Artificial Intelligence and Robotics Commons^™

Searching For Needles In The Cosmic Haystack, Thomas Ryan Devine

Graduate Theses, Dissertations, and Problem Reports (ETD)

Searching for pulsar signals in radio astronomy data sets is a difficult task. The data sets are extremely large, approaching the petabyte scale, and are growing larger as instruments become more advanced. Big Data brings with it big challenges. Processing the data to identify candidate pulsar signals is computationally expensive and must utilize parallelism to be scalable. Labeling benchmarks for supervised classification is costly. To compound the problem, pulsar signals are very rare, e.g., only 0.05% of the instances in one data set represent pulsars. Furthermore, there are many different approaches to candidate classification with no consensus on a best …

Image And Video-Based Autism Spectrum Disorder Detection Via Deep Learning, Mindi Ruan

Graduate Theses, Dissertations, and Problem Reports (ETD)

People with Autism Spectrum Disorder (ASD) show atypical attention to social stimuli and aberrant gaze when viewing images of the physical world. However, it is unknown how they perceive the world from a first-person perspective. In this study, we used machine learning to classify photos taken in three different categories (people, indoors, and outdoors) as either having been taken by individuals with ASD or by peers without ASD. Our classifier effectively discriminated photos from all three categories but was particularly successful at classifying photos of people with >80% accuracy. Importantly, the visualization of our model revealed critical features that led …

Searches For Fast Radio Bursts Using Machine Learning, Devansh Agarwal

Graduate Theses, Dissertations, and Problem Reports (ETD)

Fast Radio bursts (FRBs) are enigmatic astrophysical events with millisecond durations and flux densities in the range 0.1-100 Jy, with the prototype source discovered by Lorimer et al. (2007). Like pulsars, FRBs show the characteristic inverse square sweep in observing frequency due to propagation through an ionized medium. This effect is quantified by the dispersion measure (DM). Unlike pulsars, FRBs have anomalously high DMs, which are consistent with an extragalactic origin. Over 100 FRBs have been published at the time of writing, and 13 have been conclusively identified with host galaxies with spectroscopically determined redshifts in the range 0.003 ≤ …

Fast Decision-Making Under Time And Resource Constraints, Kyle Gabriel Lassak

Graduate Theses, Dissertations, and Problem Reports (ETD)

Practical decision makers are inherently limited by computational and memory resources as well as the time available in which to make decisions. To cope with these limitations, humans actively seek methods which limit their resource demands by exploiting structure within the environment and exploiting a coupling between their sensing and actuation to form heuristics for fast decision-making. To date, such behavior has not been replicated in artificial agents. This research explores how heuristics may be incorporated into the decision-making process to quickly make high-quality decisions through the analysis of a prominent case study: the outfielder problem. In the outfielder problem, …

Representation Learning With Adversarial Latent Autoencoders, Stanislav Pidhorskyi M.S.

Graduate Theses, Dissertations, and Problem Reports (ETD)

A large number of deep learning methods applied to computer vision problems require encoder-decoder maps. These methods include, but are not limited to, self-representation learning, generalization, few-shot learning, and novelty detection. Encoder-decoder maps are also useful for photo manipulation, photo editing, superresolution, etc. Encoder-decoder maps are typically learned using autoencoder networks.
Traditionally, autoencoder reciprocity is achieved in the image-space using pixel-wise
similarity loss, which has a widely known flaw of producing non-realistic reconstructions. This flaw is typical for the Variational Autoencoder (VAE) family and is not only limited to pixel-wise similarity losses, but is common to all methods relying upon …

Deep Learning For Image Restoration And Robotic Vision, Yixin Du

Graduate Theses, Dissertations, and Problem Reports (ETD)

Traditional model-based approach requires the formulation of mathematical model, and the model often has limited performance. The quality of an image may degrade due to a variety of reasons: It could be the context of scene is affected by weather conditions such as haze, rain, and snow; It's also possible that there is some noise generated during image processing/transmission (e.g., artifacts generated during compression.). The goal of image restoration is to restore the image back to desirable quality both subjectively and objectively. Agricultural robotics is gaining interest these days since most agricultural works are lengthy and repetitive. Computer vision is …

Autonomous Systems With Reuse: A Survey On The State-Of-The-Practice, Denny Laverne Hood Iii

Graduate Theses, Dissertations, and Problem Reports (ETD)

This problem report presents the results of an anonymous online survey that was used to collect information about software systems that use model-based software engineering, contain autonomy and where software reuse plays an important role. Recent advancements in computation ability and the emergence of decision-making algorithms have increased interest in and use of autonomy in applications areas such as aeronautics, automotive, military, and space industries. In these application domains, autonomy is used to reduce costs, reduce reaction times, and improve performance. Due to the emerging nature of autonomy, very little research has been done regarding the level of autonomy of …

Evaluation And Understandability Of Face Image Quality Assessment, Mohammad I. Nouyed

Graduate Theses, Dissertations, and Problem Reports (ETD)

Face image quality assessment (FIQA) has been an area of interest to researchers as a way to improve the face recognition accuracy. By filtering out the low quality images we can reduce various difficulties faced in unconstrained face recognition, such as, failure in face or facial landmark detection or low presence of useful facial information. In last decade or so, researchers have proposed different methods to assess the face image quality, spanning from fusion of quality measures to using learning based methods. Different approaches have their own strength and weaknesses. But, it is hard to perform a comparative assessment of …

Quantifying Human Biological Age: A Machine Learning Approach, Syed Ashiqur Rahman

Graduate Theses, Dissertations, and Problem Reports (ETD)

Quantifying human biological age is an important and difficult challenge. Different biomarkers and numerous approaches have been studied for biological age prediction, each with its advantages and limitations. In this work, we first introduce a new anthropometric measure (called Surface-based Body Shape Index, SBSI) that accounts for both body shape and body size, and evaluate its performance as a predictor of all-cause mortality. We analyzed data from the National Health and Human Nutrition Examination Survey (NHANES). Based on the analysis, we introduce a new body shape index constructed from four important anthropometric determinants of body shape and body size: body …

Coupling Numerical Simulation And Pattern Recognition To Model Production And Evaluate Carbon Dioxide Injection In Shale Gas Reservoir, Amirmasoud Kalantari-Dahaghi

Graduate Theses, Dissertations, and Problem Reports (ETD)

Massive multi-cluster, multi-stage hydraulic fractures have significantly increased the complexity of the flow behavior in shale. This has translated into multiple challenges in the modeling of production from shale wells.

Most commonly used numerical techniques for modeling production from shale wells are Explicit Hydraulic Fracture (EHF) and Stimulated Reservoir Volume (SRV). Model setup for the EHF technique is long and laborious and its implementation is computationally expensive, such that it becomes impractical to model beyond a single pad. On the other hand, identifying the extent and conductivity of SRV is a challenging proposition. SRV technique is commonly used to simplify …