Abstract

Robot-based Evaluation of Bluetooth Fingerprinting, Khuong Nguyen

Indoor tracking is an important topic for localisation. In this project, we implement a new, efficient real-time system to locate an indoor object based on Bluetooth signal and fingerprinting technique. We especially tackle the hassle of collecting fingerprinting data by designing a robot to automate the whole process. Our robot can perform complex, time-consuming data collection with great accuracy, thus opening the door for various un-seen experiments. The project has three main goals:  We build a robot to collect data;  We provide an in-depth analysis of the Bluetooth characteristic, by answering research questions; We implement a complete working Indoor Bluetooth tracking system for the Meeting room.

Applying Language Models to Language Learning, Duncan Roberts

Learn! is a popular flash-card application for Android-based mobile phones covering everything from US Navy core values to English vocabulary. But flash-cards are limited for language learning. With LearnGrammar!, teachers create language learning tasks by providing a small number of sentences that exemplify some grammatical point. The application generalises from the example set, providing students with many further examples and counter-examples to choose from in a multiple-choice question format.

Spatial indexing optimising of indoor localisation, Yordan Zaykov

Indoor tracking is an important topic for localisation. Recent work has made significant progress in this area, but real-time localisation remains an open research problem. In this project we make a step forward in this direction by using a precomputed spatial index of the building. We achieve a 14% speedup in the localisation procedure and also reduce the precomputing stage to 1/10 of the second using GPU programming. Current work on the project concentrates on evaluating the trade-offs between reduced index size and decreased performance.

TCP Performance and The Androids Sockets API , James Snee

Recent research into the power implications of the socket setup options provided by the Android API has shown strange affects occurring under different configurations. In an attempt to understand these affects a new experimental environment was built to measure and model the performance of these socket options. These models provide not only a deeper understanding of the implications of socket setup in the Android Dalvik virtual machine but can help developers build faster more efficient networked applications.