The Facility for Rare Isotope Beams (FRIB) is a new linear accelerator being funded by the U.S. Department of Energy, Office of Science and operated by Michigan State University. Once the FRIB is completed, it will enable scientists across the nation to make discoveries about the properties of rare isotopes, nuclear astrophysics, fundamental interactions, and applications for society, including medicine, homeland security, and industry.

The FRIB is sponsoring our project to design and test a controller for a 644 MHz superconducting resonant cavity. The new controller needs to be designed and tested to deal with the 644 MHz frequency, as the old controllers do not have enough analog input bandwidth. The FRIB is supplying some of the latest technology in analog and digital converters as well as a programmable microcontroller for the project.

Our goal is to design an analog signal chain to direct sample the signal from the new 644 MHz cavity for further processing using the best off-the- shelf components available. A phase locked loop will compare the cavity’s exact resonant frequency to the synchronization signal. Our project will utilize evaluation boards and a coaxial resonant structure to simulate how the cavity will respond at the FRIB.

Aptiv is a global technology company headquartered in Gillingham, England. Using industry-leading software capabilities, Aptiv is ushering in the next generation of active safety, autonomous vehicles, smart cities, and connectivity solutions.

Aptiv’s goal is to transform mobility to a portfolio of safe, green and connected solutions. As a part of accomplishing this, Aptiv set its sights on implementing the Tire Pressure Monitoring System using Bluetooth Low Energy.

Bluetooth 5.0 offers many advantages over its predecessor, with an update to Bluetooth Low Energy (BLE). BLE allows for low power consumption, a higher data rate, and longer range.

Monitoring tire pressure is key to ensuring the longevity of the tire, along with an efficient fuel economy and also plays a vital role in passenger safety. A tire with 15% under the ideal pressure lasts 1⁄2 year less than an ideal tire. At 20% under ideal pressure, fuel consumption rate increases by 4%. Finally, at 30% under ideal pressure, the tire wear rate increases by 50%.

The system level breakdown of our project is comprised of a tire pressure sensor installed on a tire communicating with a transmitter microcontroller. This microcontroller will receive the pressure data from the sensor and, using Bluetooth 5.0, will send this data to a receiver microcontroller. Finally, the receiver microcontroller will run analysis on the pressure data and transmit it through a physical connection to be displayed on a monitor.

The Autodrive Challenge is a collegiate student design competition with a focus on designing and building autonomous vehicles. The competition is sponsored by General Motors, SAE International, and many other companies involved in the autonomous vehicle industry. The competition has a three-year duration, during which student design teams from eight different universities are tasked with equipping a Chevy Bolt with the necessary technology to achieve the status of a level four autonomous vehicle.

The team has been challenged with building autonomous systems to find and maintain vehicle position within lane lines on a roadway, as well as detecting and avoiding obstacles that might be in the vehicle’s path. This project’s focus is to develop the software required to accurately detect lane lines in the environment of a vehicle, prevent the vehicle from colliding with any obstacles, and enable passing maneuvers whenever possible.

To meet the functional requirements of our project, the vehicle will have multiple distinct subsystems including perception, sensor fusion, motion planning, motion control, and vehicle interfacing.

An independent hardware system to monitor the safety and integrity of signals and systems on the vehicle is proposed. Our team will design this Safety and Integrity Monitor (SIM) system to sense and react to vehicle performance and system performance.

The vehicle performance will be monitored through an Inertial Measurement Unit (IMU) and will communicate driving quality to the main driving computer. If necessary, the SIM will disconnect the driving computer from the CAN bus, effectively disabling autonomous operation. The physical health of sensors will be monitored through voltage and current draw, while their operational health will be monitored by analyzing the data returned.

Additionally, ambient and contact temperatures will be monitored and controlled by a network of fans around the vehicle, evacuating hot air from the vehicle through vents if necessary. Finally, this device will function as a “Black Box,” data-logging all recordable metrics for later use in optimization or failure analysis.

The Circuits, Systems and Artificial Neural Networks Laboratory (CSANN) is a research group at MSU that specializes in artificial neural network algorithms. These neural networks are systems patterned after the neurons found in a human brain and have demonstrated appealing capabilities in image recognition and classification.

With the increased integration of technology on smartphones, most smartphone owners have access to the sensors needed in order to produce valid input for classifying neural networks. Giving the owner a way to interact with these networks using their phone would grant the owner the ability to receive instant processing information from the sensor data they can access.

Our team will be creating a prototype based on the idea of a portable deep neural network. The prototype will allow a smartphone user to capture an image on their phone, run the image through a classification network, and have the result of the classification displayed on the phone screen. The network will be loaded onto a portable, embedded device that will be powered by an external battery and plug into the phone.

All manufactured products need to be tested to ensure that the material integrity is upheld. In many cases, cracks or defects in a material are not visible to the human eye. As of today, the devices and techniques used to detect cracks in structures are expensive and not easily portable.

The James Dyson Foundation is seeking to develop a small inexpensive portable system to detect cracks/ defects in structures that can be displayed on an iOS app. Allowing ease of testing on manufactured surfaces will help prevent premature failures and improve the overall safety of the product. The application that our team is focusing on is an aluminum airplane wing.

A handheld wireless device comprising a differential coil array will be designed to use eddy currents to measure changes in impedance as it moves across the surface of a structure. The analog information collected from the coil array will be converted to digital by the AD5933, an impedance converter, within the handheld device. The AD5933 will perform a discrete Fourier transform which will return the magnitude of the impedance and relative phase at each frequency point. The AD5933 device will then send the digital format data to a Raspberry Pi 3+ which is connected to a Bluetooth module. The data will be sent to the iOS app, where it will be analyzed to display the coil impedances in a simple text format, and a heat graph to show concentrations of changes in detected impedance.

Team Members (L-R): Zhengze Liu, Shihao Meng, Jordie Liao, Kellen Collison, Corey Wysozan, Shu Liu

MSU Electrical and Computer Engineering Department: Voice Fitbit

Team Members (L-R): Brent Cavner, Austin Winarski, Wanxuan Chen, Ibnui Khan, Ruikai Chen