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Freshman Retention in the Fulton Schools of Engineering

The goal of this project is to implement regression models for predicting the likelihood of a freshman student in the Ira A. Fulton Schools of Engineering to return in the following spring semester. This research is built off the analysis done by Arafat and Pedroza in the spring of 2018. The models that are being

Various Artificial Intelligence Approaches Demonstrated by Chess

The main goal of this research project was to compare the effectiveness of various artificial intelligence (AI) engines in a measurable environment such as chess. In order to train the deep neural network (DNN)  an algorithm to make random movements (RM) was developed. An initial data set consisting of 20,000 games was trimmed to 702;

Student Retention in the Fulton Schools

Because of the unending need for engineers, ASU has a vested interest in maintaining and increasing the retention of their first-time freshman engineering students. This research aims to utilize linear and non-linear modeling to predict student retention probabilities and identify which tools are most successful at increasing retention. The models will provide accurate predictions of

Passively Actuated Jump Gliding Wings

This project is investigating the impact curvature, buckling, and anisotropy play when used passively to enhance jumping capability. In this paper we employ a curved laminate structure to allow a rigid link to collapse preferentially in one direction when it encounters aerodynamic drag forces. A joint of this nature could be used for passively actuated

Automated Process Planning for Multi-material Manufacturing

This project is studying methods of automating the planning of multi-material manufacturing processes through the development of a new framework for representing and computing functionally-graded materials for use in rapid prototyping applications.  This framework includes low-level operations for combining geometries together and algorithms which assist the designer in computing manufacturing-compatible sequences. These algorithms can then

LED Based Camera Pose Estimation

Augmented reality (AR) enhances the user view of the environment by overlaying the virtual information on the physical world, thereby creating an illusion to the user. For creating a good illusion, we need to accurately register the virtual world on top of real world. In fact, many applications such as AR-based classroom education demand accurate

Indoor Localization for Navigational Aides

Internet-of-Things for Smart and Connected Cities is an important research topic in computer science and engineering. The interest in IoT is enhancing indoor navigation, particularly for individuals who are blind or visually impaired. The goal of this project is to use SpotSense’s state-of-the-art indoor localization technology to transform ASU’s CUbiC Lab into an IoT testbed

Speaker Diarization using Machine Learning

Knowing about the identity of a speaker is helpful in scenarios such as meetings and telephone conversations. This task can be achieved using speaker diarization – segmenting and classifying a speech signal to the speaker identity. To perform speaker diarization, features are extracted from speech segments. An identification model is trained for the extracted features.

Removing Conflicting Interactions in Locating Arrays

When designing experiments for many different factors, two problems quickly arise.  The first is that testing all the different combinations of the factors and interactions creates an experiment that is too large to conduct in a practical amount of time. One way this problem is solved is with a combinatorial design called a locating array