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

40 篇 会议
23 篇 期刊文献

馆藏范围

63 篇 电子文献
0 种 纸本馆藏

日期分布

学科分类号

33 篇 理学
- 17 篇 物理学
- 9 篇 生物学
- 7 篇 数学
- 3 篇 化学
- 3 篇 地球物理学
- 3 篇 统计学（可授理学、...
- 2 篇 天文学
- 2 篇 地质学
32 篇 工学
- 16 篇 计算机科学与技术...
- 13 篇 软件工程
- 10 篇 核科学与技术
- 7 篇 信息与通信工程
- 5 篇 生物工程
- 2 篇 机械工程
- 2 篇 化学工程与技术
- 2 篇 航空宇航科学与技...
- 2 篇 环境科学与工程（可...
- 2 篇 生物医学工程（可授...
- 1 篇 光学工程
- 1 篇 仪器科学与技术
- 1 篇 电子科学与技术（可...
- 1 篇 控制科学与工程
- 1 篇 测绘科学与技术
8 篇 管理学
- 6 篇 图书情报与档案管...
- 3 篇 管理科学与工程(可...
- 2 篇 工商管理
2 篇 农学
- 1 篇 作物学
- 1 篇 农业资源与环境
2 篇 医学
- 1 篇 基础医学(可授医学...
1 篇 法学
- 1 篇 社会学

主题

8 篇 gravitational wa...
5 篇 optimization
5 篇 gravitational wa...
5 篇 gravitational wa...
4 篇 databases
4 篇 accuracy
3 篇 parallel process...
3 篇 libraries
3 篇 hidden markov mo...
3 篇 speech
3 篇 dark matter
3 篇 data mining
3 篇 neutron stars & ...
3 篇 feature extracti...
3 篇 machine learning
3 篇 context
3 篇 tagging
2 篇 particle dark ma...
2 篇 support vector m...
2 篇 message passing

机构

14 篇 institute for pl...
14 篇 university of so...
14 篇 indian institute...
12 篇 colorado state u...
12 篇 department of as...
12 篇 scuola di ingegn...
12 篇 king’s college l...
12 篇 the university o...
12 篇 infn sezione di ...
12 篇 dipartimento di ...
12 篇 università degli...
12 篇 ligo laboratory ...
12 篇 university of ma...
12 篇 stony brook univ...
12 篇 university of mi...
12 篇 nasa goddard spa...
12 篇 university of rh...
12 篇 montclair state ...
12 篇 infn trento inst...
12 篇 max planck insti...

作者

12 篇 r. takahashi
12 篇 j. c. bayley
12 篇 k. komori
12 篇 t. kajita
12 篇 f. hellman
12 篇 m. kinley-hanlon
12 篇 t. mcrae
12 篇 a. parisi
12 篇 t. sawada
12 篇 s. rowan
12 篇 s. m. aronson
12 篇 v. p. mitrofanov
12 篇 a. j. tanasijczu...
12 篇 g. moreno
12 篇 g. hemming
12 篇 a. z. jan
12 篇 r. c. walet
12 篇 b. f. neil
12 篇 c. kim
12 篇 g. chiarini

语言

63 篇 英文

检索条件"机构=Center for Development of Advanced Computing Pune"

共 63 条记录，以下是1-10 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：

CALIBRATION AND VALIDATION OF THE CYGNSS LEVEL 1 DATA PRODUCTS 37

CALIBRATION AND VALIDATION OF THE CYGNSS LEVEL 1 DATA PRODUC...

引用

IEEE International Geoscience & Remote Sensing Symposium

作者： Gleason, Scott Ruf, Christopher Clarizia, Maria Paola Johnson, Joel O'Brien, Andrew Chang, Paul Jelenek, Zorana Said, Faozi Soisuvarn, Seubson AAI Center for development of Advanced Computing Pune India Banasthali Vidyapith Banasthali India

ISBN: (纸本)9781509049516

This presentation will include an overview of the recently launched NASA CYGNSS mission Level 1 calibration algorithms and their on-orbit validation [1], [2]. The validation of the Level 1 calibration will be performed in several steps, including a) a detailed noise floor analysis to assess the observed on-orbit noise power levels over the open ocean, b) multiple consistency checks using a forward model and co-located ocean wind and wave truth reference data and c) a term by term error analysis of all the non-ocean corrections applied to the final sigma0 estimates. An outline of the Level 1a (calibration from raw Level 0 instrument counts to units of watts for the received power) and the Level 1b (calibration from watts to bistatic scattering cross section) algorithms are each shown below. Three key components of the Level 1a calibration will be presented, namely, an analysis of the instrument (alone) and antenna noise characteristics over the ocean, a study of the range of received power levels from the surface, and comparisons with a forward model. The key components of the Level 1b calibration presented here will include validation of the main corrections applied to arrive at a surface sigma0 estimate, including receiver antenna gain, GPS transmitter and scattering area corrections.

关键词： Calibration Sea surface Wind speed Scattering Sea measurements Surface waves

来源：评论

学校读者我要写书评

暂无评论

Conceptualizing a desktop environment for cognitively challenged people 12

Conceptualizing a desktop environment for cognitively challe...

引用

International Information Technology Conference, CUBE 2012

作者： Kumar, Shiv Nath Baijal, Sagun Chourey, Leena Ramamurthy, Aparna Sasikumar, M. Center for Development of Advanced Computing Rain Tree Marg CBD-Belapur Navi Mumbai-400614 India Center for Development of Advanced Computing Anusandhan Bhawan C-56/1 Sector 62 Noida-201307 India Center for Development of Advanced Computing Pune University Campus Pune-411007 India

ISBN: (纸本)9781450311854

In India as well as in different parts of the world, a large section of society suffers from different kinds of disabilities. These difficulties impose certain constraints on people suffering with them in following their routine life as compared to normal persons. Of these difficulties, cognitive disability is one of the most difficult to address. This disability relates to different kinds of problems with brain like amnesia, learning disability, attention problems etc. The type and nature of problem may vary from person to person. Common symptoms like slow grasping, longer response time and distraction in nature have been identified by observing the affected users at various rehabilitation centers. Using ICT based solutions, life can be made better for such persons. Computers can assist them in different ways in rehabilitation and enhancing their quality of life. The aim of this paper is to highlight the issues and challenges faced by cognitively impaired persons in using computers and to propose solution(s) so that computer usage will be easy for them. An accessible desktop environment with relevant features is conceptualized that will help user in smooth interaction with computer without distraction. Specific features of the proposed desktop are focused primarily on memory and attention based issues and provides support for affected users, parents/caretakers and teachers. The proposed solution is validated with and impacts of design have been collected from the relevant stack holders such as end users, caretakers and *** enhancements are done on open source operating system environment and are available as open source. This paper discusses the system and the rationale for the various features incorporated. Copyright 2012 ACM.

关键词： Human computer interaction

来源：评论

学校读者我要写书评

暂无评论

Parallelization of reverse time migration using MPI + OpenMP

Parallelization of reverse time migration using MPI + OpenMP

引用

2016 International Conference on advanced Communication Control and computing Technologies, ICACCCT 2016

作者： Akanksha, S. Kansara Kumar, Gardas Naresh Gujarat Technological University Gujarat India Center of Development for Advanced Computing Pune India

ISBN: (纸本)9781467395458

Increasing computational power ensures effective implementation of high-end HPC applications but does not guarantee performance. Seismic modelling is one such high computational demanding application. In oil and gas industries, reverse time migration is one of the important migration algorithms that guarantees accurate imaging results but its practical implementation gets hindered due to its high computational cost. In this paper we will discuss reverse time migration for solving 2D wave equations using finite difference method and its parallel implementation using Message Passing Interface(MPI) and OpenMP. © 2016 IEEE.

关键词： Message passing

来源：评论

学校读者我要写书评

暂无评论

Parallelization of low-level computer vision algorithms on clusters

Parallelization of low-level computer vision algorithms on c...

引用

2nd Asia International Conference on Modelling and Simulation, AMS 2008

作者： Kadam, Sanjay Scientific and Engineering Computing Group Center for Development Advanced Computing Pune University Campus Ganeshkhind Pune 411 007 Maharashtra India

ISBN: (纸本)9780769531366

In this paper we present parallel implementations of some representative low level vision algorithms on a cluster of workstations. These include convolution operation and the image restoration algorithm using Markov random field models. The convolution operation has been parallelized using the Farmer-Worker paradigm, while the image restoration algorithm has been parallelized through the Master-Worker pattern. Parallel implementations of both these algorithms have shown promising results, where the observed speedups are reasonably close to the ideal speedups. The parallel convolution operation has shown good scalability with respect to the problem size and number of processors used in parallelization. The paper elaborates on different parallelization results of the convolution operation observed after varying the image size, mask size and processor load on individual workstations. The image restoration algorithm is communication intensive. However, as the computing time between successive communications at each worker process is relatively high, the actual speedups observed are very close to the ideal speedups in this algorithm. © 2008 IEEE.

关键词： Convolution

来源：评论

学校读者我要写书评

暂无评论

Data mining: Consumer behavior analysis 3

Data mining: Consumer behavior analysis

引用

3rd IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2018

作者： Singh, Shashi Pal Kumar, Ajai Yadav, Neetu Awasthi, Rachna Aaig Center for Development of Advanced Computing Pune India Banasthali Vidyapith Banasthali Rajasthan India

ISBN: (纸本)9781538624401

The whole world is overloaded via innumerable data like scientific data, financial data, mathematical data, geographic data, employment data, and other various kinds of data. Manually analyzing or summarizing this data is impractical, the reason behind this is incredible increment in data in this generation of information and network distribution. Objective of Data mining is to extract the required information, instruction, and pattern from a huge quantity of information and data by the help of multiple tools and techniques. Therefore, it is very essential to select significant techniques to mine the database, because of the several other varieties of data in data base and every data has their individual mining techniques. In this research paper we are going to analyze the consumer behavior, that can help us not only in the retailer market/industry but we can use it in the medical, online shopping etc. Here we are talking about consumer behavior analysis, so we are taking market industry data. Data mining techniques are most conventional and sufficient technique for behavior analysis. Main target of this paper is to recognize behavior of a consumer, his/her psychological condition, consumer's decision making during purchase and then finding how much suitable methods of data mining are applied to enhance the conventional method. Here association rules are deployed to mine out required rules for customer applied in purchased data in retailers shop or organization. After that the required rule can be used for further required filed line online shopping, medical field etc. © 2018 IEEE.

关键词： Data mining

来源：评论

学校读者我要写书评

暂无评论

DNN Machine Translation for Indian Languages

DNN Machine Translation for Indian Languages

引用

International Conference on Sustainable Technology and advanced computing in Electrical Engineering, ICSTACE 2021

作者： Singh, Shashi Pal Kumar, Ajai Singh, Lenali Angra, Tanya Center for Development of Advanced Computing AAIG Pune India Banasthali Vidyapith Rajasthan Banasthali India

ISBN: (纸本)9789811943638

For developing and using various features provided by the internet by all the sections of the society, it is important to make the technology accessible irrespective of the language. So, the translation between the languages becomes important criterion. Previously, the machine translation has been used to translate between different languages, which eased the communication between people from different linguistic backgrounds. But in the last couple of years, the advancement and enhancement of Deep Neural Network (DNN) allowed us to rewrite our approach for machine translation completely. This paper discusses the process and the methodology to build the Machine translation system using Deep Neural Network (DNN) for translation between different Indian languages, using the combination of two ideas, i.e., Recurrent Neural Networks and Encoding. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep neural networks

来源：评论

学校读者我要写书评

暂无评论

A multilayer deep learning framework for auto-content tagging 5th

A multilayer deep learning framework for auto-content taggin...

引用

5th International Conference on advanced Machine Learning Technologies and Applications, AMLTA 2020

作者： Jain, Shikha Jain, Ajit Kumar Singh, Shashi Pal Banasthali Vidyapith BanasthaliRajasthan India AAI Center for Development of Advanced Computing Pune India

ISBN: (纸本)9789811533822

With the rapid and recent development of Internet of things (IoT), Bigdata, the most fundamental challenge is to explore the large volume of data from heterogeneous data sources (logs, audio, video, reports, news, social media, etc.). The tools and techniques that are being used in successful transforming of structured data into business intelligence simply do not work when it comes to unstructured data. An estimated 80% of data held by firms today are unstructured. We have powerful computations tools such as graphics processor units (GPUs) for complex calculations and Hadoop, spark for Bigdata management still technology is lacking in complete utilization of available data. Deep learning is a powerful tool to resolve the issues related to data, such as automatic extraction of complex data and representation of large volume unsupervised and uncategorized raw data. But extracted data do not have any standard format. In the survey of Bigdata mining system and IoT systems, it has been found that a layer is needed in Bigdata mining framework that can work on structured, unstructured and semi-structured data and can integrate the complete raw data. The proposed framework is a multilayer deep learning model that performed the clustering of unstructured huge text data followed by classification layer, CNN, to extract features and classify the raw data clusters in parallel manner. Conversion of unstructured data in standard format by automates content tagging helps decision-making systems to easily identify the related data and increase the data utilization. © Springer Nature Singapore Pte Ltd 2021.

关键词： Multilayers

来源：评论

学校读者我要写书评

暂无评论

Deep Neural-Based Machine Translation Using RNN for Indian Languages 7th

Deep Neural-Based Machine Translation Using RNN for Indian L...

引用

7th International Conference on advanced computing and Intelligent Engineering, ICACIE 2022

作者： Singh, Shashi Pal Kumar, Ajai Pundir, Kanishka AAIG Center for Development of Advanced Computing Pune India Banasthali Vidyapith Rajasthan Banasthali India

ISBN: (纸本)9789819950140

This study describes how to develop a machine learning system for the translation of Indian languages (Hindi, Gujarati, and Punjabi) using a form of deep neural network (DNN). We are employing a form of RNN called long short-term memory (LSTM). The state-of-the-art algorithm for sequential data is recurrent neural networks (RNNs). This research work pertaining to RNN-based translation of Indian Languages involved experimenting with nmt models (unidirectional nmt and bidirectional) and their hyperparams to devise results important from real-world perspective. We employed the neural machine translation (NMT) model to get the best results. Unlike the standard phrase-based translation system, which consists of many small sub-components that are tweaked independently, this approach allows a single system to be trained directly on the source and target text and attempts at neural machine translation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Long short-term memory

来源：评论

学校读者我要写书评

暂无评论

Automatic Evaluation for Machine Translation 1

引用

7th International Conference on Smart Trends in computing and Communications, SmartCom 2023

作者： Singh, Shashi Pal Kumar, Ajai Saxena, Aarti Verma, Richa AAIG Center for Development of Advanced Computing Pune India Banasthali Vidyapith Rajasthan Banasthali India

ISBN: (数字)9789819907694

ISBN: (纸本)9789819907687

Language is the primary mode of communication. Communication is the only way to convey our thoughts and emotions to others. But there are many languages that we don't know how to speak and we can't learn all languages so quickly which is why Machine Translation systems were invented to help us communicate with anyone from anywhere. Researchers started working on Machine Translation Systems in the 1950s and have developed various amazing techniques to make our communication easy. Machine Translation Evaluation (MTE) Methodology checks the accuracy of the translations done by Machine Translation Tools. It is extremely important because while developing the translation systems, it constantly checks the performance and helps us to make appropriate changes in the system for bringing accuracy. Mostly it is done by comparing the output of machine translation systems with the translation(s) done by Human beings, there are also some techniques that do not require any reference sentences. In our project, we are using four Automated Machine Translation Evaluation metrics which are TER (Translation Error Rate), METEOR (Metric for Evaluation of Translation with Explicit Ordering), BLEU (Bilingual Evaluation Understudy), and NIST (National Institution of Standards and Technology) for English-Hindi Translation. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Technology transfer

来源：评论

学校读者我要写书评

暂无评论

Hybrid POS classification using long short term memory 3

Hybrid POS classification using long short term memory

引用

3rd IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology, RTEICT 2018

作者： Singh, Shashi Pal Kumar, Ajai Lohan, Anu Chaudhary, Jigyasa Daksh, Pragya Aaig Center for Development of Advanced Computing Pune India Banasthali Vidyapith Banasthali Rajasthan India

ISBN: (纸本)9781538624401

A Part of Speech classifier is an important tool that is used to develop many NLP tasks. In this paper we described Deep Neural Network based architecture, that given a sentence, outputs a part-of-speech tag sequence. For Part of Speech classification, we trained a LSTM network by semi-supervised learning. We further applied a probabilistic approach to improve Part of Speech prediction of the LSTM network. Word2vec deep learning tool is used for learning high-quality distributed vector representations of words that capture a large number of precise word relationships. By using a small amount of labeled dataset of words and applying cosine similarity we assign Part of Speech tags to words. If some words don't show nearness to any word in labeled dataset, then we apply probabilistic approach for classification of unclassified words of input sentence. This hybrid neural Part of Speech classifier is evaluated over a corpus of 1000 sentences having a total of 7, 400 words;part of speech classification system achieved the accuracy of 94.71 %. © 2018 IEEE.

关键词： Long short-term memory

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7页 << < 1 2 3 4 5 6 7 > >>

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：