Exploration of Computer Vision and Deep Learning using a Nvidia Jetson Robot
Computer Vision and Deep Learning are important technologies in the field of robotics. Mobile robots equipped with cameras and deep learning algorithms are capable of autonomous navigation, obstacle avoidance, road following, pedestrian identification, traffic signal identification, and beyond. Deep Learning is field of Artificial Intelligence which allows convolutional neural networks to be trained to solve challenging computer vision problems. A Nvidia Jetson is a high-performance computer capable of running multiple neural networks in parallel. The goal of this project is to explore, implement and test deep learning and computer vision algorithms on a mobile robot, powered by a Jetson nano computer, with special emphasis on autonomous navigation.
Requirements: Completion of upper-division coursework in computer science and engineering, Project not accepting any more students in fall 22.
Duration: Fall 2022, Spring 2023
Faculty: Robert Avanzato ([email protected])
Combining Networking and Machine Learning in the Internet of Intelligence.
In this project, we will take an interdisciplinary approach to investigate the interplaying between machine learning technologies and future networking systems (e.g., 5G/6G networks) for enabling the Internet of Intelligence. We will study how high-performance networking may facilitate machine learning as well as how machine learning techniques may be applied to achieve autonomous and intelligent network control and management.
Requirements: major in IST or CMPSC, GPA 3.0 or above, have taken IST 220 or CMPSC 335, self-motivated and self-disciplined, and knowledge about machine learning is preferable.
Duration: Spring 2022, Fall 2023, Spring 2024 (no requirement to present in fair in Apr 2023
Faculty: Qiang Duan ([email protected])
Best practices of mitigating educational Artificial Intelligence implicit bias in shaping diversity, inclusion and equity
The United Nation Sustainable Development Goals (UN SDGs) emphasize achieving quality education (goal 4), gender equality (goal 5), and reduced inequalities (goal 10) to upward socioeconomic mobility, as well as creating a necessary foundation for inclusive and equitable quality education opportunities for all. The United Nations General Assembly (UNGA) recognized Artificial intelligence (AI) as a transformative technology that can impact the education sector. It further acknowledged the leveraging of AI solutions in the measurement and acceleration of progress toward the pursuit of UN SDGs goals. Emerging AI technologies offer solutions that revolutionize our daily lives' approach to education, medicine, finance, business operation, and more. AI solutions in education, particularly in higher education, are increasingly embedded in the software colleges and universities and housed different components in institutional analytics for marketing, recruiting, admission, enrollment, student support, guidance, instructional pedagogical improvement, and more. AI solutions hold tremendous promise to help higher education where AI complex algorithms can calculate the individual's demonstrated interest by tracking their interaction with instructional websites, social media posts, and emails to expand access, overcome structural barriers, and close equity gaps. AI technologies are programmed by humans, the programmers, who bring their values, world assumptions, and norms into computer programs. The programmers build implicit bias systems based on which aspects of the world they capture and which aspects they exclude unconsciously. Implicit bias is an unconscious involuntary opinion that constantly informs humans' actions. These implicit biases such as racial, gender, and religious stereotypes are now embedded in the programmers' algorithms. Consequently, the systems create data models with unjust biases that potentially propagate throughout other systems. While institutions increasingly turn to AI solutions for recruitment and student assessment, the need for accountability and the risk of structural inequities derived from human implicit bias algorithms programmed in software arises as well. For example, how an institution would manage accountability for an inequity caused by a student being disadvantaged by an algorithm’s implicit bias due to race, gender, or belonging to an underserved population. Since 2019, UNGA , UNESCO , ITU , TLTL , and NIST have been working to support the development of trustworthy and responsible AI in reducing risks of implicit bias in shaping diversity, inclusion, and equity in our communities, thus improving public trust in AI. This effort has promoted caution before welcoming AI in education (#AIforEd) without rigorous research and has sought broader efforts and support in improving trustworthiness. This proposal explores and examines the educational AI solutions and best practices in mitigating AI implicit bias algorithms to provide accountability, trustworthiness and close the diversity, inclusion, and equity gap.
Requirements: Successful completion of Cyber 100, SRA 111, SRA 221, and SRA 211 with A+.
Duration: Fall 2022 and Spring 2023
Faculty: Maryam Roshanaei ([email protected])
Can you heat up a solution by stirring? - A question from the chemistry experiment
A food blender can produce a steaming hot vegetable soup by just blending the mixtures in a very high speed! (Watch this YouTube video if you don’t believe this.) In one of the general chemistry labs, students are asked to measure the temperature of a solution when a chemical reaction is taking place. The goal of the experiment is to determine the thermal energy produced by the specific chemical reaction. The apparatus of this experiment requires constant stirring through the entire experiment to ensure the heat generated by the chemical reaction is evenly distributed in the solution. What about the heat generated by the stirring? That is what we will investigate in this project.
Requirements: Currently enrolled CHEM 111 for FA 2022 or have completed CHEM 111 with a grade of B or above.
Duration: Fall 2022, Spring 2023
Faculty: Yibai Chen ([email protected])
An LIDAR/electroencephalogram (EEG) enabled Wheelchair
This project combines LIDAR/electroencephalogram (EEG) with an electrical wheelchair to explore new possibilities and enhance the user experience.
Requirements: Have experience in programming, can work alone, highly motivated, GPA > 3.0.
Duration: Fall 2022, Spring 2023
Faculty: Yi Yang ([email protected])
Applying AI to identify painting materials
We collected the cross-section images of various painting materials and paper. We plan to apply AI to identify these materials. This could lead to new methods in art conservation.
Requirements: Have experience in programming, can work alone, highly motivated, GPA > 3.0.
Duration: Fall 2022, Spring 2023
Faculty: Yi Yang ([email protected])
The effects of high-dose vitamin C on the proliferation of gastrointestinal stromal tumor (GIST) cells
Vitamin C has long been thought to have anti-cancer properties. This project investigates whether high-dose vitamin C will decrease the proliferation of gastrointestinal stromal tumor (GIST) cells. Students will learn cell culture techniques, spectrophotometry, and data analysis.
Requirements: Successful completion of BIOL 230W. Not taking students for Fall 2022, but may have an opening for spring 2023.
Duration: Spring 2023, Fall 2023, Spring 2024
Faculty: Thomas Mcguire ([email protected])
Fabrication and Testing of "Martian Concrete" (and Other Sulfur Concrete)
Sulfur concrete is investigated as a potential in-situ construction material for the construction of habitable structures on Mars. Tasks performed by students include making small specimens of sulfur concrete using Martian regolith simulant, measuring mechanical properties using load machines, and taking careful lab notes. The research will be extended to other types of sulfur concretes for applications here on Earth, as a sustainable alternative to the ordinary Portland-cement concrete.
Requirements: Engineering or science majors with a GPA of 3.5 or above. Preference is given to students at class standing of second year or above.
Duration: Fall 2022, Spring 2023
Faculty: Masataka Okutsu ([email protected])
Evaluation of Consumer-Grade Electronics for Small Satellite Applications
We are evaluating consumer-grade sensors and microcontrollers (ESP32 and/or Arduino) for small satellite applications. Current objectives include (1) interface an accelerometer and achieve a sufficient sampling rate to measure the vibration of a sounding rocket, (2) interface a magnetometer and calibrate its measurements, (3) build a rechargeable power-managing system using solar panels, and (4) develop a capability to transmit the mission data via amateur radio.
Requirements: GPA 3.5 or above. Preference is given to students at class standing of second year or above.
Duration: Fall 2022, Spring 2023
Faculty: Masataka Okutsu ([email protected])
Pulsars Studies with Radio Astronomy
Pulsars are the remnants of exploding giant stars. Because pulsars rotate rapidly they produce a periodic signals (pulses) that can be detected with large radio telescopes. In our projects, we use a 20-meter diameter radio telescope at the Green Bank Observatory in West Virginia to detect pulsars. Our research projects support three goals: searching for undiscovered pulsars, studying the properties of pulsars, and using collections of pulsars as a galaxy-size gravitational wave detector.
Requirements: Competency in Algebra and Trigonometry. Experience with EXCEL and some background in introductory physics is preferred. Advanced projects are aided by an introductory knowledge of python.
Duration: Fall 2022, Spring 2023
Faculty: Ann Schmiedekamp ([email protected]) and Carl Schmiedekamp ([email protected])
Terrestrial and astronomical signal detection with a small radio telescope
We are constructing small, portable radio telescopes. The project involves detecting radio signals and trying to characterize their source. We are interested in distinguishing astronomical signals from signals from technology and other Earthly sources.
Requirements: Completed algebra and trig courses.
Duration: Fall 2022, Spring 2023
Faculty: Carl Schmiedekamp ([email protected])
Surface Defects Detection and Classification on Metallic Parts
Inspection of metallic parts is a challenging task and has demands in a variety of manufacturing quality control applications. Computer visions are typically used for the purpose of inspection, in particular, for detection, recognition, and classification surface features representing manufacturing imperfections. This research focuses on developing algorithms for detection, extraction, and fuzzy assessment of surface conditions that are based on the fusion of surface attributes such as texture, defect-shape, color, surface contrast, etc. We will artificially plant surface defects, e.g. dents and scratches on nominally smooth surfaces. By training a machine learning framework, we should identify how a fusion of the aforementioned surface characteristics can be used to extract quantitative information about these artificial defects.
Requirements: Proficient in python programming, Knowledge in OpenCV – image processing is a plus.
Project duration: Fall 2022, Spring 2023
Faculty: Vinayak Elangovan ([email protected])
Text Summarization
Text summarization refers to techniques of shortening lengthy sections of text to create a comprehensible summary representing pertinent information outlined in the document. This research focuses on investigating and developing various text summarization algorithms developed by past researchers, identifying the pros and cons of those techniques and establishing a problem statement to open up more research opportunities in this area. An algorithm should be developed and tested to address the concerns of the investigated techniques.
Requirements: Proficient in python programming
Project duration: Fall 2022, Spring 2023
Faculty: Vinayak Elangovan ([email protected])
Analyzing Data in Opioid Overdose Database
We developed an opioid overdose database over the last two years. Now we would like to analyze the data inside this database. Students will be working with the professors involved in this project and query the database to extract valuable data for research purposes. Previous students involved in this project has successfully hired by companies that use databases in their daily operations. Students will gain valuable data retrieval skills in this particular project.
Requirements: A- or better in IST 210, GPA 3.0 or above.
Project duration: Fall 2022, Spring 2023
Faculty: Sabahattin Ozden ([email protected]), Glenn Sterner ([email protected])
Synthesis of novel gamma-butyrolactones
Our laboratory recently completed the total synthesis of the phenolic natural product Cinncassin A1, a gamma-butyrolactone that has potential as a neuroprotective agent. We are investigating the preparation of synthetic analogues of this substance for future biological study to determine their activity.
Requirements: Students who are interested in chemistry research are welcome to apply. Requirements for consideration include completed course work in CHEM 210, 212, and 213 with a grade of B or better.
Project duration: Fall 2022 and Spring 2023
Faculty: Ahmed Nuriye ([email protected])
Using Augmented Reality to Build Virtual Warehouses
We will develop virtual warehouses using augmented reality on IPhone and Android phones. These virtual warehouses can be used to test innovative warehouse layouts before building them physically. Students will learn about Augmented Reality and JavaScript which are the important for many employers on these days. Also students will gain experience in academic writing as well as warehouse design if they want to stay in academia. We already have a working prototype and students will be enhancing this working prototype.
Requirements: Successful completion of IST 240 or IST 242, GPA 3.0 or above.
Project duration: Fall 2022, Spring 2023
Faculty: Sabahattin Ozden ([email protected])
Dynamic Fish Schooling Modeling
Schools of fish are often modelled to find configurations that find the speed, minimize drag, or maximize efficiency. These rigid configurations do not account for the dynamic behavior of a school of fish. The project intends to expand on the boids animation technique by embedding a simplistic computational fluid dynamic (CFD) model onto the swimmers to compare different types of cooperative games that may arise in a school. The computational project will have two major components: the definition of a simple CFD model via model reduction techniques and a quantitive comparison of different swimming goals.
Requirements: Should be comfortable programming in Python/Julia; successful completion of MATH220
Project duration: Fall 2022, Spring 2023
Faculty: Nate Wagenhoffer ([email protected])
Investigating antisite defects in WS2 via TEM image analysis.
Project Description: It is critical to understand the laws of quantum mechanics in transformative new technologies for computation and quantum information science applications to enable the ongoing second quantum revolution calls. Recently, spin qubits based on point defects have gain great attention since these qubits can be initiated selectively controlled and readout with high precision at ambient temperatures. The major challenge in these systems is being able to controllably generate multiqubit systems while also properly coupling the defects. Transitional metal dichalcogenides (TMDs) have a lot of potential in this field because it's easy to controllably manufacture and alter defects for scalable samples. Defects can be created in 2D materials using a variety of methods. In this research we have used proton irradiation at various energies changing between 100 keV to 4 MeV to controllably create and modulate the antisite defects on CVD-grown monolayer WS2 materials. In this project the student will identify these antisite defects by analyzing the TEM images.
Requirements: Contact faculty mentor.
Duration: Spring 2022, Fall 2022, Spring 2023
Faculty: Burcu Ozden ([email protected])
