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:

Electronics

 

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: www.vermalab.com

 

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.

 

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.

 

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.