Engineering Commons^™

Improvement Of 802.11 Protocol On Fully Programmable Wireless Radio, Eunji Lee

Undergraduate Research & Mentoring Program

The growth in the number of connected device usage has led to a rapidly increased data traffic on wireless network and the demand for access to high speed and stable Internet connection is becoming more prominent. However, current off the shelf wireless cards are not programmable or observable across layers of the standard protocol stack, which leads to poor practical performance. Thus, Wireless Open Access Research Platform (WARP), a scalable wireless platform providing programmable functionality at every layer of the network stack, has been used for the real-time implementation and improvement of 802.11 protocol.

An Analysis Of Lora Low Power Technology And Its Applications, Gomathy Venkata Krishnan

Undergraduate Research & Mentoring Program

The number of Internet of Things (IoT) devices has exponentially increased in the last decade. With the increase in these devices, there is a necessity to effectively connect and control these devices remotely. Cellular technologies cannot handle this demand since they are not cost effective and easy to deploy. This is where LoRa technology comes handy. LoRa is long-range, low-power, low cost technology that supports internet of things applications. LoRa has many advantages in terms of capacity, mobility, battery lifetime and cost. It uses the unlicensed 915MHz ISM band and can be easily deployed.

This research is focused on setting …

Generating Adversarial Attacks For Sparse Neural Networks, Jack H. Chen, Walt Woods

Undergraduate Research & Mentoring Program

Neural networks provide state-of-the-art accuracy for image classification tasks. However traditional networks are highly susceptible to imperceivable perturbations to their inputs known as adversarial attacks that drastically change the resulting output. The magnitude of these perturbations can be measured as Mean Squared Error (MSE). We use genetic algorithms to produce black-box adversarial attacks and examine MSE on state-of-the-art networks. This method generates an attack that converts 90% confidence on a correct class to 50% confidence of a targeted, incorrect class after 2000 epochs. We will generate and examine attacks and their MSE against several sparse neural networks. We theorize that …