Head-mounted eyegaze tracking allows experimenters to record the eye movements of a wearer while he interacts with a real situated environment. Analysis of the eye movements however, is difficult since motion of the wearer’s head causes objects to move relative to the head-mounted video camera. The focus of this project is to implement object recognition and motion tracking in video recorded from the head-mounted camera. Motion in the recorded environment will be matched to user’s eye movements to determine which particular object a person is looking at.
The primary goal of the proposed internship project is to develop advanced 3D visualization methods and techniques for displaying transmission lines and other components of a power system in relation to the land cover and elevation maps. A software application will be developed using modern visualization approaches and proposed algorithms. The new system will be designed in such a way that it could be easily integrated with the current energy management systems (EMS/SCADA) and assets management databases, as well as with any other required software applications used by the project sponsor.
Currently users find their desired song on a multimedia sharing website such as YouTube using text based queries. The search phrase usually contains some text information about the song such as the name of the song, the singer, or parts of the lyrics. However, this information can be easily manipulated by malicious file up-loaders, who can use this scheme to upload pirated assets. This problem can be eliminated by letting users search based on the melody of the song rather than using textual information.
The intern will be porting and extending a tool for detecting and classifying qualities of human movement which is then subsequently fed to a realtime generative visualization system as part of an artistic process. The classification scheme is based on Laban Effort qualities. The system uses accelerometers and a neural network to recognize and differentiate qualities of movement by a performer.
The exponential growth in high-bandwidth applications and devices used in backbone networks has been accompanied by a corresponding increase in power consumption, and there is a growing recognition of the need to be more energy efficient .
Brain to machine interface (BMI) is a research topic aiming to develop more direct interface between a human brain and a machine. The research is primarily motivated by desire to help humans who are in need of assistance or repair of their
Software testing and debugging take up between 30 and 50% of the development cost in embedded systems. Despite this large percentage and the associated enormous costs, only little attention has been devoted to debugging of embedded real-time systems. Apart from in-circuit emulators for standalone systems, ad-hoc methods such as blinking lights to indicate errors and morsing error codes via beepers are still widespread debugging methods.