Category: CSE projects Topics

Introduction to An Object-Oriented Graphics Engine CSE Latest Project:

An Object-Oriented Graphics Engine CSE Project is about the graphic engine which is object-oriented. Most of the users focus on the quality output and also the performance in the implementation of the graphics engine system. In this paper we have implemented object-oriented graphics system. And also the architecture of the system along with the modules is also presented. It has experimentally proved that this system provides high stability and also speed.

The paper provides the implementation of the graphics engine of 3D i.e. Gingko is given. The experiment which is conducted says that the Gingko is capable of supporting the extendable architecture and also provides the efficiency in the method.

The architecture of the Gingko includes four layers where Encapsulation layer is capable of encapsulating the graphical interfaces. The Core layer is used for implementing the main framework and also the management of the entire system. The other layer Extension provides more functions related to GUI. And the last layer of user interface is capable of providing the common API to all its users.

The main goal of this implementation of the algorithm is for providing the convenient services to all its developers and also in reduction in terms of the cost and also the time. And even the programmers are capable of developing the algorithms by their own with the help of the plug-in system. The performance of the graphics engine depends on the frame rate. In this paper for reducing the cost many experiments were also conducted. 

We can conclude that the Gingko is capable of supporting the extendable architecture and also provides the efficiency in the method.

An Efficient Image Processing Method Based on Web Services for Mobile Devices Project is about an efficient method for the processing of the image in case of the mobile devices. The present system includes the limitations in the resources which results in the degradation of the image processing system. In the existing system the computing model is centralized and implementation is difficult in the mobile devices. 

The solution is given to the above problem with the implementation of the image processing with the help of the web services. Also the processing tasks are shared among the service providers also the registry including the service requesters. 

The web based services are capable of efficient utilization of the resources of the mobile devices and the image processing tasks are distributed accordingly between them. This web based services are more efficient when compared to that of the traditional system for processing of the image. It has a lot of advantages in terms of coupling and also resources utilization in case of the heterogeneous network. 

The experiments which were conducted on the web based services show that there is an increase of around 30% in terms of the memory usage and also the response time was reduced to around 25%.  The processing of the web based image system includes three layers. 

Hence we can conclude that the web based services of image processing system is capable of overcoming the limitations of the existing traditional systems for the processing of the image.

In the existing system the computing model is centralized and implementation is difficult in the mobile devices. The web based services dominate the traditional system in terms of the memory usage and also the response time.

The CSE Project is about clustering. In the process of the cluster analysis the samples are grouped based on the similar characteristics according to the PCA scores. The ANOVA is used to compare dimensions in corresponding clusters between PCA and FA. 

PCA is a procedure that transforms highly correlated variables to a smaller number of principal components there after cluster analysis is used to divide the dimensions of the samples into three types. 

K-means clustering aims at the optimization to minimize the distance of each sample from the centre of the cluster to which the sample belongs. But the disadvantage of k-means clustering is that the number of the clusters should be specified before. Efforts have been made to find an automatic strategy to determine the number of clusters. The cluster analysis is based on the factor scores of the two key factors, i.e., height factor and girth factor. Then samples are then made into three clusters. The first cluster and the third cluster are overlapping at points of height dimensions. 

The popular methods we are using are principal component analysis (PCA) and Factor analysis (FA) though both of them are capable of reducing the dimensions of variances in sample the such that differences exists between them where PCA analyses all variance present in the data set, while FA analysis only common variances. 

We can conclude that cluster analysis plays a vital role and cluster analysis was developed to divide samples into groups with homogenous characteristics according to the PCA scores. And the ANOVA is used to compare dimensions in corresponding clusters between PCA and FA.