Digital Commons Network^™

Enhancement And Local Calibration Of Mechanistic-Empirical Pavement Design Guide, Hongren Gong

Doctoral Dissertations

The Mechanistic-Empirical Pavement Design Guide (MEPDG) represents the state-of-art procedure for pavement design. However, after more than a decade since its publication, the number of agencies that have reported entirely adopting this design system is small. Among the many causes of this phenomenon, the poor predictive accuracy of the performance prediction models is considered the most crucial one. To improve the accuracy of performance predicted by the MEPDG, a preliminary calibration was first conducted for these models with data from the pavement management system (PMS) of Tennessee, and then employed various machine learning algorithms for further improvements. Also, an approach …

Danna2: Dynamic Adaptive Neural Network Arrays, John Parker Mitchell

Masters Theses

Traditional Von Neumann architectures have been at the center of computing for decades thanks in part to Moore's Law and Dennard Scaling. However, MOSFET scaling is rapidly approaching its physical limits spelling the end of an era. This is causing researchers to examine alternative solutions. Neuromorphic computing is a paradigm shift which may offer increased capabilities and efficiency by borrowing concepts from biology and incorporating them into an alternative computing platform.The TENNLab group explores these architectures and the associated challenges. The group currently has a mature hardware platform referred to as Dynamic Adaptive Neural Network Arrays (DANNA). DANNA is a …

Characterizing Signal Transduction Networks And Biological Responses Using Computer Simulations And Machine Learning, Aaron Matthew Prescott

Doctoral Dissertations

The use of computer simulations in biology is often limited due to the lack of experimentally measured parameters. In these scenarios, parameter exploration can be used to probe biological systems and refine understanding of biological mechanisms. For systems with few unknown parameters, parameter sweeps that concurrently vary all unknown parameters are tractable. In complex systems with many unknown parameters, supervised machine learning algorithms can be used to discover parameters leading to targeted system responses. In this thesis, we study three biological problems in which we use parameter exploration methods to gain mechanistic insights. We first explore the role of altered …

Computational Analysis Of Upper Extremity Movements For People Post-Stroke, Zachariah Edward Nelson

Masters Theses

Wearable sensors have been beneficial in assessing motor impairment after stroke. Individuals who have experienced stroke may benefit from the use of wearable sensors to quantify and assess quality of motions in unobserved environments. Seven individuals participated in a study wherein they performed various gestures from the Fugl-Meyer Assessment (FMA), a measure of post-stroke impairment. Participants performed these gestures while being monitored by wearable sensors placed on each wrist. A series of MATLAB functions were written to process recorded sensor data, extract meaningful features from the data, and prepare those features for further use with various machine learning techniques. A …

Dynamics Of Collaborative Navigation And Applying Data Driven Methods To Improve Pedestrian Navigation Instructions At Decision Points For People Of Varying Spatial Aptitudes, Gengen He

Doctoral Dissertations

Cognitive Geography seeks to understand individual decision-making variations based on fundamental cognitive differences between people of varying spatial aptitudes. Understanding fundamental behavioral discrepancies among individuals is an important step to improve navigation algorithms and the overall travel experience. Contemporary navigation aids, although helpful in providing turn-by-turn directions, lack important capabilities to distinguish decision points for their features and importance. Existing systems lack the ability to generate landmark or decision point based instructions using real-time or crowd sourced data. Systems cannot customize personalized instructions for individuals based on inherent spatial ability, travel history, or situations.

This dissertation presents a novel experimental …

Neuroscience-Inspired Dynamic Architectures, Catherine Dorothy Schuman

Doctoral Dissertations

Biological brains are some of the most powerful computational devices on Earth. Computer scientists have long drawn inspiration from neuroscience to produce computational tools. This work introduces neuroscience-inspired dynamic architectures (NIDA), spiking neural networks embedded in a geometric space that exhibit dynamic behavior. A neuromorphic hardware implementation based on NIDA networks, Dynamic Adaptive Neural Network Array (DANNA), is discussed. Neuromorphic implementations are one alternative/complement to traditional von Neumann computation. A method for designing/training NIDA networks, based on evolutionary optimization, is introduced. We demonstrate the utility of NIDA networks on classification tasks, a control task, and an anomaly detection task. There …

3d Robotic Sensing Of People: Human Perception, Representation And Activity Recognition, Hao Zhang

Doctoral Dissertations

The robots are coming. Their presence will eventually bridge the digital-physical divide and dramatically impact human life by taking over tasks where our current society has shortcomings (e.g., search and rescue, elderly care, and child education). Human-centered robotics (HCR) is a vision to address how robots can coexist with humans and help people live safer, simpler and more independent lives.

As humans, we have a remarkable ability to perceive the world around us, perceive people, and interpret their behaviors. Endowing robots with these critical capabilities in highly dynamic human social environments is a significant but very challenging problem in practical …

Mitigation Of Catastrophic Interference In Neural Networks And Ensembles Using A Fixed Expansion Layer, Robert Austin Coop

Doctoral Dissertations

Catastrophic forgetting (also known in the literature as catastrophic interference) is the phenomenon by which learning systems exhibit a severe exponential loss of learned information when exposed to relatively small amounts of new training data. This loss of information is not caused by constraints due to the lack of resources available to the learning system, but rather is caused by representational overlap within the learning system and by side-effects of the training methods used. Catastrophic forgetting in auto-associative pattern recognition is a well-studied attribute of most parameterized supervised learning systems. A variation of this phenomenon, in the context of feedforward …

Online Multi-Stage Deep Architectures For Feature Extraction And Object Recognition, Derek Christopher Rose

Doctoral Dissertations

Multi-stage visual architectures have recently found success in achieving high classification accuracies over image datasets with large variations in pose, lighting, and scale. Inspired by techniques currently at the forefront of deep learning, such architectures are typically composed of one or more layers of preprocessing, feature encoding, and pooling to extract features from raw images. Training these components traditionally relies on large sets of patches that are extracted from a potentially large image dataset. In this context, high-dimensional feature space representations are often helpful for obtaining the best classification performances and providing a higher degree of invariance to object transformations. …

Computer Modeling And Signal Analysis Of Cardiovascular Physiology, Henian Xia

Doctoral Dissertations

This dissertation aims to study cardiovascular physiology from the cellular level to the whole heart level to the body level using numerical approaches.

A mathematical model was developed to describe electromechanical interaction in the heart. The model integrates cardio-electrophysiology and cardiac mechanics through excitation-induced contraction and deformation-induced currents. A finite element based parallel simulation scheme was developed to investigate coupled electrical and mechanical functions. The developed model and numerical scheme were utilized to study cardiovascular dynamics at cellular, tissue and organ levels. The influence of ion channel blockade on cardiac alternans was investigated. It was found that the channel blocker …

Primary User Emulation Attacks In Cognitive Radio - An Experimental Demonstration And Analysis, Benjamin James Ealey

Masters Theses

Cognitive radio networks rely on the ability to avoid primary users, owners of the frequency, and prevent collisions for effective communication to take place. Additional malicious secondary users, jammers, may use a primary user emulation attacks to take advantage of the secondary user's ability to avoid primary users and cause excessive and unexpected disruptions to communications. Two jamming/anti-jamming methods are investigated on Ettus Labs USRP 2 radios. First, pseudo-random channel hopping schemes are implemented for jammers to seek-and-disrupt secondary users while secondary users apply similar schemes to avoid all primary user signatures. In the second method the jammer uses adversarial …

A Kernelized Genetic Algorithm Decision Tree With Information Criteria, James Michael Lanning

Doctoral Dissertations

Decision trees are one of the most widely used data mining models with a long history in machine learning, statistics, and pattern recognition. A main advantage of the decision trees is that the resulting data partitioning model can be easily understood by both the data analyst and customer. This is in comparison to some more powerful kernel related models such as Radial Basis Function (RBF) Networks and Support Vector Machines. In recent literature, the decision tree has been used as part of a two-step training algorithm for RBF networks. However, the primary function of the decision tree is not model …