We just finished the implementation and testing a virtual walk around for 3 different parts of our school. The first is the small park in front of the school, the second is our lab and third is the 9th-floor roof garden of our lab building. Navigation is carried out by a Gaze-based implementation (you look at the menu item the eye movement is tracked and you are transported into that specific scene). We have left the audio on (the park sounds of people playing, lab playing Bollywood music, and the roof with the noise of wind). This is to demonstrate that audios can be switched dynamically along with the scene. While there is still a lot of issues to fix in terms parallelization for performance and lag, scaling, deployment, distribution, etc. This is cool, right?We just finished a implementation and testing of virtual walk around for 3 different parts of our school. The first scene is the small park in front of the school library, the second scene is our lab and third scene is the 9th-floor roof garden of our lab building. Navigation is carried out by a Gaze-based implementation (you look at the menu item the eye movement is tracked and you are transported into that specific scene). We have left the audio on (the park sounds of people playing, lab playing Bollywood music, and the roof with the noise of wind). This is to demonstrate that audios can be switched dynamically along with the scene. While there is still a lot of issues to fix in terms parallelization for performance and lag, scaling, deployment, distribution, etc. This is cool, right?

One of the teams in my teaching course is playing around with block chains. What do you think about the team's prototype?

Our goal of clinical VR applications has high integration requirements (integration of heart rate sensing, temperature sensing, patient head movement, post-session questionnaire, etc.). So we start playing around by building early prototypes for simpler applications like bank teller automation. A sneak peek of the past 30 odd hours of work (watch in HD). We are now trying to add intelligent conversation support for dynamic Q&A.

Electrical transformers are capital machinery and the global transformers market is expected to grow to $46.9 billion by 2020. Key drivers of the global growth are generation capacity additions to cater to increased electrification, high replacement rates in developed countries, and strict grid stability requirements owing to renewable energy. This an early prototype that enables automatic identification of requirements from a given RfQ (Request for Quotation) document for a competitive outreach.

Excessive Topic Generation (ETG) is a preprocessing framework for topic term (referred to as key term) generation, visualization, and analysis. ETG inherits the topic generation characteristics from Latent Dirichlet Allocation (LDA) with a further capability to generate word distance relationships among key terms. The results obtained from ETG analysis is further used as input for k-means for clustering and application programming interface (API) Google graph. This prototype is a link for simplified ETG application for a quick visualiztion. Click here for the extended GPU version of ETG that provides a detailed probablistic values for each iteration along with a range of additional over the top functionality for advanced users.

A prototype security solution for a tablet sharing ecosystem project called Grab-A-Tab build as an user experience design project during my doctoral degree course work.