(Poster is private)
In U.S., about 63% of households include pets. However, certain pets (such as dogs) have the instinct to run away from the house. Yet, it is impossible for the pet owners to watch their pets all the time. Therefore, a portable and inexpensive handheld tracking system can be a useful tool for helping the owners to watch their pets.
This project intend to employ iBeacon, which is a technology released by Apple Inc., to build a tracking system. The iBeacon technique can achieve distance measurements based on the Received Signal Strength (RSS). The RSS value will change as the distance between Beacon and the signal receiving device change. Moreover, the iBeacon tag device for pets is small (in the size of a quarter) enough to put on the collar of a pet. The application will store the information of beacons (including UUID, which is used to distinguish different beacons) that provide by users, and continually detect the signal from the beacons. When the signal is not strong enough, which means the Beacon is out of the controllable range, then the application will alert the user.
Recent advances in image recognition have been catalyzed by progressions in the applications of convolutional neural networks (CNN) and deep learning (DL). In traditional artificial intelligence (AI), neural networks (NN) were represented in a “shallow” fashion; dictating only one dimensional vectors at various layers. Furthermore past networks were often confined to three main layers: input, hidden and output layers. This rigidity of the structure not only contrasted the NN’s derivation from complicated biological neural systems but also limited their capability of categorizing inputs of various sizes and orientations (like images.) CNN's sought to solve this problem by representing a NN in terms of 3D volumes in which a kernel is moved in a sliding manner over subsections of an input volume and convolved with these regions to generate a k-layer output volume. This output volume is comprised of filtered versions of the previous volume which help detect recognizable features while maintaining important spatial features. This project created a deep CNN which leverages the Java library DeepLearning4j to demonstrate these techniques and provide a simple program which attempts to categorize input images into one of 5 classes.
There are two types of hearing loss, conductive and sensorineural. The former simply reduce the sound level as it passes through the external ear canal to the middle ear, so amplifying comes close to restore hearing to normal. On the other hand, sensorineural hearing impairment results from a defect in the inner ear or the cochlea nerve. Most of the time, this condition cannot be medically or surgically corrected. This is also the most common form of hearing lost and amplification of sound alone is ineffective. However, we can combine many other techniques to manipulate sound to treat this condition. Thus, we create Distinct Sound to help patients with sensorineural hearing loss.
Author(s): Kathryn Jaslikowski Computer Science Nicholas Bomm Computer Science Phil Howell Computer Science Wills Ward Computer Science
Advisor(s): Donnell Payne Computer Science Samantha Powell Nutritional Sciences
Location: Session: 2; 3rd Floor; Table Number: 4
The purpose of this capstone project is to aid Meals On Wheels, Inc. (MOWI) of Tarrant County with in-home consultations for low-income and low-mobility clients. MOWI can only provide a client with 10 meals per week. A professor in the TCU Nutritional Sciences Department approached our group about creating a web application to provide easy-to-make, low-cost recipes in order to supplement the meals delivered. We then developed an application - easybites.org - that allows dietitians to create recipe and shopping lists based on client food preferences, allergies, and appliance/utensil restrictions. Dietitians can then print a PDF file of the recipes and shopping lists in-home for clients to keep. Foods, recipes, and stores can be dynamically added, edited, and deleted from the database by administrators and interns. We also calculate the nutritional information for each recipe using a USDA Nutrient Database API to ensure that the MOWI clients are able to see the nutrition content of the recipes.
Author(s): Rebecca Ruch Computer Science Cameron Diou Computer Science Harrison Engel Computer Science Steven Garcia Computer Science Will Taylor Computer Science
Advisor(s): Donnell Payne Computer Science Lisa Ball Computer Science
Location: Session: 2; B0; Table Number: 3
Expanding Your Horizons Network (EYHN) is a 501(c)3 nonprofit organization dedicated to providing gateway STEM (Science, Technology, Engineering, and Math) experiences to middle and high school girls that spark interest in activities and careers within these fields. EYHN accomplishes this through role-model led conferences with hands on STEM activities and workshops.
These conferences are hosted by various organizations across the country. In Fort Worth, an annual EYHN conference is hosted by Texas Wesleyan University (TxWes). Each year, this conference hosts hundreds of student participants and requires dozens of leaders, volunteers, and presenters. Handling a conference of this size requires significant organizational effort, with a bulk of pre-conference administrative work going to registering participants and creating a good schedule for the event. In previous years, organizers at TxWes used a scheduling and registration system created by TCU students in 2005. However, this program is out of date and no longer useable making a replacement necessary.
This Project, Scheduling Your Horizons (SYH), creates a replacement system for TxWes that allows TxWes organizers to register participants and generate a schedule for the conference. It does so in a modern, user-friendly manner, with an emphasis on platform independence and maintainability to extend the lifespan of the application.
Author(s): James Stewart Computer Science Michael Giba Computer Science Quang Nguyen Computer Science Son Nguyen Computer Science Thaddeus Rix Computer Science
Advisor(s): Liran Ma Computer Science Billy Farmer Computer Science Donnell Payne Computer Science
Location: Session: 1; 2nd Floor; Table Number: 8
TCU’s previous Student Research Symposium site provided an outdated submission-review system for the Michael and Sally McCracken Student Research Symposium, an event growing in popularity. The old system was mostly a front-end to a primarily manual collection of procedures to collect, review, and present research projects. There was a growing need to make a more robust system that can provide smart interfaces for various users that allows for secure submitting, balloting, and administration.
The new system provides a host of automated processes that facilitate in the management of the SRS event from year to year, including such things as automatic archiving of previous year’s information. This is possible due to a myriad of free technologies such as Django. To complement the many processes we have automated, we have created tools for administrators to change information in the website without entering the codebase. Among the automated processes and features that help with administration, we have embedded advanced algorithms which reduce the need for human involvement, such as cost-analysis table assignments, a procedure that once required hours of laborious calculations.