Our sustainable future: Remote sensing, smart systems, and innovative devices

REU students in Yellowstone National Park

Lidar measuring drone propeller

REU student piloting drone

 

 

The active research groups of the Montana State University Electrical and Computer Engineering Department will offer approximately 8–12 REU positions for Summer 2025. The REU program will run from May 26–August 1, 2025.

  • Participants must be full-time undergraduates, and have at least one academic term remaining after the program. Applicants from underrepresented groups are particularly welcome and encouraged to apply.  Applicants must be U.S. Citizens or permanent residents.
  • The REU program is intended to provide an introductory research experience to undergraduate students considering graduate education and a career in research. Applicants should address how this experience might facilitate their pursuit of these goals in their application.
  • Travel costs to/from Bozeman, $1,000 for meals, and residence hall lodging will be provided. REU students will also receive a $7,000 stipend.

Prospective Project Descriptions

Nanofabrication of Optical Devices (Mentors: Prof. Wataru Nakagawa, Prof. David Dickensheets) 

Significance: 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 and photonic devices based on nanostructures in silicon and related materials, optimized for applications in polarimetry, optical imaging, and LIDAR. Objectives: Functional optical devices based on silicon nanostructures will be designed, fabricated, and characterized in support of ongoing projects in the group. Role of REU participant: The participating student will be given an introduction to these fabrication technologies and hands-on training in working in MSU’s clean room facility. With support from team 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. The student will also be trained to use relevant characterization tools (e.g. profilometer, scanning electron microscope) to investigate the fabricated structures.

Cyber-Physical Power System Data Generation for Resilience and Security Studies (Mentors: Prof. Shamsun Nahar Edib, Prof. Brad Whitaker)

Significance: The integration of information and communication technologies (ICT) with traditional power infrastructure has transformed power systems into cyber-physical systems. This evolution enables advanced monitoring and control capabilities, enhancing system resilience and operational security. However, the scarcity of real-world datasets remains a significant barrier, largely due to confidentiality and security concerns associated with real-world power systems. Without such data, researchers face challenges in conducting studies on cyber-physical resilience, such as anomaly detection and vulnerability analysis. This project aims to address this challenge by generating realistic and open-source cyber-physical power system datasets that can support research efforts while safeguarding sensitive information. Objective: The project aims to: 1) utilize existing power system datasets (e.g., load profiles) to generate synthetic data that replicates real-world patterns, and 2) develop corresponding communication network traffic profiles to simulate interactions between physical and cyber layers. These datasets will provide a valuable resource for advancing data-driven research on cyber-physical power systems, addressing the current gap in publicly available integrated datasets. Role of REU Participant: The REU participant will use machine learning techniques (e.g., generative adversarial networks) to analyze and expand existing physical power system datasets and model corresponding communication traffic profiles to reflect cyber-physical interactions. The student will validate the generated datasets by testing them on power system models, ensuring alignment with realistic operational behaviors.

Elucidating Neuroplasticity in Neuronal Networks under Space-related Magnetic Field Gradient Changes (Mentor: Prof. Anja Kunze)

Significance: Exploration missions to the Moon and Mars, with their reduced gravity conditions, pose unique challenges to astronaut health, particularly concerning the neural adaptations required in microgravity. While short-term disorientation and sensory-motor impairment have been observed during spaceflight, the underlying neural mechanisms and the adaptation speed of neuronal circuits to altered gravitational forces remain poorly understood. Objective: This project focuses on investigating the neuroplasticity effects induced by magnetic field gradients that mimic microgravity-like conditions on human neuronal networks. Building on the Kunze Lab’s innovative multi-modal magnetic field gradient neuronal cell assay, the research will examine how mechanical forces, and magnetic gradients influence neuronal signaling, organization, and functional recovery over time. Role of REU Participant: The REU students will delve into the world of neuromodulation and utilize fluorescence microscopy to elucidate one or two neuronal cell behavioral changes associated with neuroplasticity. Hands-on experiences will include techniques for capturing growing and maintaining human neurons, recording neuronal signals, preparing and analyzing data-omics from cell assays, and understanding the complexities of neuronal adaptation to external stimuli. Thus, the REU students are expected to work in a biosafety class II, wet-lab environment within a highly collaborative research group environment with graduate and undergraduate peers, and participate and present in weekly lab meetings. Strong communication skills, an ability to work in a team, and enthusiasm for interdisciplinary research are essential. In summary, this project provides a unique opportunity to engage in state-of-the-art neuroengineering research, and to contribute to the development of strategies to support astronaut health during extended missions.

Semiconductor Process Development (Mentor: Dr. Andrew Lingley)

Significance: 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 develop and characterize a variety of semiconductor manufacturing processes. Additionally, with the push to onshore semiconductor manufacturing, we provide opportunities to spark interest in semiconductors and microelectronics, and do so in an inclusive, supportive environment. Objectives: This summer, our REU student will help develop a multi-user MEMS (Microelectromechanical System) process. Examples of MEMS devices are digital micromirrors that power all movie theater projectors and gyroscopes and accelerometers that determine the orientation and acceleration of smart phones. We would like to develop a MEMS course at MSU comprising one semester of design and one semester of in-lab fabrication. In this class, students would use a defined microfabrication process flow, or recipe, capable of turning a silicon wafer into a variety of microscale sensors or actuators. Although we have an outline for a basic process to create piezoresistors, cantilevers, membranes, electrodes, heaters, and other elements, this process needs to be vetted with a variety of experiments to determine the feasibility and individual process steps. Role of REU participant: The participant will work within a team of peers and will learn about cleanroom safety, operation, and etiquette, including proper dress code and gowning procedures. They will be assigned a process development task leading to a MEMS course that is commensurate with their experience, skills, and interests. Previous examples include experimenting with safer developers for photolithography, updating the texturing process for our solar cell class, and characterizing the photoresist removal rate and uniformity of several plasma ashing recipes. These projects will provide introductory experiences for motivated students to learn about micro- and/or nano-fabrication and the semiconductor industry, with a broad range of potential applications and careers. Prior cleanroom and lab experience is not required.

Long-Term Natural Soundscape Assessment and Description (Mentor: Prof. Rob Maher)

Significance: The natural acoustical environment of a park or wilderness area provides important information about the composition, diversity, and health of the natural ecosystem. The soundscape comprises biological sounds from animal communication, environmental sounds of wind, rain, and moving water, and anthropogenic sound of human activity. Objectives: The project requires (a) the creation of analytical software for automatically identifying soundscape components and performing statistical characterization of recordings that are potentially hundreds of hours in duration, and (b) experimenting with machine learning models and validation approaches to segmenting, identifying, and classifying sounds in environmental recordings. Role of REU participant: The participating student will gain a theoretical and practical understanding of environmental acoustics (the propagation, reflection, absorption, and attenuation of sound in the atmosphere), gain experience designing and conducting field research, participate in scientific interpretation and documentation of acoustical recordings, and conceive of new means for automated acoustical processing and analysis. 

Optical Metamaterial Characterization (Mentor: Prof. Wataru Nakagawa)

Significance: The Nano Optics group is developing and testing nanostructured 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. Objectives: An optical characterization system will be used to measure the spectral and polarization properties of the metamaterials under test. As needed modifications or improvements to the system will be implemented, including laboratory automation and signal processing/analysis tools. Role of REU participant: The REU student will be trained on the operation of the optical characterization 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. 

Potato Virus Y Detection with Hyperspectral Imaging and Machine Learning (Mentors: Prof. Paul Nugent, Prof. Brad Whitaker)

Significance: This project aims to develop a novel remote sensing approach to detect Potato Virus Y (Potyviridae, PVY) infection in potato plants using hyperspectral cameras mounted on an unpiloted aerial system (UAS). The use of hyperspectral imaging technology can capture the reflectance spectrum of an object at each pixel of an image and identify subtle changes in plant color as an indicator of PVY infection. As part of this project, we are developing machine learning technology trained on hyperspectral data to enable rapid, whole-field surveys of PVY, assisting in disease management. Objectives: The goal for this work is to test the developed machine learning and hyperspectral imaging techniques against traditional methods involving manual inspection and DNA sequencing of plant leaves. Role of REU participant: The REU participant will be expected to learn about and use signal processing and machine learning techniques applied to data collected from hyperspectral imagers. The student will develop algorithms to autonomously calibrate hyperspectral images and determine the presence of PVY infection.

Hyperspectral Signal Processing and Real-Time Classification (Mentor: Prof. Ross Snider)

Significance: Hyperspectral remote sensing can produce orders of magnitude more data than color cameras which makes it impractical/undesirable to store or transmit this voluminous data for further processing.  Objectives: The Snider Lab is collaborating with the Optical Remote Sensor Laboratory (ORSL) and a local company Resonon that manufactures hyperspectral cameras to process and classify hyperspectral data in real-time.  Role of REU participant: The REU participant will be expected to learn about hyperspectral signal processing and help embed machine classification algorithms in Field Programmable Gate Arrays (FPGAs) so that hyperspectral imaging can be applied in a real-time setting (e.g. drone-based real-time classification of hyperspectral color signatures).

Optical Sensing of Prescribed Fire Smoke (Mentor: Prof. Joseph Shaw)

Significance: Because of the increasing threat of wildfire, we are studying prescribed fires and their physical and social impacts as a tool for reducing the harm done by wildfires. This multi-institutional and multi-disciplinary study includes the development of smart optical sensor systems that will be used with embedded machine learning and artificial intelligence algorithms to characterize fire fuels before and after prescribed fires and to characterize the physical properties of the smoke emitted by these fires. This will be done in partnership with studies of the societal impacts of prescribed fires and people’s perceptions of prescribed fires with different levels and types of information. Objectives: Optical sensor systems will be deployed outdoors for measuring properties of smoke and other aerosols in the atmosphere. Role of REU participant: The participating students will assist graduate students and faculty in deploying optical and electronic sensor systems to measure properties of smoke and other aerosols in the atmosphere. Laboratory measurements also will be performed to calibrate the sensors before and after outdoor deployment. Data from the laboratory and field measurements will be analyzed and intercompared to obtain cross-validated data. 

River Algae Imaging (Mentor: Prof. Joseph Shaw)

Significance: The Gallatin River flows out from Yellowstone National Park and is one of three rivers that join together at Three Forks, Montana to form the headwaters of the Missouri River. One of the nation’s premier fly-fishing rivers, the Gallatin is heavily used for recreation, tourism, and as a source of fresh water. It was declared in 2023 by the EPA to be an “impaired waterway” because of nuisance algae blooms that are increasing in severity and spatial extent. We are collaborating with the Montana Department of Environmental Quality (DEQ) to provide hyperspectral images from drones that fly over the river and multispectral images from fixed-location, low-cost imagers we are developing. The images are used to detect algae blooms, determine their spatial extent, and study their causes. Objectives: Low-cost river imager and calibration system prototypes will be tested and validated in the lab and in the field. Role of REU participant: The participating students will participate in experiments to calibrate and validate low-cost imaging and calibration systems in both laboratory and outdoor field measurements. The students also will assist a multi-disciplinary team in collecting ground-truth algae samples that will be analyzed in our collaborators’ ecology labs. Prior experience with photography or imaging is helpful, as is interest in optical systems design and calibration, and image analysis with Matlab and related software.

Underwater Machine Vision for Scene Characterization and Anomaly Detection (Mentor: Prof. Brad Whitaker)

Significance: Autonomous unmanned underwater vehicles have the potential to support many applications, including national defense, autonomous exploration and navigation, and search and rescue missions. One major problem inhibiting general robotic submarine systems is that they are often expected to be deployed in many different, and sometimes never-before-seen, environments. Such environments include human-made facilities, freshwater lakes, coastal seawater, and the deep ocean. The visual background and types of anticipated objects associated with different environments may make it infeasible for a single perception algorithm to perform well in all circumstances. Objectives: In this research project, the Whitaker Lab will create a framework for environmentally aware computer vision. Using such a framework, the algorithm will sense the current environment and rely on training data from a similar environment to make perception inferences. We hypothesize that an adaptive perception algorithm will lead to greater success in identifying anomalous underwater objects. Role of REU participant: The REU participant will be expected to learn about machine vision and transfer learning techniques. The student will also collect and label data from multiple underwater environments in order to train different vision algorithms. This work will enable the submarine to identify targets of interest, including anomalies, in labeled and unlabeled data collected using underwater cameras.

Application Information

  • To apply, please use the NSF Education and Training Application portal (https://etap.nsf.gov/). Our program is listed as "Our sustainable future: Remote sensing, smart systems, and innovative devices (Montana State ECE)."
  • As part of the application you will be asked to provide a ranked list of the available projects. We will consider your top three choices in the selection process.
  • Application deadline is February 12, 2025. Offers will be made on a rolling basis starting in early March 2025 until all positions are filled (likely in early April 2025).

Questions? Please contact Prof. Wataru Nakagawa (nakagawa@montana.edu) or Prof. Bradley Whitaker (bradley.whitaker1@montana.edu).