Improving Big Data Visual Analytics with Interactive Virtual Reality

For decades, the growth and volume of digital data collection has made it challenging to digest large volumes of information and extract underlying structure. Coined 'Big Data', massive amounts of information has quite often been gathered inconsistently (e.g from many sources, of various forms, at different rates, etc.). These factors impede the practices of not only processing data, but also analyzing and displaying it in an efficient manner to the user. Many efforts have been completed in the data mining and visual analytics community to create effective ways to further improve analysis and achieve the knowledge desired for better understanding. Our approach for improved big data visual analytics is two-fold, focusing on both visualization and interaction. Given geo-tagged information, we are exploring the benefits of visualizing datasets in the original geospatial domain by utilizing a virtual reality platform. After running proven analytics on the data, we intend to represent the information in a more realistic 3D setting, where analysts can achieve an enhanced situational awareness and rely on familiar perceptions to draw in-depth conclusions on the dataset. In addition, developing a human-computer interface that responds to natural user actions and inputs creates a more intuitive environment. Tasks can be performed to manipulate the dataset and allow users to dive deeper upon request, adhering to desired demands and intentions. Due to the volume and popularity of social media, we developed a 3D tool visualizing Twitter on MIT's campus for analysis. Utilizing emerging technologies of today to create a fully immersive tool that promotes visualization and interaction can help ease the process of understanding and representing big data.Comment: 6 pages, 8 figures, 2015 IEEE High Performance Extreme Computing Conference (HPEC '15); corrected typo

Laser space rendezvous and docking tradeoff

A spaceborne laser radar (LADAR) was configured to meet the requirements for rendezvous and docking with a cooperative object in synchronous orbit. The LADAR, configurated using existing pulsed CO2 laser technology and a 1980 system technology baseline, is well suited for the envisioned space tug missions. The performance of a family of candidate LADARS was analyzed. Tradeoff studies as a function of size, weight, and power consumption were carried out for maximum ranges of 50, 100, 200, and 300 nautical miles. The investigation supports the original contention that a rendezvous and docking LADAR can be constructed to offer a cost effective and reliable solution to the envisioned space missions. In fact, the CO2 ladar system offers distinct advantages over other candidate systems

Point cloud management techniques for a multihit ladar imaging camera system

Lidar imaging is a powerful measurement technique where a laser pulse is shone onto an object and the beam reflected back is recovered at some solid-state detector. The time elapsed is counted so an automated measurement of the distance to the target is obtained, without any further calculation. The concept is also referred to as ladar or time of-flight imaging. Different scanning mechanisms have been proposed to recover complete 3D images out of this pointwise approach. Most popular recent applications involve landing aids, object recognition, self-guided vehicles and safeWith the incorporation of optical sensors into the machine vision technology, a full new field has emerged to revolutionize different technologies such as self-driving, 3D scanners and printers or virtual reality. However, new technologies come with new techniques and methodologies to manipulate them. Point Clouds were born as the data storage system and a collection of challenges came with them. One of these challenges consists in processing them in order to obtain the best description of the real world. Hence, it is necessary to have a tool to evaluate the quality of those Point Cloud in order to analyze their quality. In this MSc thesis we developed a mathematical approach for Point Cloud quality evaluation and implanted by Matlab. The full mathematical development as well as the structure of the code and the different tools used to acquire and manipulate Point Clouds are described and introduced along the thesis. A final analysis of the methodology showed there is still a lot of work to do. Several questions appeared and need to be solved in order to grow in this field

A Statistical Approach to Fusing 2-D and 3-D LADAR Systems

LADAR (LAser Detection and Ranging) systems can be used to provide 2-D and 3-D images of scenes. Generally, 2-D images possess superior spatial resolution but without range data due to the density of their focal plane arrays. A 3-D LADAR system can produce range to target data at each pixel, but lacks the 2-D system\u27s superior spatial resolution. The 3-D system is limited by its hardware, specifically its imaging array. Currently developers are investigating ways to change the pixel size in the 3-D LADAR imaging array, but the costs of this research is quite expensive and technically robust. It is the goal of this work to develop an algorithm using an Expectation Maximization approach to estimate both 3-D LADAR range and the bias associated with a 3-D LADAR system. The algorithm developed demonstrates both spatial and range resolution improvement over standard interpolation techniques using both real and simulated 3-D and 2-D LADAR data