Clarity Worlds is a next generation presentation software based on ideas of 3D, continuity and interactivity. We are looking forward to integrating VR into our environment, which will allow user interaction with 3D objects in VR, and, potentially, multi-user presentations in virtual spaces. The project will be based on integrating existing OpenGL 3/4 rendering pipeline with OpenVR library and introducing new integration primitives. The programming is being done in C#.
Resistive memory is a new technology based on a passive circuit element called Memristor, which changes its resistance value based on the current flowing through it. Memristors are nanoscale elements that can be easily integrated with a typical VLSI manufacturing process. Therefore, memristors can be combined with existing structures to create new circuits. Memristors have a list of unique properties, such as non-volatility, non-linearity, and sensitivity to process that make them particularly attractive for security applications. One such application is a memristive hardware secure hash function, based on a memristor crossbar structure. This application uses differential read and requires high accuracy in the read path. In this project, the students will design a high precision differential sense amplifier required for an accurate read from the array based on the state-of-the-art technology.
At the first stage, the students will study the literature related to high precision differential current sense amplifiers.
One of the security field related problems is a person re-identification via security cameras. In general, an object re-identification and in particular a person re-identification process faces a few challenges such as low resolution cameras and illumination changes. Therefore, a unique algorithm which overcomes those difficulties was developed.
In my work I have implemented this algorithm with C++ opencv and developed an android application which uses it.
With the advent of the Internet it is now possible to collect hundreds of millions of images. These images come with varying degrees of label information. Semi-supervised learning can be used for combining these different label sources. However, it scales polynomially with the number of images. In this work we utilize recent results in machine learning to obtain highly efficient approximations for semi-supervised learning that are linear in the number of images. This enables us to apply semi-supervised learning to a large database of images gathered from the Internet.
In our project we have implemented one of the new and the promising algorithms that can be found in the computer science community.
This Algorithm is taken from the SSL (Semi Supervised Learning) field. It uses a small amount of classified data to classify a large data base effectively.