Muhammed Elsayed

Windows Azure - IIS media services team (Nimbus).

Worked on creating a Framework for validating the output of the SmoothStreaming encoders used by Microsoft media services.

Smooth Streaming is the productized version of technology first used by Microsoft to deliver on-demand video of the 2008 Summer Olympics for NBCOlympics.com. By dynamically monitoring local bandwidth and video rendering performance, Smooth Streaming optimizes content playback by switching video quality in real-time.

Smooth Streaming provides media companies with a better way to make full HD on the web a reality. It offers code-free deployment and simplified content management for content creators and content delivery networks. For content viewers, the improved video viewing experience brings the reliability and quality of HD television to their favorite video websites.

For more information please check this:
http://www.iis.net/

Worked on a project called PaveMyDrive supervised by Microsoft Research Project Hawaii during Mobile Computing Course I attended at Alexandria University in cooperation with E-Just (Egypt-Japan University of Science and Technology).

For more information about the project please check this:
https://sites.google.com/site/pavemydrive2012/home

Check-in authentication system.Generation and matching for fingerprinting a logical location based on ambient sound, light, color, and human movement and NFC tags leading to creating location-authentication system.

Intafeen project.(intafeen.com): the first Arab mobile location-based social networking game that enables you to socialize and compete with your friends based on your location.
I worked on the Check-in authentication system by reading NFC tags carrying information about the location and generating unique fingerprint for each location using mobile sensors and NFC (Near Field Communication) in addition to GSM/Wi-Fi/GPS.

Wathaek project . (Intelligent Document Server with Classifying and Clustering capabilities).I worked on indexing, searching and updating documents and then classifying and clustering features using NLP, Machine Learning, clustering and classifying algorithms practicing Agile Development process.