Prospective project descriptions for the ECE REU program 2023 can be found below:

Spectral River Imaging

Mentor: Prof. Joe Shaw (2 students)

In this project, students will help conduct outdoor experiments using drone-mounted and tripod-mounted hyperspectral, multispectral, and polarization imagers to detect and map harmful algal blooms and monitor water quality in rivers. Between field experiments, the students will assist with calibrating, processing, and georeferencing imagery. Analysis will be done using primarily the Matlab programming language. This project will involve interdisciplinary collaborations with students and faculty with expertise in optical engineering, remote sensing, and river ecology. For this project, interest and experience in environmental science, photography, imaging, and optical systems will be helpful.

Nanofabrication of Optical Devices

Mentors: Prof. Wataru Nakagawa, Prof. David Dickensheets (1–2 students)

Micro- and nano-fabrication technologies based on silicon have enabled a vast range of technological advances, including microprocessors, integrated systems, and optoelectronics. The Nano Optics group uses these manufacturing technologies to produce functional optical devices based on nanostructures in silicon and related materials, optimized for applications in polarimetry, optical imaging, and LIDAR. The participating student will be given an introduction to these fabrication technologies and hands-on training in working in a clean room facility. Working with group members, the REU student will be assigned a sequence of steps in the fabrication process chain to understand and optimize, and to assist in fabricating functional optical devices in our cleanroom facility.

Optical Metamaterial Characterization

Mentor: Prof. Wataru Nakagawa (1–2 students)

The Nano Optics group is developing and testing composite optical materials (metamaterials) with engineered polarization properties for sensing and other applications. The optical properties including polarization characteristics of these devices must be thoroughly measured and analyzed in order to understand their performance and give feedback to the design and manufacturing processes. This involves using an optical characterization system to measure the spectral and polarization properties of the metamaterials under test, including laboratory automation and signal processing/analysis tools. The REU student will be trained on the operation of this system, assist in its calibration and testing, perform measurements on fabricated metamaterial devices, and potentially assist in making improvements to the system. Prior experience with Matlab, LabView, polarimetry, and/or benchtop optical measurements desirable but not required. 

LIDAR Processing and Target Identification with Machine Learning

Mentor: Prof. Brad Whitaker (1–2 students)

The Whitaker Lab is collaborating with the Optical Remote Sensor Laboratory (ORSL) and Spectrum Lab to develop machine learning approaches for identifying targets in cluttered LIDAR environments. The techniques have applications from environmental monitoring (detecting fish in lakes or insects in a field) to military surveillance (detecting flying drones or camouflaged materials). In this REU project, we will use signal processing and machine learning techniques to identify targets of interest in labeled and unlabeled data collected using LIDAR or other optical remote sensing devices.

Machine Learning for Software Quality Assurance

Mentor: Prof. Brad Whitaker (1–2 students)

The Whitaker Lab is developing a machine learning approach to cybersecurity and software quality assurance. Most commercially developed software relies heavily on large, disparate libraries of source code. Unfortunately, not all software is created equal, and many libraries and packages have severe cybersecurity vulnerabilities. Machine learning models have the potential to spot unintentional vulnerabilities in software that draws from many different sources. In addition, deep neural networks may be able to interact with other software quality assurance tools to help the user visualize the locations and severity of detected vulnerabilities. In this REU project, we will develop machine learning methods for detecting and visualizing software vulnerabilities, improving the ability of programmers to develop secure software.

Characterization of MEMS Mirrors in LIDAR Systems

Mentor: Dr. Andrew Oliver (1 student)

Microelectromechanical systems, or MEMS, is the technology of building sophisticated mechanical structures at micro-scale - so called "micromachines." MEMS micromirrors for laser beam scanning have emerged as enabling components for medical endoscopes, virtual reality headsets and LIDAR (LIght Detection And Ranging) cameras for smart and autonomous cars. Researchers at MSU are designing and building new types of micromirrors and optical sensors for many applications, including long-range LIDAR.  This is an exciting opportunity for a hands-on undergraduate student to help develop this next generation of optical sensors and devices.  Specifically, we are looking for an undergraduate student to help characterize the MEMS mirrors and to measure their performance inside a lidar system.  This project will involve an introduction to and learning about optics and optical measurement techniques, microcontroller programming, lidar, control systems and statistics.  They will also learn about Matlab and how to present experimental results.  Prior experience is not required since we will train you.

Optical Characterization of Evoked Signal Propagation Patterns in Nano-directed Neurite Networks

Mentors: Prof. Anja Kunze, Connor Beck (2 students)

Stimulating neuronal cell activity to overcome neuro-degenerative processes such as the loss of synaptic connections are achieved through electrical, chemical, or biomechanical signals. Recent advancements in the field of neuroengineering are based on nanoparticle-mediated stimulation of neuronal activity, either based on mechanical forces, heat, or light interactions. Exposing neurite networks to nanoparticle-mediated stimulation for two weeks may permanently imprint a directionality within an unorganized neurite network. The efficiency of these methods is monitored through electrophysiology or fluorescent microscopy, e.g., by labeling and analyzing intracellular calcium concentrations (Ca2+ signals) with fluorescent markers. In this project, the REU student will characterize the direction of evoked calcium signal propagation from fluorescence microscopy in random and subcellular force-mediated organized neurite network patterns. This project will expose the REU student to the state-of-the-art and current challenges of light-based methods to analyze neuronal cell behavior and signaling in neuroengineering.  At the end of the REU project, the student will have learned to capture neuronal signals through fluorescent-light microscopy, generating and recording electrophysiological signals in mechanically stimulated neurite networks. Furthermore, the student will be embedded in a vibrant lab dynamic in the Kunze Neuroengineering Lab, consisting of several graduate and undergraduate students. Strong communication skills, weekly participation in lab meetings, and team-working attitudes are expected. 

Semiconductor Process Development

Mentor: Dr. Andrew Lingley (1 student)

The Montana Microfabrication Facility is a resource for Montana State University, external academics, and commercial entities that provides affordable access to a range of micro and nanofabrication equipment. We support applications ranging from fundamental physics to biology, microfluidics, MEMS and MOEMS, and sensors. Our mission is to accelerate and simplify the process of designing and fabricating micro and nanoscale devices for scientific research, device development, and prototyping. To better achieve this mission, we need to understand and characterize semiconductor equipment and processes so our users can achieve their goals quicker. We will work with 1 REU student to better characterize one of the following processes, depending on the student's interest: 1) anodic bonding for microfluidics, 2) glancing angle electron beam evaporation for control over nanoscale crystal growth, 3) reactive sputtering for thin-film dielectrics. The participating student will be trained to work in the microfabrication (cleanroom) facility, as well as on the specific fabrication steps for the chosen process. This project will provide introductory experience for a motivated student to learn about micro- and/or nano-fabrication, with a broad range of potential applications. Prior cleanroom experience, while welcome, is not required; prior hands-on laboratory experience strongly preferred.

Image Recognition of Microbial Cells During Mechanical Loading

Mentor: Prof. Stephan Warnat (1–2 students) 

In natural environments, most microorganisms exist as organized communities known as biofilms. Biofilms are complex communities composed of multiple microorganisms and organic matter attached to surfaces that often cause microbial-induced corrosion. Studying biofilm growth is challenging owing to its heterogeneous structure and diverse microbial composition. The Warnat lab wants to develop an image recognition system that differentiates the diverse composition. The created database will be used in future work to determine mechanical biofilm properties.  Image recognition algorithms need to be developed in Matlab for an autonomous detection machine on a single board computer such as the NVIDIA Jetson. Matlab offers existing functions that can be tested and optimized for biofilm characterization. The realized algorithm is expected to measure size and location in the field of view of beads and one defined biofilm-forming microorganism.  Students will work and learn in an interdisciplinary team of biologists, physicists, mechanical and electrical engineering at the Center for Biofilm Engineering.