Engineering Commons^™

On Resource-Efficiency And Performance Optimization In Big Data Computing And Networking Using Machine Learning, Wuji Liu

Dissertations

Due to the rapid transition from traditional experiment-based approaches to large-scale, computational intensive simulations, next-generation scientific applications typically involve complex numerical modeling and extreme-scale simulations. Such model-based simulations oftentimes generate colossal amounts of data, which must be transferred over high-performance network (HPN) infrastructures to remote sites and analyzed against experimental or observation data on high-performance computing (HPC) facility. Optimizing the performance of both data transfer in HPN and simulation-based model development on HPC is critical to enabling and accelerating knowledge discovery and scientific innovation. However, such processes generally involve an enormous set of attributes including domain-specific model parameters, network transport …

Network Management, Optimization And Security With Machine Learning Applications In Wireless Networks, Mariam Nabil

Theses and Dissertations

Wireless communication networks are emerging fast with a lot of challenges and ambitions. Requirements that are expected to be delivered by modern wireless networks are complex, multi-dimensional, and sometimes contradicting. In this thesis, we investigate several types of emerging wireless networks and tackle some challenges of these various networks. We focus on three main challenges. Those are Resource Optimization, Network Management, and Cyber Security. We present multiple views of these three aspects and propose solutions to probable scenarios. The first challenge (Resource Optimization) is studied in Wireless Powered Communication Networks (WPCNs). WPCNs are considered a very promising approach towards sustainable, …

Benchmarking Small-Dataset Structure-Activity-Relationship Models For Prediction Of Wnt Signaling Inhibition, Mahtab Kokabi

Masters Theses

Quantitative structure-activity relationship (QSAR) models based on machine learning algorithms are powerful tools to expedite drug discovery processes and therapeutics development. Given the cost in acquiring large-sized training datasets, it is useful to examine if QSAR analysis can reasonably predict drug activity with only a small-sized dataset (size < 100) and benchmark these small-dataset QSAR models in application-specific studies. To this end, here we present a systematic benchmarking study on small-dataset QSAR models built for prediction of effective Wnt signaling inhibitors, which are essential to therapeutics development in prevalent human diseases (e.g., cancer). Specifically, we examined a total of 72 two-dimensional (2D) QSAR models based on 4 best-performing algorithms, 6 commonly used molecular fingerprints, and 3 typical fingerprint lengths. We trained these models using a training dataset (56 compounds), benchmarked their performance on 4 figures-of-merit (FOMs), and examined their prediction accuracy using an external validation dataset (14 compounds). Our data show that the model performance is maximized when: 1) molecular fingerprints are selected to provide sufficient, unique, and not overly detailed representations of the chemical structures of drug compounds; 2) algorithms are selected to reduce the number of false predictions due to class imbalance in the dataset; and 3) models are selected to reach balanced performance on all 4 FOMs. These results may provide general guidelines in developing high-performance small-dataset QSAR models for drug activity prediction.

Data And Sensor Fusion Using Fmg, Semg And Imu Sensors For Upper Limb Prosthesis Control, Jason S. Gharibo

Electronic Thesis and Dissertation Repository

Whether someone is born with a missing limb or an amputation occurs later in life, living with this disability can be extremely challenging. The robotic prosthetic devices available today are capable of giving users more functionality, but the methods available to control these prostheses restrict their use to simple actions, and are part of the reason why users often reject prosthetic technologies. Using multiple myography modalities has been a promising approach to address these control limitations; however, only two myography modalities have been rigorously tested so far, and while the results have shown improvements, they have not been robust enough …

Scatter Estimation And Correction For Experimental And Simulated Data In Multi-Slice Computed Tomography Using Machine Learning And Minimum Least Squares Methods, Cornelia Wang

McKelvey School of Engineering Theses & Dissertations

Current research aims to reduce the stopping power ratio prediction error in the inputs to the proton therapy planning process to less than 1%, which allows for improved radiation therapy planning. Our present study on reducing SPR error neglects the effect of scattering, which can increase SPR error by as much as 1-1.5%. The idea is that for each source-to-detector pair, 24 mm collimation data is close to 3 mm collimation data but with increased signal due to scattering. The goal is to estimate 3 mm collimation data from 24 mm collimation data. Pairs of sinograms, both experimental data and …

Machine Learning For Analog/Mixed-Signal Integrated Circuit Design Automation, Weidong Cao

McKelvey School of Engineering Theses & Dissertations

Analog/mixed-signal (AMS) integrated circuits (ICs) play an essential role in electronic systems by processing analog signals and performing data conversion to bridge the analog physical world and our digital information world.Their ubiquitousness powers diverse applications ranging from smart devices and autonomous cars to crucial infrastructures. Despite such critical importance, conventional design strategies of AMS circuits still follow an expensive and time-consuming manual process and are unable to meet the exponentially-growing productivity demands from industry and satisfy the rapidly-changing design specifications from many emerging applications. Design automation of AMS IC is thus the key to tackling these challenges and has been …

Generative Learning In Smart Grid, Samer M. El Kababji

Electronic Thesis and Dissertation Repository

If a smart grid is to be described in one word, that word would be ’connectivity’. While electricity production and consumption still depend on a limited number of physical connections, exchanging data is growing enormously. Customers, utilities, sensors, and markets are all different sources of data that are exchanged in a ubiquitous digital setup. To deal with data complexity, many researchers recently focused on machine learning (ML) applications in smart grids. Much of the success in ML is attributed to discriminative learning where models define boundaries to categorize data. Generative learning, however, reveals how data is generated by learning the …

Development Of A Highly Sensitive Pressure Sensing System With Custom-Built Software For Continuous Physiological Measurements, Masoud Panahi

Masters Theses

In this work, a pressure sensing system was designed and fabricated by developing a highly sensitive cone-structured pressure sensor with a custom-built software for physiological monitoring applications. A novel highly sensitive cone structured porous polydimethylsiloxane (PDMS) based pressure sensor capable of detecting very low-pressure ranges was developed for respiration monitoring. The pressure sensor was fabricated using a master mold, a hybrid-structured dielectric layer, and fabric-based electrodes. The master mold with inverted cone structures was created using a rapid and precise three-dimensional (3D) printing technique. The dielectric layer, with pores and cone structures, was prepared by annealing a mixture of PDMS, …

Power System Stability Assessment With Supervised Machine Learning, Mirka Mandich

Masters Theses

Power system stability assessment has become an important area of research due to the increased penetration of photovoltaics (PV) in modern power systems. This work explores how supervised machine learning can be used to assess power system stability for the Western Electricity Coordinating Council (WECC) service region as part of the Data-driven Security Assessment for the Multi-Timescale Integrated Dynamics and Scheduling for Solar (MIDAS) project. Data-driven methods offer to improve power flow scheduling through machine learning prediction, enabling better energy resource management and reducing demand on real-time time-domain simulations. Frequency, transient, and small signal stability datasets were created using the …

Impact Assessment, Detection, And Mitigation Of False Data Attacks In Electrical Power Systems, Sagnik Basumallik

Dissertations - ALL

The global energy market has seen a massive increase in investment and capital flow in the last few decades. This has completely transformed the way power grids operate - legacy systems are now being replaced by advanced smart grid infrastructures that attest to better connectivity and increased reliability. One popular example is the extensive deployment of phasor measurement units, which is referred to PMUs, that constantly provide time-synchronized phasor measurements at a high resolution compared to conventional meters. This enables system operators to monitor in real-time the vast electrical network spanning thousands of miles. However, a targeted cyber attack on …

Impact Assessment, Detection, And Mitigation Of False Data Attacks In Electrical Power Systems, Sagnik Basumallik

Dissertations - ALL

The global energy market has seen a massive increase in investment and capital flow in the last few decades. This has completely transformed the way power grids operate - legacy systems are now being replaced by advanced smart grid infrastructures that attest to better connectivity and increased reliability. One popular example is the extensive deployment of phasor measurement units, which is referred to PMUs, that constantly provide time-synchronized phasor measurements at a high resolution compared to conventional meters. This enables system operators to monitor in real-time the vast electrical network spanning thousands of miles. However, a targeted cyber attack on …

Achieving Differential Privacy And Fairness In Machine Learning, Depeng Xu

Graduate Theses and Dissertations

Machine learning algorithms are used to make decisions in various applications, such as recruiting, lending and policing. These algorithms rely on large amounts of sensitive individual information to work properly. Hence, there are sociological concerns about machine learning algorithms on matters like privacy and fairness. Currently, many studies only focus on protecting individual privacy or ensuring fairness of algorithms separately without taking consideration of their connection. However, there are new challenges arising in privacy preserving and fairness-aware machine learning. On one hand, there is fairness within the private model, i.e., how to meet both privacy and fairness requirements simultaneously in …

Machine Learning-Based Side-Channel Analysis On The Advanced Encryption Standard, Jack Edmonds, Tyler Moon

Electrical and Computer Engineering Senior Theses

Hardware security is essential in keeping sensitive information private. Because of this, it’s imperative that we evaluate the ability of cryptosystems to withstand cutting edge attacks. Doing so encourages the development of countermeasures and new methods of data protection as needed. In this thesis, we present our findings of an evaluation of the Advanced Encryption Standard, particularly unmasked and masked AES-128, implemented in software on an STM32F415 microcontroller unit (MCU), against machine learning-based side-channel analysis (MLSCA). 12 machine learning classifiers were used in combination with a side-channel leakage model in the context of four scenarios: profiling one device and key …

Indoor Navigation Using Convolutional Neural Networks And Floor Plans, Ricky D. Anderson

Theses and Dissertations

The goal of this thesis is to evaluate a new indoor navigation technique by incorporating floor plans along with monocular camera images into a CNN as a potential means for identifying camera position. Building floor plans are widely available and provide potential information for localizing within the building. This work sets out to determine if a CNN can learn the architectural features of a floor plan and use that information to determine a location. In this work, a simulated indoor data set is created and used to train two CNNs. A classification CNN, which breaks up the floor plan into …

Holistic Control For Cyber-Physical Systems, Yehan Ma

McKelvey School of Engineering Theses & Dissertations

The Industrial Internet of Things (IIoT) are transforming industries through emerging technologies such as wireless networks, edge computing, and machine learning. However, IIoT technologies are not ready for control systems for industrial automation that demands control performance of physical processes, resiliency to both cyber and physical disturbances, and energy efficiency. To meet the challenges of IIoT-driven control, we propose holistic control as a cyber-physical system (CPS) approach to next-generation industrial automation systems. In contrast to traditional industrial automation systems where computing, communication, and control are managed in isolation, holistic control orchestrates the management of cyber platforms (networks and computing platforms) …

A Dynamic Active Noise Control System For Live Music Attenuation, Elliot James Krueger

Graduate Research Theses & Dissertations

This thesis proposes a system design that will be suitable for applying active noise control (ANC) effectively to live musical instruments. The design consists of three parts: a signal separation section, an instrument classification section, and the active noise control section. The signal separation section will split up the music signals. The instrument classification section will identify the signals, and the ANC section will attenuate the music signal based on the previous information from the other sections. The two instruments of focus will be the trombone and tuba for their low frequency and ability to be quite loud in a …

Wind Turbine Parameter Calibration Using Deep Learning Approaches, Rebecca Mccubbin

Electronic Theses and Dissertations

The inertia and damping coefficients are critical to understanding the workings of a wind turbine, especially when it is in a transient state. However, many manufacturers do not provide this information about their turbines, requiring people to estimate these values themselves. This research seeks to design a multilayer perceptron (MLP) that can accurately predict the inertia and damping coefficients using the power data from a turbine during a transient state. To do this, a model of a wind turbine was built in Matlab, and a simulation of a three-phase fault was used to collect realistic fault data to input into …

Machine Learning Approach For Vigilance State Classification In Mice, Anik Muhury

Theses and Dissertations--Electrical and Computer Engineering

Sleep has a significant impact on cognitive abilities such as memory, reaction time, productivity, and creative thinking; however, there are many aspects of this important activity that are not clearly understood. Over the last century, researchers have developed technology and animal models to assist in the study of sleep. Manual sleep scoring is time consuming, reduces productivity, and is impacted by human scorer subjectivity. On the other hand, automatic sleep stage categorization can enhance consistency and reliability, aiding professionals in identifying sleep related health problems.

In recent times various studies reported significant achievements for automatic vigilance detection and overcome the …