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Live Room Merger: A Real-Time Augmented Reality System for Merging Two Room Scenes

Published in VRIC, 2017

A real-time augmented reality system is built to replace the background of the user’s room (the ‘observer room’ or ‘local room’) with a 360-degree live video of another room (the ‘remote room’). The user can see the merged room captured by an RGBD camera mounted on the VR headset. A 360-degree image of the remote room is converted into a simple box-like room structure model in real time. The model is loaded into Unity and replaces the background of the observer room, with the result then displayed in a VR head-mount device.

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Scalable and Efficient Construction of Suffix Array with MapReduce and In-Memory Data Store System

Published in CoRR, 2017

Suffix Array (SA) is a cardinal data structure in many pattern matching applications, including data compression, plagiarism detection and sequence alignment. However, as the volumes of data increase abruptly, the construction of SA is not amenable to the current large-scale data processing frameworks anymore due to its intrinsic proliferation of suffixes during the construction. That is, ameliorating the performance by just adding the resources to the frameworks becomes less cost- effective, even having the severe diminishing returns. At issue now is whether we can permit SA construction to be more scalable and efficient for the everlasting accretion of data by creating a radical shift in perspective. Regarding TeraSort [1] as our baseline, we first demonstrate the fragile scalability of TeraSort and investigate what causes it through the experiments on the sequence alignment of a grouper (i.e., the SA construc- tion used in bioinformatics). As such, we propose a scheme that amalgamates the distributed key-value store system into MapReduce to leverage the in-memory queries about suffixes. Rather than handling the communication of suffixes, MapReduce is in charge of the communication of their indexes, which means better capacity for more data. It significantly abates the required disk space for constructing SA and better utilizes the memory, which in turn improves the scalability radically. We also examine the efficiency of our scheme in terms of memory and show it outperforms TeraSort. At last, our scheme can complete the pair- end sequencing and alignment with two input files without any degradation on scalability, and can accommodate the suffixes of nearly 6.7 TB in a small cluster composed of 16 nodes and Gigabit Ethernet without any compression.

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