ECE Senior Design 2022

Team 302: Frequency Multiplier

The signal generator and oscilloscope are the heart and mind of electrical engineering. They are used to create and read signals, respectively, and are vital for learning and real-world testing. However, cost and usability limit these devices. They cost thousands of dollars, do not always have matching ranges, and require manual adjustments for any level of use cases. We hope to solve these problems by creating a signal generator that can cover most oscilloscope ranges for under a $100, as well as automate the process of testing through a computer program.

There are two major parts to our project, the physical multiplier, and the computer controls. Our multiplier takes a user input and outputs a faster version of a signal. It can also work independently, creating a signal of any value within range. The computer controls set a multiplication value or signal but can also run an automated test through a range of signals. In addition, all the data maybe viewed on a computer interface and saved on the computer local drive.

No matter the user, our signal generator prototype will make electrical engineering testing and learning easier and more affordable. 

Team (L to R):
Senior Design Team Members

Andrew Benton, Jackson Bruce, Patrick Hollis, Elijah Parsanko & Delton Spencer

Advisor(s):

Jinyeong Moon Ph.D.

Sponsor(s):

Keysight Technologies

Team 301: SoutheastCon Hardware Competition 2022

The annual IEEE SoutheastCon Hardware Competition is a robotics design contest. This year’s competition is held in the birthplace of Mardi Gras, Mobile, Alabama, and thus is Mardi Gras-themed. Our task is to design a small-scale model of a driverless parade float that follows a parade route, collects and throws beads and pushes a marshmallow. Additionally, we can earn points by including a display and music that demonstrates our school spirit. Our display will be a screen with the images of FSU and FAMU logos, and the music will be FSU’s War Chant and FAMU’s War Cry.

There are three rounds to the competition, with 10, 15, and 20 beads available in each round. At the start of each round, our robot follows a painted line located in the center of the game board. It uses a camera with image recognition to detect beads located on poles hanging over the track. Using its robotic arm, it collects these beads and deposits them into a holding area. Once the beads are collected, the robot continues along the parade route unless more beads are detected. Along the way, the robot places the beads in a launcher to throw into nets along the route, also detected by using image recognition. Each round will have one marshmallow on the board that represents a member of the crowd that is on the road. If the robot senses a marshmallow, an extendable arm located on the bottom of the robot will move the marshmallow to an alleyway on the side of the parade route. Points are earned for each bead collected and thrown into a net, for each marshmallow pushed into an alleyway, and for completing the course. Our goal is to have our robot finish the obstacle course and the tasks in the least amount of time to maximize points.

Team (L to R):
Senior Design Team Members

Melissa Emery, Kelvin Hamilton, Destiny Law, Raymond Martinez & Allison Rosenbaum

Advisor(s):

Bruce Harvey, Ph.D.

Sponsor(s):

FAMU-FSU Engineering

Team 303: Automated Non-Destructive Cleaning of Solar Panels

New technology is being created every day to help the need for renewable energy. One of the most promising renewable energy sources is solar power. Solar panels are used to harness the solar power in both homes and businesses and their use is growing rapidly.

Over the course of use, solar panels provide renewable energy to homes, but sustain a lot of wear on their ability to produce energy. One of the main drawbacks to solar panel productivity is typically in the form of dirt or debris that builds up over time and is baked into the panel by the hot solar rays. This project aims to develop a device that automates the process of cleaning solar panels.

Currently, buildup is removed by cleaning the panel using brushes and cleaning solution. The main benefit of the device will be to bring down the time spent manually cleaning the solar panels and put that time towards other productive tasks.

This automated cleaning device moves across the entire solar panel to clean it, removing dirt and debris. The mechanical side of the project creates the cleaver structure of the device to make it lightweight, efficient and balanced on the solar panel. The electrical side of the project powers the device and allows it to move around the panel. Device movement is controlled by multiple motors and sensors tracking movement across the panels. 

The main benefactors of this device are solar panel companies and solar panel owners. They will benefit by having cleaner solar panels which will make them more efficient and able to produce more power.

Team (L to R):
Senior Design Team Members

Edward Corlett (ECE), Justin Green (ECE), Kristen Pepper (ECE), David Sailor (ECE), Caterina Arnold (ME) & Tanner Buis (ME)

Advisor(s):

Jerris Hooker, Ph.D. & Peter Stenger

Sponsor(s):

Raa Tech

Team 304: Image Recognition for Padmounted Equipment

Pad-mounted transformers are responsible for lowering voltages to the standard household levels. Florida Light and Power (FPL) includes devices called fault current indicators within their transformers. When an area has lost power, these indicators detect whether their transformer’s current is faulty. However, FPL teams must manually check the indicator within each transformer to locate a fault, which is a time consuming and challenging procedure. We were tasked with detecting faulted transformers using computer vision, inspired by how FPL’s existing drone program locates damage on powerlines.

Our design includes a physical beacon that visually indicates faults and a computer vision system that recognizes the beacon. The beacon must be reliable and weather resistant while securely mounted to the transformer’s exterior, it must also connect internally to the fault current indicator. When the beacon receives power, a spring releases it into an upright position and its LED light turns on. The FPL Air drone program captures video of the beacon.

The second design component is a computer vision system that can detect the transformer, beacon and beacon state utilizing the algorithm You Only Look Once (YOLO) to accurately detect objects in real time footage.

We developed a method of visually indicating faults and detecting them using computer vision. The solution speeds the process of locating a faulted transformer and reduces the time it takes for FPL to return power to an area.

Team (L to R):
Senior Design Team Members

Erwin Gage (ECE), Samuel Hammermaster (ECE), Erin Murphy (ECE), Kent Logue (ME), & Jordan Wilkerson (ME)

Advisor(s):

Rodney Roberts, Ph.D.

Sponsor(s):

Florida Power and Light

Team 305: Data Collection and Aggregation (Tame the Beast)

The Naval Air Warfare Center Training Systems Division (NAWCTSD) is a Navy training systems center in Orlando, Florida. NAWCTSD works to test and implement new equipment on aircraft platforms. The test points and results are currently on handwritten sheets. Manually transferring the data from the test into a database makes the process time-consuming and inefficient.

To address this issue, we developed a new system for collecting and organizing test data. This application can be used on any tablet. It allows an operator to input the test data electronically and then output the results to a local server. The application can also export the collected test results into a .csv file. NAWCTSD can use the collected results to evaluate their new equipment.

Our application’s code works with both Windows and Android devices. For testing, we implemented it on a variety of mobile electronic devices. The application is easy to use and works in a secure environment. NAWCTSD can now collect and analyze test results directly on the application instead of writing them by hand and manually typing each test on Excel.

Team (L to R):
Senior Design Team Members

Kaden Diaz (ECE), Eliott Ford (ECE), Michael Brouillette (ECE), John Mijares (ECE), Chase Toepp (IME), & Sherlanda Auguste (IME)

Advisor(s):

Reginald Perry, Ph.D.

Sponsor(s):

National Security Innovation Network, Nav