A Hybrid Spatial Indexing Structure of Massive Point Cloud Based on Octree and 3D R*-Tree

The spatial index structure is one of the most important research topics for organizing and managing massive 3D Point Cloud. As a point in Point Cloud consists of Cartesian coordinates (x,y,z), the common method to explore geometric information and features is nearest neighbor searching. An efficient spatial indexing structure directly affects the speed of the nearest neighbor search. Octree and kd-tree are the most used for Point Cloud data. However, Octree or KD-tree do not perform best in nearest neighbor searching. A highly balanced tree, 3D R*-tree is considered the most effective method so far. So, a hybrid spatial indexing structure is proposed based on Octree and 3D R*-tree. In this paper, we discussed how thresholds influence the performance of nearest neighbor searching and constructing the tree. Finally, an adaptive way method adopted to set thresholds. Furthermore, we obtained a better performance in tree construction and nearest neighbor searching than Octree and 3D R*-tree.

Higher Payload Capacity in DNA Steganography using Balanced Tree Data Structure

In recent years, with the huge expansion of internet and communication technology, the data transmission rate has increased exponentially. The threat of unauthorized penetration to the secret messages during transmission has become a major concern for data integrity. Cryptography and Steganography are two well-known techniques used to secure and hide confidential data from the intruders. Cryptography is used to obscure the secret message whereas Steganography embeds the messages into a cover media and conceals the presence of secret information. In DNA steganography, DNA molecular sequence is used as a cover medium. In the field of steganography, Payload capacity is the measurement of hiding intended messages in cover media. The Capacity of hiding messages in cover media is one of the prime challenges in the field of steganography. The principal study of this research is to provide a new framework using DNA steganography that provides a higher payload capacity. We have used balanced tree data structures for message encoding where the leaf node contains the intended message. This unique process of message encoding and decoding guarantees a payload capacity of ≥ 0.50.

Parallelize DSW Algorithm

In this project, we propose a parallelized DSW algorithm, useful to periodically rebalance an arbitrary unbalanced BST. The proposed algorithm splits the tree into several subtrees and concurrently do ``Tree-to-Vine'' to these subtrees using multiple threads, and finally connects them into a complete vine; It also splits the vine into several semi-equal sized subvines and do ``Vine-to-Tree'' to these subvines using multiple threads, and combines them into a complete balanced tree. Our extensive experiments including BST generation, ``Tree-to-Vine'', and ``Vine-to-Tree'' parallelization comparisons show that this parallelized DSW algorithm performs much better compared to its counterpart sequential DSW algorithm.

A Novel Index Method for K Nearest Object Query over Time-Dependent Road Networks

Knearest neighbor (kNN) search is an important problem in location-based services(LBS) and has been well studied on static road networks. However, in real world, road networks are often time-dependent; i.e., the time for traveling through a road always changes over time. Most existing methods forkNN query build various indexes maintaining the shortest distances for some pairs of vertices on static road networks. Unfortunately, these methods cannot be used for the time-dependent road networks because the shortest distances always change over time. To address the problem ofkNN query on time-dependent road networks, we propose a novel voronoi-based index in this paper. Furthermore, we propose a novel balanced tree, namedV-tree, which is a secondary level index on voronoi-based index to make our querying algorithm more efficient. Moreover, we propose an algorithm for preprocessing time-dependent road networks such that the waiting time is not necessary to be considered. We confirm the efficiency of our method through experiments on real-life datasets.