Project # 12, Smart Walker REU student: Jonathan Guilbe Graduate mentors: Sharare Zehtabian, Siavash Khodadadeh Faculty mentor(s): Dr. Turgut, Dr. Boloni.

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Project #6: Processing data from a visual IoT sensor of Fluid/Structure interactions with Machine Learning REU Students: Alexis Downing and Pete Orkweha.
Project 6: Processing data from a visual IoT sensor of fluid/structure interactions with machine learning REU Students: Pete Orkweha & Alexis Downing.
Project #6: Processing data from a visual IoT sensor of Fluid/Structure interactions with Machine Learning REU Students: Alexis Downing and Pete Orkweha.
Project 1: IoT-enabled smart mobility devices for aging and rehabilitation REU Students: Kelly Fullin & Nafisa Mostofa Graduate mentors: Sharare Zehtabian.
Project 4: IoT-enabled peer-to-peer energy trading REU Students: Eric Elliott & Nicholas Shanklin Graduate mentors: Sharare Zehtabian Faculty mentor(s):
Project 4: IoT-enabled peer-to-peer energy trading REU Students: Eric Elliott & Nicholas Shanklin Graduate mentors: Sharare Zehtabian Faculty mentor(s):
Project 1: IoT-enabled smart mobility devices for aging and rehabilitation REU Students: Kelly Fullin & Nafisa Mostofa Graduate mentors: Sharare Zehtabian.
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Project # 12, Smart Walker REU student: Jonathan Guilbe Graduate mentors: Sharare Zehtabian, Siavash Khodadadeh Faculty mentor(s): Dr. Turgut, Dr. Boloni Week 3 (May 29 – June 1, 2018) Accomplishments: Worked through the algorithm that I found last week. Implemented a check of the linear regression of a column of the depth image. Problem & Solutions The algorithm that I found was using a very different scale for their threshold parameters (their depth values cap at around 200 while ours cap at around 6000), so we need to find our own experimentally. There’s 4 different parameters we need to check that results in thousands of combinations. Since we can’t possibly find tresholds manually, I created another program that cycles through all the combinations and logs how successful they are so we can pick numbers with a high amount of correct answers and a low amount of false negatives. Plans for next week: Collect images and measurements for a data set. Run the data set through the program I created. Test how well it works in real time! NSF REU Research Experience on Internet of Things 2018