ECE Projects – Fall 2024

In 2022, 801 motorists and pedestrians lost their lives in roadside work zone accidents, highlighting the urgent need for improved traffic monitoring to enhance safety. This project aims to develop a cost-effective, scalable system to track vehicle speeds in work zones using Valtech Mobility’s Hardware Development Kit (HDK).

Valtech Mobility, a joint venture between Valtech and the Volkswagen Group, is a leader in connected car technologies and traffic management solutions.

Our project will integrate radar speed-tracking into the HDK over a standard serial bus to accurately measure vehicle speeds. In addition, we will explore using Bluetooth-enabled HDKs to track vehicles by detecting unique MAC addresses and calculating their speed based on timestamp comparisons over a set distance.

By combining radar precision with Bluetooth connectivity, our project provides a comprehensive and flexible solution for monitoring traffic in work zones. The system can be seamlessly integrated into existing infrastructure, enhancing safety for both workers and motorists.

Michigan State University

Team Members (left to right)

Hisham-Bryan Burgol
Farmington Hills, Michigan

Boston Meisner
Pinckney, Michigan

Kevin Kapllaj
Novi, Michigan

Sami Shahin
Canton, Michigan

Jaxon Hancock
Muskegon, Michigan

Valtech Mobility

Project Sponsors

Angela Fessler
Detroit, Michigan

Project Facilitator

Dr. Daniel Morris

Fraunhofer USA is a research group that focuses on innovation and development in electrical, software, material, and biological technologies. With an office in East Lansing, Michigan, Fraunhofer partners with MSU students and faculty on semiconductor research in coatings and diamond technologies.

Diamond is a naturally non-conductive material in its pure state but becomes semiconductive when doped with Boron atoms. These electrical properties, as well as its mechanical hardness and resistance to temperature changes and corrosion, lead to diamond becoming an increasingly important material in electronic components. As electronics are designed to function at higher frequencies and in hostile environments, diamond-based chips are needed in larger quantities at lower costs.

Manufacturing diamond-based chips is often done through the long and expensive process of diamond lithography, where a crystal is grown in a lab and cut to size for use. Diamond crystals grown this way may warp the chips as the mechanical crystal structure is extremely rigid.

This project aims to find a cost-effective alternative to diamond lithography. By selectively dispensing diamond seeding solution onto silicon wafers, diamond crystals can be grown in desired patterns without the need for cutting. This will be done with a modified 3D printer and inkjet printhead controlled by custom software for autonomous dual axis movement and solution dispensing. Images generated from CAD software will be processed and compiled into commands, then sent to an Arduino that controls the printer motors and inkjet printhead.

Michigan State University

Team Members (left to right)

Pritham Kura
Farmington Hills, Michigan

Andrew Barton
Eden Prairie, Minnesota

Noah Sanders
Benton Harbor, Michigan

Mate Narh
Accra, Ghana

Tuan Nguyen
Hanoi, Vietnam

Fraunhofer USA, Michigan

Project Sponsors

James Siegenthaler
East Lansing, Michigan

Project Facilitator

Dr. William Harokopus

Facility for Rare Isotope Beams (FRIB) is a facility on Michigan State University’s campus with a powerful isotope accelerator. The accelerator generates high- intensity beams of stable atomic nuclei and propels them to high velocities causing collisions that produce rare isotopes. Safely transporting these beams includes transporting the beam through various components. This is a non-interceptive diagnostic, meaning that the beam characteristics are not changed by this transportation. Dipole and quadrupole movements are crucial for steering an ion beam, as external electric fields are used to guide the polarized beam in different directions. The advantage of using a quadrupole moment for directing an ion beam is that it can be manipulated in both horizontal and vertical directions. A quadrupole moment is the next step of analysis at FRIB. A 4-button Beam Position Monitor (BPM) can be used to analyze this quadrupole moment. Currently, a 1-wire system models this system accurately instead of using the actual beam. An impedance has been calculated for this 1-wire system and circuit boards have already been fabricated. This circuit couples with a 50 Ω output from a Vector Network Analyzer (VNA). This means that the load, or in this case, circuit, should have an impedance of 50 Ω. Since the VNA input matches the circuit impedance, it can be stated that this system exhibits impedance matching. Therefore, a modulating input travels through the wire system, then through an initial impedance matching circuit board. After the signal (AC current) passes through this board, the signal goes through the BPM and another identical impedance matching board before it meets a 50 Ω termination.

The goal for this project is to have a working PCBA and VNA setup with a matching impedance for the 4-wire setup. Ideally, an RF splitter and attenuator circuit would be used for further analysis of this 4-wire system.

Michigan State University

Team Members (left to right)

Oliver Umeokolo
Detroit, Michigan

Paul Merritt
Rochester Hills, Michigan

Erik Firehammer
West Olive, Michigan

Nolan Janke
Canton, Michigan

Alex Bejin
Northville, Michigan

MSU Facility for Rare Isotope Beams

Project Sponsor

Steven Lidia
East Lansing, Michigan

Project Facilitator

Dr. Oleksii Karpenko

The Michigan State Solar Racing team designs, builds, and tests solar powered cars. These solar powered cars are designed with a cross-country race in mind, where the car must travel thousands of miles over the course of a few weeks while being completely powered by the sun and street legal.

The solar powered vehicles created by the Solar Racing Team require a large rechargeable battery pack. This battery pack is rated for 155V and 50A. It is created from hundreds of 3.6V lithium-ion battery cells. These cells are connected in separate modules and come together to form the complete battery pack. This battery must be tested to ensure safety and to maximize efficiency.

The goal of this project is to create an intelligent test system to test the vehicle battery at the individual battery cell, module, and pack level. This system must be able to test the capacity of the battery, the internal resistance of the battery, and perform stress tests to test the maximum current of the battery. This system must be able to communicate to and from a PC with user-friendly software. This software can be used to run the various tests and display data as requested by the Solar Racing Team.

This system will be implemented by the creation of a PCB using Altium Circuit Designer. This PCB will contain the required electronics for the capacity tests along with the stress tests. It will be controlled with an STM32 microcontroller development board as requested by the team. The PCB will connect to the battery at either the cell, module, or pack level and run tests as desired by the users. Separate electronics must be used for the high voltage pack vs. the low voltage capacity tests.

Michigan State University

Team Members (left to right)

Nic Ho
Grand Rapids, Michigan

Adrinil Dennis
Los Angeles, California

Nick Lowe
Milford, Michigan

Henry Grimsby
Ann Arbor, Michigan

Cameron Tenkel
Rochester, Michigan

MSU Solar Racing Team

Project Sponsors

Owen Winegar
East Lansing, Michigan

Project Facilitator

Dr. Edward Gebara

Michigan State University Infrastructure Planning and Facilities (MSU IPF) is responsible for planning, building, and maintaining the physical environment of the campus. With about 5,467 acres of property and 24.5 million square feet of buildings, there is a continuous need of accurate and reliable data to efficiently monitor and save energy usage costs. This includes electricity, steam, water, and natural gas.

Currently, identifying drift in these meters is a reactive process, based on data collected up to a month before analysis. Additionally, the system is a manual process requiring labor time to complete. This can lead to delayed identification of inaccuracies, inefficient use of labor, and potential errors in utility tracking.

The goal of this project is to create a workflow that will make possible the automated prediction and identification of drift on various IPF utility meters.

We will be using Sartorius Simca, a multivariate data analysis software, to draw conclusions from the historical metering data. The system will enable early detection of metering anomalies and proactive recalibration of meters.

Michigan State University

Team Members (left to right)

Colin McNeal
Howell, Michigan

Rohin Deolekar
Rochester Hills, Michigan

Jason Polakowski
Rockford, Michigan

Nathan Zylka
West Palm Beach, Florida

Noah Dreiling
Laramie, Wyoming

MSU IPF Building Performance Services

Project Sponsor

Jason Vallance
East Lansing, Michigan

Project Facilitator

Dr. Sunil Chakrapani

Currently, Radiation Engineers (REs) are required to evaluate power electronic products at Michigan State University Facility for Rare Isotope Beams (FRIB). Oftentimes, numerous products need to be evaluated, which requires following detailed instructions, time-consuming intervention due to the switching of product hardware after each test, and having to wait for radiation to dissipate in the testing chamber, not to mention the limited availability of the Linear Particle Accelerator itself. Engineers come from all over the country to perform tests in an environment that, if not careful, can be rather dangerous due to the radiation. Additionally, considering FRIB’s limited schedule, customers need to use their time as efficiently as possible. Texas Instruments provided relevant information that shows that using the Linear Particle Accelerator costs approximately $2,500 per hour. Manually changing each device individually takes 30 to 40 minutes, and with an average of ten devices tested per session, would mean that without any automation $15,000 would be wasted.

For this reason, our project aims to design and automate the hardware connection of the Device Under Test (DUT) to the instrumentation during Single Event Effects (SEE) radiation testing of space-grade power electronic products at the Michigan State University FRIB facility. The hardware automation will facilitate the precise positioning of Integrated Circuits (ICs) in front of the beamline while also automating the connection changeover of instrumentation, thus enabling REs to evaluate many products without requiring the manual, time-consuming intervention of switching product hardware after disabling the SEE heavy ion beam line. Ultimately, the implementation of this system will increase efficiency while decreasing the use of resources and reducing expenses for Texas Instruments.

Michigan State University

Team Members (left to right)

Colin Watkins
Iron Mountain, Michigan

Rafael Gonzalez Zuniga
Reynosa Tamaulipas, Mexico

Aaron Elkin
West Bloomfield, Michigan

Michael Charlton
Bloomfield Hills, Michigan

Marcelo da Paz Leal
Monterrey, Mexico

Texas Instruments

Project Sponsors

Justin Nuttall
Dallas, Texas

Project Facilitator

Dr. Sergey Baryshev

Texas Instruments (TI) is a global leader in analog and embedded processing technologies with a mission to make electronics more efficient, affordable, and innovative. With expertise in microcontrollers, sensors, and integrated circuits, TI is at the forefront of advancements in sensing, control, and processing. As a project sponsor, TI provides state-of-the-art components and technical support to drive the development of this Smart Software Controlled Foam Dart Launcher, enabling the practical application of their technologies in a hands-on engineering project.

The Smart Software Controlled Foam Dart Launcher project aims to create a high-performance dart launcher that integrates TI’s advanced hardware to enhance accuracy, safety, and efficiency. At the heart of the launcher is a brushless motor- controlled flywheel, which enables precise propulsion of foam darts. TI microcontrollers and sensors are embedded to enable key features like jam detection and thermal monitoring, ensuring smooth operation and preventing mechanical issues. These components work together to deliver real-time feedback and efficient power management throughout the launcher’s operation.

To maximize user control and flexibility, a custom software interface is designed to calibrate, monitor, and control the launcher’s various settings. Users can adjust parameters such as number of darts and modes while the system processes input data from sensors and hardware components for optimal performance. TI’s embedded systems allow for seamless communication between the hardware and software, making the device easy to operate and ensuring that it meets the highest standards of functionality and safety.

This dynamic design emphasizes both safety and user experience, making the launcher an interactive and reliable device, with TI’s embedded technologies providing the essential control and connectivity between components.

Michigan State University

Team Members (left to right)

Gavin Boomsma
New Lenox, Michigan

Anirudh Srigiriraju
Troy, Michigan

Ipek Kuzkaya
Istanbul, Türkiye

Peyton Wallace
Clarkston, Michigan

Jon Toomey
Rochester Hills, Michigan

Texas Instruments

Project Sponsor

Robert Clifton
Dallas-Fort Worth, Texas

Project Facilitator

Dr. Nicholas Miller

Michigan State University’s D-CYPHER lab, led by Vaibhav Srivastava, and the NeEWS laboratories, led by Subir Biswas, focus on advancing technology through cutting-edge research in various fields, innovating solutions to enhance human interaction with automated systems.

The Interactive Robot System project, sponsored by Michigan State’s D-CYPHER and NeEWS laboratories, aims to revolutionize remote collaboration by integrating advanced AI capabilities into an existing sound-activated robot named “Call-E.” This initiative focuses on improving communication dynamics during virtual meetings, ensuring participants can engage more effectively.

Building on a previous mobile robot design that utilized a microphone array for sound localization to identify and approach active speakers, this project introduces Generative Pre-trained Transformer (GPT) technology. The enhanced system will not only enable the robot to follow conversations but also analyze the dialogue in real time, offering insights into participant engagement and group dynamics.

By integrating GPT functionalities, the robot facilitates smoother conversations by notifying participants when they need to speak up or give space for others to contribute. It will also improve the flow of communication outside of a meeting through the reports and overviews it generates after a meeting is complete, enabling absent participants to catch up on what they missed or simply to refresh the memory of those who were in it. Automating meeting reports with GPT can minimize human error and potentially recall key aspects of a meeting more accurately than relying on memory, especially when compared to manual notetaking and meeting report creation.

Michigan State University

Team Members (left to right)

Jacob Zirin
Naperville, Illinois

Brinn Rider
Petoskey, Michigan

Tommy Hart
Howell, Michigan

Matthew Woodring
Canton, Michigan

Brian Dwyer
Detroit, Michigan

MSU D-CYPHER and NeEWS Laboratories

Project Sponsor

Subir Biswas
East Lansing, Michigan

Vaibhav Srivastava
East Lansing, Michigan

Project Facilitator

Dr. Erin Purcell

GenoPalate is a nutritional support service that utilizes the unique genetic DNA of the customer to generate a personalized nutritional report. The customer sends in their DNA via a saliva sample to GenoPalate’s lab where it is analyzed and used to generate the report that helps the customer understand what foods and nutrients their body processes best based on their genetic code. From here the customer can utilize the GenoPalate app to better understand their report in an interactive way.

The GenoPalate app is a great tool for the customer to understand and use the data from their genetic report to live a healthier lifestyle. The current “optimal foods” page works by providing food scores based on the customer’s DNA report and the nutritional facts about common foods. This gives the customer an excellent way to determine what foods will help (or harm) their nutritional goals. Although this “optimal foods” page is an excellent way for customers to find foods, improvements to the page can be made to provide a better customer experience.

The focus of this project will be to upgrade and update the optimal foods page by providing a filtering and sorting mechanism to better find foods. The filtering mechanism will be a way to filter out what foods are shown based on details such as type, individual properties, and customer-selected ranges of nutrients and vitamins. The sorting mechanism will be a way to list the foods in various ways such as alphabetically, by score, by category, or by other nutritional properties.

For this project we will be utilizing React Native, the food algorithms and data provided by GenoPalate, and the current GenoPalate app to add the desired sorting and filtering mechanisms to the page.

Michigan State University

Team Members (left to right)

Chris Martin
Northville, Michigan

Shalord Okello
East Lansing, Michigan

Simon Landaverde
Grand Rapids, Michigan

Ashton Waclawski
Grand Rapids, Michigan

GenoPalate Inc.

Project Sponsor

Talal Malick
Milwaukee, Wisconsin

Hannah Pekarek
Milwaukee, Wisconsin

Project Facilitator

Dr. Panagiotis Traganitis

Large neural networks, recast as deep neural networks (DNNs) in the mid-2000s, altered the machine learning landscape by outperforming other approaches in many tasks. This increasing use of deep learning is creating incentives for adversaries to manipulate DNNs so as to force misclassification of inputs. This is when an attacker uses adversarial samples, an input crafted to cause learning algorithms to misclassify. Novel algorithms that can produce samples that are correctly classified by humans while being misclassified by a DNN, have been introduced in the past. This is possible by the algorithms maintaining a low modification average of input features per sample. This project’s goal is to utilize Theano to build and train a deep learning model to classify these handwritten digits, then further explore these vulnerabilities in deep learning models through adversarial attacks.

This project will demonstrate how small, imperceptible changes in input data can fool machine learning models while remaining unaltered to the human eye. To do so it will implement adversarial attacks on neural networks using either an original dataset or a premade dataset like MNIST, a collection of handwritten digits (0-9). The basis of this work is rooted in the research paper ‘The Limitations of Deep Learning in Adversarial Settings’ in which they formalize and discuss the space of adversaries against DNNs. In this research paper they evaluate the vulnerability of different sample classes, defining a hardness measure, which will be used in this project to better measure, evaluate, and compare the difficulty of altering specific source-target class pairs.

Michigan State University

Team Members (left to right)

Mathieu Chapaton
Sterling Heights, Michigan

Blake Morris
Macomb, Michigan

Faris Sweis
Macomb, Michigan

Rashed Alumalla
Umm Al Quwain, United Arab Emirates

MSU Broadband
Access Wireless Communication Lab

Project Sponsor

Tongtong Li
East Lansing, Michigan

Project Facilitator

Dr. Jian Ren

The PUMA Lab, which is a part of the Nondestructive Evaluation Lab, specializes in physical ultrasonic testing especially for structural evaluation. This line of work inspired the creation of the “High Power Amplifier Fabrication for Acoustic Testing of Rail Tracks” project. The main goal in this project is to create a high-power amplifier capable of generating 1500A peaks while also not exceeding +/- 70V. Additionally, we require a 4-cycle tone burst in the output, which will be used to excite our Electromagnetic Acoustic Transducer (EMAT) and be used for structural evaluation of railroad tracks.

Our team will aim to utilize Eagle to design a compact PCB containing our amplifier and driver circuits while meeting the specified requirements. To initially test the functionality of our board we will make use of QSPICE to simulate our circuit. Eventually, when our design passes our desired simulation results, we will go onto breadboard testing along with implementing our cooling solution. We will repeat the same cooling solution with our fabricated PCB.

With our PCB possessing the capability of exciting our EMAT, we will be able to analyze the ultrasonic waves that are sent through portions of the rail track and returned. By examining any differences in wavelength, we will be able to determine if there are any faults in the tracks.

Michigan State University

Team Members (left to right)

Aidan Statetzny
Northville, Michigan

Noah De Back
Sparta, Michigan

Chris Haddad
Plymouth, Michigan

Kihong Kim
Daegu, South Korea

Josh Kreder
Northville, Michigan

MSU PUMA Lab

Project Sponsor

Sunil Chakrapani
East Lansing, Michigan

Project Facilitator

Dr. Premjeet Chahal

Aerial nondestructive evaluation (NDE) has the potential to provide safe, efficient, and reliable evaluation methods for critical infrastructure. Vast benefits to workplace safety and the public may be realized when a drone is used to expand the scope of an evaluation or provide inspections for formerly inaccessible infrastructure.

While the use of commercial drones may be cost-effective for larger organizations, the goal of this project is to improve access to drone-based evaluation by developing a low-cost, fit-for-purpose drone for infrastructure inspection. This durable and economical solution would enable a user to swap out technology to explore additional evaluation techniques while avoiding the costly consequences of a commercial drone malfunction or accident.

This project entails the adaptation of a roughly $70 drone received from MSU’s Physical Ultrasonics, Microscopy, and Acoustics (PUMA) Lab equipped with brushless motors, electric speed controllers, and a KK Multicopter flight controller on a hexacopter frame with an integrated power distribution board. The drone’s capabilities will be continuously expanded by first integrating and mounting a camera and infrared (IR) sensor to collect and write close-up video and IR data to an onboard SD card via a lightweight Raspberry Pi, and cleaning and processing the data post-flight.

In iteratively improving this minimum viable product, adding functionality tailored to the end user while testing the possible capabilities of a low-cost drone, this project presents a route for new users to explore aerial nondestructive evaluation without costly buy-in and commitment. Subsequently, the use of drone- based infrastructure evaluation may be accessible to a broader range of consumers, providing invaluable benefit to the public by ensuring the safe and continuous operation of critical infrastructure.

Michigan State University

Team Members (left to right)

Maria Khalilova
Novi, Michigan

Isabelle Lenhardt
Ann Arbor, Michigan

Poulomi Dey
Troy, Michigan

Eleanor Murray
Dearborn, Michigan

Reeve Fernandes
Mangalore, India

MSU Nondestructive Evaluation Lab

Project Sponsor

Yiming Deng
East Lansing, Michigan

Ciaron Hamilton
East Lansing, Michigan

Project Facilitator

Dr. Tongtong Li

Molecular communication is an innovative bio-inspired communication technology that involves the transfer of information through the exchange of molecules. This method shows promise for interconnecting electrical devices at the macroscale, interfacing with biological cells for biomedical applications. In molecular communication channels, information is encoded in the concentration of molecules. The molecules then diffuse through the environment to reach the receiver.

Previous studies have explored molecular communication channels utilizing the diffusion-convection process. The process focuses on noise sources and channel performance metrics, such as capacity, data rate, and bit error rate under various conditions. These molecular communication channel setups typically include wind-tunnel-like fan arrays for directional airflow, alcohol atomizers to transmit molecules, and sensor arrays as receivers. However, challenges such as maintaining steady airflow, accurately controlling fan speeds, and ensuring precise molecule detection have been present in these experiments.

The team looks to further characterize the macroscale communication channel by conducting various tests, such as adjusting fan speeds and directions, transmission molecule amounts, transmitter-receiver distance, and transmitter duration. Turbulent airflow will be introduced by controlling the existing fans, adding a new one to the side of the channel, and fan gratings. The microcontroller boards that control the fans, sensors, and atomizers will be upgraded to a more capable, singular board, and the communication circuitry for the sensors will be improved. A detection algorithm will be developed to interpret received signals, along with a novel modulation program to increase data rate, while reducing bit error rate.

Michigan State University

Team Members (left to right)

Ethan Bernier
Rochester Hills, Michigan

Adam Jaraki
Grosse Pointe, Michigan

Justin Littleton
Sterling Heights, Michigan

Tharwath Chowdhury
Sterling Heights, Michigan

MSU UNLUTURK LAB

Project Sponsor

Bige Unluturk
East Lansing, Michigan

Project Facilitator

Dr. Robert McGough

The goal of this project is to design an automated phosphate sensing system for agricultural practices, which is crucial in mitigating the harmful effects of excessive phosphate runoff into rivers. High phosphate levels cause rapid algae growth, which depletes dissolved oxygen (DO) in the water, leading to aquatic ecosystem collapse. While phosphorus is essential for wildlife and vegetation, elevated concentrations reduce DO, threatening marine life and contaminating drinking water sources. Reducing phosphate levels helps protect marine ecosystems and maintain water quality.

This system is comprised of three main components. The first is the development of an automated dispensing mechanism that accurately combines reagents with phosphate present in water samples, ensuring precise mixing and consistent delivery for each test cycle. The second component involves photodetection, utilizing a sensor such as a photodiode or phototransistor to capture the signal produced when phosphate reacts with the reagent. The third component is data analysis, where the photodetection signal is processed to determine the phosphate concentration in the water.

To ensure system efficiency, particularly in real-time phosphate monitoring, extensive testing is conducted across various phosphate concentrations and reagent mixtures. This identifies the ideal reagent composition that produces a detectable color change at a specific wavelength, which is then measured by the photodiode. The reagent is dispensed incrementally until the correct wavelengths are detected. The goal is to create a robust system capable of providing continuous data on phosphate concentrations, enabling timely interventions to prevent ecological damage.

Michigan State University

Team Members (left to right)

Georgia Bolek
Traverse City, Michigan

Remy Van Wert
Canton, Michigan

Brendan Mack
Clarkston, Michigan

Sebastian Spaenle
Macomb County, Michigan

Gabrielle Price
Muskegon, Michigan

MSU Smart Microsystems Lab

Project Sponsor

Alexander George
East Lansing, Michigan

Project Facilitator

Dr. Mauro Ettorre

The MSU Solar Car Racing team started 24 years ago, competing in both the American Solar Challenge (ASC) and Formula Sun Grand Prix (FSGP). They are a racing team that is designing a single occupant port canopy four- wheel solar car. The current vehicle, codenamed Cynisca, was taken to the 2024 competition but was kept from competing due to incomplete and poor designs.

Currently, one of the poor designs hindering the team from competing is being caused by the data buses on the vehicle being overloaded, causing data to be lost. The overloaded buses are due to only having two CAN nodes and using the slower CAN 2.0 protocol, as opposed to the faster FDCAN protocol. There is also no GPS system or data storage system.

This means if the car loses power due to a fault state, there isn’t any way to view what the fault was after the vehicle shuts down as the information is not stored.

The goals for us are to centralize the CAN system with support for three lines, control vehicle lights while adhering to regulations, add a GPS system to be able to track the car, and an SD card to monitor the car’s systems by logging necessary information.

Our design will consist of an enclosed PCB, with a mounted STM32 Nucleo Development Board for all processing needs. The board must be able to communicate with the solar car via CAN to toggle the lights (headlights, turn signals, hazards, brakes, etc.). These lights must comply with all ASC and FSGP competition racing regulations. There also must be support for three FDCAN lines. Using the FDCAN lines, the board must properly route all relevant signals to their respective systems without error. The PCB must include a GPS module to deliver useful tracking information for the team, as well as an SD card for data logging even after power is removed from the vehicle.

Michigan State University

Team Members (left to right)

Lucas Wolstencroft
Novi, Michigan

Nireimathiyan Sundaramsomu
Okemos, Michigan

Lukas Herrmann
Plymouth, Michigan

Zoltan Kovacs
Troy, Michigan

Shane Liu
Shenzhen, China

MSU Solar Racing Team

Project Sponsor

Owen Winegar
East Lansing, Michigan

Project Facilitator

Dr. Jeffrey Nanzer

With the increasing development of autonomous vehicles and smart transportation systems, Vehicle- to-Everything (V2X) communication has become a critical element in ensuring the safety, reliability, and efficiency of connected vehicle ecosystems. However, ensuring that V2X communications are secure and resistant to eavesdropping or unintentional interferences poses a significant challenge.

To address these issues that are associated with V2X communications security, the team is designing a custom phased antenna array to achieve directional modulation techniques through phase vector synthesis and spatial amplitude dynamics, while ensuring that the system is lightweight and inexpensive to implement. In addition, Software-Defined Radio (SDR) technology is used in conjunction with AES and RSA to transmit encrypted data with directional modulation techniques serving to enhance the overall security of the system by adding an additional physical layer of security.

To demonstrate the practical application of this technology, the phased array system will be integrated with a drone and will communicate with a stationary ground receiver. This design will implement multiple key parts such as a handshake protocol for initial connection, beamforming to detect the receiver node, and encrypted music streaming for secure communications.

To evaluate the final design, the Bit Error Ratio (BER) will be measured to ensure that it is low when the drone and ground systems are broadside to ensure that only the desired recipient is able to receive the data, and the playback of the audio stream enables for an auditory check that the data is correctly transmitted without any serious degradation.

Michigan State University

Team Members (left to right)

Akash Bedi
Bloomfield Hills, Michigan

Sangwoon Jeong
Rochester Hills, Michigan

Balaji Ganeshbabu
Rochester Hills, Michigan

Ben Toaz
Ionia, Michigan

Lance Moy
Troy, Michigan

Electromagnetics Research Group

Project Sponsor

Jacob Randall
Novi, Michigan

Project Facilitator

Dr. Premjeet Chahal