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Decoding of LDPC Block Codes over Convolutional Codes with Channels
Gaurav Vijay, Ravindra Prakash Gupta

Abstract
Decoding of LDPC block codes over Convolutional codes with channels have been shown to be capable of achieving the same capacity-approaching performance as LDPC block codes with iterative message-passing decoding. However, for comparing block and convolutional codes tied to the implementation complexity of trellis based decoding are irrelevant for message-passing decoders. In this paper, we shows a comparison of LDPC block and convolutional codes based on several factors. In this paper the erasure channel are studied. Of special interest will be maximum distance profile (MDP) convolutional codes. These are codes which have a maximum possible column distance increase. This is shown how this strong minimum distance condition of MDP convolutional codes help us to solve error situations that maximum distance separable (MDS) block codes fail to solve. For this, two subclasses of MDP codes are defined: reverse-MDP convolutional codes and complete-MDP convolutional codes. Reverse-MDP codes have the capability to recover a maximum number of erasures using an algorithm which runs backward in time. Complete-MDP convolutional codes are both MDP and reverse-MDP codes. They are capable to recover the state of the decoder under the mildest condition. It is shown that complete-MDP convolutional codes perform in many cases better than comparable MDS block codes of the same rate over the erasure channel.
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Climate Change Effect on Tuber Crops Yield in the Tropics
Yahaya Mijiyawa, Timothy Denen Akpenpuun, Busari Rasheed Amao

Abstract
Climatic data were obtained from the National Bureau of Statistics (NBS) for a decade (2002 – 2011) while crop yield data for the same period were sourced from Kwara State Agricultural and Development Project (KWADP). Both climatic and crop yield data were analyzed using correlation analytical techniques, multiple regression and trend analysis in order to evaluate the impact of climate on the yield of the most important tuber crops in Kwara State, Nigeria viz: cassava, yam, and sweet potato. The result obtained shows that the impact of climate on yield is significant for yam and cassava at 95% probability levels, however, insignificant for sweet potato. The implication of this is that climate has a strong linear correlation with yam and cassava within the years under review. Tuber crops yield in the study area can be improved upon by supplementing rain-fed cultivation with irrigation and application of modern agricultural techniques and operations by the farmers.
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Performance Analysis and Fabrication on a Turbocharger in Two Stroke Single Cylinder Petrol Engine
Mohammad Israr, Amit Tiwari, Mahendra Labana, Anshul Gangele

Abstract
Turbochargers are used throughout the automotive industry as they can enhance the output of an internal combustion (IC) engine without the need to increase its cylinder capacity. The emphasis today is to provide a feasible engineering solution to manufacturing economics and “greener” road vehicles. It is because of these reasons that turbochargers are now becoming more and more popular in automobile applications. The new turbocharger is coupled to an air-water intercooling system to decrease the inlet air temperatures. This project analyzed the intercooling system and tested the final design in the vehicle. The results show that the cooling system Components purchased are adequate for this system. The aim of this paper is to provide are view on the techniques used in turbocharging used in two stroke single cylinder petrol engine by this to increase the engine output and reduce the exhaust emission levels. This paper is to analyze a turbocharger system in a two stroke petrol engine. The ideal turbocharger design would be smaller than the system purchased. The paper will also create speed sheets for use in calculating the necessary parameters for another turbocharger system, or to modify the current system.
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Implementation of Distributed Data Mining Jobs for Grid Environment
Jyoti, Anil Saini

Abstract
Data mining technology is not only compiled from efficient and effective algorithms, fulfilled as standalone cores. Instead, it's constituted by complex applications formulated in the non-trivial interaction among hardware and software system Components, running on large scale distributed surrounds. This final feature turns out to be both the cause and the effect of the inherently circularised nature of data, on one face, and, on the other side, of the spatiotemporal complexity that characterises a lot of DM applications. For a growing number of application areas, Distributed Data Mining (DDM) is therefore a critical technology. In this research paper, after reviewing the open problems in DDM, we describe the DM jobs on Grid environments. We'll introduce the blueprint of Knowledge Grid System.
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.:: IMPORTANT DATES ::.
Volume 5, Issue 1 
(January 2018)

Submission: 15 December 2017
Publication: January 2018

 

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