The goal of this project is to enhance a previously established dashboard which was done by another senior design team. This senior design project aims to design, develop, and demonstrate a web-based dashboard for displaying grid and market conditions and performing data analysis. Our team's design solution features enhanced security, multiple new charts that include real-time and historical data, and a subtraction tool for chart analysis.

Team Members

Ajay Ambati
Ajit Ambati
Parth Dargan
Sathvik Gujja
Nishanth Navali
Randunu Thrikawala

The Magic Charger is an onboard charging system for electric vehicles that would use the already available traction inverter in an E.V to charge its battery. To do this we leverage the bidirectional nature of the traction inverter by controlling the power flow into and out of the inverter. Using an electrically controlled contactor switch the inverter is set to either charge the onboard battery using the grid as a source, or power the vehicle’s motors from the battery.

Imagine a world where your car adapts to your driving needs. In this world, our driving experience can be enhanced when the car can infer the internal state of the driver. Towards this end, we devised and executed an experiment to determine whether consumer wearable health monitoring devices can detect driver stress. If this stress can be predicted, the car’s controls can adapt dynamically to help the driver deal with their situation on the road. To complete this task, we used a driving simulator to enact a series of high and low stress driving scenarios on our participants.

Our project demonstrates a novel blockchain-based implementation of the Vehicular Ad-Hoc Network (VANET). In our implementation, the blockchain network is utilized in order to facilitate communication  between vehicles and roadside units (RSUs). Our approach focuses on using Hyperledger and Veins in order to construct simulations of vehicles using the VANET, with Hyperledger powering the blockchain network and Veins serving as our simulation environment.

Tenaris, a leading industrial pipe production company, proposed this project to automate their manual straightness checks. Team i18 envisioned a machine learning solution. We constructed a vision system using a Triton 24.5 MP Model sensor to capture pipes in production settings and feed them into a machine learning model. This model, built from Tenaris pipe images, identifies flawed pipes based on a 3-5 second video. The Team created a web-hosted user interface, allowing factory managers to view the model's diagnosis and provide their validation with a few simple clicks.

The N-Gauge Battery Charger and Power Sharing prototype is based on a TI charger design for hot-swappable battery packs. Some medical devices need redundancy and flexibility in regard to battery configuration. There is a trend with large cart-based medical systems to use a uniform scalable approach across products with various power and battery capacity requirements. This battery charger focuses on a solution that is scalable to larger battery packs and maximizes system efficiency (As medical devices need to run as long as possible for patient safety).

Our team has built a multiple-robot system that can transport chips to different testing stations, streamlining the chip testing and validation process by moving them at the most efficient speed in the safest way possible. The design is focused on a smaller scale than an actual lab due to budgetary and time constraints, but it's meant to be easily scalable. We combined concepts such as mechanics, circuitry, and various software algorithms to create two robots that can navigate and coordinate with one another while communicating with a central server.  

The LTE Shield TM4C Multi-purpose Breakout Board brings LTE CAT-M1 and NB-IoT communication capabilities to the TM4C123 LaunchPad. The board contains GPIO and sensor peripherals that allow users to virtually read to and write from pins from a website.

Team Members

Malik Bajunaid
Diego Davila
Kenneth Emeremnu
Alex Liu
Pedro Mendez
Colby Renouf
Christian Tolentino

Our project is a Hardware Abstraction Layer (HAL) for the use of the Rust programming language in robotics applications on the Texas Instruments MSP430 series. This HAL allows developers to interface with the MSP430’s hardware more efficiently and in a more intuitive manner. Rust has multiple unique features that make it suited to embedded programming, including an emphasis on memory safety and easy package management with Cargo.

Our project is a teleconferencing robot for hybrid office environments that will provide users with a virtual presence. This will provide a solution to the lack of physical presence for remote employees in the work environment. The robot will provide a certain degree of freedom to interact and explore its environment independently through the use of locomotion and height variation.