Improve How We Make Things

2020-2021 Capstone Design Projects

Project Name: Designing high-speed control FPGA platform for WBG battery charging applications

Project Client: UBC Martin Ordonez Lab

Technology: Computer & Electronics

Project Description: The high-speed control FPGA platform for WBG battery charging applications is a system that is designed to provide efficient, fast, and safe battery charging for Electric Vehicles. Current Electric Vehicle chargers either optimize power efficiency by reducing power losses, or fast charging at low power efficiency and high safety risks. Our team is aiming to design a system that is capable of addressing both power efficiency and charging speed, while ensuring the safety.

On top of the current industrial charger circuit design, our team selected better electric components that are more suitable for Electric Vehicle battery charging. Furthermore, our team implemented a controller to monitor the voltage, current, and temperature level of the battery being charged to prevent possible damages. Although our system was able to control the voltage and current feeding to the battery, the voltage and current fluctuation is still very large resulting in more heat and power loss. Additionally, in order to ensure safety, the voltage fed to the battery must be controlled within the limit at all time to prevent damages, even in the situation of intensive noises from surroundings. Our team decided to use a PID controller instead of the PI controller usually used by the industry, to provide more flexibility in voltage overshoot and settling time, supported with emergency shutdowns for high voltage, current, and temperature. Our design has a resilient circuit design that is capable of withstanding fast charging, and a controller that controls the voltage and current level.


Project Name: Data Acquisition Framework for Physics Experiments using Xilinx SoCs and PetaLinux

Project Client: TRIUMF – Canada’s national laboratory for particle and nuclear physics

Technology: Computer & Electronics

Student Contacts: Atahan Akar: https://www.linkedin.com/in/atahanakar/, Masoud Mokhtari: https://www.linkedin.com/in/masoud-m/, Nadhem Rojbi: https://www.linkedin.com/in/nadhem-rojbi/, Songzhu Zhang: https://www.linkedin.com/in/songzhu-zhang/, Yuanhong Shao: jacob.gtgt@gmail.com

Project Description: Many physics experiments are carried out at TRIUMF, Canada’s particle accelerator center, in collaboration with research institutes around the world. An essential component to all these experiments is the Data Acquisition (DAQ) system which receives experiment waveforms, processes the data in real time, and transmits the results to users for analysis.

The development methods of DAQ systems at TRIUMF are demanding, requiring engineers to develop new hardware and software for each experiment. This is limiting the number of experiments per year and the speed at which these are carried out, making it difficult for TRIUMF and its partners to reach their targets.

We have developed a configurable DAQ system which reduces the time needed to set up a new experiment from one month to a few days. Our design supports over 30 different input configurations while providing the same functionality as previous systems which only support one. Our system is also capable of handling significantly large amounts of data at extremely high speeds, enabling a more detailed analysis of experiment results.

By using our system, and thus reducing the experiment setup time, both time and financial resources can be focused on accelerating experiments and expediting scientific discoveries.


Project Name: Lab-in-a-pack

Project Client: Sudip Shekhar (UBC)

Technology: Computer & Electronics

Student Contacts: vsira@student.ubc.ca

Project Description: It has been over a year since COVID-19 forced many of us to work from home. For ECE students, this has meant video lectures, conference calls and—perhaps most challenging— no lab access. But projects in the lab are fundamental to an ECE education. Experimentation fosters scientific thinking, creativity and is the reason many students get excited about ECE. Along with lab work comes the practical and hands-on experience required of future graduates. So it is important that students have access to quality test and measurement equipment to perform labs from home, but even before COVID-19 this has been a challenge. Test and measurement equipment like oscilloscopes and power supplies are expensive and the alternatives are limited.
That’s why our team is excited to introduce the Lab-in-a-Pack. The Lab-in-a-Pack is a four-in-one tool that includes an oscilloscope, function generator, multimeter and power supply for under 50CAD. It is small and portable and fits right into a backpack. The Lab-in-a-Pack is simple and easy to use. It connects into a user’s PC via USB where the user interacts with the Lab-in-a-Pack’s GUI. More advanced users can interact directly with the Lab-in-a-Pack through an API.
For more information contact our team at vsira@student.ubc.ca.


Project Name: Computational Quantum Chemistry Hardware Accelerator

Project Client: Eigen Research Inc.

Technology: Computer & Electronics

Student Contacts: William Qu – willqu@outlook.com, Xianda Sun – sunxd@student.ubc.ca, Andrada Zoltan – zoltandrada@gmail.com, Yash Dhandhania – yashd@alumni.ubc.ca, Karthik Ravichandran – karthik.ravichandran25@gmail.com

Project Description: The field of medical drug and cancer research requires simulations of large protein molecules to study their properties. One important property needed in most simulations is the lowest energy state of a quantum system. The Variational Monte Carlo (VMC) algorithm is a widely used method that computes this property. VMC uses a statistical approach to iteratively guess the lowest energy state until it converges to a solution. Unfortunately, the algorithm’s runtime increases exponentially with the number of particles in the given quantum system. However, VMC is highly parallelizable, which makes it a good candidate for hardware acceleration.
The project’s client, EigenResearch, wishes to study the feasibility of implementing this algorithm on a Field Programmable Gate Array (FPGA). FPGAs allow for the development of customized hardware that uniquely implement a user-defined functionality. The Quantum Chemistry Hardware Accelerator project implements a proof-of-concept VMC solution on an FPGA. Using parallelization techniques and a custom hardware architecture, the team shows that an FPGA solution is possible and can outperform a CPU implementation if designed correctly from the ground up.


Project Name: High-Efficiency Smart Lithium Charger using Gallium Nitride (GaN) technology

Project Client: GluxKind Technologies

Technology: Computer & Electronics

Student Contacts: Will Ries – william.ries@alumni.ubc.ca, Yingrui Yang – Jhinjing0608@gmail.com, Grant Andersen – g.andersen@alumni.ubc.ca, Igor Vuckovic – igor.vuckovic@alumni.ubc.ca, Arslan Bhatti – mabhatti@alumni.ubc.ca

Project Description: Our project is to design and build a smart battery charger that can take a user input to dictate the type of charging used at any given time. Charging can range from fastest which comes at the cost of battery health, to slowest which maximizes it as well as anything in between. Our client will use our charger to test multiple batteries for their application, and the customizability will allow them to revise their battery choice in the future, as well as offer the end user a more customized experience.
Our client gave us several technical challenges that we needed to achieve to ensure a comfortable user experience. Our design had to fit within 750 cubic centimeters so that it is easily portable, can be updated wirelessly for a comfortable user experience, as well as supplying the necessary power to the battery to run the stroller.


Project Name: Automated AI Photogrammetry Apparatus

Project Client: UBC Studios

Technology: Digital Media, Web & Mobile Apps

Student Contacts: capstone2021.PL114@gmail.com

Project Description: The world is using more digital learning methods; one emerging method is the use of 3D models to aid faculties and students that rely on analyzing physical objects, such as bones or antiques.
UBC Studios currently has a photogrammetry system capable of creating 3D models, however, it is limited to small objects that can be placed on a turntable. To improve this system, we designed and simulated a mobile photogrammetry robot. Utilizing its free ranging wheels and vertical chain drive, the robot is able to scan large objects, such as furniture. Additionally, the system is automated to reduce workload and time spent creating the 3D scans. The robot autonomously determines photo locations, uses VR tracking to orient the camera, and takes a photo of the object at each location.
The system will enable UBC Studios and the university as a whole to have a turnkey 3D scanning system in place, allowing objects to be scanned autonomously. Now UBC Studios can continue to create an open category of objects, accessible to both faculty and students, aimed toward improving digital learning.


Project Name: Control hub for modern food production

Project Client: SEEDS Sustainability Program/UBC Campus and Community Planning

Technology: Computer & Electronics

Student Contacts: ubc.capstone.121@gmail.com

Project Description: This project aims to reduce urban food insecurity by reducing the cost and labour requirements of monitoring aquaponic or hydroponic food-growing systems. Designed specifically for hobbyist or small-scale growers, our project monitors critical system variables such as pH, temperature, and water-level and provides autonomous control. Critical notifications, real-time information, control settings, and system-history is available to users on a mobile application.
Our work contributes a low-cost, open-source hardware design, and IoT development framework that will allow hobbyists to expand and modify the system for their own needs. By simplifying the communication protocols, growers can focus on adding new sensors instead of worrying about data-security.
Our system can also operate on battery power in the event of a power-outage, a critical failure in aquaponic systems. This feature allows growers to be notified and to begin to take corrective action within the minute of the outage.


Project Name: Ultra-Fast CMOS Failure Test Unit (UF-FTU)

Project Client: Ivanov group from UBC SOC labs

Technology: Computer & Electronics

Student Contacts: Ketan Desai: ketszim@gmail.com, Abdullah Mubushar: abdullahmubushar@gmail.com, Jerry Liu: jerryl1996@gmail.com, Keerthana Aparacherla: keerthana.reddy@outlook.com, Jiaqi Yue: yuejiaqi1@gmail.com

Project Description: Transistors are in virtually every device we use, cell phones, televisions and even cars. As transistors age, they develop imperfections which result in reduced performance over time. Researchers at the UBC System-on-a-Chip lab are studying methods to reverse this process. By controlling the environment of the transistors, some of these imperfections can be removed. Due to the rapid nature of this healing process, a specialized measurement device was built.
Using reconfigurable hardware such as a field programmable gate array, four high speed analog to digital converters and eight gigabits of sample memory, the device provides a high-performance platform for future testing. A modular front end allows for the device to be utilized in multiple applications, giving the researchers a flexible tool in their studies.
The device provides a foundation to further understand the fundamental physics that cause transistor healing.


Project Name: Visually Enhanced Lesion Scope (VELscope®) Product Design Update

Project Client: LED Dental Inc.

Technology: Computer & Electronics

Project Description: The VELscope is a medical device produced by LED Dental and used by oral health professionals to help identify oral tissue abnormalities caused by conditions such as cancer. The device emits violet-blue light to stimulate the natural fluorescence of the mouth; abnormal tissue tends to fluoresce abnormally, which makes it easily identifiable against the typical fluorescence patterns of healthy tissue.
The latest model, VELscope Vx, has improved the quality of oral health care for the last decade. However, it faces technical challenges which we seek to address. As a wireless handheld device, the VELscope Vx is powered by a rechargeable battery which requires replacement over the device’s lifetime. Its LEDs and power circuitry produce heat which is removed using a cooling fan that produces significant noise. The VELscope is also sometimes confused as a standalone diagnostic tool, as opposed to an adjunctive tool alongside traditional examination methods under white light.
Our design implements an improved battery pack with higher capacity battery cells and battery protections to allow it to last for the device’s entire lifespan. Instead of a fan, we use passive cooling, made feasible by an overall reduction in power consumption due to higher-efficiency LEDs and power circuits. Finally, addressing the use-case confusion, we have introduced a white light mode to encourage the use of traditional methods under white light.


Project Name: Precision electron microscopic imaging to accelerate COVID-19 drug discovery

Project Client: UBC

Technology: AI & Machine Learning

Student Contacts:

Project Description: Team TL119’s capstone project is focused in the field of Cryo-EM imaging, a process that uses an electron microscope to image individual cellular structures at a molecular resolution. Cryo-EM allows us to view properties of individual molecules and improve drug design.
Our project is targeted towards the lab technicians of the Cryo-EM Drug Design research program at UBC. The technicians feed samples into an electron microscope, where sections are magnified to image areas where the particle of interest is visible. A single sample contains millions of potential areas for particle image collection. Selecting regions for imaging is slow and relies heavily on the operator’s intuition. With the current manual selection process, around 95% of the sample’s regions are never imaged, with no guarantee that the 5% chosen were the best regions.
Our team designed a deep learning model to predict areas where the highest-quality particle images can be found. Our program provides lab technicians with evidence-based guidance on where to collect images, increasing the yield of useful images and reducing the time needed to fully image a sample. Furthermore, our work can be integrated into a future pipeline for fully automated data collection, drastically accelerating molecular research and drug design.