REU: 2022

Doyup Kwon
Deep-Learning Based Traffic Prediction on Las Vegas Highways

This project aims to determine the optional conditions for deep learning-based traffic prediction on given stretch of highway in Las Vegas through experiments and expand the capabilities of the traffic prediction systems currently implemented in the Las Vegas urban area.

Eric Volotao
Rover Mapping and Navigation

The goal of this project is to develop a more robust mapping and localization environment for mobile robots. The model is based on Turtlebot3 robot platform that includes odometer, IMU and Lidar sensors.

Jae Canetti
Walking Time Approximation for Smart Parking

Smart Parking application aims to anticipate openings in full parking lots at crowded venues or in high congested or parking deprived areas. It aims for traffic decongestion, reduces emissions, and improve efficiency.

Jennifer Kroon
Modelling of Dynamic Walking Robot

This project aims to determine the best foot model design using Collision Control Algorithm to increase energy efficiency and reduce the mechanical cost of transport for prosthetics and bipedal robots.

Kyle Luna
Generating Synthetic Data for UAV Autonomous Detection and Assessment

The aim of this project is to synthetically generate datasets of vehicles from an aerial view with various angles and altitudes, which can be used as training datasets for car detections, traffic congestion and accident severity. Synthetic data generation can offer more efficient data generation while having flexibility for different scenarios.

Naomi Halbersleben
Traffic Prediction Plotting/ Decision Support Tool

This project aims to create a data dashboard with synchronizes charts displaying various information such as historical traffic speeds, traffic volume, road occupancy and traffic predictions.

Obioma Okechukwu
Software Defined Radio Communication for V2X

The goal of this project is to develop a Software Defined Radio (SDR) system that allows for various types of communication between a vehicle and its surroundings (V2X).

Samuel Sokalzuk
Real Time Pedestrian Detection using YOLOv5 and Jetson Nano

The purpose of the project is to design a low-cost Pedestrian Detection System (PDS), that detects and counts the number of pedestrians at an intersection and transmit this data to Regional Transportation Commission (RTC) to aid in traffic light regulation or to the drivers using an app to alert when pedestrians are present.

Steven Nguyen
Control for Safe Braking of Autonomous Vehicles

Braking is the most fundamental safety precaution needed for any autonomous vehicles, and this project aims at developing a control algorithm for vehicles’ longitudinal motion, implement and test the safe breaking on actual vehicle while maintaining a comfortable ride for passengers.

Tyler Harris
Vehicle-to-Everything (V2X) Communications with Software Defined Radio

A critical aspect of ‘Smart Cities’ is creating a safe, efficient, and effective ways to communicate information from vehicles to its surroundings, called Vehicle-to-Everything (V2X) communications. The purpose of this project is creating a reliable and efficient way to enable a car to communicate in a V2X fashion.

REU: 2021

Abraham Castaneda
Benchmarking the Wally RISC-V Processor

The aim of this project is to use the CoreMark benchmark to evaluate the Wally RISC-V processor, compare it to existing commercial processors, and improve its performance.

Ana Maria Smith
Modeling Human Walking Dynamics

This project aims to model the Dynamic Walking Platform to determine which foot shape and control model minimizes the mechanical cost of transport. This research can be implemented in prosthetics and robots to increase efficiency, comfort, and reduce chances of injury.

Andres Graterol
U.S. Traffic Sign Recognition – Can We Bridge the Gap?

The rise in prevalence of ADAS in the United States is going to require classification systems that are trained on U.S. Traffic Signs. This research aims to determine whether it can begin to bridge the gap in image classification models that are trained based on these signs.

Cole Moreno
Vehicle to Everything (V2X) Communications using Software Defined Radio

Determine if the Software Defined Radio infrastructure is a viable option for V2X systems Implement customized demodulation schematics through MATLAB and Simulink using the Software Defined Radio architecture.

Dinh Hoang
Pedestrian Detection in Autonomous Vehicles Using AI and Computer Vision

This work utilizes on computer vision & deep learning techniques for environmental perception and pedestrian detection with the use of RGB & IR thermal camera (IR for better night-time detection).

Edgar Sanchez
Software Defined Radio Interface

The goal of the project is to use a Software Defined Radio (SDR) implemented in MATLAB to create a communication system between vehicles and various entities that make use of an SDR interface.

Erin Searcy
Visualizing Highway Sensor Data

Design a dashboard to visualize historical traffic measurements (speed, occupancy, and flow) and display prediction of future conditions using Python.

Gabrielle King
Solar and Load Forecasting

This model analyzes real historical data taken from homes in the Las Vegas valley to create an accurate forecast model of the short-term load that can be used by utilities (for optimal system operation) as well as individual residential customers (for bill management).

Kyla Sannadan
Traffic congestion prediction using multi-source historical and real-time traffic flow data

This project uses autonomous and crowd-sourced data to design a traffic prediction model with increased realizability, coverage and improve accuracy.

Marco Infantado
Video Capture and Streaming for Neural Network Training

The objective is to write a player for video capture-store-play for RGB cameras as well as multispectral LiDAR. This captured data can be used to train and test Neural Network capable of recognizing all the images in a frame and ordering them according to the distance from the capturing source and direction (path).

Mae Kjaer
Design & Analysis of Quantum Dot/Plasmonic Enhanced Solar Cells

The objective of this project is to design, analyze and optimize a high efficiency solar cell structure enabled by the UNLV fabrication technique.

Michael Lazeroff
Smart Parking System with time Prediction

A proof of concept for a smart parking system which can estimate the time when parking spaces will become available, and a mobile application front to relay information to users in real-time.

Michael Stepzinski
Autonomous Accident Detection and Assessment

This project aims at developing methods of autonomous accident detection and assessment to evaluate the performance and computational cost of these methods.