Congratulations to the Master of Engineering students class of 2024! Many students in the cohort participated in the Fung Institute End of Year Showcase 2024 on Thursday, May 2, 2024. Tranportation Enginnering MEng students Nuha Anfaresi, Sydney Chen, Caoying Huang, Nozomu Kitamura, and Mike Santos were on hand to present their captone projects to Berkeley students, faculty, and alumni that focused on cutting edge work in engineering and innovation for social impact.
Machine Learning for Snow Depth Image Denoising for Improved Water Resources Management
Team: Shofi Latifah Nuha Anfaresi [CEE], Daniel Garcia Mijares [ME], Prin Seetapan [ME], Guanyu Zhao [ME]Advisor(s): Van Carey
Project ID: 78
By the year 2050, agricultural water scarcity is projected to rise in over 80% of the world’s agricultural land. This exacerbates the difficulties faced by more than 122 million individuals globally struggling with food insufficiency. Airborne Snow Observatories Inc. has developed snow maps capable of predicting water availability from snow. However, errors in the data observations necessitate manual correction by highly specialized engineers. Our team is developing an automated algorithm to compress this knowledge, enabling the identification and elimination of noise. This algorithm will reduce time and resource usage, ultimately enhancing decision-making for data users.
Cloud City
Team: Jhan-Shuo Liu [EECS], Nozomu Kitamura [CEE], Qingyang Hu [EECS]
Advisor(s): JD Margulici
Our project aims to revolutionize urban planning decision-making by introducing a low-code framework that simplifies the creation of data-driven tools. Specifically, we target two critical challenges as proof of concept: mitigating traffic congestion through predictive traffic flow analysis and streaming data transformations for decision-making. Utilizing a low-code approach with digital twins, we aim to construct an accurate machine-learning model and create an intuitive dashboard for data analysis with cloud computing. This endeavor seeks to alleviate traffic bottlenecks and demonstrate the potential of low-code platforms for non-technical users to develop advanced applications, fostering broader innovation in urban planning.
Utilizing Transformable Vertiport Topologies for Evolving eVTOL Demands
Team: Sydney Chen [CEE], Caoying Huang [CEE], Thomas Perera [IEOR], Mike Santos [CEE]
Advisor(s): Jasenka Rakas
Urban Air Mobility (UAM) is a new aviation concept that will transport people and goods in urban metropolitan areas using electric Vertical Takeoff and Landing aircraft (eVTOL).
The purpose of this project is to develop a framework/tool for vertiport site and vertiport size selection to support the implementation of eVTOL services in the airport access, able to balance passenger experience criteria and UAM operational constraints.Estimating passenger demand for eVTOL operations between vertiports and airports, and determining the maximum number of houry eVTOL operations at vertiports are important metrics for safe and reliable UAM operations.
See more projects from the event: https://funginstitute.berkeley.edu/capstone-showcase-2024/#1682536665051...