Engineering Commons^™

Creating A 3d Printed Bipedal Robot’S Ankle And Foot With Human-Like Motion, Tylise E. Fitzgerald

Undergraduate Research & Mentoring Program

Humanoid robots are being created to replace humans in dangerous situations, assist overworked humans, and improve our quality of life by completing chores. However, current bipedal robots haven’t matched the performance of humans and are still impractical for commercial use.

One of the Agile and Adaptive Robotics Lab’s goals is to create a humanoid robot whose anatomy is similar to the human body. If this can be accomplished, we can have a functioning model of the human body that we can adjust to improve both humanoid robots’ functions and the functionality of our own human bodies. This specific project looks …

Material Parameter Estimation Of Thin Wafers With Terahertz Time-Domain Spectroscopy, Kirk R. Jungles

Undergraduate Research & Mentoring Program

Terahertz Time Domain Spectroscopy(THz TDS) is a spectroscopic technique that can be implemented to perform non destructive material parameter extraction on a variety of materials. Accuracy of these material parameters is often limited by statistical variation between measurements and insufficient knowledge of the thickness of the slabs being measured.

The goal of this project was to develop an in house procedure that would allow us to perform THz TDS on thin wafers using an up to date signal processing algorithm that would provide accurate predictions for the thickness of the wafers, reliable estimations of the wafer’s material parameters, and demonstration …

Omni-Gravity Hydroponics System For Spacecraft, Tara M. Prevo

Undergraduate Research & Mentoring Program

Effective omni-gravity hydroponics will allow astronauts to supplement nutrition and further close the life cycle of water in orbit, lunar, and Martian conditions. This project determines the operational limits of the test cells for the Plant Water Management Hydroponics mission. A scaled 1-g channel was designed by Rihana Mungin to mimic full-scale performance in microgravity that could be tested terrestrially. This project sought to find the limits of operation of the 1-g test cells and identify failure modes that could pose a safety risk in space. The cells were filled at increments of 20% and cycled from 0.184 to 8.33 …

Simulation Of Human Balance Control Using An Inverted Pendulum Model, Joshua E. Caneer

Undergraduate Research & Mentoring Program

The nervous system that human beings use to control balance is remarkably adaptable to a wide variety of environments and conditions. This neural system is likely a combination of many inputs and feedback control loops working together. The ability to emulate this system of balance could be of great value in understanding and developing solutions to proprioceptive disorders and other diseases that affect the human balance control system. Additionally, the process of emulating the human balance system may also have widespread applications to the locomotion capabilities of many types of robots, in both bipedal and non-bipedal configurations.

The goal of …

The Applications Of Grid Cells In Computer Vision, Keaton Kraiger

Undergraduate Research & Mentoring Program

In this study we present a novel method for position and scale invariant object representation based on a biologically-inspired framework. Grid cells are neurons in the entorhinal cortex whose multiple firing locations form a periodic triangular array, tiling the surface of an animal’s environment. We propose a model for simple object representation that maintains position and scale invariance, in which grid maps capture the fundamental structure and features of an object. The model provides a mechanism for identifying feature locations in a Cartesian plane and vectors between object features encoded by grid cells. It is shown that key object features …

Exploring And Expanding The One-Pixel Attack, Umairullah Khan, Walt Woods

Undergraduate Research & Mentoring Program

In machine learning research, adversarial examples are normal inputs to a classifier that have been specifically perturbed to cause the model to misclassify the input. These perturbations rarely affect the human readability of an input, even though the model’s output is drastically different. Recent work has demonstrated that image-classifying deep neural networks (DNNs) can be reliably fooled with the modification of a single pixel in the input image, without knowledge of a DNN’s internal parameters. This “one-pixel attack” utilizes an iterative evolutionary optimizer known as differential evolution (DE) to find the most effective pixel to perturb, via the evaluation of …

No-Reference Image Denoising Quality Assessment, Si Lu

Computer Science Faculty Publications and Presentations

A wide variety of image denoising methods are available now. However, the performance of a denoising algorithm often depends on individual input noisy images as well as its parameter setting. In this paper, we present a noreference image denoising quality assessment method that can be used to select for an input noisy image the right denoising algorithm with the optimal parameter setting. This is a challenging task as no ground truth is available. This paper presents a data-driven approach to learn to predict image denoising quality. Our method is based on the observation that while individual existing quality metrics and …