ECE Projects – Spring 2025

MSU Bikes Service Center has highlighted the need for a safer, more inviting atmosphere for all users of the shared roadway. Red- light running is a significant problem in almost every intersection, yet current traffic laws in Michigan do not allow for cameras to be used in detection and photography of the license plate of the violating vehicle. A police officer must be an eyewitness to the red-light running, but since it’s nearly impossible to tell the timing of the light from the opposing side of an intersection, many such cases go unreported. Our project aims to decrease the fatal impact of neglected traffic signals.

We designed a radar sensor system to detect an oncoming car traveling at an excessive speed in the vicinity of the intersection, where cross traffic or pedestrians may be in harm’s way. Our design is freestanding and compact enough to be integrated into various intersection models. We focused our design on campus communities per the needs of our client, being focused on heavily trafficked areas like Michigan State University’s roadways.

We will trigger an alert system to notify motorists from all directions of the oncoming threat using visual and audible alarms. The social shaming element of the alert system should also encourage the red-light runner to make a better decision in future intersections. Blatant red-light running is a dangerous threat to innocent people who are following traffic laws. Our red-light runner project is a potential remedy to this growing problem.

Michigan State University

Team Members (left to right)

Geoffrey Rajesh
Novi, Michigan

Aaron Cordts
Ann Arbor, Michigan

Ben Zuzga
Macomb, Michigan

Abigail Bilyeu
Grand Rapids, Michigan

Norah Daley
Marquette, Michigan

MSU Bikes Service Center

Project Sponsors

Tim Potter
East Lansing, Michigan

Project Facilitator

Dr. Premjeet Chahal

The Facility for Rare Isotope Beams (FRIB) at Michigan State University contains the world’s most powerful heavy-ion accelerator. With this particle accelerator, Michigan State University helps scientists better understand the physics of nuclei, nuclear astrophysics, fundamental interactions, and applications for society.

The FRIB works with high intensity beams that require safe transportation. To facilitate this, beams require measuring and monitoring of beam size and distribution using non-interceptive diagnostics with devices called beam position monitors (BPMs). BPMs are placed along the path of the beam, and they operate by measuring the capacitance between the beam and the button on the BPM. However, their current beam position monitoring systems face a critical limitation: they lack a reliable method to simulate variation in beam quadrupole moment, which hinders the ability to analyze and improve beam dynamics.

The goal of this project is to change the way this calibration procedure takes place. First, the input signal is split into four paths, enabling the calibration of quadrupole moments. After the signal is split, our circuit then attenuates each of the four channels independently, at configurable steps, to enable multiple shapes to be accounted for. The four channels will then undergo a similar treatment for phase shifting, further increasing the possible number of ways to calibrate these sensors.

Our design utilizes a custom printed circuit board (PCB), enabling reproducibility and precision. Our design also includes widely adopted SMA connection points, making it both versatile and cost-effective. The inclusion of a microcontroller makes this device easy to use and intuitive to the user.

Michigan State University

Team Members (left to right)

Jack Dorris
Houston, Texas

Sandhiya Suresh
Chennai, Tamil Nadu, India

Jacob Lucas
Brighton, Michigan

Jack Bruienne
Ann Arbor, Michigan

Nathan Joseph
DeWitt, Michigan

MSU Facility for Rare Isotope Beams

Project Sponsors

Steven Lidia
East Lansing, Michigan

Project Facilitator

Dr. Oleksii Karpenko

Over the last two decades, communication security at the physical layer has increased in importance for wireless communication. Commercial, automotive, and aerospace applications are beginning to require more wireless sensors and transceivers, thus requiring strict compliance guidelines to mitigate unintentional interference or improve robustness to hostile interference from foreign devices. These emerging concerns must be addressed to ensure safe and acceptable wireless system performance for a range of applications. The previously developed C-band Phased array communication system project enables researchers and engineers to prototype communication and sensing systems with the HackRF One software-defined-radio (SDR).

The secure communication system will be implemented in a V2X environment between a car and a drone, which enables experimentation with future 6G satellite communication networks. The integration of the C-band phased array provides a versatile platform for exploring a wide range of communication and sensing applications. Due to university restrictions on piloting drones, the majority of the tests will be conducted when the drone is in a static position.

Traditional radar sensing and tracking will be developed to further improve robustness of the communication system and validate the combination of software and physical layer communication security techniques.

Michigan State University

Team Members (left to right)

Arjun Bhat
Canton, Michigan

Bennett Marr
Okemos, Michigan

Matt Rochna
Canton, Michigan

Andrew Wojciechowski
Pinckney, Michigan

Sherwin Shiran
Okemos, Michigan

Keegan DeGeode
Hudsonville, Michigan

MSU Electromagnetic
Research Group
(EMRG)

Project Sponsors

Jacob Randall
East Lansing, Michigan

Project Facilitator

Dr. Jeffrey Nanzer

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. The ability of this design of diamond to maintain mechanical hardness while being a viable conductor in microelectronics makes for an increasingly valuable manufacturing resource. As electronics are designed to function at higher frequencies and in hostile environments, diamond-based electronics are needed in larger quantities at lower costs. Manufacturing diamond- based chips is often done through the long and expensive process of diamond lithography.

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 Linux software for autonomous 3D movement and solution dispensing. Images will be processed and converted into a binary matrix that is used by the microcontroller to control the stepper motors and printhead to precisely dispense the diamond seeding solution.

Michigan State University

Team Members (left to right)

Bowen Wang
Beijing, China

Jack Perry
Lewiston, Michigan

Conner Wilczewski
Novi, Michigan

Gabe Humbert
Three Rivers, Michigan

Jack Geisler
Temperance, Michigan

Fraunhofer USA, Center Midwest

Project Sponsors

Alex Ho
East Lansing, Michigan

James Siegenthaler
East Lansing, Michigan

Project Facilitator

Dr. Mohammed Ben-Idris

Great Lakes Crystal Technologies is a semiconductor supplier in East Lansing, Michigan, specializing in production of high-quality diamond substrates and wafers for applications in electronics, quantum computing, and optics. Since 2019, the company has developed and utilized its proprietary Chemical Vapor Diamond Disposition (CVD) reactors to produce the high-quality substrates. Initially, the CVD reactors were controlled and monitored by LabVIEW, a data acquisition and machine control programming platform.

As part of the company’s ongoing commitment to improving operational efficiency and scalability, Great Lakes Crystal Technologies sought to enhance the reliability and simplicity of their reactors to support future advancements in diamond substrate production. The objective of this project was to design and implement a Programmable Logic Controller (PLC) capable of managing and monitoring the complex processes within a single CVD reactor.

To replace the existing LabVIEW-based control system, the PLC needs to communicate seamlessly with a variety of sensors utilizing a unified communication protocol, integral to the reactor’s operation. The key challenge was ensuring proper connectivity and consistent data flow, as current systems based on serial communication had to be transitioned to a new architecture for interactions with the PLC, thereby enabling ease of communication and data collection while maintaining operational integrity.

With the implementation of a PLC, Great Lakes Crystal Technologies can now explore the world of automation, enabling quicker process flows and requiring minimal human intervention. Additionally, the upgraded PLC system offers long-term security benefits at the operating system level, ensuring resilience against potential vulnerabilities.

Michigan State University

Team Members (left to right)

Jim Allen
Beverly Hills, Michigan

Andrew Bastian
Birmingham, Michigan

Pranshu Dixit
Mumbai, India

Nathan Grimmer
Northville, Michigan

John Tysar
New Baltimore, Michigan

Great Lakes Crystal Technologies

Project Sponsors

Andrew Kovalchick
East Lansing, Michigan

Project Facilitator

Dr. Nelson Sepulveda

Henry Ford Health, a leader in innovation health care solutions, is building Destination Grand, a world- class facility set to open in 2029, which will integrate advanced robotic systems for day-to-day operations, including the transport of pathological specimens within the medical laboratory area. To prepare for this future, the Pathology Department aims to evaluate and implement a robotic transport system in their current facility. Our mission is to design and develop a robotic system capable of safely and efficiently transporting pathological specimens within the existing hospital environment. This system will serve as a prototype for the advanced robotic solutions planned for Destination Grand.

Pathological specimens need to be transported across a predetermined route that includes multiple turns, delivery points, and an overpass connecting two buildings. Our robot will navigate this route autonomously, avoiding obstacles and pedestrians, and be equipped with features such as adjustable height, navigation sensors, and recharging functionality, and it will be designed to transport specimen boxes of predetermined dimensions.

A user interface will enable staff to easily command the robot for deliveries or returning to base. By collaborating with Henry Ford Health, we aim to create a reliable and efficient robotic transport system that not only meets the needs of the current facility but also lays the groundwork for the future of healthcare logistics at Destination Grand. This project represents a step forward in integrating robotics into healthcare, improving operation efficiency, and enhancing patient care.

Michigan State University

Team Members (left to right)

Benjamin Hackman
Vicksburg, Michigan

Jackie Dinh
Grand Rapids, Michigan

Matthew Hull
Milford, Michigan

Pranesh Muthukumar
Coimbatore, India

Yi-Hung Kan
Taipei, Taiwan

Henry Ford Health

Project Sponsors

James Adams
Detroit, Michigan

Adam Baldwin
Detroit, Michigan

Vikas Relan
Detroit, Michigan

Project Facilitator

Dr. Tom Clark

Existing food tracking and recommendation applications— such as Yuka, MyFitnessPal, and Lifesum—primarily focus on calorie tracking and dietary logging. While these tools help users monitor their overall food intake, they fall short when it comes to providing personalized, genetics- based recommendations. GenoPalate addresses this gap by offering a platform that helps users discover foods best matched to their genetic profiles, paving the way for more precise and individualized nutrition guidance.

Earlier versions of the GenoPalate app provided fundamental nutritional data and basic food suggestions but lacked robust sorting and filtering functionalities. Although recent updates introduced color-coded food scores and improved categorization, additional refinements are needed to boost user interactivity and real-time personalization. Our project builds on these foundation features by implementing a more user-friendly interface, enhanced filtering and sorting options, and customizable visibility controls.

Central to this initiative is a redesigned food index page featuring a clear, color-coded scoring system that identifies healthier (green) versus less healthy (red) choices at a glance. A refined filtering mechanism enables users to exclude items they cannot or prefer not to eat—whether due to allergies or personal preferences—while a new toggle feature lets them hide or unhide foods with ease. Users can sort items by score, food group, or nutrient content, and they will also have access to a diet profile editor. This editor supports diverse dietary preferences—from vegan (excluding meat, fish, dairy, and eggs) to vegetarian (excluding meat and fish) and pescatarian (excluding meat)—so individuals can swiftly identify foods that align with their unique needs. By offering these robust personalization tools, our project ensures that users can tailor their dietary experience to fit their goals and restrictions.

Michigan State University

Team Members (left to right)

Sean Gavin
Lake Grove, New York

Sawyer McClure
Cadillac, Michigan

Joseph Funke
Rochester Hills, Michigan

Nick Pecktol
Macomb, Michigan

Tanner Mason
Marine City, Michigan

GenoPalate Inc.

Project Sponsors

Asif Naseem
Wauwatosa, Wisconsin

Hannah Pekarek
Wauwatosa, Wisconsin

Project Facilitator

Dr. Yiming Deng

Wyatt’s Creative Works is a startup company that showcases creative software solutions to everyday used apps. Founded in 2022 in Grand Blanc, Michigan, the company is a publishing and innovation production studio that is focused on creating projects designed for minimalistic organization tools for reminders and taking notes.

The main problem with electronic organizational tools is the need to download multiple apps to use important features such as calendar events, reminders, and note taking. Additionally, there are no specialized tools for organizing information regarding specific activities like grocery shopping, bills and expenses, as well as travel plans. Our team’s focus is to create two projects for Wyatt’s Creative Works, LLC: the Modern Organizational App, and the Modern Notes App, with the use of a minimalistic design.

Our team will implement a cross-platform app that focuses on organizational tools including a feature for notifications and a built-in calendar. We will continue to expand on the project from what a previous team did by using Flutter for the cross-platform API tools specifically for subscriptions and choice of language feature. The main additions will require a sign-in and a login page for each user with Firebase authentication.

In addition, our team implemented the Modern Notes app tool for creating custom notes with a flipbook design as well as a toolbar for choosing designs for notes. Utilizing the same software tools as the organizational app, it will serve to create custom notes depending on the subject of choice.

Each of the products will service a multilingual customer base with organizational and note taking tools. Cloud accessibility with Firebase will enhance the user experience with offline access to each app.

Michigan State University

Team Members (left to right)

Tim Earle
Macomb, Michigan

Nolan Schroeder
Birmingham, Michigan

Rami Imran
Ann Arbor, Michigan

Talal Alkhaled
Kuwait City, Kuwait

Peter Polega
St. Joseph, Michigan

Wyatt’s Creative Works, LLC

Project Sponsors

Marquonda Wyatt
Grand Blanc, Michigan

Project Facilitator

Dr. Navid Yazdi

As urban populations grow and traffic congestion worsens, the need for safer, more efficient transportation systems becomes increasingly urgent. Issues such as high accident rates, inefficient fuel consumption, and limited mobility for individuals with disabilities highlight the potential benefits of advancing autonomous vehicle technologies. Semi-autonomous vehicles can be critical in reducing human error, optimizing traffic flow, and enhancing transportation accessibility.

In response to these challenges, the goal of this project is to develop a hardware and software environment that replicates the characteristics of a semi-autonomous vehicle control system, like Tesla’s Autopilot. The system will include a benchmarking routine utilizing a well-known machine learning model (CNN/DNN). With the usage of the Raspberry Pi 5, the project will integrate a first/third-party AI acceleration kit, a first-party camera, and actuators controlled through the Pi’s GPIO.

The miniature car will be trained to perform essential autonomous driving tasks, such as flexible speeds, navigating curves, following other vehicles, parking, and responding to traffic signals and pedestrians. Key requirements set by the sponsor include computer vision and the implementation of an AI accelerator for enhanced processing and computation.

Design constraints include budget limitation, workspace size, car mobility, and the challenge of adapting image processing to a lower vantage point compared to standard datasets. Two design approaches are considered: using a pre- made RC car kit for ease and compatibility or building the car from scratch for greater customization and cost efficiency.

The goal is to complete the hardware setup and initiate the software development phase by the end of the semester, ensuring the foundation for an effective semi-autonomous vehicle prototype.

Michigan State University

Team Members (left to right)

Billur Haskara
Macomb, Michigan

Brandon Trela
Plymouth, Michigan

Gunnar Karlstrom
Clarkston, Michigan

Colten Zehnder
Frankenmuth, Michigan

Chidera Ikpeama
Belleville, Michigan

MSU Cyber Security Lab

Project Sponsors

Jaxon Hancock
East Lansing, Michigan

Project Facilitator

Dr. Nihar R. Mahapatra

Deep learning, a type of machine learning (ML) that uses artificial neural networks to learn from data in a similar manner to the human brain, has become increasingly prevalent in technology today in a variety of applications. However, although it has many benefits, there are some vulnerabilities that, when exploited, can cause these models to have adverse results. This project focuses on adversarial attacks, which are one of the main weak points of ML systems. Adversarial attacks consist of intentionally altering inputs with small perturbations that are virtually undetectable to the human eye with the goal of forcing the system to misclassify the input. The consequences of these attacks can be extremely dangerous, especially when the ML model is used in safety-critical systems. For example, if an attacker were able to slightly alter an autonomous vehicle’s camera input so that it misclassifies a stop sign as a speed limit sign, it could cause the vehicle to drive into cross traffic, which would be devastating.

The primary goal of the project is to develop adversarial attack methods to cause handwritten digits to successfully be misclassified with minimal modifications to the input. Furthermore, an intuitive graphical user interface will be developed to enable users to interact with the system, visualize adversarial examples, and adjust attack parameters dynamically. The program will be run on embedded hardware and enable users to draw on a digital tablet or a whiteboard for camera capture to use as input for the model, which will bridge the gap between theory and real-world implementation.

By developing ways to exploit the vulnerabilities in these ML models, engineers can better understand the weaknesses of these models and develop stronger defenses against them, enhancing the robustness of ML systems.

Michigan State University

Team Members (left to right)

Tolu Oshin
Midland, Michigan

Justin Skipper
Grand Rapids, Michigan

Chris Dadisho
Farmington Hills, Michigan

Someshwar Maji
Rochester Hills, Michigan

Owen Wurzer
Pinckney, Michigan

MSU Cyber Security Lab

Project Sponsors

Jaxon Hancock
East Lansing, Michigan

Project Facilitator

Dr. Daniel Morris

Our project explores the integration of brain-to- computer interface technology to enable hands- free control of IoT (Internet of Things) devices. Specifically, we are developing a system that enables users to control a remote-controlled (RC) car using brain signals. This project is divided into two main phases.

In the first phase, we establish a wireless communication system between a computer and the RC car. The car will initially be controlled using a keyboard or mouse, enabling us to ensure smooth directional movement with minimal latency. This phase serves as the foundation for responsive communication before introducing brain control.

The second phase involves integrating an EEG headset (EMOTIV EPOC X) to capture signals from the user’s brain, which are then processed using MATLAB. The processed signals are mapped to specific commands such as moving forward, left, or right. These commands then will be transmitted to an Arduino-based RC car. By utilizing signal processing techniques and reducing noise interference, we aim to achieve reliable real-time control using only brain activity.

This project demonstrates the potential of hands-free control technology, with applications in accessibility, robotics, and human-computer interaction. Our work contributes to advancements in assistive technology, enabling individuals with mobility impairments to interact with devices in new and intuitive ways.

Michigan State University

Team Members (left to right)

Abdulrahman Alharbi
Medina, Saudi Arabia

Josh Lauzon
Livonia, Michigan

Daniel Qin
Canton, Michigan

Evan Bennett
Clarkston, Michigan

Matthew Frazee
Portland, Michigan

MSU Broadband
Access Wireless
Communications Lab

Project Sponsors

Jinxian Deng
East Lansing, Michigan

Ming Gu
East Lansing, Michigan

Evan Sun
East Lansing, Michigan

Project Facilitator

Dr. Premjeet Chahal

This project is to design an impedance-matching network which will optimize power transmission to and from arbitrary transducers. Impedance- matching circuits reduce the amount of reflected energy and can be approached in multiple ways. For the purposes of this project, an impedance-measuring circuit and two impedance-matching circuits will be designed. The impedance-measuring circuit will measure the impedance of a transducer at a given frequency. The two impedance-matching circuits will maximize power transmission from a 50 Ohm high- power amplifier to the measured transducer, and the power transmission from the measured transducer to a low-noise amplifier. Testing equipment, transducers, and other necessary lab equipment will be provided. A proper impedance-matching network results in optimized power usage and increased transducer performance with a plethora of applications including nondestructive evaluation and testing, ultrasonics, electromagnetics, radio communications, micro- sensing and more.

Michigan State University

Team Members (left to right)

Thomas Bonnen
Milford, Michigan

Mohsen Anas
Toronto, Ontario

Xzandria Jozwiak
Boyne City, Michigan

Dwight Reed
Detroit, Michigan

Nic Poma
Howell, Michigan

MSU Physical Ultrasonics, Microscopy
and Acoustics (PUMA) Lab

Project Sponsors

Sunil Chakrapani
East Lansing, Michigan

Project Facilitator

Dr. Sunil Chakrapani

With the purpose of combining bioengineered materials with advanced manufacturing, Li Labs aims to develop bio-integrated technologies that enable us to precisely record, understand, and modulate biology. Led by Professor Jinxing Li, Ph.D. and a team of doctoral candidates, postdoctoral scholars, and research assistants, the Li Lab strives to apply these technologies to interrogate complex nervous circuits and microbial activities, with the goal of reshaping connections between the synthetic and biological worlds.

Our project involves creating a portable, wireless ECG for long-term use. An ECG is a non-invasive test that uses electrodes to view the electrical activity of the heart.

We want to collect accurate, thorough results that can detect and alert abnormal heart activity.

Our task is to improve and finalize an ECG system from the initial circuit board stage which contains components such as an MCU, CPU, Bluetooth, and battery. For the hardware phase, we are to add an SD card for memory, improve the battery life, and minimize the size of the board. For the electrode phase, we must determine the electrode patch type and material, design an effective electrode layout, and magnetically connect the circuit to the electrode leads. For the software phase, we must integrate a machine learning algorithm to detect heart abnormalities and develop a user-friendly app.

Michigan State University

Team Members (left to right)

Sydney Chap
Northville, Michigan

Brandon Curtis
Clinton Twp., Michigan

Reed Scott
Milford, Michigan

Jeni Fischer
Macomb Twp., Michigan

Tung Pham
Hai Phong, Vietnam

MSU Li Lab@IQ

Project Sponsors

Jinxing Li
East Lansing, Michigan

Project Facilitator

Dr. Robert McGough

Li Lab is dedicated to developing bio-integrated technologies to advance human and planetary health through the creation of new diagnostics, therapeutics, and sustainable materials. They focus on three main domains: micro/nanorobotics, neural interfaces and bioelectronics, and engineered living materials. Li Lab has enabled access to their advanced manufacturing equipment and materials to aid in the success of this project, which aims to develop a high-density microneedle array for advanced neuromodulation and neurorecording applications. The main goal is to design and fabricate microneedles using state-of-the-art two-photon polymerization technology, optimizing their size and geometry to enhance signal stability while minimizing tissue damage.

The project involves a multi-phase testing approach. Ex-vivo and in-vitro experiments will be conducted to analyze the microneedle array properties and its ability to provide reliable and relevant measurements during the next phase of testing. In-vivo testing will then be performed on locusts with assistance from Li Lab to evaluate the safety, functionality, and overall performance of the microneedle arrays in a biological setting.

By leveraging cutting-edge fabrication techniques and rigorous testing protocols, this project seeks to contribute to the advancement of neurotechnology, paving the way for more effecting and minimally invasive neural interfaces.

Michigan State University

Team Members (left to right)

Jack Hutchison
Buffalo, New York

Ian McNorton
Dearborn, Michigan

Herb Harmon
Novi, Michigan

Samuel Webber
Rochester Hills, Michigan

Katy Samoy
Plymouth, Michigan

MSU Li Lab@IQ

Project Sponsors

Yulu Cai
East Lansing, Michigan

Jinxing Li
East Lansing, Michigan

Project Facilitator

Dr. Matthew Hodek

PoliMOVE-MSU is a team that competes in the Indy Autonomous Challenge (IAC). IAC organizes racing competitions among 17 universities from around the world to program fully autonomous racecars and compete in a series of events at iconic tracks. PoliMOVE-MSU had secured a top spot in the newest IAC competition. Our team is tasked with developing a 1/10th scale autonomous vehicle capable of navigating a track without human intervention. A major focus is developing a reactive vehicle that can optimize its speed, accuracy, and navigation. The result will be a high- speed scalable platform that mirrors real-world F1 vehicle development with a data-driven testing environment.

The requirements for this project are to create a scaled autonomous race car that follows the F1/10th style of build given a specific budget. The vehicle testing environment must be created to mimic a track. The vehicle must be scaled from an F1 car, with a vehicle reaching speeds of 20 mph to mimic the average top speed of 200 mph on an F1 car. The software side of the project is to implement robust data logging, processing, and storage to mimic the full-scale vehicle’s system, and it needs to be ROS-functional for command line operations.

Multiple sensors will be used for this project, which include LIDAR, IMU, and mounted cameras. A LIDAR unit plots the surrounding area of the vehicle by projecting light to create a map of obstacles. An IMU measures the current orientation and velocity which will be used to self-regulate position. A mounted camera can track the visibility of the vehicle in relation to its obstacles, which can work with LIDAR to track obstacles and match both views to get a complete map of its surroundings.

Michigan State University

Team Members (left to right)

Marcus Pytel
Rochester, Michigan

Gene Ruan
Rochester, Michigan

Grant Brisley
Rochester, Michigan

Jori Larner
Lansing, Michigan

Michael Li-Liu
Sterling Heights, Michigan

PoliMOVE-MSU

Project Sponsors

Shaunak Bopardikar
East Lansing, Michigan

Pragyan Dahal
East Lansing, Michigan

Project Facilitator

Dr. Edward Gebara

This project, Development of a Field-Use Heavy Metal Microfluidic Test Platform, is to create a portable, low- cost electrochemical sensing system for detecting heavy metals in water and soil samples. The project utilizes novel carbon fiber electrodes and a low-cost custom potentiostat developed by Fraunhofer USA and the MSU Microtechnology Lab in a fully integrated sensing system intended for in- situ monitoring of water and soil samples from agricultural operations. Existing commercial solutions are expensive and designed for laboratory conditions, making them poorly suited to real-time, on-site monitoring.

The system developed by our team incorporates an automated microfluidic sample processing structure, a Bluetooth enabled control PCB, and a control mobile application to enhance system usability and testing efficiency. The new system enables remote control and monitoring and enhanced data analysis through the addition of the Bluetooth hardware and software to the potentiostat PCB, along with the dedicated control and monitoring mobile application. These improvements—along with the new monolithic sample preparation structure and controls—also enable streamlined, automated sample preparation to improve ease of use and robustness of tests.

By introducing these capabilities, our team capitalizes on the new electrode technology to make a more robust, efficient, and cost-effective sensing system compared to traditional laboratory-based heavy metal testing methods. In doing so, our project makes these measurements more accessible for researchers and environmental agencies, lowering barriers to monitor these compounds and protect human health through our agriculture systems.

Michigan State University

Team Members (left to right)

Samuel Rabick
Kalamazoo, Michigan

Elise Wright
Chesterfield, Michigan

Matthew Clark
Mount Pleasant, Michigan

Nyah Williams
Detroit, Michigan

Grant James
Okemos, Michigan

Michigan Translational Research and Commercialization Innovation Hub & Fraunhofer USA

Project Sponsors

Mohammad Kafi Kangi
East Lansing, Michigan

Wen Li
East Lansing, Michigan

James Siegenthaler
East Lansing, Michigan

Project Facilitator

Dr. Ming Han

Effective navigation and mapping in unknown environments is a significant challenge, especially in underground drainage pipe systems where traditional localization tools like GPS are unavailable. At Michigan State University’s Smart Microsystems Lab, our team is developing a SLAM-based robot to autonomously explore and map underground drainage networks. This intelligent pipe inspection and water monitoring system will provide farmers with a 3D reconstruction of their drainage infrastructure, helping to optimize water management for improved soil health and crop yield.

Drainage pipes play a crucial role in preventing waterlogging and maintaining sustainable agricultural practices, yet their layouts often remain undocumented after installation. Our goal is to deploy a wheeled robot equipped with a magnetometer and other sensors to traverse these networks, collect data, and generate an accurate 3D map of our environment.

Our project consists of four key components. First, our robot must integrate a LiDAR sensor and be capable of running SLAM (Simultaneous Localization and Mapping) algorithms to create real-time maps of its environment. Second, we are developing autonomous navigation algorithms that enable the robot to avoid collisions, make intelligent path decisions, and fully explore unknown pipe systems. Third, we are designing a graphical user interface (GUI) to visualize the mapped networks, making them accessible for analysis and maintenance. Finally, we are constructing a test pipeline with varied paths and elevations to rigorously evaluate our system’s capabilities.

By implementing this advanced mapping technology, we aim to provide an essential tool for precision agriculture, enabling farmers to manage their drainage systems more effectively and sustainably.

Michigan State University

Team Members (left to right)

Maximillian Pytel
Rochester Hills, Michigan

Fahad Hakami
East Lansing, Michigan

Grant Heinlen
Troy, Michigan

Medala Yang
Clarkston, Michigan

Tegan Warren-Green
Canton, Michigan

Smart Microsystems Lab

Project Sponsors

Xiaobo Tan
East Lansing, Michigan

Xinyu Zhou
East Lansing, Michigan

Project Facilitator

Dr. Mauro Ettorre

Nondestructive evaluation (NDE) provides safe, efficient, and reliable methods for assessing critical infrastructure. By employing a ground drone, inspections can be conducted with precision and stability along a predictable route. This approach improves the efficiency of inspection and contributes to enhanced infrastructure safety.

The primary objective of this project is to design and build a custom Unmanned Ground Vehicle (UGV) for NDE of railway infrastructure. While previous work focused on using aerial drones to inspect hard-to-reach structures, our project shifts the focus to an unmanned, rail-adapted ground drone. This transition enables close-up, and continuous inspections of railway tracks and associated infrastructure.

Building on the work of a previous capstone team, a key component of our project is the integration of a sensor box. Instead of utilizing an aerial platform, the sensor box will be mounted on the ground-based drone to capture close-up, low-resolution video data for detecting structural issues in rail systems. By adopting this ground-based approach, the aim of the project is to enhance the effectiveness of railway inspections, particularly for aging and critical infrastructure.

The wheeled drone is powered by a ~ 60-watt motor, delivering torque to the wheels through a geared sprocket system. The motor also serves as the braking mechanism by reversing the current, eliminating the need for additional mechanical brakes. The frame features an H-shaped design, integrating all essential components, including the battery, sensors, and control boards, within the rails for a compact and efficient layout. The chassis is constructed using a combination of wood for cost efficiency and aluminum in key areas to ensure structural rigidity.

Michigan State University

Team Members (left to right)

Grant Woodford
Haslett, Michigan

Wyatt Donley
Ada, Michigan

Zimu Zhou
Canton, Michigan

Adarsh Vatts
New Delhi, India

Parker Strach
Chelsea, Michigan

MSU Nondestructive Evaluation Lab

Project Sponsors

Yiming Deng
East Lansing, Michigan

Zebadiah Miles
East Lansing, Michigan

Project Facilitator

Dr. William Harokopus