Projects are available for BMES members with paid membership to participate in to develop skills inside and outside of the classroom and have a hands-on chance to apply what they've learned onto real-life and professional applications.

Projects This Year

TBA, check back as it gets closer to the school year!

Past Projects

2018 - 2019
Fall Quarter
COMSOL Learning Session:
​Designed to be an introductory course for those who are interested in learning about the fundamentals of COMSOL. This entails both solid and fluid mechanics within the COMSOL software, as well as how to analyze results within the software.

​Slides and files used can be found here.
Winter Quarter
MATLAB Learning Session:
Topics covered include image processing, signal processing, and more.

Slides and files used can be found here.
Spring Quarter
Senior Design Tips and Prep Workshop:
This workshop is designed to help underclassmen get an idea as to what to get ready for senior design.
2017 - 2018
​Electromyographically Controlled Servo Motor:
​This project consists of using surface electrodes along with the concept of Electromyography (EMG) to detect electricity generated by muscles when flexing, then creating a circuit that would filter out electrical noise while accepting a range of frequencies that action potentials are propagated at, and lastly using LabVIEW along with Arduino to process the EMG signal and control a small servo's angle based on the intensity of the subject's forearm flexion.
​Heart Rate Detection using Photoplethysmography:
This project consists of using a TCRT1000 sensor, composed of an infrared diode light source and a phototransistor light detection circuit, to measure the infrared light reflected when placed on a subject's finger. The idea behind this project has to do with oxygenated hemoglobin's high absorption of infrared light and pulsatile arterial blood flow, which is based on the concept of Photoplethysmography (PPG). Taking the raw data of the TCRT1000 sensor, we use a filter circuit to allow only a specific range of frequencies that correspond to a subject's pulsatile arterial blood flow to pass, and then using an Arduino to measure the frequency of the filtered waveforms, we are able to determine the subject’s heart rate.