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Articles 1 - 11 of 11

Full-Text Articles in Physical Sciences and Mathematics

Investigating Machine Learning Techniques For Gesture Recognition With Low-Cost Capacitive Sensing Arrays, Michael Fahr Jr. May 2020

Investigating Machine Learning Techniques For Gesture Recognition With Low-Cost Capacitive Sensing Arrays, Michael Fahr Jr.

Computer Science and Computer Engineering Undergraduate Honors Theses

Machine learning has proven to be an effective tool for forming models to make predictions based on sample data. Supervised learning, a subset of machine learning, can be used to map input data to output labels based on pre-existing paired data. Datasets for machine learning can be created from many different sources and vary in complexity, with popular datasets including the MNIST handwritten dataset and CIFAR10 image dataset. The focus of this thesis is to test and validate multiple machine learning models for accurately classifying gestures performed on a low-cost capacitive sensing array. Multiple neural networks are trained using gesture ...


The Effects Of Mixed-Initiative Visualization Systems On Exploratory Data Analysis, Adam Kern Apr 2020

The Effects Of Mixed-Initiative Visualization Systems On Exploratory Data Analysis, Adam Kern

Engineering and Applied Science Theses & Dissertations

The main purpose of information visualization is to act as a window between a user and data. Historically, this has been accomplished via a single-agent framework: the only decisionmaker in the relationship between visualization system and analyst is the analyst herself. Yet this framework arose not from first principles, but from necessity: prior to this decade, computers were limited in their decision-making capabilities, especially in the face of large, complex datasets and visualization systems. This thesis aims to present the design and evaluation of a mixed-initiative system that aids the user in handling large, complex datasets and dense visualization systems ...


Toward Efficient Automation Of Interpretable Machine Learning Boosting, Nathan Neuhaus Jan 2020

Toward Efficient Automation Of Interpretable Machine Learning Boosting, Nathan Neuhaus

All Master's Theses

Developing efficient automated methods for Interpretable Machine Learning (IML) is an important and long-term goal in the field of Artificial Intelligence. Currently the Machine Learning landscape is dominated by Neural Networks (NNs) and Support Vector Machines (SVMs), models which are often highly accurate. Despite high accuracy, such models are essentially “black boxes” and therefore are too risky for situations like healthcare where real lives are at stake. In such situations, so called “glass-box” models, such as Decision Trees (DTs), Bayesian Networks (BNs), and Logic Relational (LR) models are often preferred, however can succumb to accuracy limitations. Unfortunately, having to choose ...


Seeing Eye To Eye: A Machine Learning Approach To Automated Saccade Analysis, Maigh Attre May 2019

Seeing Eye To Eye: A Machine Learning Approach To Automated Saccade Analysis, Maigh Attre

Honors Scholar Theses

Abnormal ocular motility is a common manifestation of many underlying pathologies particularly those that are neurological. Dynamics of saccades, when the eye rapidly changes its point of fixation, have been characterized for many neurological disorders including concussions, traumatic brain injuries (TBI), and Parkinson’s disease. However, widespread saccade analysis for diagnostic and research purposes requires the recognition of certain eye movement parameters. Key information such as velocity and duration must be determined from data based on a wide set of patients’ characteristics that may range in eye shapes and iris, hair and skin pigmentation [36]. Previous work on saccade analysis ...


Transfer Learning Approach To Multiclass Classification Of Child Facial Expressions, Megan A. Witherow, Manar D. Samad, Khan M. Iftekharuddin Jan 2019

Transfer Learning Approach To Multiclass Classification Of Child Facial Expressions, Megan A. Witherow, Manar D. Samad, Khan M. Iftekharuddin

Electrical & Computer Engineering Faculty Publications

The classification of facial expression has been extensively studied using adult facial images which are not appropriate ground truths for classifying facial expressions in children. The state-of-the-art deep learning approaches have been successful in the classification of facial expressions in adults. A deep learning model may be better able to learn the subtle but important features underlying child facial expressions and improve upon the performance of traditional machine learning and feature extraction methods. However, unlike adult data, only a limited number of ground truth images exist for training and validating models for child facial expression classification and there is a ...


Computer Vision-Based Traffic Sign Detection And Extraction: A Hybrid Approach Using Gis And Machine Learning, Zihao Wu Jan 2019

Computer Vision-Based Traffic Sign Detection And Extraction: A Hybrid Approach Using Gis And Machine Learning, Zihao Wu

Electronic Theses and Dissertations

Traffic sign detection and positioning have drawn considerable attention because of the recent development of autonomous driving and intelligent transportation systems. In order to detect and pinpoint traffic signs accurately, this research proposes two methods. In the first method, geo-tagged Google Street View images and road networks were utilized to locate traffic signs. In the second method, both traffic signs categories and locations were identified and extracted from the location-based GoPro video. TensorFlow is the machine learning framework used to implement these two methods. To that end, 363 stop signs were detected and mapped accurately using the first method (Google ...


2018 Ieee Signal Processing Cup: Forensic Camera Model Identification Challenge, Michael Geiger Jun 2018

2018 Ieee Signal Processing Cup: Forensic Camera Model Identification Challenge, Michael Geiger

Honors Theses

The goal of this Senior Capstone Project was to lead Union College’s first ever Signal Processing Cup Team to compete in IEEE’s 2018 Signal Processing Cup Competition. This year’s competition was a forensic camera model identification challenge and was divided into two separate stages of competition: Open Competition and Final Competition. Participation in the Open Competition was open to any teams of undergraduate students, but the Final Competition was only open to the three finalists from Open Competition and is scheduled to be held at ICASSP 2018 in Calgary, Alberta, Canada. Teams that make it to the ...


Deep-Learned Generative Representations Of 3d Shape Families, Haibin Huang Nov 2017

Deep-Learned Generative Representations Of 3d Shape Families, Haibin Huang

Doctoral Dissertations

Digital representations of 3D shapes are becoming increasingly useful in several emerging applications, such as 3D printing, virtual reality and augmented reality. However, traditional modeling softwares require users to have extensive modeling experience, artistic skills and training to handle their complex interfaces and perform the necessary low-level geometric manipulation commands. Thus, there is an emerging need for computer algorithms that help novice and casual users to quickly and easily generate 3D content. In this work, I will present deep learning algorithms that are capable of automatically inferring parametric representations of shape families, which can be used to generate new 3D ...


Mouse Vs. Machine: The Game, Cafferty Aiko Frattarelli Jan 2017

Mouse Vs. Machine: The Game, Cafferty Aiko Frattarelli

Senior Projects Spring 2017

Many modern video games built by big name companies are coded by a group of people together using, and possibly modifying, an already designed game engine. These games usually have another group of people creating the artwork. In this project, I coded and designed a video game from scratch, as well as created all the artwork used in the game. The player controls a mouse character who fights a variety of monsters. In order to create the complexity of the game, I implement basic neural networks as the enemy artificial intelligence, i.e. the decision making process of the enemy ...


Generating A Multipliciy Of Policies For Agent Steering In Crowd Simulation, Cory D Boatright, Mubbasir Kapadia, Jennie Shapira, Norman I. Badler Sep 2015

Generating A Multipliciy Of Policies For Agent Steering In Crowd Simulation, Cory D Boatright, Mubbasir Kapadia, Jennie Shapira, Norman I. Badler

Center for Human Modeling and Simulation

Pedestrian steering algorithms range from completely procedural to entirely data-driven, but the former grossly generalize across possible human behaviors and suffer computationally, whereas the latter are limited by the burden of ever-increasing data samples. Our approach seeks the balanced middle ground by deriving a collection of machine-learned policies based on the behavior of a procedural steering algorithm through the decomposition of the space of possible steering scenarios into steering contexts. The resulting algorithm scales well in the number of contexts, the use of new data sets to create new policies, and in the number of controlled agents as the policies ...


Computer Sketch Recognition, Richard Steigerwald Jun 2013

Computer Sketch Recognition, Richard Steigerwald

Master's Theses and Project Reports

Tens of thousands of years ago, humans drew sketches that we can see and identify even today. Sketches are the oldest recorded form of human communication and are still widely used. The universality of sketches supersedes that of culture and language. Despite the universal accessibility of sketches by humans, computers are unable to interpret or even correctly identify the contents of sketches drawn by humans with a practical level of accuracy.

In my thesis, I demonstrate that the accuracy of existing sketch recognition techniques can be improved by optimizing the classification criteria. Current techniques classify a 20,000 sketch crowd-sourced ...