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Dhole, Avinash
- Download Time Estimation and Reduction
Authors
1 Computer Science & Engineering, Raipur Institute of Technology, Raipur, IN
2 Department of C.S.E., Raipur Institute of Technology, Raipur, IN
Source
Research Journal of Engineering and Technology, Vol 4, No 1 (2013), Pagination: 36-39Abstract
Increasing use of internet results the network traffic and slow data transfer speed. Because every file, data is transferred from one computer to another computer through internet the user need a consistent speed for uploading the data as well as download speed. Upload can be defined as the process of transferring data from client computer to server and downloading can be defined as the processes of transferring from server to client computer. Bandwidth can be a considered as the major factor affecting the download time. Bandwidth can be defined as the maximum amount of information a communication channel can carry. When working on the download speed for a file user need an estimator tool for prediction. This paper presents the basic idea of download process and some mathematical prediction methods such as Regression Analysis for download time estimation. It also covers the factors which can affect the download process and total download time. This paper proposes a framework for regression application for estimating the download time. After Estimation of download time the technique for download time reduction is required. In a network the file is shared by using the concept of Mirror servers. Download Time can be reduced by selecting the best mirror server or selecting the suitable set of mirror servers. Here for selection of mirror servers we consider the major criteria among all the Qos is bandwidth and file size.Keywords
Bandwidth, Regression, Linear, Non Linear, MIME , Mirror Servers, Download Time.
References
- Raj guru Abhijit, Dr D.B. Kulkarni ,”File Download Delay Reduction Through Parallelization “Second International Conference on Emerging Trends in Engineering and Technology, ICETET-09
- Yuanjia n Xing, Zhi Yang, Chi Chen, Jilong Xue, and Ya fe i Dai Department of Computer Science and Technology Peking University, China {xyj, yangzhi, chenchi, x jl, dyf}@n et.pku.edu.cn., “On the QoS of Offline Download in Retrieving Peer-side File Resource” 2011 International Conference on Parallel Processing
- L.Guo,S.Chen,Z.Xiao,E.Tan,X.Ding and X.Z hang, “Measurements, analysis and Modelling of Bit Torrent-like systems” in IMC ’05.
- http://www.cs.gmu.edu/~menasce/cs700/files/SimpleRegression.pdf
- Applied Regression Analysis.pdf
- Natural Language Words Analysis for Affective Scene Generation from Written Text Using Artificial Neural Network
Authors
1 Raipur Institute of Technology, Madir Hasod, Raipur, IN
2 Pt. Ravishankar Shukla University, Raipur (C.G.), IN
Source
Research Journal of Engineering and Technology, Vol 3, No 1 (2012), Pagination: 1-5Abstract
This paper presents an artificial neural network approach to word analysis to generate the 3 dimension scene or image from the textual description. We start with the recognition of characters and then form the words from these characters. The words used in natural language will have some special meaning and gives some information. Each word represents some inherited properties of some of the objects. The properties of each word will depend on the object being used in the sentence. Therefore the word itself gives lots of information about the objects. The neural network approach to lexical classifications is the first step to find the objects and its properties. The next step is neural network based approach to word classification is extracting words attribute and then relating it with the other words using artificial neural network. The multilayer feedforward neural network will be used. Here we will analyze the different parts of the speech with their inherit properties which the word have in the sentence.
A central issue in cognitive neuroscience today concerns how distributed neural networks in the brain that are used in language learning and processing can be involved in non-linguistic cognitive sequence learning. This issue is informed by a wealth of functional neurophysiology studies of sentence comprehension, along with a number of recent studies that examined the brain processes involved in learning non-linguistic sequences, or artificial grammar learning (AGL). The current research attempts to reconcile these data with several current neurophysiologically based models of sentence processing, through the specification of a neural network model whose architecture is constrained by the known cortico-striato- thalamo-cortical (CSTC) neuroanatomy of the human language system. The challenge is to develop simulation models that take into account constraints both from neuranatomical connectivity, and from functional imaging data, and that can actually learn and perform the same kind of language and artificial syntax tasks. Thus different distributed neural networks will be trained and integrated in such a way that it understands the language as being understand by the human being.
Keywords
Part of Speech, Neural Network, Cognitive Neuroscience, Computational Linguistic, Perception Network.- An Effective Method for Transferring Color to Gray Scale Image Using Luminance Matching without Human Intervention
Authors
1 Department of Computer Science and Engineering, Raipur Institute of Technology, Raipur (C.G.) 492101, IN