Digital Commons Network^™

Electronic Note-String Detector, Gavin Garcia-Rossi, Tommy Smail

Electrical Engineering

As the virtual space has become a dominant part of everyone’s day-to-day lives, many normal face-to-face interactions and services have not yet been facilitated by adapting technology. One of these prevailing areas is music lessons. Over Zoom meetings, or other virtual platforms, it is tremendously challenging to teach students. These challenges include recognizing student mistakes audibly and visually, and being able to give confident feedback on the incorrect notes played by learning musicians. Without having to delve into improving the complex systems that would be required to improve audio, video, and connection quality of these connections, we have another solution …

Controlled Manipulation And Transport By Microswimmers In Stokes Flows, Jake Buzhardt

All Dissertations

Remotely actuated microscale swimming robots have the potential to revolutionize many aspects of biomedicine. However, for the longterm goals of this field of research to be achievable, it is necessary to develop modelling, simulation, and control strategies which effectively and efficiently account for not only the motion of individual swimmers, but also the complex interactions of such swimmers with their environment including other nearby swimmers, boundaries, other cargo and passive particles, and the fluid medium itself. The aim of this thesis is to study these problems in simulation from the perspective of controls and dynamical systems, with a particular focus …

Ensuring Non-Repudiation In Long-Distance Constrained Devices, Ethan Blum

Honors Theses

Satellite communication is essential for the exploration and study of space. Satellites allow communications with many devices and systems residing in space and on the surface of celestial bodies from ground stations on Earth. However, with the rise of Ground Station as a Service (GsaaS), the ability to efficiently send action commands to distant satellites must ensure non-repudiation such that an attacker is unable to send malicious commands to distant satellites. Distant satellites are also constrained devices and rely on limited power, meaning security on these devices is minimal. Therefore, this study attempted to propose a novel algorithm to allow …

Chemical Strategies To Mitigate Electrostatic Charging During Coffee Grinding, Joshua Méndez Harper, Christopher H. Hendon

Electrical and Computer Engineering Faculty Publications and Presentations

The process of grinding coffee generates particles with high levels of electrostatic charge, causing a number of detrimental effects including clumping, particle dispersal, and spark discharge. At the brewing level, electrostatic aggregation between particles affects liquid-solid accessibility, leading to variable extraction quality. In this study, we quantify the effectiveness of four charge mitigation strategies. Our data suggests that adding small amounts of water to whole beans pre-grinding, or bombarding the grounds with ions produced from a high-voltage ionizer, are capable of de-electrifying the granular flows. While these techniques helped reduce visible mess, only the static reduction through water inclusion was …

Design Of A Low-Power Delay-Locked Loop-Based 8x Frequency Multiplier In 22 Nm Fdsoi, Naveed, Jeff Dix

Electrical Engineering Faculty Publications and Presentations

A low-power delay-locked loop (DLL)-based frequency multiplier is presented. The multiplier is designed in 22 nm FDSOI and achieves 8× multiplication. The proposed DLL uses a new simple duty cycle correction circuit and is XOR logic-based for frequency multiplication. Current starved delay cells are used to make the circuit power efficient. The circuit uses three 2× stages instead of an edge combiner to achieve 8× multiplication, thus requiring far less power and chip area as compared to conventional phase-locked loop (PLL) circuits. The proposed 8× multiplier occupies an active area of 0.09 mm2. The measurement result shows ultra-low power consumption …

Virtual Structures Based Autonomous Formation Flying Control For Small Satellites, Tyson K. Smith

All Graduate Theses and Dissertations, Fall 2023 to Present

Many space organizations have a growing need to fly several small satellites close together in order to collect and correlate data from different satellite sensors. To do this requires teams of engineers monitoring the satellites orbits and planning maneuvers for the satellites every time the satellite leaves its desired trajectory or formation. This task of maintaining the satellites orbits quickly becomes an arduous and expensive feat for satellite operations centers. This research develops and analyzes algorithms that allow satellites to autonomously control their orbit and formation without human intervention. This goal is accomplished by developing and evaluating a decentralized, optimization-based …

Optical And Electrical Interfaces For Biological Structures, Mehdi Bandali

Boise State University Theses and Dissertations

This dissertation presents novel solutions for recording optical and electrical data from biological structures. CHAPTER ONE discusses the 16×16 Time correlated SPAD image array I developed in a 180 nm HV CMOS process for single-molecule localization super-resolution microscopy (SRM). This image array has a maximum frame rate of 80 MHz and a fill factor of 17.4 %. To demonstrate SRM with this imaging array, we localized gold nanoparticle displacement at 40 nm. CHAPTER TWO discusses stimulation artifacts' time and frequency domain characteristics and potential strategies for canceling this interference on neural recording. CHAPTER THREE discusses an artifact-resilient neural recording front-end …

Characterization Of Human Mobility From Cellular Data, Zaid Matloub

Theses and Dissertations

This dissertation investigates human mobility patterns using crowd-sourced cellular network data from different Metropolitan Statistical Areas (MSAs) in the United States, spanning the Houston, New York-Newark, NJ City, and 13 other significant MSAs. By focusing on prominent spatial mobility parameters highlighted in existing literature, the study unveils consistent findings regarding the predictability of human mobility across diverse time scales and geographic regions. The research underscores the significance of selecting appropriate sampling thresholds based on the mobility parameters being examined, the size of the dataset, and available computational resources. Through a meticulous analysis, it emerges that while values such as mean …

Robust Uncertainty Estimation Framework In Deep Reinforcement Learning For Active Slam, Bryan Joseph Pedraza

Theses and Dissertations

Autonomous mobile robots are essential in various domains such as industry, manufacturing and healthcare. Navigating autonomously and avoiding obstacles are crucial tasks that involve localizing the robot to explore and map unknown environments without prior knowledge. Simultaneous localization and mapping (SLAM) present significant challenges. In this study, we introduce a new approach to address robust navigation and mapping of robot actions using Bayesian Actor-Critic (A2C) reinforcement learning. The A2C framework combines policy-based and value-based learning by dividing the model into two components: (1) the policy model (Actor) determines the actions based on the state, and (2) the value model (Critic) …

Melanoma Detection Based On Deep Learning Networks, Sanjay Devaraneni

Electronic Theses, Projects, and Dissertations

Our main objective is to develop a method for identifying melanoma enabling accurate assessments of patient’s health. Skin cancer, such as melanoma can be extremely dangerous if not detected and treated early. Detecting skin cancer accurately and promptly can greatly increase the chances of survival. To achieve this, it is important to develop a computer-aided diagnostic support system. In this study a research team introduces a sophisticated transfer learning model that utilizes Resnet50 to classify melanoma. Transfer learning is a machine learning technique that takes advantage of trained models, for similar tasks resulting in time saving and enhanced accuracy by …

A Tessellation Of Engineers, Grace Jenkins Welsh

University Honors Theses

A capstone project is the culmination of what a student has learned while pursuing an undergraduate degree. A project of this magnitude requires a lot of time and dedication from every member of the team. This essay looks back at the experience of an electrical engineering student participating in the design of an auxiliary power and protection of a planned substation. The author describes the team's work, how they performed as a team, and their involvement with the professional sponsor and advisor. The author examines the experience with the goal of informing and encouraging prospective engineering students who may be …

Accelerating Machine Learning Inference For Satellite Component Feature Extraction Using Fpgas., Andrew Ekblad

Theses and Dissertations

Running computer vision algorithms requires complex devices with lots of computing power, these types of devices are not well suited for space deployment. The harsh radiation environment and limited power budgets have hindered the ability of running advanced computer vision algorithms in space. This problem makes running an on-orbit servicing detection algorithm very difficult. This work proposes using a low powered FPGA to accelerate the computer vision algorithms that enable satellite component feature extraction. This work uses AMD/Xilinx’s Zynq SoC and DPU IP to run model inference. Experiments in this work centered around improving model post processing by creating implementations …

Model-Free Methods To Analyze Pmu Data In Real-Time For Situational Awareness And Stability Monitoring, Sean Kantra

All Dissertations

This dissertation presents and evaluates model-free methodologies to process Phasor Measurement Unit (PMU) data. Model-based PMU applications require knowledge of the system topology, most frequently the system admittance matrix. For large systems, the admittance matrix, or other system parameters, can be time-consuming to integrate into supporting PMU applications. These data sources are often sensitive and can require permissions to access, delaying the implementation of model-based approaches. This dissertation focuses on evaluating individual model-free applications to efficiently perform functions of interest to system operators for real-time situational awareness. Real-time situational awareness is evaluated with respect to central digitization where the PMU …

A Scalable Transmission And Distribution Co-Simulation Platform For Ibr-Heavy Power Systems, Yousu Chen

All Dissertations

The grid is evolving rapidly to meet the requirements of the clean energy transition. This evolution involves an increasing penetration of renewable energy resources and new complexities with a larger number of devices and controls spread across transmission and distribution networks. The boundary between transmission and distribution becomes blur. Consequently, we face significant challenges in managing the fundamental shift in power system physics. The ubiquitous use of Inverter Based Resources (IBRs) throughout the transmission and distribution systems has made it more difficult for the grid to maintain grid stability under dynamic conditions. Therefore, there is a strong need to explore …

Electrical Characteristics Of In-House Produced Micro/Nanodielectrics, Dewan Mahmudullah

Graduate Theses and Dissertations (2019 - present)

Dielectric characterization is very important across a wide range of applications, including the design and optimization of capacitors, electrical insulation, and electronic devices. It plays a vital role in providing valuable insights for material selection, performance evaluation, and the development of advanced dielectrics. The permittivity of dielectrics is a fundamental property of dielectric materials that quantifies their ability to store and dissipate energy in the presence of an electric field. Studies for developing advanced dielectrics and accurate measurements of the dielectric constant are essential for understanding and optimizing the performance of various electrical and electronic systems. The objective of this …

Towards A Spaceworthy Cots Graphics Processing Unit: Hardware Performance Counter Based Symptomatic Fault Detection, Antonio E. Teijeiro

Open Access Theses & Dissertations

Ionizing radiation remains an obstacle to bringing graphics processing units (GPU) to space. Since radiation-hardened GPU chips are technically infeasible at the moment, an emphasis has been placed on the adaptation of commercial-off-the-shelf (COTS) GPUs to the space domain. At present, GPU error detection methods require redundant computation. This thesis work explores the utilization of hardware performance counters, special registers useful for monitoring internal GPU hardware events, for symptom-based, lightweight error detection. Hardware performance counters are successfully utilized for the detection of anomalous single event upsets in the L0 instruction cache, the load store unit, the arithmetic and logic unit, …

Hpc-Enabled Fast And Configurable Dynamic Simulation, Analysis, And Learning For Complex Power System Adaptation And Control, Cong Wang

All Dissertations

This dissertation presents an HPC-enabled fast and configurable dynamic simulation, analysis, and learning framework for complex power system adaptation and control. Dynamic simulation for a large transmission system comprising thousands of buses and branches implies the latency of complicated numerical computations. However, faster-than-real-time execution is often required to provide timely support for power system planning and operation. The traditional approaches for speeding up the simulation demand extensive computing facilities such as CPU-based multi-core supercomputers, resulting in heavily resource-dependent solutions. In this work, by coupling the Message Passing Interface (MPI) protocol with an advanced heterogeneous programming environment, further acceleration can be …

An Improved Genetic Algorithm Based Fractional Open Circuit Voltage Mppt For Solar Pv Systems, Aakash Hassan, Octavian Bass, Mohammad A. S. Masoum

Research outputs 2022 to 2026

To extract the maximum power from solar PV, maximum power point tracking (MPPT) controllers are needed to operate the PV arrays at their maximum power point under varying environmental conditions. Fractional Open Circuit Voltage (FOCV) is a simple, cost-effective, and easy to implement MPPT technique. However, it suffers from the discontinuous power supply and low tracking efficiency. To overcome these drawbacks, a new hybrid MPPT technique based on the Genetic Algorithm (GA) and FOCV is proposed. The proposed technique is based on a single decision variable, reducing the complexity and convergence time of the algorithm. MATLAB/Simulink is used to test …

Implementation Of Adas And Autonomy On Unlv Campus, Zillur Rahman

UNLV Theses, Dissertations, Professional Papers, and Capstones

The integration of Advanced Driving Assistance Systems (ADAS) and autonomous driving functionalities into contemporary vehicles has notably surged, driven by the remarkable progress in artificial intelligence (AI). These AI systems, capable of learning from real-world data, now exhibit the capability to perceive their surroundings via a suite of sensors, create optimal routes from source to destination, and execute vehicle control akin to a human driver.

Within the context of this thesis, we undertake a comprehensive exploration of three distinct yet interrelated ADAS and Autonomy projects. Our central objective is the implementation of autonomous driving(AD) technology at UNLV campus, culminating in …

Cyber-Threat Detection Strategies Governed By An Observer And A Neural-Network For An Autonomous Electric Vehicle, Douglas Scruggs

All Theses

A pathway to prevalence for autonomous electrified transportation is reliant upon accurate and reliable information in the vehicle’s sensor data. This thesis provides insight as to the effective cyber-attack placements on an autonomous electric vehicle’s lateral stability control system (LSCS). Here, Data Integrity Attacks, Replay Attacks, and Denial-of-Service attacks are placed on the sensor data describing the vehicle’s actual yaw-rate and sideslip angle. In this study, there are three different forms of detection methods. These detection methods utilize a residual metric that incorporate sensor data, a state-space observer, and a Neural-Network. The vehicle at hand is a four-motor drive autonomous …

Custom-Designed And Collaborative Unmanned Systems With Learning-Based Applications, Robert Zanone

All Theses

This paper presents a culmination of work utilizing custom-designed autonomous vehicles and learning based simulations applied to real world applications across three distinct research domains. The first focuses on deploying autonomous drones in an agricultural setting for plant phenotyping. A telescoping arm is used to deploy a camera into the plant foilage for under-the-canopy imaging. The system utilizes on-board image processing to identify early-stage issues such as diseases, bacteria, and pests in crop plants to provide data for crop loss mitigation. The system takes advantage of the maneuverability of drones allowing for quick random sampling when traversing large areas.

In …

Qasm-To-Hls: A Framework For Accelerating Quantum Circuit Emulation On High-Performance Reconfigurable Computers, Anshul Maurya

Theses and Dissertations

High-performance reconfigurable computers (HPRCs) make use of Field-Programmable Gate Arrays (FPGAs) for efficient emulation of quantum algorithms. Generally, algorithm-specific architectures are implemented on the FPGAs and there is very little flexibility. Moreover, mapping a quantum algorithm onto its equivalent FPGA emulation architecture is challenging. In this work, we present an automation framework for converting quantum circuits to their equivalent FPGA emulation architectures. The framework processes quantum circuits represented in Quantum Assembly Language (QASM) and derives high-level descriptions of the hardware emulation architectures for High-Level Synthesis (HLS) on HPRCs. The framework generates the code for a heterogeneous architecture consisting of a …

Multivariate Analysis Approach To Factor-Affected Tuberculosis Disease, Zuli Agustina Gultom, Farid Akbar Siregar, Mahardika Abdi Prawira Tanjung, Al-Hamidy Hazidar

Knowledge Engineering and Data Science

Tuberculosis is a disease caused by infection with the mycobacterium tuberculosis complex. Tuberculosis attack organ besides the lung, such as the pleura, lining of the brain, lining of the heart, lymph gland, bones, joint, skin, intestines, kidney, urinary tract, and genital. This disease is found in densely populated settlements with poor sanitation, lack of ventilation and sunlight and lack of rest. Moreover, the factors that will be analyzed in this research are Population Density (X1), Number of HIV/AIDS (X2), number of toddlers who experience nutrition (X3), Number of toddlers who experience BCG immunization (X4), number of toddlers who get exclusive …

Systematic Literature Review On Ontology-Based Indonesian Question Answering System, Fadhila Tangguh Admojo, Adidah Lajis, Haidawati Nasir

Knowledge Engineering and Data Science

Question-Answering (QA) systems at the intersection of natural language processing, information retrieval, and knowledge representation aim to provide efficient responses to natural language queries. These systems have seen extensive development in English and languages like Indonesian present unique challenges and opportunities. This literature review paper delves into the state of ontology-based Indonesian QA systems, highlighting critical challenges. The first challenge lies in sentence understanding, variations, and complexity. Most systems rely on syntactic analysis and struggle to grasp sentence semantics. Complex sentences, especially in Indonesian, pose difficulties in parsing, semantic interpretation, and knowledge extraction. Addressing these linguistic intricacies is pivotal for …

Evidence Of Students’ Academic Performance At The Federal College Of Education Asaba Nigeria: Mining Education Data, Arnold Adimabua Ojugo, Christopher Chukwufunaya Odiakaose, Frances Emordi, Rita Erhovwo Ako, Winifred Adigwe, Kizito Eluemonor Anazia, Victor Geteloma

Knowledge Engineering and Data Science

One main objective of higher education is to provide quality education to its students. One way to achieve the highest level of quality in the higher education system is by discovering knowledge for prediction regarding enrolment of students in a particular course, alienation of traditional classroom teaching model, detection of unfair means used in online examination, detection of abnormal values in the result sheets of the students, and prediction about students’ performance. The knowledge is hidden among the educational data set and is extractable through data mining techniques. The present paper is designed to justify the capabilities of data mining …

Eeg Classification While Listening To Murottal Al-Quran And Classical Music Using Random Forest Method, Heni Sumarti, Fahira Septiani, Agus Sudarmanto, Wahyu Caesarendra, Rizki Edmi Edison

Knowledge Engineering and Data Science

This study is aimed to classify the brain activity of adolescents associated with audio stimuli; murottal Al-Quran and classical music. The raw data were filtered using Independent Component Analisys (ICA) and followed by band-pass filter in Python on the Google Colab Extraction was processed with Power Spectral Density (PSD) and the Random Forest Method in Weka Machine Learning was used for classification. The research results showed the same results between the two types of stimulation, namely the order of brain waves from highest to lowest were delta, alpha, theta and beta. The average brain waves of teenagers when given murottal …

Comparison Of Machine Learning Algorithms For Species Family Classification Using Dna Barcode, Lala Septem Riza, M Ammar Fadhlur Rahman, Yudi Prasetyo, Muhammad Iqbal Zain, Herbert Siregar, Topik Hidayat, Khyrina Airin Fariza Abu Samah, Miftahurrahma Rosyda

Knowledge Engineering and Data Science

Classifying plant species within the Liliaceae and Amaryllidaceae families presents inherent challenges due to the complex genetic diversity and overlapping morphological traits among species. This study explores the difficulties in accurate classification by comparing 11 supervised learning algorithms applied to DNA barcode data, aiming to enhance the precision of species family classification in these taxonomically intricate plant families. The ribulose-1,5-bisphosphate carboxylase-oxygenase large sub-unit (rbcL) gene, selected as a DNA barcode locus for plants, is used to represent species within the Amaryllidaceae and Liliaceae families. The experimental results demonstrate that nearly all tested models achieve accurate species classification into the appropriate …

Space-Time-Frequency Characterization In Electromagnetic Near-Field Scanning: A Data-Driven Approach, Yanming Zhang, Lijun Jiang

Electrical and Computer Engineering Faculty Research & Creative Works

Near-field scanning has become a widely used tool for diagnosing electromagnetic compatibility and electromagnetic interference issues. However, the transient signal of the device under test often contains multiple frequency bands and exhibits nonstationarity, which poses a challenge for near-field scanning analysis. To address this issue, this article presents a method for analyzing time-resolved near-field radiation based on multiresolution dynamic mode decomposition (MRDMD). The MRDMD approach recursively and hierarchically applies the traditional dynamic mode decomposition (DMD) method. The distribution of DMD eigenvalues determines the slow and fast modes in each level's decomposition, where the slow modes are preserved, and the fast …

Data-Driven Scheme For Joint Estimation Of Direction-Of-Arrival And Frequency With Uniform Linear Array, Yanming Zhang, Lijun Jiang, Hsi Tseng Chou

Electrical and Computer Engineering Faculty Research & Creative Works

This article presents a two-step dynamic mode decomposition (TSDMD) approach to estimate the direction of arrival (DOA) and frequency for multiple narrow-band sources with single uniform linear array (ULA). The proposed algorithm first decomposes the receiving spatial-temporal data into temporal dynamic modes and corresponding spatial spectrum that yields the DOA information. Then the decomposed temporal dynamic modes are analyzed via the augmented DMD to retrieve the frequency information. The estimated DOAs and frequencies are automatically paired without the extra computational cost with such a temporal-spectral correlated decomposition scheme. Also, the obtained spatial spectrum distribution implies the actual number of the …

Gpu Based Monte Carlo Estimation Of Eddy Current Losses In Electromagnetic Coil-Core System, Adwaith Ravichandran

Theses and Dissertations

A novel parallelizable probabilistic approach to model eddy currents in AC electromagnets is presented in this research. Consequently, power loss associated with the formation of these eddy currents is estimated and validated using experimental data. Furthermore, predicting the effect of ferromagnetic alternating field enhancement on power loss in the source excitation winding has been an active area of research. Unlike a stationary field, an alternating sinusoidal field diffuses partially into the ferromagnetic material leading to a predictably sub-optimal field enhancement. To model these physics, finite element techniques employ nonlinear iterative solvers which are time consuming. A novel method is developed …