2022 Research Projects

Projects are posted below; new projects will continue to be posted. To learn more about the type of research conducted by undergraduates, view the archived symposium booklets and search the past SURF projects.

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:


Internet of Things (9)

 

Agricultural Data Pipeline and Integration with Models 

Description:
Digital Agriculture, at its best, builds upon decades of discipline research with some integration of new IoT sensors and communication pathways as well as public resource data such as weather, soil, and topography. One challenge to be addressed is to more fully document the backstory or fuller context of situations so that artificial intelligence and machine learning can be more complete and robust. Another is the integration of mechanistic (descriptive of the fundamental science) models that might be biological, physical, chemical, logistical, economic, etc. in origin. The better parameterization of these models and even auto-population of initial conditions can stem from data sets and data streams. In this project, the student will extract biophysical model(s) from literature and other simulations to meld model + data. It will require interoperability focus and that involves wise choices of data architecture and an integration with data pipelines (often based on open source tools). The end game is to provide better insight (including probabilities, when applicable) for tactical and strategic cropping decisions while preserving security and privacy.
Research categories:
Internet of Things, IoT for Precision Agriculture
Preferred major(s):
  • No Major Restriction
Desired experience:
Knowledge of cropping systems and coding desired.
School/Dept.:
Agricultural and Biological Engineering
Professor:
Dennis Buckmaster

More information: iot4ag.us

 

Development of Immersive Mixed-reality Environment for IoT-Human interaction  

Description:
The emergence of the Internet of Things (IoT) has transformed our world with billions of interconnected smart devices. It has received ubiquitous adoption in numerous industries such as healthcare transportation, manufacturing, and agriculture. Most IoT systems nowadays are empowered by AI technology to automate a lot of tasks with little human intervention. However, designing and customising IoT devices for individual needs still remains challenging for lots of end users with limited technical background , which greatly hinders the IoT’s mass adoption. In order to bridge this gap and lower the entry barrier, we plan to take full advantage of the immersive technology(AR/VR), which allows common users to create, author and debug IoT behaviours effortlessly inside a simulated virtual environment. Specifically, the undergrad will participate in design and fabrication of unique IoT devices for a variety of tasks and integrating them to an overarching virtual environment. The undergrad will also get involved in the user evaluation process. This work will eventually lead to the submission of a paper to a top-tier ACM conference.
Research categories:
Deep Learning, Human Factors, Internet of Things, Learning and Evaluation
Preferred major(s):
Desired experience:
Applicants should have a general interest in developing mixed-reality applications and designing electronic hardware. Applicants with experience in some (not all) of the following are preferred: PCB design, embedded programming, C#, Unity,3D-CAD Software, deep learning.
School/Dept.:
Electrical and Computer Engineering
Professor:
Karthik Ramani

More information: https://engineering.purdue.edu/cdesign/wp/

 

High-efficiency solar-powered desalination  

Description:
Water and energy are tightly linked resources that must both become renewable for a successful future. The United Nations predicts that 6 billion people will face water scarcity by 2050. This warrants the need to develop efficient and realizable engineering solutions for desalination using the vast availability of solar energy.
This project aims to design, prototype, and test novel configurations for membrane-based desalination (reverse osmosis), powered by solar-thermal engines. The student will be part of a team of graduate and undergraduate students responsible for process design, thermal-fluid modeling and simulation, hydraulic circuit prototyping and testing, and experimental data analysis.
All students will be required to read relevant, peer-reviewed literature and keep a notebook or log of weekly research progress. At the end of the semester or term, each student will present a talk or poster on their results.
Research categories:
Ecology and Sustainability, Energy and Environment, Fluid Modelling and Simulation, Internet of Things, Nanotechnology, Thermal Technology
Preferred major(s):
  • No Major Restriction
Desired experience:
Applicants should have an interest in thermodynamics, water treatment, and sustainability. Applicants with experience in some (not all) of the following are preferred: experimental design and prototyping, manufacturing, Python, LabView, EES, MATLAB, 3D CAD Software, & Adobe Illustrator. Rising Juniors and Seniors are preferred.
School/Dept.:
Mechanical Engineering
Professor:
David Warsinger

More information: www.warsinger.com

 

In-Sensor Computing with Ferroelectric Resonators 

Description:
Edge computing is a growing necessity for the Internet of Things (IoT) given the demand for sensor networks collecting and communicating information to central processing points. The power required to transmit data at high bandwidth is prohibitive, and solutions for efficient lower level computation at each sensor node are required. Ferroelectrics (FEs), with unique hysteresis properties, are currently under investigation for in-memory computing. In this project, we will leverage the combined benefits of nonlinear piezoelectricity and hysteresis of ferroelectrics in the context of MEMS resonators to explore oscillatory computation for resonant sensors. Goals of this project include analysis and simulation of computational schemes based on existing large-signal FE models recently developed in our group, as well as experimental prototyping using existing ferroelectric resonators also previously designed in the HybridMEMS Lab. This would be the first experimental demonstration of FE resonant computation.
Research categories:
Internet of Things, Nanotechnology
Preferred major(s):
  • Electrical Engineering
  • Computer Engineering
  • Mechanical Engineering
School/Dept.:
ECE
Professor:
Dana Weinstein

More information: https://engineering.purdue.edu/hybridmems/

 

Industrial IoT Implementation and Machine Learning for Smart Manufacturing 

Description:
The student will work with PhD students on implementation of IoT technology on manufacturing machines and processes, database development, dashboard development, and machine learning for smart manufacturing.
Research categories:
Big Data/Machine Learning, Deep Learning, Fabrication and Robotics, Internet of Things
Preferred major(s):
  • Mechanical Engineering
  • Computer Engineering
  • Computer Science
School/Dept.:
Mechanical Engineering
Professor:
Martin Jun

More information: https://web.ics.purdue.edu/~jun25/

 

Infield study of long term virtual and augmented reality-based training for vocational skilling 

Description:
Welding is a skill that requires manual dexterity, adept psychomotor skills, and attention to numerous details of the process. Virtual Reality (VR)-based simulators for welding have gained popularity in recent decades and have been integrated with in-person training methods to provide hands-on practice to learners for improving the necessary psychomotor skills. Previous research studies have shown that respondents agree with using VR-based welding simulators as a tool to develop basic welding skills in new trainees. Using a VR welding simulator, the trainee’s understanding was much clearer when doing the welding process, and welding skills also developed. In addition, the simulator helps the trainee redo the exercises without considering the wastage of workpiece material and access to other equipment. Considering these advantages offered by VR-based training methods, our research would focus on the systematic study to explore and evaluate the usage of the technology to facilitate user experience and the development of psychomotor skills required for welding. The student researcher would be needed to help conduct experiments with field subjects. He/she would collect data during the investigations and later help perform statistical analysis of the data. This work will eventually lead to submitting a paper to a top-tier ACM conference.
Research categories:
Human Factors, Internet of Things, Learning and Evaluation, Other
Preferred major(s):
  • Mechanical Engineering
Desired experience:
Applicants with experience in the following are preferred (But not necessary) : Unity, 3D-CAD Package, Conducting experiment and data collection, Statistical Analysis
School/Dept.:
Mechanical Engineering
Professor:
Karthik Ramani
 

Making Blockchains/Cryptocurrencies Secure and Scalable for Billions 

Description:
The cryptocurrency boom has seen millions of people adopting digital assets; the recent economic successes of Bitcoin and several other blockchains/cryptocurrencies have enthused a broad population to explore them. The diversity in the needs and objectives of these cryptocurrency users is vast, ranging from just being enthused by technology to trading, sometimes even using all of their savings. With increasing adoption and valuation, the attacks on the system have also seen a rise. Moreover, the current systems cannot scale beyond a few million users. There is a desperate need to improve the blockchain architectures towards combating these issues. In collaboration with the blockchain industry, this project considers the security and scalability aspects of real-world blockchain solutions for finance, supply-chain, precision agriculture, and beyond.
Research categories:
Cybersecurity, Internet of Things
Preferred major(s):
  • Computer Science
  • Computer Engineering
  • Mathematics - Computer Science
  • Computer Engineering Technology
  • Cybersecurity
Desired experience:
Programming proficiency/interest in Golang and Rust will be necessary. Knowledge about distributed systems, blockchains, cryptocurrencies will be useful.
School/Dept.:
Computer Science
Professor:
Aniket Kate
 

Real-Time Measurements of Volatile Chemicals in Buildings with Proton Transfer Reaction Mass Spectrometry 

Description:
The objective of this project is to utilize state-of-the-art proton transfer reaction mass spectrometry (PTR-MS) to evaluate emissions and exposures of volatile chemicals in buildings. My group is investigating volatile chemical emissions from consumer and personal care products, disinfectants and cleaning agents, and building and construction materials. You will assist graduate students with full-scale experiments with our PTR-MS in our new Purdue zEDGE Tiny House and process and analyze indoor air data in MATLAB.
Research categories:
Big Data/Machine Learning, Ecology and Sustainability, Energy and Environment, Engineering the Built Environment, Environmental Characterization, Human Factors, Internet of Things
Preferred major(s):
  • No Major Restriction
Desired experience:
Preferred skills: experience with MATLAB, Python, or R. Coursework: environmental science and chemistry, physics, thermodynamics, heat/mass transfer, and fluid mechanics.
School/Dept.:
Lyles School of Civil Engineering
Professor:
Nusrat Jung

More information: https://www.purdue.edu/newsroom/stories/2020/Stories%20at%20Purdue/new-purdue-lab-provides-tiny-home-for-sustainability-education.html

 

Surface sound and AI based machine monitoring for smart manufacturing 

Description:
A stethoscope-based surface sound sensor has been developed at Purdue and this sensor is being used to monitor manufacturing equipment. Deep learning will be used to classify machine and process conditions using the sound sensor. Student will work with PhD student to implement sound sensors on machines in local manufacturing companies, collect data, build database and dashboard, and develop AI models.
Research categories:
Big Data/Machine Learning, Deep Learning, Fabrication and Robotics, Internet of Things
Preferred major(s):
  • Mechanical Engineering
  • Computer Engineering
  • Electrical Engineering
  • Computer and Information Technology
  • Computer Science
School/Dept.:
Mechanical Engineering
Professor:
Martin Jun

More information: https://web.ics.purdue.edu/~jun25/