John C. Linford > Research Projects

Powered by CMSimple


Research Projects

Doctoral Research

I earned my Ph.D. as a member of the Computational Science Laboratory in the Department of Computer Science at Virginia Tech. I applied emerging heterogeneous and homogeneous multi-core chipsets to high-performance computing problems.  Read my dissertation if you'd like to know more.

Most of my work concerned large-scale atmospheric models, such as WRF, STEM, and CMAQ. The computer systems I frequently use include the IBM BlueGene and IBM BladeCenter installations at Forschungszentrum Jülich, the Apple-based System X supercomputer and PlayStation 3 clusters at Virginia Tech.

I was a member of the Air VT air quality research group led by Dr. Linsey Marr of Civil and Environmental Engineering from 2005 - 2007. Air VT conducts over half of Virginia Tech's on-going air quality projects, including research on nano particles in the atmosphere, measuring air pollutant emissions, and modeling current air quality on the urban and regional scales. My course of study is expected to conclude in May 2010.

Urban and Regional Scale Air Quality Modeling

Authors: John C. Linford and Linsey Marr.

One-third of the country's population lives in areas with unhealthy ambient ozone levels, and control strategies aimed at reducing precursor emissions of volatile organic compounds and nitrogen oxides have not always succeeded. Photochemical air quality models can be used to test the effects of projected emissions reductions. Recent research has focused on the influence of gaseous motor vehicle emissions on ozone formation, in particular, why ambient ozone tends to be higher on weekends than on weekdays in many urban areas. Investigation of this phenomenon using a three-dimensional photochemical model has shown that higher weekend ozone is due mainly to the decrease in emissions associated with reduced diesel truck traffic on weekends. Models can be used not only as a policymaking tool but also to improve the understanding of the emissions, chemistry, and meteorology used to drive them.

Sustainable Mobility Learning Laboratory

Authors: John C. Linford, Lisa Shires, Doug Nelson, Hesham Rahka, Wallace Allen, and Linsey Marr.
The Sustainable Mobility Learning Laboratory is an online laboratory for college freshmen and high school seniors. Visitors can design their own automobile and test it on a virtual race track to see the overall impact of their design on the environment. Results are generated in real-time by scaling data generated by EPA-approved air quality models.
The lab also contains educational modules which discuss the human impact on the environment and forms of sustainable mobility. The laboratory was presented at the Virginia Tech Dean's Energy Conference, 2006. Go to the lab.

Using Physiological Features to Quantify Benefits of Immersion (Class Project)

VR Research Authors: John C. Linford, Ricky Castles, Andrew Love
Abstract: In this paper we detail an experiment which uses physiological features of users(heart rate and skin conductance) in virtual environments to quantify the benefits of two components of immersion: passive haptics and stereo rendering. Our findings show that physiological features are a statistically significant method of determining the effect of an immersive component on a user's stress level.

Throughout the experiment both heart rate and skin conductivity were measured. Heart rate was measured using the Polar T31 chest strap. Skin conductivity, also known as the galvanic skin response (GSR), was measured using a simple, homebuilt circuit.Data was sampled using Measurement Computing's USB-1208FS, a USB-based data acquisition module. During the study, participants wore a small backpack that contained the data acquisition module, the wireless heart rate receiver, and an analog signal buffering circuit. Subjects described the physiological equipment as less distracting than the HMD and tracking system.

We decided to use a stressful virtual environment in order to evoke an easily-measured physiological response. We chose a "pit environment" because such environments have been shown to elicit strong user responses, even after multiple exposures. The Effective Virtual Environments research group at the University of North Carolina at Chapel Hill provided us with an older copy of their pit environment model, which we adapted to match our needs and our physical environment. The underlying environment software and event handlers were written in C using the SVE toolkit version 2.1.Our pit environment consists of a nondescript 10'x10' "training room" and a 12'x12'"pit room," separated by a wooden door. Gravity does not apply to the user in the environment; if he steps off the walkway he will not fall. Download the paper.