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25,827 Matching Results

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Behavioral and Neural Correlates of Speech Perception Outcomes in Adults with Cochlear Implants

Postlingually deafened cochlear implant (CI) adults have large variability in speech perception abilities. While CIs are one of the most successful neural prosthetic devices, they are not able to adequately provide fine structure cues which results in a degraded signal for the listener to interpret. While behavioral measures remain the gold standard for determining speech perception abilities, an objective measure is needed for patients who are unable to provide reliable behavioral responses. Behavioral, cognitive, and neural measures were collected in this study to identify potential neural biomarkers that correlate with speech perception performance. Behavioral experiments evaluated participants' abilities to identify, discriminate, and recognize words as well as sentences in quiet and in noise. Cognitive measures were assessed to determine the roles of attention, impulse control, memory, and cognitive flexibility on speech recognition. Auditory event-related potentials (ERP) were obtained with a double oddball paradigm to produce the mismatch negativity (MMN) response, which has been shown to have associations with phonetic categorical perception at the group level. The results indicated that executive function is highly predictive of speech performance and that the MMN is associated with categorical perception at the individual level. These findings are clinically relevant to determining appropriate follow-up care …

Date: December 2021

Creator: Manning, Jacy

System: The UNT Digital Library

Online Testing of Context-Aware Android Applications

This dissertation presents novel approaches to test context aware applications that suffer from a cost prohibitive number of context and GUI events and event combinations. The contributions of this work to test context aware applications under test include: (1) a real-world context events dataset from 82 Android users over a 30-day period, (2) applications of Markov models, Closed Sequential Pattern Mining (CloSPAN), Deep Neural Networks- Long Short Term Memory (LSTM) and Gated Recurrent Units (GRU), and Conditional Random Fields (CRF) applied to predict context patterns, (3) data driven test case generation techniques that insert events at the beginning of each test case in a round-robin manner, iterate through multiple context events at the beginning of each test case in a round-robin manner, and interleave real-world context event sequences and GUI events, and (4) systematically interleaving context with a combinatorial-based approach. The results of our empirical studies indicate (1) CRF outperforms other models thereby predicting context events with F1 score of about 60% for our dataset, (2) the ISFreqOne that iterates over context events at the beginning of each test case in a round-robin manner as well as interleaves real-world context event sequences and GUI events at an interval one achieves …

Date: December 2021

Creator: Piparia, Shraddha

System: The UNT Digital Library

Integrating Multiple Deep Learning Models to Classify Disaster Scene Videos

Recently, disaster scene description and indexing challenges attract the attention of researchers. In this dissertation, we solve a disaster-related multi-labeling task using a newly developed Low Altitude Disaster Imagery dataset. In the first task, we realize video content by selecting a set of summary key frames to represent the video sequence. Through inter-frame differences, the key frames are generated. The key frame extraction of disaster-related video clips is a powerful tool that can efficiently convert video data into image-level data, reduce the requirements for the extraction environment and improve the applicable environment. In the second, we propose a novel application of using deep learning methods on low altitude disaster video feature recognition. Supervised learning-based deep-learning approaches are effective in disaster-related features recognition via foreground object detection and background classification. Performed dataset validation, our model generalized well and improved performance by optimizing the YOLOv3 model and combining it with Resnet50. The comprehensive models showed more efficient and effective than those in prior published works. In the third task, we optimize the whole scene labeling classification by pruning the lightweight model MobileNetV3, which shows superior generalizability and can disaster features recognition from a disaster-related dataset be accomplished efficiently to assist disaster recovery.

Date: December 2021

Creator: Li, Yuan

System: The UNT Digital Library