URMP Student Participants

Meet this year's Undergraduate Research & Mentoring Program student participants. These students work over several terms with a faculty mentor to complete an exciting research project.

Luis Brennan

Faculty Mentor: Dr. David Burnett, ECE

Project Title: Electrically Small Antennas for single chip mote wireless sensor networks

Project Abstract: Using single chip motes for wireless sensor networks dramatically lowers the scale factor and expense for network nodes.  This improvement in scale factor however poses serious problems, especially regarding antenna design, for common RF communication protocols like 2.4GHz Bluetooth Low Energy (BLE), whose free space wavelength is well over 10 times the size of a single chip mote.  This study will design and investigate various electrically small antenna designs in order to quantify the effectiveness of less than ideal configurations constrained by planar width, surface area, and volume.  Specifically a variety of PCB based and wire antenna configurations with a variety of sizes will be simulated, built and tested.

Junyi Feng

Faculty Mentor: Dr. Ameeta Agrawal, CS

Project Title: Summarization Model

Project Abstract: This project is about developing automatic summarization models using some of the latest machine learning for natural language processing (ML + NLP) algorithms. While existing summarization algorithms have made great progress toward generating fluent, coherent, and even factually consistent summaries, what they are missing is their ability to generate more diverse summaries. In other words, the goal of this project is to generate not only 'good' but also 'fairer' summaries by preserving the diverse perspectives occurring in the input text. The input data will consist of natural language text (e.g., news articles, tweets, etc.), and the expected output is a concise summary of the input.

Patrick Gibson

Faculty Mentor: Dr. Jun Jiao, MME

Project Title:  Investigation of Low Temperature Growth Parameters for Scalable Graphene Films Suitable for Graphene-Based Silicon-CMOS Applications

Project Abstract: Following Intel co-founder Gordon Moore’s prediction, the semiconductor industry has doubled the number of transistors per square inch on integrated circuits (ICs) every two years for more than four decades. While this trend continues to be a unique feature of the semiconductor industry, CMOS-based ICs scaling toward single-digit nanometer dimensions are facing tremendous challenges caused by the quantum limit, current leakage, thermal constraints, signal/power integrity, and device parameter variability. In order to sustain silicon-CMOS technology and achieve deeper nanometer scaling with improved device performance, attention in the semiconductor field has turned to graphene. In 2015, the Executive Summary Report of International Technology Roadmap for Semiconductors (ITRS) named graphene a promising “ballistic conductor” that could be a CMOS-compatible material for three-dimensional (3D) device architecture in the new era of “3D power scaling.” Graphene is predicted to have the greatest impact on geometric scaling due to its high mobility, which is desirable in CMOS field effect transistor (MOSFET) channels. In this Intel-funded project, we plan to develop a scalable technique for low-temperature (within 400–600C) growth of graphene with controlled properties by a chemical vapor deposition (CVD) process. A systematic experimental investigation will be carried out. The results will be comparatively analyzed, and the correlations of the synergistic effects among the growth parameters will be established. This will lead to the identification of optimal parameters for direct deposition of graphene films that could be readily integrated with the silicon and complementary metal-oxide-semiconductor (CMOS) process for nanoscaled electronic fabrication. The students, who are supported by the Undergraduate Research Mentorship Program (URMP), will be involved in growing graphene and participating in design, fabrication and measurement of related nanodevices. The URMP students will also learn how to operate an inductively coupled chemical vapor deposition (ICPCVD) reactor and other thermal CVD reactors for graphene growth, the electron microscopes for imaging, and the probe station for electrical measurements. 

Nicole Henderson

Faculty Mentor: Dr. Bob Bass, ECE

Project Title: Development EC Systems to manage dispatch of residential loads to provide grid services

Project Abstract: Our research group has developed an aggregation server that coordinates dispatch of distributed energy resources (DERs) such as water heaters, electric vehicles and residential-scale battery-inverter systems.  The server uses the SEP 2.0 protocol (IEEE 2030.5), an open-source Energy Grid-of-Things (EGoT) framework, to exchange information between the aggregation server and DER clients.  The aggregation server, based on an Entity Component System architecture, coordinates the dispatch of thousands of DERs to provide grid services for an electric utility.  For this project, the student will work with a team on the design, build, and testing of EC Systems to manage the dispatch of DER for specific grid services.

Laura Israel

Faculty Mentor: Dr. Wu-chang Feng, CS

Project Title: Cybersecurity Curriculum and Activities

Project Abstract: The project seeks to develop content for a 10-week course intended for incoming freshmen, and potentially even high school students. The focus is on building labs, games, and other activities, with the eventual goal being to introduce non-CS students to topics in cybersecurity.

Arthur Jerome

Faculty Mentor: Dr. Alex Hunt, MME

Project Title: 3D Printed Electronics Mounting

Project Abstract: The Agile and Adaptive robotics lab has a quadruped robot that is in the process of being rewired. In order to help streamline the wiring of the quadruped robot, a new electronics mounting platform must be created. This will take the form of a 3d printed head that will store custom built circuit boards as well as any other necessary electronic hardware. This will make it much easier to work on the robot because there will be far fewer wires to disconnect and reconnect. This will also make the electronics much more modular making it simple to swap or add any components. Lastly this will improve the aesthetics of the bot slightly by decreasing the amount of wires and hiding some electronics underneath a custom case. Overall, this project will make it easier to work on the robot by streamlining the electronics.

Nihar Koppolu

Faculty Mentor: Dr. Christof Teuscher, ECE

Project Title: Computation with Biochemical Oscillators

Project Abstract: Molecular computing is a promising computational paradigm, in which computational functions are evaluated at the nanoscale, with potential applications in smart molecular diagnostics and therapeutics. However, despite recent advances in the field, prospects for direct application of these techniques to solve real-world problems are limited by the lack of robust interfaces between molecular computers and biological and chemical systems. The goal of this project is to model and simulate biochemical oscillators and to harness them for solving computational problems.

Lynn Nguyen

Faculty Mentor: Dr. Marek Perkowski, ECE

Project Title: Explorations in Interactive Robots and Quantum Machine Learning

Project Abstract: Working with the humanoid robot HR-OS5, we will explore robot-human interaction. Equipped with Dynamixel servos, this robot can learn sequences of motions, similar to classical animation. These behaviors can also be programmed or modified by software based on various machine learning technologies. Along with robot motion programming, animation, voice technologies and computer vision, this will allow us to control the robot's behaviors in unexpected situations. We will make use of preexisting machine learning and vision software available at PSU.

Additionally, we will be learning about quantum algorithms and machine learning. Quantum algorithms like the Grover algorithm to robots could speed up many practically important problems in computer-aided design, robotics, machine learning and optimization. We hope to apply these concepts to control a small robot with a simple quantum algorithm through the Internet.

Hongzu Pan

Faculty Mentor: Dr. Diane Moug, CEE

Project Title: Many Small Problems Application for an Undergraduate Geotechnical Engineering Class

Project Abstract: The overall goal of this project is to develop a web-based application for an undergraduate geotechnical engineering class. The application will produce short, auto-graded problems that allows students to master basic engineering concepts before moving onto more challenging homework or exam problems. The URMP student for this project will work with Dr. Moug to program a few problems for the application, then have the application tested during spring quarter's offering of the class.

Michael Ridenour

Faculty Mentor: Dr. Ameeta Agrawal, CS

Project Title: Evaluating Summarization Models

Project Abstract: The goal of this project is to develop better metrics for evaluating the performance of automatic summarization models using methods from natural language processing and machine learning. Most current evaluation metrics rely on human-written reference summaries for comparison and only focus on n-gram overlap between the system and reference summaries. Thus they are labor-intensive, and they often fail to account for meaning-preserving lexical diversity in the summary. Developing better evaluation metrics will help streamline the process and will give a more comprehensive measure of summarization quality.

Andrew Rodman Fink

Faculty Mentor: Dr. Christof Teuscher, ECE

Project Title: Building Logic Gates with Memristive Oscillators

Project Abstract: Memristors are passive, nonlinear circuit elements. They can be used to implement oscillators. Oscillators, on the other hand, can be used to realize logic gates. The purpose of this project is to use memristive oscillators to build logic gates. We will design, test, and evaluate such systems and compare them to other realizations of logic gates.

Nicholas Sylvia

Faculty Mentor: Dr. Raúl Bayoán Cal, MME

Project Title: Investigating Solar PV Panel Soil Mitigation

Project Abstract: As the world continues to fight the onset of climate change, renewable energy has become the main solution to obtaining sustainability. Solar panels are an important part of the renewable energy sector, yet after just weeks of use they suffer from efficiency loss due to their surfaces soiling. This research project investigates how dust on a solar panel can be efficiently removed. The experiments will be performed drop tower runs where the droplet is ejected onto a dirty surface. The measurements will be collected by determining the amount of dust the droplet collected from impacting the surface. The results will be used to determine best practices to achieve an effective means to alleviate PV panel soiling.

Questions? Please contact ajh26@pdx.edu.

View past participants, student posters and abstracts at PDX Scholar.