Stellar Spine is a real-time brace compliance sensor for children with idiopathic scoliosis. These children are prescribed to wear a brace by their doctors for a certain number of hours a day. Stellar Spine consists of a sensor built into the brace and a mobile application. The sensor determines if the brace is being worn, and the mobile application provides a method to view that compliance data as well as incentivize the children to wear their brace through fun constellations. By providing real-time data over Bluetooth, we aim to increase brace compliance.

SmartNest is a human presence detection system used in smart home applications. With SmartNest, a home automation system can translate passive human behavior into actions normally triggered via voice activation, button presses, or other active input. This system is composed of sensor modules equipped with thermal cameras which send data to a central server for processing. This server then analyzes the thermal images, searching for heat signatures that match those emitted by humans.

The Automated Team Formation (ATF) system is a web-based application that uses students’ responses on a given survey data to generate teams. The website will give professors the flexibility to choose questions from a provided template and allow them to create their own questions. The objective for this project is to implement a website that allows ECE professors to create teams based on their discrepancies. This will let the professors be in full control of their definition on what makes a good team.

Compared to current market solutions for 3D Object Scanners, the design implemented is customizable and affordable. With the help of the open source freeLSS software, The Object Scanner has adjustable resolution, time delay, rotation steps, light exposure, and more. This allows for the user to counterbalance resolution with scan time. Additionally, the freeLSS software captures the color of objects and produces a 3D rendering, giving users an idea of what the scanned object would look like.

As computation-heavy applications become more prevalent and complex, the demand for developers to provide reasonably short response times for their users becomes a harder and harder problem to solve. Lots of the algebraically intensive work rests on computer architects, who aim to increase the speed of digital computations at the hardware level. Much of the modern progress in the field of computer architecture has been developed through improving linear algebra calculations, which are ubiquitous in modern computing systems.

Our system is composed of two seperate learning modules or subsystems. Module 19 builds on top of TI’s existing Bluetooth Module 19 by adding an Internet Of Things (IOT) component. Specifically, we will be utilizing the Blynk app to control circuitry on the RSLK over Bluetooth. Module 20 teaches how Wi-Fi works at both a chip level and in the realm of IOT by also utilizing the Blynk app to control circuitry on the RSLK, but now over Wi-Fi.

Our team developed the Deep Learning Hardware Accelerator (DLHA), a coprocessor designed to run deep neural networks faster and more power-efficiently than general purpose processors alone. Convolutional neural networks have revolutionized applications in image processing, robotics, and autonomous driving. However such methods are computationally very intensive, which prevents deployment in constrained embedded systems like mobile, IoT or in-vehicle platforms.

Our project’s main goal was to bring awareness to improper posture and eye strain during prolonged computer sessions by implementing a technological solution. To achieve this goal, our developed final product is comprised of a combination of hardware and software. The hardware solution utilizes an Arduino Nano to continually read data from a Time of Flight sensor and Thermal Camera. The processed data is fed into a UWP based windows application, where it shows a visual display of the information on a clean user interface.

The binding of DNA can be expressed in networks to build the next generation of logic gates. Researchers will simulate experiment outcomes to reduce resource waste using this web tool, which seeks to solve an NP-complete problem using a SAT solver.

The heat generated in laptops by the CPU (Central Processing Unit) and GPU (Graphics Processing Unit) is largely wasted. This project determines the feasibility of harnessing this untapped heat by converting it to power using thermoelectric generators, or TEGs, which convert heat to voltage. If laptops were able to recycle their own heat into usable power, built-in laptop batteries would have to supply less power to the laptop over time, effectively increasing the laptop's battery life.