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Shekhar, Aishwarya
- Effectiveness of Kd- Tree in Ray Tracing of Dynamic Point Clouds
Authors
1 School of Computing and Information Technology, Jaipur, IN
Source
Networking and Communication Engineering, Vol 7, No 4 (2015), Pagination: 133-137Abstract
A kd-tree is basically defined as a data structure which is mainly used for accumulating a fixed set of points in a k-dimensional space. Kd-tree is predominantly a binary tree. Kd-tree is attracting an increasing interest in recent decades for ray tracing due to its advantages in generating detailed images based on natural physical behaviors, flexibility, and ease of coding. Both point-based representations and ray tracing provide means to efficiently show very complicated 3D models. Computational effectiveness has been the main focus of past work on ray tracing point-sampled surfaces. Kd-tree solves the complication in searching nearest neighbors of a given point in k dimensional space. Kd-trees are also used in pattern recognition and machine learning. Nearest neighbor search algorithm on kd-trees can be slightly improved to allow incremental nearest neighbor search. Kd-tree was invented by john Louis Bentley and it is an abstraction of a binary search tree. It is mainly used in a large amount of graphics applications, inclusive of nearest neighbor search in point cloud (Set of data point) modeling. Kd-tree is also used for accumulating a group of points in the Cartesian co-ordinate plane means in 3-D space. Kd-tree establishment can also be cast-off for dynamic point clouds for accelerating the nearest neighbor queries. Kd-trees are mainly used for Nearest neighbor searching, Query processing in sensor network, Database searching using multiple keys, Fingerprint matching and Optimization ray tracing.
Keywords
Kd-Tree, Ray Tracing, Point Cloud, Splitting Hyperplane.
- A Very Robust Dedicated and Verified Technique by Applying the Hybridity of Cloud for Deduplication of Data
Authors
1 Manipal University, Jaipur, IN
2 Manipal University at Jaipur, IN
Source
Networking and Communication Engineering, Vol 7, No 3 (2015), Pagination: 109-113Abstract
This paper presents a review study on the data deduplication or single instancing originally refers to the removal of needless data. In this process, duplicate data is expunged, leaving only one copy means single instance of the data to be accumulated. Though, indexing of each and every data is still maintained. Data deduplication is an approach for minimizing the part of storage space an organization required to retain its data. In most of the company, the storage systems carry identical copies of numerous pieces of data. Deduplication terminates these additional copies by saving just one copy of the data and exchanging the other copies with pointers that assist back to the primary copy. To ignore this duplication of the data and to preserve the confidentiality in the cloud here we are applying the concept of hybrid nature of cloud. A hybrid cloud is a fusion of minimally one public and private cloud. To secure the confidentiality of sensitive data while encouraging deduplication, the convergent encryption technique must be used to encrypt the data before expanding. For better shielding the data security, this paper makes an attempt to formally address the problem of data deduplication.
Keywords
Confidentiality, Deduplication, Data Compression, Hybridity of Cloud.- Monitoring Aspects of Cloud Over the Big Data Analytics Using the Hadoop for Managing Short Files
Authors
1 Banasthali Vidyapith, Tonk, Rajasthan, IN
2 Nic, Delhi, IN
3 Manipal University, Jaipur, IN