Research Projects

Projects are posted below; new projects will continue to be posted through February. To learn more about the type of research conducted by undergraduates, view the 2018 Research Symposium Abstracts.

2019 projects will continue to be posted through January!

This is a list of research projects that may have opportunities for undergraduate students. Please note that it is not a complete list of every SURF project. Undergraduates will discover other projects when talking directly to Purdue faculty.

You can browse all the projects on the list or view only projects in the following categories:


A New Ignition Technology for Lean-burn Combustion Engines

Research categories:  Aerospace Engineering, Mechanical Engineering
School/Dept.: Aeronautics & Astronautics
Professor: Li Qiao
Preferred major(s): Mechanical or Aerospace Engineering
Desired experience:   Thermodynamics, fluid mechanics, experimental skills, design experience

The gas turbine and internal combustion engine industries are pushing towards lean-burn combustion. Lean combustion means a small amount of fuel (much lower than the stoichiometric condition) is supplied and burned in the combustion chamber. The biggest advantage of lean-burn combustion is that it can lower emissions. However, lean-burn technologies have several challenges. One of the challenges is ignition, which becomes difficult for lean fuel/air mixtures. A potential solution is to use hot turbulent jets to ignition a lean mixture, rather than a spark plug. The hot jets are generated by burning a near-stoichiometric mixture in a small volume called pre-chamber.

This research will investigate the ignition behavior of a lean mixture by a hot turbulent jet. The undergraduate researcher will work closely with a graduate student on experiments. High-speed imaging techniques will be applied to visualize the jet penetration and ignition processes in a combustor at Zucrow Laboratories.


Active Learning: Choosing the Right Data for Machine Learning

Research categories:  Computer Engineering and Computer Science
School/Dept.: Electrical and Computer Engineering
Professor: Yung-Hsiang Lu
Preferred major(s): Computer Engineering, Computer Science, Electrical Engineering, Computer and Information Technology
Desired experience:   Computer Programming

Machine learning can be classified into different categories. One is called supervised learning: each piece of data is associated with a correct answer (also called label). Since machine learning is not perfect, labeling usually needs human efforts and can be very expensive. Another type of learning is unsupervised learning: there is no correct answer and this is frequently used in clustering data into groups. Active learning is somewhat in between. Unsupervised learning is used to cluster data and identify the data that is distinct and should be labeled.

This project will use public datasets of images (or videos) as the foundation for training machine models (supervised learning). Then, new data is clustered to discover which should be labeled. This is part of the CAM2 (Continuous Analysis of Many CAMeras). CAM2 discovers, retrieves, and analyzes vast amounts of real-time data from worldwide network cameras.

More information:


Additive Manufacturing (3D Printing) of Solid Propellants

Research categories:  Aerospace Engineering, Chemical, Material Science and Engineering, Mechanical Engineering
School/Dept.: ME
Professor: Steven Son
Preferred major(s): ME, AAE, ChE or MSE
Desired experience:   Junior or senior level students are preferred. Aptitude and interest in graduate school also desirable. Good laboratory or hands on work experience desirable.

Significant advancements have been made in the fabrication of energetic materials with additive manufacturing (AM) processes. The geometric flexibility of AM has been touted, but little has been done to combine complex geometries with spatially-varying thermodynamically optimized materials in solid propellants. Investigation of the intersection of these areas is needed to fulfill the potential of tailorability of AM processes for propellant optimization. The propellant grains result in complex geometries. Recent development of an ultrasonic-vibration assisted direct write printing system at Purdue has opened a range of new materials for printing. Steps are being taken to combine AM techniques in a single, multi-nozzle printer to allow continuous fabrication of a propellant with two or more major components. This project will focus on printing thermodynamically optimized solid propellants in with a range of internal geometries and investigating their effects with classical and more recent diagnostic techniques.


Adhesives at the Beach

Research categories:  Bioscience/Biomedical, Chemical, Environmental Science, Life Science, Material Science and Engineering, Physical Science
School/Dept.: Department of Chemistry
Professor: Jonathan Wilker
Preferred major(s): Biology, Biomedical Engineering, Chemical Engineering, Chemistry, Materials Engineering
Desired experience:   This project will involve aspects of marine biology (e.g., working with live mussels), materials engineering (e.g., measuring mechanical properties of adhesives), and chemistry (e.g., making surfaces with varied functionalities). Few people at any level will come in with knowledge about all aspects here. Consequently we are looking for adventurous students who are wanting to roll up their sleeves, get wet (literally), and learn several new things.

The oceans are home to a diverse collection of animals producing intriguing materials. Mussels, barnacles, oysters, starfish, and kelp are examples of the organisms generating adhesive matrices for affixing themselves to the sea floor. Our laboratory is characterizing these biological materials, designing synthetic polymer mimics, and developing applications. Characterization efforts include experiments with live animals, extracted proteins, and peptide models. Synthetic mimics of these bioadhesives begin with the chemistry learned from characterization studies and incorporate the findings into bulk polymers. For example, we are mimicking the cross-linking of DOPA-containing adhesive proteins by placing monomers with pendant catechols into various polymer backbones. Adhesion strengths of these new polymers can rival that of the cyanoacrylate “super glues.” Underwater bonding is also appreciable. In order to design higher performing synthetic materials we must, first, learn all of the tricks used by nature when making adhesives. Future efforts for this coming summer will revolve around work with live mussels. Plans for experiments include changing the water, surfaces, and other environmental conditions around the animals. Mechanical performance of the resulting adhesives will be quantified and compared. Microscopy and other methods will be used to further understand the factors that dictate how these fascinating biological materials can function under such demanding conditions.


Applications of Deep Reinforcement Learning

Research categories:  Computer Engineering and Computer Science
School/Dept.: IE
Professor: Vaneet Aggarwal
Preferred major(s): CS, EE, Math, Stats

Reinforcement learning refers to goal-oriented algorithms, which learn how to attain a complex objective (goal) or maximize along a particular dimension over many steps; for example, maximize the points won in a game over many moves. Our group is involved in coming up with fundamental algorithms for many aspects in reinforcement learning, and applying them to problems in social networks and transportation.


Building Computer Systems Software for AI and IoT

Research categories:  Computer Engineering and Computer Science
School/Dept.: ECE
Professor: Felix Lin
Preferred major(s): ECE or CS
Desired experience:   Enthusiasm in programming, exploring, and building.

While the mobile computers are still flourishing, we are quickly embracing a variety of new computing paradigms -- wearables, IoT, and VR headsets, just to name a few. They challenge the way we design and build computer software today. In this project, you will be involved in redefining systems software to make computers smarter, faster and cooler. This project will give you a lot of fun in hacking Linux, frameworks, and various modern hardware.

Underrepresented minority students are particularly encouraged to apply.

More information:


Building Software for Environmental Modeling

Research categories:  Agricultural, Computer Engineering and Computer Science, Environmental Science, Other
School/Dept.: Agricultural and Biological Engineering
Professor: Dharmendra Saraswat
Preferred major(s): Agricultural Engineering , Civil Engineering, Computer Science or related disciplines
Desired experience:   Programming skills in any language with some experience in frontend and backend web development is desired.

Agricultural and Biological Engineering Department has contributed several tools for environmental modeling community. It is a challenge to review and understand old codes with minimum documentation. This project involves modernizing an environmental modeling software written primarily in Perl. In this project, the SURF student will first assess the current application, create a plan for the new iteration in collaboration with the project supervisor, get a head start on developing the new application and document the process. The SURF student will work with a staff programmer.


Characterization of Decomposition and Detonation of Cocrystal Explosives

Research categories:  Aerospace Engineering, Chemical, Material Science and Engineering, Mechanical Engineering
School/Dept.: ME
Professor: Steven Son
Preferred major(s): ME, AAE, ChE or MSE
Desired experience:   Junior or Senior UG students preferred. Good lab skills are highly desired.

Cocrystal explosives offer the possibility improved safety and performance over conventional materials. The SURF student would assist graduate students in the study of novel cocrystal explosives. Both slow heating and detonation experiments and detonation experiments will be designed and performed.


Data Visualization and Analysis for IoT Based Smart Irrigation System

Research categories:  Agricultural, Civil and Construction, Computer Engineering and Computer Science, Environmental Science, Other
School/Dept.: Agricultural and Biological Engineering
Professor: Dharmendra Saraswat
Preferred major(s): Agricultural Engineering, Civil Engineering, Environmental Engineering, Computer Science or related disciplines
Desired experience:   Programming skills in any language with some experience in statistics is desired.

It is reported that currently almost 33 percent of the global population is affected by water scarcity and by 2030, this figure is expected to climb up to almost 50 percent. Around 60 percent of the water used for irrigation is wasted, either due to evapotranspiration, land runoff, or simply inefficient, primitive irrigation application methods. This realization has brought attention to smart irrigation – powered by the internet of things (IoTs) – that can be a better way of managing water stress on a global basis. In this project, the SURF student will customize commercially available software to analyze and visualize data, perform calculations/combine new data, run time-based calculations, plot functions for visual understanding and perform sophisticated analysis by combining data from several field nodes. The SURF student will work with Project Supervisor and a staff programmer.


Design and Analysis of Novel Approaches for Packaging of Li-Ion Batteries for Automotive Applications

Research categories:  Computational/Mathematical, Mechanical Engineering, Mechanical Systems, Other
School/Dept.: School of Mechanical Engineeing
Professor: Thomas Siegmund
Preferred major(s): Mechanical Engineering

E-mobility is a key driver of future transportation systems. E-vehicles rely on energy storage in batteries, and such batteries packages need to be integrated into the overall vehicle structure under consideration of structural and thermal design considerations. This research project will advance novel solutions to do so. The SURF student will work on CAD model design, simulations and experiments on simulated Li-ion battery packages for mechanical and thermal safety.


Development of New Approaches for Biological Imaging and Materials Design using Mass Spectrometry

Research categories:  Chemical, Innovative Technology/Design, Material Science and Engineering
School/Dept.: Chemistry
Professor: Julia Laskin
Preferred major(s): Chemistry, biochemistry, chemical engineering, computer science, electrical engineering, materials engineering
Desired experience:   We are looking a different skill set for different aspects of the project. If you are excited about science and dedicated to research, you will find an excellent environment in our lab.

We have two projects in the lab. In one project, we develop new analytical approaches for imaging of numerous biomolecules in biological systems. We need help in running experiments, analyzing data, and development of new computational approaches, which will streamline data analysis and facilitate biological discoveries. In another project, we develop unique instruments for designing layered coatings using beams of complex ions. In this project, we need help with the synthesis of relevant precursor molecules, their characterization using mass spectrometry and other analytical techniques, ion deposition on surfaces, and surface characterization.


Engineering of the Tumor Microenvironment of Pancreatic Cancer

Research categories:  Bioscience/Biomedical, Mechanical Engineering
School/Dept.: Mechanical Engineering
Professor: Bumsoo Han
Preferred major(s): Mechanical or Biomedical Engineering Majors
Desired experience:   Course work on solid and fluid mechanics are required - Basic programming skill on Matlab - Basic wet lab skills are preferred, but not required.

Pancreatic ductal adenocarcinoma (PDAC) poses a significant challenge with dismal 7% 5-year survival rates. Ineffective treatment of PDAC is linked primarily to poor drug delivery through a dense PDAC stroma and to elevated drug resistance of pancreatic cancer cells. These are largely correlated to the complex tumor microenvironment (TME) of PDAC. Due to its complexity, it is extremely difficult to identify promising molecular targets and to devise innovative strategies for efficient delivery of molecules at the PDAC TME. In order to address this technical challenge, this project aims to develop and validate engineered tumor models based on microfluidics and tissue engineering technologies. Students in this project are expected to learn about microfabrication, biomechanics, biotransport and fluorescence microscopy and analysis.


Enhance the Burn Rate of Solid Propellants

Research categories:  Aerospace Engineering, Mechanical Engineering
School/Dept.: Aeronautics & Astronautics
Professor: Li Qiao
Preferred major(s): Aerospace Engineering
Desired experience:   Thermodynamics, aerodynamics, propulsion

Composite solid propellants are a major source of chemical energy for most of the solid rockets in use today with applications to space, ballistic, tactical and assist propulsion, and are made up of three components: binder, energetic fuel and oxidizer. Enhancing the burn rates of solid propellants is crucial for improving performance of solid rocket motors in terms of higher thrust, simplified propellant grain geometry, and reduced overall size and weight of the propulsion system.

In this research, the undergraduate student will work closely with a graduate student to explore methods to enhance the burn rates of solid propellants. The nature of the research is experimental, involving materials synthesis and characterization, combustion measurement using high-speed infrared camera, and data collection and analysis.


Evaluate Epigenetic Effects on Transgene Expression

Research categories:  Bioscience/Biomedical, Chemical
School/Dept.: Davidson School of Chemical Engineering
Professor: Chongli Yuan
Preferred major(s): Chemical Engineering
Desired experience:   Previous research experience required

Transgene expression can be potentially regulated via epigenetic marks. We are making synthetic chromatin containing different histone modifications and assess their impact on transgene activity. Participating students will learn about molecular cloning, transcription assays and other molecular/cellular bio techniques.


Illumination of Damage through Microtomography

Research categories:  Aerospace Engineering, Computer Engineering and Computer Science, Industrial Engineering, Material Science and Engineering, Mechanical Engineering
School/Dept.: Aeronautics and Astronautics
Professor: Michael Sangid
Preferred major(s): AAE, ME, MSE, EE, CSE, or IE
Desired experience:   Students are expected to work with Image Processing and Visualization tools, as well as Matlab.

Damage in structural materials is often difficult to quantify, instead we rely on large scale component level testing and curve fitting. With the advent of advanced microtomography, we have the ability to identify damage inside the bulk of the material, in which the samples are subjected to mechanical loading. Thus, in this project, microtomography scans will be reconstructed and the damage in the form of voids or cracks will be characterized and quantified in several material systems (including carbon fiber reinforced composites and Ti-6Al-4V produced via additive manufacturing). The interaction of damage with microstructural features will be assessed, in order to achieve a physics-based understanding of material failure.


Indoor Air Pollution Research: From Nano to Bio

Research categories:  Agricultural, Bioscience/Biomedical, Chemical, Civil and Construction, Environmental Science, Life Science, Mechanical Systems, Nanotechnology, Physical Science
School/Dept.: Civil Engineering
Professor: Brandon Boor
Preferred major(s): Students from all majors are welcome to apply.
Desired experience:   Interest in studying contaminant transport in the environment, human health, air pollution, HVAC and building systems, microbiology, nanotechnology, and atmospheric science. Experience working in a laboratory setting with analytical equipment and coding with MATLAB, Python, and/or R. Passionate about applying engineering fundamentals to solve real-world problems.

Airborne particulate matter, or aerosols, represent a fascinating mixture of tiny, suspended liquid and solid particles that can span in size from a single nanometer to tens of micrometers. Human exposure to aerosols of indoor and outdoor origin is responsible for adverse health effects, including mortality and morbidity due to cardiovascular and respiratory diseases. The majority of our respiratory encounters with aerosols occurs indoors, where we spend 90% of our time. Through the SURF program, you will work on several ongoing research projects exploring the dynamics of nanoaerosols and bioaerosols in buildings and their HVAC systems.

Nanoaerosols are particles smaller than 100 nm in size. With each breath of indoor air, we inhale several million nanoaerosols. These nano-sized particles penetrate deep into our respiratory systems and can translocate to the brain via the olfactory bulb. These tiny particles are especially toxic to the human body and have been associated with various deleterious toxicological outcomes, such as oxidative stress and chronic inflammation in lung cells. Bioaerosols represent a diverse mixture of microbes (bacteria, fungi) and allergens (pollen, mite feces). Exposure to bioaerosols plays a significant role in both the development of, and protection against, asthma, hay fever, and allergies.

Your role will be to conduct measurements of nanoaerosols and bioaerosols in laboratory experiments at the Purdue Herrick Laboratories, as well as participate in a field campaign at Indiana University - Bloomington in collaboration with an atmospheric chemistry research group. You will learn how to use state-of-the-art air quality instrumentation and perform data processing and analysis in MATLAB.

More information:


Low-cost user-friendly biosensors for animal health

Research categories:  Agricultural, Bioscience/Biomedical, Electronics, Innovative Technology/Design, Life Science, Material Science and Engineering, Mechanical Systems
School/Dept.: Agricultural and Biological Engineering
Professor: Mohit Verma
Preferred major(s): Biomedical engineering, biological engineering, electrical engineering, mechanical engineering, or other relevant fields
Desired experience:   To be successful at this position, you should have a GPA>3.5, prior experience working in a wet lab (ideally experience with bacterial culture and DNA amplification), experience building electromechanical devices, and the ability to work in a team.

Infectious diseases are a leading cause of economic burden on food production from animals. For example, bovine respiratory diseases lead to a loss of ~$480/animal. Current methods for tackling these diseases includes the administration of antibiotics by trial-and-error. This approach leads to failure of treatment in up to one-third of the cases. In addition, it also leads to a proliferation of antibiotic resistance in pathogens.

Our research project focuses on developing a low-cost user-friendly biosensor based on paper that can detect which pathogen is causing the disease and whether it exhibits antibiotic resistance. Such a biosensor would provide a readout to the farmer or the veterinary physician and suggest which antibiotics are likely to be successful.

The SURF student will have three objectives: i) design primers for detecting pathogens associated with bovine respiratory diseases, ii) build a device for processing the sample and extracting DNA that can be amplified by the biosensor, and iii) build a device for detecting colorimetric/fluorometric output from the biosensor.

More information:


Micro/nano Scale 3D Laser Printing

Research categories:  Mechanical Engineering, Mechanical Systems, Nanotechnology
School/Dept.: Mechanical Engineering
Professor: Xianfan Xu
Preferred major(s): Mechanical Engineering, Physics, Materials Engineering, Chemical Engineering, Electrical Engineering
Desired experience:   Junior or Senior standing, GPA>3.6

The ability to create 3D structures in the micro and nanoscale is important in many fields including electronics, microfluidics, and tissue engineering and is an emerging area of research and development. This project deals with the development and testing of a setup for building microscopic 3D structures with the help of a femtosecond laser. A method known as two photon polymerization is typically used to fabricate such structures in which a polymer is exposed to laser and at the point of the exposure the polymer changes its structure. Moving the laser in a predefined path helps in getting the desired shape and the structures are then built in a layer by layer fashion. The setup incorporates all the steps from a designing a CAD model file to slicing the model in layers to generating the motion path of the laser needed for fabricating the structure. In order to make a solid and stable structure, investigation of better materials and optimization of the process parameters is needed. Besides, possible improvements to the control algorithms used in the setup can be done to increase the efficiency of the process and build the structures faster.


Monitoring Bacterial Contamination in Biologics

Research categories:  Agricultural, Bioscience/Biomedical, Chemical, Mechanical Systems
School/Dept.: Mechanical Engineering
Professor: Arezoo Ardekani
Preferred major(s): Biomedical engineering, chemical engineering, biological engineering

Biologics comprised 22% of major pharma companies in 2013 and is expended to grow to 32% of sales in 2023. Biologics are large complex molecules that are created by microorganisms and mammalian cells. They are polypeptides or proteins such as monoclonal antibodies, cytokines, fusion proteins used in vaccines, cell therapies, gene therapies, etc. Impurities such as aggregates, cell debris, bacterial and viral contamination can negatively impact the manufacturing process. In this project, we will focus on developing methods for monitoring bacterial contamination.


Multiphase Fluid Flows in Tight Spaces

Research categories:  Bioscience/Biomedical, Chemical, Computational/Mathematical, Physical Science
School/Dept.: Mechanical Engineering
Professor: Ivan Christov
Preferred major(s): Mechanical Engineering, Chemical Engineering, Applied Mathematics, Computational Science
Desired experience:   1. Thorough understanding of undergraduate fluid mechanics. 2. Programming experience with high-level language such as Python or MATLAB. 3. Experience with shell/command-line environments in Linux/Unix; specifically, remote login, file transfers, etc. 4. Experience researching difficult questions whose answers are not found in a textbook. 5. Desire to learn about new fluid mechanics phenomena and expand computational skillset.

Multiphase flows are fluid flows involving multiple fluids, multiple phases of the same fluid, and any situation in which the dynamics of an interface between dissimilar fluids must be understood. Examples include water displacing hydrocarbons in secondary oil recovery, a mixtures of particle-laden fluids being injected into a hydraulically fractured reservoirs ("fracking"), introduction of air into the lungs of pre-maturely born infants to re-open their liquid-filled lungs and airways, and a whole host of other physico-chemical processes in biological and industrial applications.

The goal of this SURF project will be to study, using computational tools such as ANSYS Workbench and/or the OpenFOAM platform, how multiphase flows behave in tight spaces. To accomplish this goal, the SURF student will work with a PhD student. Specifically the dynamics of interfaces between different phases and/or fluids will be studied through numerical simulation, and the effect of the flow passage geometry will be addressed. Some questions that we seek to address are whether/how geometric variations can stabilize or destabilize an interface and whether/how geometry affects the final distribution of particles in particle-laden multiphase flow passing through a constriction/expansion. Applications of these effects to biological and industrial flows will be explored quantitatively and qualitatively.

More information:


Network for Computational Nanotechnology (NCN) / nanoHUB

Research categories:  Chemical, Computational/Mathematical, Computer Engineering and Computer Science, Electronics, Material Science and Engineering, Mechanical Systems, Nanotechnology, Other
Professor: NCN Faculty
Preferred major(s): Electrical, Computer, Materials, Chemical or Mechanical Engineering; Chemistry; Physics; Computer Science; Math
Desired experience:   Serious interest in and enjoyment of programming; programming skills in any language. Physics coursework.

NCN is looking for a diverse group of enthusiastic and qualified students with a strong background in engineering, chemistry or physics who can also code in at least one language (such as Python, C or MATLAB) to work on research projects that involve computational simulations. Selected students will typically work with a graduate student mentor and faculty advisor to create or improve a simulation tool that will be deployed on nanoHUB. Faculty advisors come from a wide range of departments: ECE, ME, Civil E, ChemE, MSE, Nuclear E, Chemistry and Math, and projects may be multidisciplinary. To learn about this year’s research projects along with their preferred majors and requirements, please go to the website noted below.

If you are interested in working on a nanoHUB project in SURF, you will need to follow the instructions below. Be sure you talk about specific NCN projects directly on your SURF application, using the text box for projects that most interest you.

1) Carefully read the NCN project descriptions (website available below) and select which project(s) you are most interested in and qualified for. It pays to do a little homework to prepare your application.

2) Select the Network for Computational Nanotechnology (NCN) / nanoHUB as one of your top choices.

3) In the text box for Essay #2, where you describe your specific research interests, qualifications, and relevant experience, you may discuss up to three NCN projects that most interest you. Please rank your NCN project choices in order of interest. For each project, specify the last name of the faculty advisor, the project, why you are interested in the project, and how you meet the required skill and coursework requirements.

For more information and examples of previous research projects and student work, click on the link below.


Preparing engineers to address climate change and its implications on sustainability: modeling impact of college experiences on students

Research categories:  Civil and Construction, Educational Research/Social Science
School/Dept.: Engineering Education
Professor: Allison Godwin
Preferred major(s): All STEM majors invited to apply
Desired experience:   Some experience in statistics and programming languages is preferred. All other skills including human subject research ethics, statistical analysis in R, data management, will be taught.

Engineers are an essential part of solving the effects of climate change and must not only be aware of the issues but empowered to make change to reduce and shift the impact of humans on the planet. This research investigates engineering students' experiences during undergraduate programs that predict their beliefs about climate change and empowerment to address its related implications for sustainability in their careers. This study is the first of its kind to explore how experiences in college impact students' climate change beliefs and interest to address related implications for sustainability. This project is a collaborative effort between the Virginia Tech Charles E. Via, Jr. Department of Civil and Environmental Engineering Myers-Lawson School of Construction and the Purdue University School of Engineering Education.

This SURF research project uses national survey data from ~4,000 senior engineering design students collected in 2018 along with 7,673 first-year student responses collected in 2014 to model how student experiences during undergraduate education may influence their understanding of climate change and desire to address sustainability in their future engineering careers. The SURF student will use multilevel modeling (this modeling technique will be taught to any interested student) to analyze how student beliefs, student experiences, and institutional contexts may influence students attitudes and actions over time. The student will learn complex statistics in the programming language R, analyze data and interpret findings, and write up their results for journal publication. The student will also interface with faculty and another undergraduate summer research student at Virginia Tech.


Processing of innovative satellite remote sensing data for ocean and snow remote sensing

Research categories:  Aerospace Engineering, Computer Engineering and Computer Science, Electronics, Environmental Science, Physical Science
School/Dept.: AAE
Professor: James Garrison
Preferred major(s): ECE, AAE, Physics, EAPS
Desired experience:   Good programming skills, signal processing (ECE 301 or AAE301). Experience with software defined radio (USRP) will be a plus.

Reflectometry is a new approach to Earth remote sensing in microwave frequencies, using reflections of Global Navigation Satellite System (GNSS, e.g. GPS, Galileo, etc ...) signals from land and ocean surfaces as illumination source in a bistatic radar configuration. Through observing measurable changes in the properties of these signals, various features of the reflecting surfaces can be inferred.

Ocean surface winds is the most developed application for GNSS-Reflectometry (GNSS-R), with the launch of the CYGNSS constellation by NASA in 2016. CYGNSS data has been collected during the 2017 and 2018 Hurricane seasons, showing some capability for wind field measurements at a high spatial resolution. New models and algorithms are required, however, to optimally process these data and extract wind vectors with high sensitivity, especially at the higher wind speeds present in hurricanes. Development of these new models and algorithms requires the collection of high-quality data under carefully controlled conditions along with in situ training data provided by independent sources. With this goal in mind, Purdue has developed a wideband GNSS-R signal recorder which will be flown on the P-3 “Hurricane Hunter” aircraft operated by NOAA. This aircraft is capable of operating in extremely high winds and penetrating the Hurricane eye wall, in order to collect data inside developing tropical cyclones. GNSS-R data collected in this experiment will be compared with wind speed observations from other instruments on the P-3 aircraft, other satellite data, and model results. These comparisons will be used to develop and improved model for the extraction of ocean winds from CYGNSS and future satellite missions.

Snow Water Equivalent (SWE) is a representation of the total water stored in the snow pack. This is an important climate variable for the prediction of fresh water supplies as well as applications such as hydroelectric power. A new application of GNSS-R is measuring SWE as a change in phase of the reflected signal, a result of the slower propagation of the signal through the snow layer. Spaceborne measurements of SWE using GNSS-R have never been conducted. Special collections of CYGNSS data were conducted this year, in which raw signals (no on-board processing or compression) were collected in arcs spanning snow-covered regions in the Himalayan mountains.

SURF projects are proposed to support these two research goals for CYGNSS data. Both will involve extensive programming and data processing, using a “software defined radio” method that essentially implements all signal processing in software to operate on the full-spectrum of the recorded signal.

Applicants should have very strong programming skills, some knowledge of basic signal processing.


Programming 3D and environmental data acquisition into iFly -- a mobile iOS app

Research categories:  Computer Engineering and Computer Science, Life Science
School/Dept.: Entomology
Professor: Trevor Stamper
Preferred major(s): Computer science or engineering, or biological sciences
Desired experience:   Must have programming knowledge in Swift programming language. Mobile device iOS programming experience is highly desired.

The student researcher will be programming in Swift language on the iFly project to allow environmental sensor systems and 3D sensing systems to input data directly into the app. Student researcher will also be improving other functions of the software to build a better user experience.


Remote sensing of soil moisture and forest biomass using P-band Signals of Opportunity: Model development and experimental validation

Research categories:  Agricultural, Aerospace Engineering, Electronics, Environmental Science, Physical Science
School/Dept.: AAE
Professor: James Garrison
Preferred major(s): ECE, AAE, Physics, ABE
Desired experience:   Basic signal processing (AAE 301 or ECE 301 or equivalent) desired. Students should know how to use basic hand tools, and be willing to work outdoors in agricultural or forest environments. A drivers license and reliable access to a car is required for field work.

Root Zone Soil Moisture (RZSM), defined as the water profile in the top meter of soil where most plant absorption occurs, is an important environmental variable for understanding the global water cycle, forecasting droughts and floods, and agricultural management. No existing satellite remote sensing instrument can measure RZSM. Sensing below the top few centimeters of soil, often through dense vegetation, requires the use of microwave frequencies below 500 MHz, a frequency range known as “P-band”. A P-band microwave radiometer would require an aperture diameter larger than 10 meters. Launching such a satellite into orbit will present big and expensive technical challenge, certainly not feasible for a low-cost small satellite mission. This range for frequencies is also heavily utilized for UHF/VHF communications, presenting an enormous amount of radio frequency interference (RFI). Competition for access to this spectrum also makes it difficult to obtain the required license to use active radar for scientific use.

Signals of opportunity (SoOp) are being studied as alternatives to active radars or passive radiometry. SoOp re-utilizes existing powerful communication satellite transmissions as “free” sources of illumination, measuring the change in the signal after reflecting from soil surface. In this manner, SoOp methods actually make use of the very same transmissions that would cause interference in traditional microwave remote sensing. Communication signal processing methods are used in SoOp, enabling high quality measurements to be obtained with smaller, lower gain, antennas.

Under NASA funding, Purdue and the Goddard Space Flight Center have developed an airborne prototype P-band remote sensing instrument to demonstrate the feasibility of a future satellite version. Complementing this technology development, a field campaign will be conducted for its third year the Purdue Agricultural research fields. This campaign will make reflected signal measurements from towers installed over instrumented fields. Measurements will be obtained over bare soil first, and then throughout the corn or soybean growth cycle. Complementing these remote sensing measurements, a comprehensive set of ground-truth data will also be collected for use in developing models and verifying their performance.

In Spring 2019 an additional experiment, using a small Unpiloted Aerial Vehicle (UAV), will be conducted in a forested area in collaboration with the School of Forestry and Natural Resources (FRN).

Work under this project will involve installing microwave electronic equipment in the field, writing software for signal and data processing, and making field measurements of soil moisture and vegetation properties.

Students interested in this project should have good programming skills and some experience with C, python and MATLAB. They should also have a strong background in basic signal processing. Experience with building computers or other electronic equipment will also be an advantage. Students should be willing to work outdoors and have an interest in applying their skills to solving problems in the Earth sciences, environment, or agriculture.

The project will involve regular travel to and from the local research field, so students should have a driving license and access to a car.


Sensing the Human Factors in Laparoscopic and Robotic Surgery

Research categories:  Bioscience/Biomedical, Computer Engineering and Computer Science, Industrial Engineering, Mechanical Systems
School/Dept.: Industrial Engineering
Professor: Denny Yu
Preferred major(s): Industrial Engineering, other
Desired experience:   Human Factors, Matlab, Machine Learning, Healthcare, Medical Device Design

Work-related musculoskeletal disorders (MSDs) among surgeons are becoming more common. The purpose of this project is to use sensors to measure ergonomic risks and assess interventions to surgeons during laparoscopic and robotic surgery. This work will leverage sensing technology (e.g., motion tracking, pressure map, electromyography) to monitor surgeons’ ergonomics to ultimately develop recommendations on minimizing MSDs and how to better design an operating room.

The SURF student will participate in data collection in the operating room at Indiana University School of Medicine, data analysis and interpretation, and write his/her results for a journal publication. The student will regularly communicate his/her progress and results with faculty, graduate mentors, and surgeon collaborators.


Smart Manufacturing using IoT and Machine Learning

Research categories:  Computer Engineering and Computer Science, Innovative Technology/Design, Mechanical Engineering
School/Dept.: Mechanical Engineering
Professor: Martin Jun
Preferred major(s): Mechanical Engineering, Computer Engineering, or Computer Science
Desired experience:   Virtual reality programming, mechatronics, CAD design and programming for graphics, signal processing and data analysis, machining, etc.

Autonomous operation and decision making during manufacturing processes and production are important. Using IoT technologies, machine-to-machine, machine-to-human communication and data generation are achieved and machine learning algorithms are used for data analysis and decision making. The student will work on virtual reality (VR) based visualization of data achieved from IoT devices connected to CNC machine and robots and analyze data using machine learning.


ThermoConc as a Building Envelope for Electricity Generation and Space Heating and Cooling

Research categories:  Material Science and Engineering, Mechanical Systems, Physical Science
School/Dept.: Civil Engineering
Professor: Ming Qu
Preferred major(s): Material engineering or mechanical engineering
Desired experience:   1. Good skills with experiments and data acquisition; 2. Good writing and presentation skills; 3. Solid background in thermoelectric theory.

NSF research project aims to create a new strategy to reduce building energy consumption while enhancing thermal comfort by using thermoelectric concrete envelope to heat or cool indoor space without the need of additional power source. The student will help to characterize the new TE Concrete and evaluate the performance of ThermoConc both theoretically and practically.