This course can also be taken for academic credit as ECEA 5318, part of CU Boulder’s Master of Science in Electrical Engineering degree. The final course emphasizes hands-on building of an application using real-time machine vision and multiple real-time services to synchronize the internal state of Linux with an external clock via observation. Compare actual performance to theoretical and analysis to determine scheduling jitter and to mitigate any accumulation of latency. The verification of the final project will include comparison of system timestamp logs with a large set of images which can be encoded into a video. The final report will be peer reviewed and the captured frames and video uploaded for scripted assessment. Course Learning Outcomes: ● Outcome 1: Decompose a problem and set of basic real-time requirements into software modules and Linux POSIX real-time threads ● Outcome 2: Analyze services in terms of C (execution time), T (request period), and D (deadlines for completion) to establish feasibility and margin for meeting requirements ● Outcome 3: Design and construct a solution for a native Linux system equipped with a webcam to verify and demonstrate system synchronization using machine vision processing