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Side swipe accidents occur primarily when drivers attempt an improper lane change, drift out of lane, or the vehicle loses lateral traction. Past studies of lane change detection have relied on vehicular data, such as steering angle, velocity, and acceleration. In this paper, we use three physiological signals from the driver to detect lane changes before the event actually occurs. These are the electrocardiogram (ECG), galvanic skin response (GSR), and respiration rate (RR) and were determined, in prior studies, to best reflect a driver's response to the driving environment. A novel system is proposed which uses a Granger causality test for feature selection and a neural network for classification. Test results showed that for 30 lane change events and 60 non lane change events in on-the-road driving, a true positive rate of 70% and a false positive rate of 10% was obtained.

In this paper is a description of a simple virtual reality (VR) - controlled atmosphere brazing (CAB) furnace created using the virtual reality technology based on the physics of a simple thermal model. Within this VR-CAB furnace, engineers ‘walk’ through the furnace, and ‘observe’ the heating process inside the furnace. This VR-CAB furnace provides the framework which will ultimately allow engineers to control and improve the heat exchange processes in a realistically simulated environment, and study the effects of various process parameters that control product yield.