal extent of flooded areas. However, presence of clouds and shadow limit the utility of these images. Numerous digital algorithms are available for enhancing such images and highlighting areas of interest. These algorithms range from simple to complex, and the time required to process these images also varies considerably. For disaster response, it is important to select an algorithm that can enhance the quality of the images in relatively short time. This study compared the relative performance of five traditional (Histogram Equalization, Local Histogram Equalization, Contrast Limited Adaptive Histogram Equalization, Gamma Correction, and Linear Contrast Stretch) algorithms for enhancing post-flood satellite images. Flood images with different levels of clouds and shadows were enhanced and output generated were evaluated in terms of processing time and quality as measured by Blind/Reference less Image Spatial Quality Evaluator (BRISQUE), a no-reference image quality metric. Findings from this study will provide valuable information to image analysts for selecting a suitable algorithm for rapidly processing post-flood satellite images. © 2020 International Society for Photogrammetry and Remote Sensing. All rights reserved.

关键词： Floods

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24页 << < 2 3 4 5 6 7 8 9 10 11 > >>

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：