Other Computer Engineering Commons^™

Machine Learning And Deep Learning Approaches For Gene Regulatory Network Inference In Plant Species, Sai Teja Mummadi

Dissertations, Master's Theses and Master's Reports

The construction of gene regulatory networks (GRNs) is vital for understanding the regulation of metabolic pathways, biological processes, and complex traits during plant growth and responses to environmental cues and stresses. The increasing availability of public databases has facilitated the development of numerous methods for inferring gene regulatory relationships between transcription factors and their targets. However, there is limited research on supervised learning techniques that utilize available regulatory relationships of plant species in public databases.

This study investigates the potential of machine learning (ML), deep learning (DL), and hybrid approaches for constructing GRNs in plant species, specifically Arabidopsis thaliana, …

Adapting Deep Learning For Underwater Acoustic Communication Channel Modeling, Li Wei

Dissertations, Master's Theses and Master's Reports

The recent emerging applications of novel underwater systems lead to increasing demand for underwater acoustic (UWA) communication and networking techniques. However, due to the challenging UWA channel characteristics, conventional wireless techniques are rarely applicable to UWA communication and networking. The cognitive and software-defined communication and networking are considered promising architecture of a novel UWA system design. As an essential component of a cognitive communication system, the modeling and prediction of the UWA channel impulse response (CIR) with deep generative models are studied in this work.

Firstly, an underwater acoustic communication and networking testbed is developed for conducting various simulations and …

An Experimental Study Towards Underwater Propulsion System Using Structure Borne Traveling Waves, Shreyas Suhas Gadekar

Dissertations, Master's Theses and Master's Reports

The method of generating steady-state structure-borne traveling waves underwater in an infinite media creates abundant opportunities in the field of propulsive applications, and they are gaining attention from several researchers. This experimental study provides a framework for harnessing traveling waves in a 1D beam immersed under quiescent water using two force input methods and providing a motion to an object floating on the surface of the water.

In this study, underwater traveling waves are tailored using structural vibrations at five different frequencies in the range of 10Hz to 300Hz. The resulting fluid motion provides a propulsive thrust that moves a …

High-Performance Spectral Methods For Computer-Aided Design Of Integrated Circuits, Zhiqiang Zhao

Dissertations, Master's Theses and Master's Reports

Recent research shows that by leveraging the key spectral properties of eigenvalues and eigenvectors of graph Laplacians, more efficient algorithms can be developed for tackling many graph-related computing tasks. In this dissertation, spectral methods are utilized for achieving faster algorithms in the applications of very-large-scale integration (VLSI) computer-aided design (CAD)

First, a scalable algorithmic framework is proposed for effective-resistance preserving spectral reduction of large undirected graphs. The proposed method allows computing much smaller graphs while preserving the key spectral (structural) properties of the original graph. Our framework is built upon the following three key components: a spectrum-preserving node aggregation and …

Hometracker: A Household Information Feedback System For Food/Energy/Water Metabolism, Nichole Mackey

Dissertations, Master's Theses and Master's Reports

The Food, Energy and Water Conscious (FEWCON) project seeks to understand how food, energy and water (FEW) as independent resources within households are connected. In the main study of the project, intervention messages that link household FEW consumption to equivalent climate consequences are pushed to the households. The goal of the FEWCON study is to determine potential intervention messages that influence household FEW consumption behavior.

A key component of the FEWCON study is a web application named HomeTracker (Household Metabolism Tracker) which collects FEW consumption data within households, then uses this data to select consumption-specific feedback to the homeowners. To …

Anomaly Inference Based On Heterogeneous Data Sources In An Electrical Distribution System, Yachen Tang

Dissertations, Master's Theses and Master's Reports

Harnessing the heterogeneous data sets would improve system observability. While the current metering infrastructure in distribution network has been utilized for the operational purpose to tackle abnormal events, such as weather-related disturbance, the new normal we face today can be at a greater magnitude. Strengthening the inter-dependencies as well as incorporating new crowd-sourced information can enhance operational aspects such as system reconfigurability under extreme conditions. Such resilience is crucial to the recovery of any catastrophic events. In this dissertation, it is focused on the anomaly of potential foul play within an electrical distribution system, both primary and secondary networks as …

Design Automation For Carbon Nanotube Circuits Considering Performance And Security Optimization, Lin Liu

Dissertations, Master's Theses and Master's Reports

As prevailing copper interconnect technology advances to its fundamental physical limit, interconnect delay due to ever-increasing wire resistivity has greatly limited the circuit miniaturization. Carbon nanotube (CNT) interconnects have emerged as promising replacement materials for copper interconnects due to their superior conductivity. Buffer insertion for CNT interconnects is capable of improving circuit timing of signal nets with limited buffer deployment. However, due to the imperfection of fabricating long straight CNT, there exist significant unidimensional-spatially correlated variations on the critical CNT geometric parameters such as the diameter and density, which will affect the circuit performance.

This dissertation develops a novel timing …

Low-Cost Open-Source Gmaw-Based Metal 3-D Printing: Monitoring, Slicer, Optimization, And Applications, Yuenyong Nilsiam

Dissertations, Master's Theses and Master's Reports

Low-cost and open-source gas metal arc welding (GMAW)-based 3-D printing has been demonstrated yet the electrical design and software was not developed enough to enable wide-spread adoption. This thesis provides three novel technical improvements based on the application of mechatronic and software theory that when combined demonstrate the ability for distributed digital manufacturing at the small and medium enterprise scale of steel and aluminum parts. First, low cost metal inert gas welders contain no power monitoring needed to tune GMAW 3-D printers. To obtain this data about power and energy usage during the printing, an integrated monitoring system was developed …

An Algorithm For Reconstructing Three-Dimensional Images From Overlapping Two-Dimensional Intensity Measurements With Relaxed Camera Positioning Requirements, With Application To Additive Manufacturing, Siranee Nuchitprasitchai

Dissertations, Master's Theses and Master's Reports

Cameras are everywhere for security purposes and there are often many cameras installed close to each other to cover areas of interest, such as airport passenger terminals. These systems are often designed to have overlapping fields of view to provide different aspects of the scene to review when, for example, law enforcement issues arise. However, these cameras are rarely, if ever positioned in a way that would be conducive to conventional stereo image processing. To address this, issue an algorithm was developed to rectify images measured under such conditions, and then perform stereo image reconstruction. The initial experiments described here …

Performance Comparison Of Binarized Neural Network With Convolutional Neural Network, Lopamudra Baruah

Dissertations, Master's Theses and Master's Reports

Deep learning is a trending topic widely studied by researchers due to increase in the abundance of data and getting meaningful results with them. Convolutional Neural Networks (CNN) is one of the most popular architectures used in deep learning. Binarized Neural Network (BNN) is also a neural network which consists of binary weights and activations. Neural Networks has large number of parameters and overfitting is a common problem to these networks. To overcome the overfitting problem, dropout is a solution. Randomly dropping some neurons along with its connections helps to prevent co-adaptations which finally help in reducing overfitting. Many researchers …

Representation And Analysis Of Multi-Modal, Nonuniform Time Series Data: An Application To Survival Prognosis Of Oncology Patients In An Outpatient Setting, Jennifer Winikus

Dissertations, Master's Theses and Master's Reports

The representation of nonuniform, multi-modal, time-limited time series data is complex and explored through the use of discrete representation, dimensionality reduction with segmentation based techniques, and with behavioral representation approaches. These explorations are done with a focus on an outpatient oncology setting with the classification and regression analysis being used for length of survival prognosis. Each decision of representation and analysis is not independent, with implications of each decision in method for how the data is represented and then which analysis technique is used. One unique aspect of the work is the use of outpatient clinical data for patients, which …