For this project, we have researched, designed, and tested various approaches and tools for actively combatting and reporting scammers. Through scambaiting, we have gathered intelligence on how different scams operate and have used this knowledge to produce repeatable social engineering tactics to gather actionable intelligence. Additionally, we use our collective technical skills to design seemingly realistic personalities and financial institutions to ensure the collection of intel and use that to investigate the scam operations in order to find anything further before creating a report and submitting to the proper authorities.

The purpose of the Agricultural IoT Capstone is to provide a battery-powered sensor node that measures soil moisture and atmospheric conditions at a garden site and then uses low-power, long-range communications to transmit this data so it can be stored on a server located off-site. This server will then provide end-users with an intuitive and easy to way to view data about their garden and make optimal choices about water conservation.

For our capstone project we decided that we wanted to challenge ourselves with some mobile development. Therefore, we decided to build a replica of the 2014 iOS game found in the App Store "2048"

Research and design a wrist wearable camera solution where the feed is accessible to view and record through a mobile application.

WeFit is a social network in the form of an iOS application that is geared towards users with healthy lifestyles and those that are looking to improve their overall health.

The purpose of this project is to provide a secure boot controller for commercial off-the-shelf FPGA. The boot controller must be capable of authenticating and decrypting runtime images from flash memory to ensure the integrity of the image to prevent malicious code injection. During the boot process, partial reconfiguration will be implemented to maintain data, memory, and connections of static regions of the FPGA while loading the new runtime.

This project is a Real Time Face Tracking and Animation software. The goal implemented is an optimization process that fits a 3D face to a 2D one in an image.

Our project is to integrate machine learning models within the CASA product suite. The modeler will evaluate the effectiveness of models on specific data sets. This will enable the automating aspect of the model selection process.

Our project goal was to create a fast GPIO module for the GOWIN Semiconductor Ministar board. We wanted to beat the GPIO speeds of competing boards on the market, and achieve transfer speeds of up to 200 Mb. We also wanted this module to be easily usable for consumers.

A vehicle repair solutions platform, it takes inputted vehicular questions and concerns and matches the natural language against our database to procure expert solutions to the user. The solution data inside is collected through manual input and/or webscraping api. Overtime the system will become smarter as it continues to grow its wealth of knowledge and its ability to match a proper solution to a given problem.