Data Electrical Engineering

Gage Johnathan Haas Phone: 504-***-**** Email: *****@***.***

SUMMARY

Experience in circuit design, programming, robotics, data acquisition, machine learning, computer vision, neural networks, data science, predictive data modeling, and other electronic projects.

Passed the FE exam and am eligible for EIT licensure.

Coursework in Electrical Engineering and Computer Engineering topics:

Circuits, Power Systems, Electronics, Signal Processing, Physics, Mathematics, Embedded Systems, & Programming.

TECHNICAL SKILLS

Languages

Proficient in C, C++, Python, and Assembly Language.

Experience with SQL, DAX, and Linux/Windows shell scripting.

Software

Circuit Analysis & Simulation Environments: Multisim, Logisim, MATLAB, LabVIEW.

Fluent with Linux command line, and QT Integrated Development Environment for GUI design.

Used Tensorflow and OpenCV libraries for machine learning, neural network, and computer vision applications.

Electronics

Soldering and Bread-Board prototyping for circuits, as well as testing with lab equipment.

Raspberry Pi, Arduino, Beaglebone, and Motorola HCS12 micro-controller programming and interfacing.

EXPERIENCE

University of New Orleans – Undergraduate Robotics Research Assistant 10/01/18 – Present

Tolmas Scholarship Recipient

Assisted in developing computer vision and image processing algorithms, to help achieve autonomous operation of a small-scale vehicular robot that was used in the IEEE robotics competition. This robot placed 5th out of 30 teams.

Utilized Tensorflow and OpenCV’s machine learning libraries to re-train a previously trained convolutional neural network for object detection. This method is also known as transfer-learning.

Developed and implemented pre-processing of an image data-set in order to train the object detection model. This object detection model can successfully identify blocks with the letters A-F with the correct letter. It can also identify obstacles in its path, as well as the robot’s orientation to the building it is delivering the lettered-block to.

Utilized OpenCV functions and ray tracing to determine the distance and angle to an identified target or obstacle.

Established communication via SCI between the raspberry pi and arduino micro-controllers. This allows the Arduino to request for the raspberry pi to take an image and test for what’s in the image. The raspberry pi then communicates the result back to the Arduino which drives the robot’s motors in response.

Utilized IR and ultrasonic sensors to supplement the image processing capabilities, this creates an effective feedback system that uses both sensors and image processing for target identification, obstacle avoidance, and target delivery all using autonomous motion.

Entergy New Orleans – Data Analytics Intern for the Internal Audit Group 12/08/18 – Present

Learned SQL to develop database queries to support internal audit’s monitoring for fraud and testing of controls.

Cleaned Data-Sets acquired from queries and then used statistical analyses such as Benford Analysis, Linear and Logistic Regressions, and other Excel functions to identify trends or cases of fraud, such as duplicate transactions.

Used data-analytics to identify fraudulent expenses, as well as identified gaps in the company’s internal controls.

NASA Marshall Space Flight Center’s Space Hardware and Robotics Academy – Research Associate

Robotic Lunar Lander Propulsion System Development 5/27/2018- 8/4/2018

Investigated battery sizing for an electrically powered propellant pump, for a lunar lander’s propulsion system.

Assisted with programming of a control system for a DC Brushless motor that powered the pumps, using LabVIEW.

Created LabVIEW software for data acquisition and control of a cryogenic valve testbed, used to model a lunar lander’s reaction control system. The CDAQ and other hardware used was also mounted next to the testbed.

Calibrated signal conditioners for connection to pressure transducers, flow-meters, and thermo-couples.

NASA Ames Research Center – Embedded Systems Intern

Intelligent Systems Division – Prognostics and Diagnostics Group 1/8/2018-4/27/2018

Designed a display unit demonstrating the Generic Software Architecture for Prognostics (GSAP) that was implemented on the Raspberry Pi. This software allows the user to predict a time when a component will fail, under current conditions. My project utilized prognostics on an 18650 Lithium Ion battery.

Display unit consists of a battery that’s interfaced with external electronic circuits, IC’s, sensors, and a Raspberry Pi. Sensors read data, such as battery voltage, current drawn from a load, and battery temperature. This data is then converted to a digital signal and read through the Raspberry Pi’s SPI interface into the GSAP software. The GSAP then implements Monte Carlo predictions to determine when the battery will die.

Programmed in C++ a real-time data graphing GUI for the GSAP to run on the Raspberry Pi. The GUI graphs the battery data and displays the prediction for end of life. GUI is utilized in the display unit.

Programmed the GUI to allow the user to control the GSAP program. GUI Code facilitated inter-process communication between the programs to share data, such as the end of life prediction for the battery. The GUI also allowed for debugging of the GSAP using logs from the program.

Created a transistor switching circuit that allowed the user to reverse the polarity of a motor being powered by the battery. This control was in the form of a button in the GUI. The button ran a shell script causing a voltage signal to be sent to transistors, which forced a path for the current to flow.

Red Stick Robotics – Lead Instructor

5/1/2016 – 9/1/2016

I taught robotics summer camps geared towards high school students. I lead and instructed groups of over 30 students and organized a robotics competition at the end of each camp.

Students learned basic C++ programming as well as how to interface sensors on Vex Robots. Students were taught how to drive motors using a video game styled controller, as well as how to autonomously program the robot and make use of ultrasonic and touch sensors.

Students were shown how to program video games and other activities using the Raspberry Pi.

EDUCATION

University of New Orleans B.SC Electrical Engineering with a focus in Computer Engineering Minor in Mathematics GPA 3.10 / 4.00

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