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In that project, the focus was on interaction of mobile Internet Tablet devices with other household electronics, such as TVs and the like, as well as the exploration of a time-centric desktop computing paradigm. However, the icecast-based broadcasting architecture there is similar (albeit far more primitive, lacking security features, and restricted to only local devices as targets) to what I aim to implement here. Having learned more about the constraints and architecture of mass-video broadcasting over Icecast, I believe I now have the necessary knowledge to deliver a production-ready implementation of the described multimedia-broadcasting activity.
 
In that project, the focus was on interaction of mobile Internet Tablet devices with other household electronics, such as TVs and the like, as well as the exploration of a time-centric desktop computing paradigm. However, the icecast-based broadcasting architecture there is similar (albeit far more primitive, lacking security features, and restricted to only local devices as targets) to what I aim to implement here. Having learned more about the constraints and architecture of mass-video broadcasting over Icecast, I believe I now have the necessary knowledge to deliver a production-ready implementation of the described multimedia-broadcasting activity.
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In addition, I have also worked with video APIs (primarily FFmpeg's libavcodec/libavformat, and OpenCV, though I have used GStreamer on another project) as part of my current undergaduate research project focused on emotion recognition based on facial and speech features, so I am familiar with how to perform real-time video and audio capture and addressing the usual issues that show up in terms of laggy capture performance or locking threads; in this case this should be even simpler as no data processing must be done.
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In addition, I have also worked with video APIs (primarily FFmpeg's libavcodec/libavformat, and OpenCV, though I have used GStreamer on another project) as part of my current undergaduate research project (unfortunately currently proprietary) focused on emotion recognition based on facial and speech features, so I am familiar with how to perform real-time video and audio capture and addressing the usual issues that show up in terms of laggy capture performance or locking threads; in this case this should be even simpler as no data processing must be done.
    
Regarding development pace, I will have no other commitments this summer so I will be able to spend my full time on development.
 
Regarding development pace, I will have no other commitments this summer so I will be able to spend my full time on development.
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