2021 Senior Projects Conference

Multiple Guitar Effects Unit

Team Name: Multiple Guitar Effects Unit

Team Members: Nate Chopin, Elzin Hitchens, Cole Perilloux, Suede Taylor

Advisors: Dr. Matthew Hartmann, Dr. Prashanna Bhattarai

The goal of our senior design project is to design a multiple guitar effects unit that will process and modify an electric guitar signal using three different effects circuits: overdrive, fuzz, and tremolo. Our design primarily targets consumers, who are guitar players looking for a user-friendly device, that is innovated based on common guitar effect problems. (These problems include minimal versatility, manual daisy chain order switching, and various units.) To solve this, each effect includes adjustments for sound alteration using potentiometers and/or switches with LED on/off recognition. They are also interconnected using true by-pass switching which enables the user to switch the order in which the guitar signal goes through the effects by employing an internal switching network.

Surgical Metal Detector

Team Name: Surgical Metal Detector

Team Members: Mason Campbell, John Coughlin, Daniel Goss, Mark Nussbaum

Advisors: Dr. Matthew Hartmann, Dr. Prashanna Bhattarai

In dangerous environments such as military combat or explosives storage, there is a hazard of bullets, grenades, or shrapnel hitting a victim. Even with no fatal injuries, there are still risks of lead poisoning, infection, and the fragment moving to cause further damage (fragment migration). In most cases, the victim can be taken to a hospital with large scanners to locate the metal before performing surgery, solving the issue. However, such hospitals are not always accessible for victims.

Our device is inspired by research from the article, “Implementing metal detector technology and a navigation system in the removal of shrapnel,” in the Computer Aided Surgery journal. In cases of fragment migration, the detector/navigation system decreased location time by 275%. It is also assumed that the metal detection tool alone would greatly assist surgeons during their operation even if a navigation system was not accessible. Our project plans to encompass this surgical metal detection system in one mobile, cost-efficient unit.

Frequency and Location Correlator over Narrow Bands

Team Name: FALCON-B

Team Members: Ian Carney, Michael Krzystowczyk, William Lee, Frank Polermo

Advisors: Dr. Matthew Hartmann, Dr. Prashanna Bhattari

FALCON-B is a passive radar system that utilizes an array of four antennas to measure the distance and bearing of an incoming signal. FALCON-B finds the relative power of the incoming signal and uses the signal-to-noise ratio (SNR) to determine the distance in feet and direction within 50 feet. FALCON-B can be used for home and business security, or for those interested in the location vector from incoming signals. The data collection is done through RTL Software Defined Radio, and approximately one million samples of IQ data are collected each second. The distance-finding process utilizes an equation that was derived by measuring and recording the collected SNR values at various distances in Microsoft Excel and is calculated using the antenna data with the highest SNR. The direction-finding subcomponent done by FALCON-B utilizes the network of four antennas. By collecting the two highest SNR values, FALCON-B can determine the approximate octant in which the incoming signal is located. Radio shielding is utilized to block each antenna from 180 degrees of its field of view, so the SNR collection process can be more precise. Through SNR collection, FALCON-B determines the distance and approximate bearing of the incoming signal.

Smart Outlet

Team Name: Smart Outlet

Team Members: Adrian Eugene, Colton Oberthier, Vu Vu, Mason Youberg

Advisors: Dr. Matthew Hartmann, Dr. Prashanna Bhattarai

The Smart Outlet device is a load consumption monitoring system that measures and displays the voltage, current, real power, apparent power, and power factor based on a load of a 120 Volt RMS system. The smart outlet design project acts as a model for higher voltage industrial plants and is used as an additional solution for smaller-scale predictive maintenance. Through continuous data collection from voltage and current sensors, the smart outlet uses the zero-crossing method to display the power factor the load consumes. The data the smart outlet records is used to troubleshoot abnormal operations and provide feedback for overcurrent.

The smart outlet product has a primary focus for industrial use with additional features that can be useful for common household consumers as well. The exterior design of the smart outlet is similar to a common household power strip with the addition of an LCD monitor placed in between two electrical sockets. The LCD displays the electrical values consumed by the various loads plugged into the socket using both voltage and current sensors which are configured between the supply and the load. The smart outlet LCD has the function to display multiple screens of information for each socket by using its touch-screen capability. To increase the safety and reliability of the smart outlets’ components and the connected load, the smart outlet incorporates an overcurrent protection relay system that is set to trip at 10 amps. Another feature the smart outlet device implemented is the ability to control the device’s ON/OFF setting through a web browser’s IP address that is connected to the user’s Wi-Fi.

Remote Controlled Lawnmower

Team Name: Remote Controlled Lawnmower

Team Members: Adam Carlisle, Edward Chappel, Benjamin Finch

Sponsor: Brenden Mertz

Advisors: Dr. Matthew Hartmann, Dr. Prashanna Bhattarai

We converted a normal electric lawnmower into a remote-controlled lawnmower that can be operated with the user inside of a house while the mower is in the yard. The mower has a video feed, backup sensor, and numerous safety devices to maximize usability and reliability.

Boat Obstruction Avoidance System

Team Name: Boat Obstruction Avoidance Team (BOAT)

Team Members: Brayden Davis, Steven Powell, Joseph Smith, Travis Williams

Advisor: Dr. Matthew Hartmann

The boat obstruction avoidance system is designed to both alert and divert away from unseen obstructions under the surface of the water. This will be done by utilizing three sonars, one forward-facing and two at 45 degrees from the center. This will provide an adequate field of view to know both that there is an obstruction out front, and in which direction the system needs to turn. The system also includes a three-tier LED system that shows the operator if there is an obstruction 8 feet away, 6 feet away, and 4 feet away, at which point the obstruction system will take over. The colors of each tier vary as well, with the 8-foot marker being green, the 6-foot marker being yellow, and the 4-foot marker being red, at which point all LEDs will be on and the system will be in avoidance mode. Avoidance mode is when the linear actuators will be told which direction to turn based on the range finders’ input. The actuators may also be controlled when there is not an obstruction with a joystick input from the operator.

Data Acquisition System for Particle Physics Experiments

Team Name: DAQula

Team Members: Mitchell Adams, Scott Hotard, Josh Jones, Nick Trichel

Sponsor: Dr. Rakitha Beminiwattha

Advisors: Dr. Matthew Hartmann, Dr. Prashanna Bhattarai

A data acquisition system is a multi-system, modular device that captures data from sensors. As a modular system, a data acquisition system is configurable and expandable to suit experimental design. The group’s data acquisition system is designed to capture data from an analog signal generated by a cosmic ray impacting a scintillator. A change of energy state within the scintillator material generates a photon, and a photomultiplier tube connected to the scintillator generates a current. The photomultiplier tube uses this current signal to produce an amplified voltage signal. An analog to digital converter translates the signal to discrete data. The data is recorded in real-time via a network connection to a lab computer. The data acquisition system has a single 8-channel analog to digital converter capable of external, internal, or software triggering. The system produces high-resolution data with a 12-bit digitizer with a sampling rate of 250 MS/s. The system is highly configurable with programmable event size and pre/post-trigger. The system will provide a tool for Louisiana Tech to expand its experimental capabilities in the future by offering accessible, high-quality data that can be customized to the user.

Electric Heating for Fly Ash Based Geopolymer Cement

Team Name: Geo-Heater Team

Team Members: Stephen Timothy Gordon, Kurt Ashton Glorioso, Diego Arturo Segura Ibarra

Sponsor: Dr. Shaurav Alam

Advisor: Dr. Prashanna Bhattarai

The goal of our project is to develop an electric heating system for fly ash-based geopolymer cement (FABGC). Our heating system improves on the existing solution implemented by Dr. Shaurav Alam by reducing the time needed to heat the samples to a target temperature of 50-70 degrees Celsius and reducing the size of the heating system. We have developed two approaches to electrically heat the FABGC. The first approach is induction heating. Our induction heating circuit works by generating an oscillating magnetic field in an induction coil, samples placed inside the coil are then heated by eddy currents induced in the sample. The second approach we have been developing is dielectric heating. Dielectric heating utilizes alternating electric fields to rapidly switch the direction of dipoles which generates friction and in turn heat. To produce this dielectric heating effect, we have repurposed a microwave oven magnetron. The magnetron used by microwave ovens emits high-frequency electromagnetic waves that are used to create dipole motion. By modifying the circuitry of a microwave oven, we can control the magnetron using a custom control system.

Crisis Situations in Self-Driving Cars via Novel Machine Learning

Team Name: Machine Learning in Self-Driving Cars

Team Members: Mark Hidalgo, Nolan Matthews, Jim Vanchiere, Alex Yoes

Advisor: Dr. Jinyuan Chen

As a relatively new technology, many Americans still do not believe in the safety of self-driving vehicles, but we believe that exploring crisis management and decision-making is an important step in helping sway public opinion in favor of this technology. For our project, we have been exploring the application of machine learning (ML) to assess and act on crisis situations in self-driving cars. To demonstrate this, we have fabricated a remote control (RC) car fitted with various sensors that will utilize ML algorithms to demonstrate the basic functionality of self-driving cars. Our design utilizes a Raspberry Pi 4B which serves as our primary controller and runs the ML algorithms which teach the car to navigate lanes and recognize objects such as traffic signs. Overall, our project aims to explore a novel machine learning topic that will help a model autonomous vehicle navigate a certain crisis situation. By doing this, we hope that our project may help improve autonomous vehicle technology going into the future.