ITS and the Fung Institute Highlight Master of Engineering Students Capstone Projects

May 27, 2021

ME Capstone showcase 2019

Professor Alexandre Skabardonis poses with two of his teams at the 2019 Masters of Engineering Capstone Project Showcase in May 2019.

Every year, the Institute of Transportation Studies faculty and lead researchers engage with the Master of Engineering program at UC Berkeley, which encompasses engineering coursework with classes in leadership and business management. This year, with the course being completely online, the program saw an increase in student enrollment, many of whom chose transportation courses, projects and activities that crossed over into ITS Berkeley. Near the end of the program, students complete a capstone project that integrates their business and technical courses into one final project. Students work closely with faculty advisors to address a specific industry challenge often partnering with a with private business. On May 6, the Fung Institute shared the 10th annual UC Berkeley Master of Engineering (MEng) Capstone Showcase online, featuring both an interactive exhibition, formal presentations and awards.

The Institute of Transportation Studies would like to recognize the outstanding work done by the Master of Engineering students in the field of transportation. Thank you also to our many faculty advisors who provide leadership and oversight to these students:

Unmanned Underwater Vehicle with Wireless, LED-based Optical Communication System for Ocean Exploration

Team: Rebecca Sung [ME], James Liao [ME], Henry Fong [ME]
Advisor(s): Reza Alam [ME], Alexandre Immas [ME]

81% of the ocean remains unexplored, due to the ineffectiveness of traditional, wireless technology for underwater communication. Current methods mostly utilize acoustic communication, which is hampered by its low speed and limited bandwidth. Our team is laying the foundation for a new wireless optical communication method which localizes and transfers data using a communication link formed by a swarm of unmanned underwater vehicles (UUVs). The advantage of this new approach is twofold: it improves communication speed and bandwidth, and also allows longer range and depth for ocean exploration than is currently possible.

A System That Answers Cybersecurity Questions

Team: Liu Rui [EECS], Songwen Su [EECS], Yifei Zhang [EECS]
Advisor(s): Dawn Song [EECS], Peng Gao [EECS]

There is a cyber attack happening every 39 seconds. But many people and companies do not have enough knowledge about the current trends or behaviors of cyber attacks and how to prevent them. Our team is developing a security domain question answering system that aims to promote cybersecurity knowledge sharing. Our solution is a web page user interface that uses both Bidirectional Encoder Representations from Transformers (Bert) language model and knowledge-graph based approach to provide users with easy access to precise answers of security questions.

SafeTport - The Creation of a Crowdsourcing Application for Transportation Safety

Team: Jasper Shih-Pu Lee [CEE], Han Wang [CEE], Kim Muy Ly [CEE]
Advisor(s): Alexander Skabardonis [CEE], Offer Grembek [CEE]

Every year, over 1 million people are killed on roadways around the world. Our team has created an application that allows users to voice their concerns about infrastructure designs that make them feel unsafe. Through the data received by our mobile application, we can identify which road features make road users feel unsafe during their travels, so that local municipalities can proactively identify and improve dangerous components of their infrastructure.

GridMod: Decarbonizing the Energy Sector through Cost-Effective Electric Grid Modernization

Team: Sarah Gunasekera [ME], Bogdan Cristei [IEOR], Elliott Suen [CEE], Sydney Holgado [CEE]
Advisor(s): Seth Hoedl [Post Road], Gabriel Gomes [ME]

Global energy demand is expected to rise by 50% by the year 2040 – the energy grid of the future calls for decarbonization, digitalization and decentralization. We must transition away from a centralized energy system that relies on fossil fuels to a more distributed system that prioritizes renewables. Our team built a tool that uses a data driven approach to find optimal modernized grid configurations in order to ensure rapid and cost-effective decarbonization.

Improving the Map Construction System in an Open Sourced Augmented Reality Toolkit

Team: Michael Khorram [EECS], Tiantian Wang [EECS]
Advisor(s): Allen Yang [EECS]

OpenARK is an open sourced augmented reality software development kit whose goal is to allow developers to rapidly prototype AR applications. Our team’s goal is to improve the accuracy of the SLAM system in OpenARK which is responsible for tracking the device’s location relative to its environment. Our primary improvements include the merging of independent maps caused by the re-initialization of the SLAM system in low-feature environments, as well as the replacement of the current image feature detector with an improved one based on new research.

Minimizing Robots Travel Distance in an E-Commerce Warehouse to Improve Warehousing Efficiency

Team: Xiao Zhang [CEE], Fengyu Zhu [IEOR], Raven Han [CEE]
Advisor(s): Max Shen [IEOR], Cristobal Pais [IEOR]

The scale of e-commerce has been growing rapidly, and this growth has brought remarkable opportunities to transform the traditional warehouses into robot-based e-commerce warehouses. E-commerce warehouses have more complex functions and thus it is of great importance to improve their efficiency to meet the needs of customers. Our project aims at finding out the best locations of picking stations so as to minimize the total travel distances of the working robots. We will mainly use integer programming methods with complex constraints that match the real warehouse conditions. We will also study how optimal picking stations locations change due to different warehouse layouts. By doing several simulations, we hope to give e-commerce companies conclusive rules on how to design an efficient warehouse.

Motion-Tracked VR Experiences to Improve Balance and Posture Rehabilitation [Blue Goji]

Team: Derek Ho [ME], Calvin Shih [ME], Anna Wolfe [ME], Xingshuo Yan [CEE]
Advisor(s): Daniel Daugherty [Blue Goji], Austin Peck [Blue Goji], Siyuan Ren [Blue Goji], Coleman Fung [Blue Goji], Gabriel Gomes [ME]

An estimated 13 million people are currently living with an adult-onset brain disorder, such as Alzheimer’s disease, stroke, and Parkinson’s disease in the US. These individuals are more prone to falling, and the rehabilitation of declining balance and postural stability is a challenging process. With the Blue Goji Infinity system, we have created a dynamic testing environment that utilizes VR gaming to direct users with tasks that challenge balance and posture. Using gameplay data synchronized with movement data collected through force sensors and a depth camera, we are able to track and improve the rehabilitation of neurological disorders.

Adaptive Drone-Swarm Mapping for Wildfires

Team: Junhao Yu [ME], Zhanqin Huang [ME]
Advisor(s): Tarek Zohdi [ME]

Real-time mapping of complex environments after multi-location wildfires cannot be completed by traditional remote sensing methods like satellites and aircrafts. These methods are either slow, tricky to deploy, or have limited flexibility. Our drone-swarm mapping solution combines Machine Learning and commercial drones to implement a competitive alternative. The result is an efficient, adaptive, and cost effective solution that can track and predict wildfire progression.

Enhancing Physicians’ Prognoses using Deep Learning: An Ergonomic UI to Find Similar Patient Groups and Medical Trends [UCSF Bakar]

Team: Bo Zhou [BIOE], Sixtine Lauron [IEOR], James Corbitt [BIOE], Samuel Harreschou [NE]
Advisor(s): Gabriel Gomes [ME], Gundolf Schenk [UCSF]

Electronic health records consist of text-heavy clinical notes that document disease history of patients : this unstructured data makes it very challenging to extract information and examine medical trends. Our solution? By applying NLP and deep learning on hospital notes, we created a patient profiling search engine : our web-based user interface allows physicians to study for each new patient the medical history and medication reactions of its N nearest neighbors in terms of symptoms, lab results, diagnoses and medications. By taking a glance at our 3D patient clusters, doctors can adjust their medical prognosis and preferable treatments decisions.

Improving Robotic Navigation of Indoor Spaces Using Remote Imaging and Reinforcement Learning

Team: Jacob Szymkowski [BioE], Jianshe Guo [ME], Ziang Deng [ME], Kalle Suzuki [ME], Jiahao Zhao [ME], Chunyu Jin [ME], Weibo Huang [ME]
Advisor(s): Gabriel Gomes [ME]

Onboard computer vision for robots is often limited in field of view and in localization potential, which are both vital for precise navigation. Our objective is to apply reinforcement learning to develop a framework that improves a compact robot using remote imaging systems, improving the quality of data available for navigation. This supplies the robot with information that onboard sensors cannot provide, and has the potential to reduce labor costs in a multitude of fields by expanding the range of environments robots can navigate.

A Collision-Resilient Flying Robot [Squishy Robotics]

Team: Joey Zhu [ME]
Advisor(s): Alice Agogino [ME], Mark Mueller [ME], Clark Zha [ME], Doug Hutchings [Squishy Robotics]

Quadcopters and other flying robots are typically fragile and unable to operate in rough, unknown environments typical of disaster zones. Our team has designed and assembled a collision resilient flying robot that utilizes a rigid tensegrity shell and can carry a wide range of payloads, including the heritage Squishy Robotics HazMat payload. The rigid icosahedron tensegrity shell, mounting apparatus, and control system allow the quadcopter to continue operations even after a 6.5 [m/s] collision.

Optimizing Traffic in the Age of Navigation Apps

Team: Michael Zhang [EECS], Roham Ghotbi [EECS]
Advisor(s): Alexandre Bayen [EECS]

The city of Fremont is facing a significant challenge that is impacting the quality of life for its residents and businesses. Regional cut-through traffic, led by navigation applications, is clogging local roadways with motorists that do not live or work in Fremont. Our team is working on a dynamic traffic model that provides accurate simulations on various time periods. The resulting model can contribute to understand and mitigate congestion without decreasing road capacity in the real-world.

Beyond-Line-of-Sight Drone Navigation Through Real-time Onboard 3D Resconstruction

Team: Shubha Jagannatha [EECS], Maggie Zhang [EECS], Weiyan Zhu [EECS] Advisor(s): Allen Yang [EECS]

Beyond-line-of-sight drone navigation is currently unsafe due to the absence of tools to accurately perceive and understand a drone’s surroundings. Our team is working on visualizing a drone’s environment through Virtual Reality in real-time, making remote drone piloting much easier and safer. The solution we present is an end-to-end pipeline for remote piloting using a drone-mounted stereo camera, computer vision algorithms, and a Unity visualizer.

BISTRO: Using Traffic Simulation and Toll Zones in San Francisco to Reduce Congestion

Team: Albert Loekman [EECS], Christina Lu [EECS], Haochong Xia [EECS]
Advisor(s): Alex Bayen [EECS], Jessica Lazarus [EECS]

According to the San Francisco County Transportation Authority, car trips are expected to grow by 36% from 2015 to 2050, disproportionally increasing congestion and travel costs for low-income communities. Through BISTRO, we provide an open-source traffic simulation tool and graphical dashboard for policymakers to make traffic policies on how to combat these issues. To do this, we calibrated a traffic simulator for San Francisco and developed an algorithm to find the best locations and prices for a toll zone based policy. The dashboard will show how traffic conditions improve after implementing these toll zones.

Racing to Deliver a Comprehensive Autonomous Driving Research Solution

Team: Zizhao Gong [ME], Michael Cui [ME], James Cheney [ME], Chufan Guo [ME], Jiuqi Wang [ME], Yiliang Sun [ME]
Advisor(s): Allen Yang [EECS]

Autonomous driving is on the verge of changing the world, but the development needed to master the complexity of full autonomous operation is difficult to achieve with current platforms. Simulators are cost effective and accessible, but not realistic enough to test all situations. Testing with real vehicles is perfectly realistic, but expensive and complicated to implement in research. Berkeley’s Robot Open Autonomous Racing (ROAR) program addresses this need with a research platform that includes cross-compatible simulation and 1/10 scale physical environments. The Winning ROAR!! Team is making improvements to the ROAR dual environment platform as well as proving its effectiveness by developing and racing autonomous vehicle agents on it.

ML 101: A Learning Tool for Machine Learning with Real-Life Data

Team: Siyi Qu [IEOR], Jiayue Tao [IEOR]
Advisor(s): Gabriel Gomes [ME]

As individuals, we are exposed to, connected to and creating open-source data every day. With the existing machine learning techniques, predictions could be made more easily than ever, but the sheer number of available models overwhelms people. In this project, the most popular machine learning tools, from support-vector machine to neuron network, will be used to make high-accuracy predictions based on numerical, text and image data. Our goal is to help people without technical background understand, apply and gain insights on machine learning models in a more efficient and effective manner, with real-life examples (e.g., Titanic, Airbnb and American Sign Language).

Data Driven Framework to Democratize Deep Learning

Team: Cem Koc [EECS], Eric Liu [EECS], Jiayi Wang [EECS], Yujie Xu [EECS]
Advisor(s): Alvin Cheung [EECS]

The ongoing AI arms race sparked by Krishevsky et al. in 2012 with breakthrough artificial deep neural network performance has taken the world by storm. As AI has advanced, there has also been massive growth in AI frameworks and tools. Unfortunately, learning how to use these deep learning frameworks has been a challenge for engineers, researchers, and laity due to a steep learning curve. With our research, we aim to increase accessibility and productivity of researchers, industry professionals, and tech-curious people in machine learning by providing a data-driven model for searching, generating, and summarizing AI code.