Projects

Room Localization on Smartphones

Designing a room localization system that is practically useful in real-world environments is challenging. First, due to the complex multi-path propagation problem,Wi-Fi signals obtained by smartphones are dynamic and noisy. Such noise obscures the unique relationship between Wi-Fi signals and individual rooms. Second, existing room localization methods require labor-intensive manual annotation of individual rooms. The process is time-consuming and expensive, which is a key limitation of existing room localization applications. Third, knowledge of indoor floorplans is often required by room localization application. However, indoor floorplans are either unavailable or obtaining them requires slow, tedious, and error-prone manual labor. In addition, the overhead of room localization, e.g., energy consumption, imposed on personal smartphones must be low. In this project, we are working on a novel indoor room localization system to tackle above challenges.

Android Sensing Library

source code http://code.google.com/p/android-sensing-lib/

Mobile System for Socially-Collaborative Environmental Monitoring

We are developing a Mobile Air Quality Sensing system, MAQS for short, that allows individuals to continuously and inexpensively measure the air quality they are exposed to, as well as giving them a framework to understand what the measurements indicate, and a place to discuss and explore their finding with fellow users. The MAQS system consists of wearable personal monitors, M-Pods, an Android-based mobile phone app (MAQS App) , and an online social network (MAQS Network). The M-Pods send readings over Bluetooth to the user's smart phone running the MAQS app. The app then allows the user to view his or her personal air quality measurements in real time, offers some simple analysis tools, and uploads the data to a server whenever the smart phone is connected to a WiFi network. The server's database can then be accessed via a web interface, which takes the form of an online social network, so a user can query measurement data and communicate with other users.

Trip Detection and Monitoring on Mobile Device

The proliferation of mobile devices such as cellphones makes it possible to understand users' locations and daily trips more easily and provide information services in context. In this project, we focus on trip and location detection, which is a fundamental issue in location based services (LBS). We are working on algorithms of automatic, accurate, timely, and energy-efficient trip/location detection on mobile devices.

Opensource Python API for Google Maps

source code http://code.google.com/p/pygmaps/

the Python Package Index http://pypi.python.org/pypi/pygmaps/0.1.1

Mobile Sensing and Driving Behavior Analysis for Emerging Electric-Drive Vehicles

Graph-based Social Network Analysis and Social-based Information Filtering

Python Web Crawler

source code http://code.google.com/p/python-crawler/

the Python Package Index http://pypi.python.org/pypi/crawler/0.1.1