Its official! I’ve graduated. Man, what an experience. This has been one of the most rewarding decisions of my life. Its hard to imagine what will happen next. I have day-dreamed and focused on grad school for so long that I didn’t spend much effort looking past this moment.
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Tag: CMU
American Sign Language Recognition System
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This project was part of my Carnegie Mellon University graduate coursework. The project spanned two courses: Computer Vision and Machine Learning. I worked in a team with Justin Farrell and Matt Eicholtz. Our Matlab code is available on my GitHub page. Feel free to use it and take what you need. If you do we only ask that you reference our efforts (and the effort of those who we relied on). You can get some more information from our poster.
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Poster |
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GitHub repository |
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Paper (for Machine Learning course) |
Machining and Anodizing
To use the CMU machine shop full time, I had to take a small course on machining. The class was straight forward, and I knew about 75% of what was taught. I did learn a lot though and picked up some new tricks. We learned the main concepts through two small projects, a pencil holder and a plumb-bob, performing the cutting operations ourselves. Once the skill was demonstrated, we moved on, so most people didn’t finish the projects. However, I spent a little time after class putting the finishing touches on the parts. I’ve machined a million parts for previous jobs but never had the opportunity to display them. While these two parts are pretty basic and not a good indicator of my skill level, I thought it would be fun to “do it right”.
CMU Mechatronics Project: Multi-shot Cannon
The challenge
Design and build a machine that fires Nerf foam balls and hits static and moving targets with great accuracy and speed.
The team
Four engineers joined forces to accept the challenge.
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Justin Farrell (firing), Melvin Rayappa (vision, coding), Jason Atwood (system integration), and Rachel Jackson (loading, aiming)
The machine
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Figure 1. Depiction of overall mechatronic system with key components highlighted.
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