Electrical and Computer Engineering Commons^™

Adaptive Backstepping Nonsingular Fast Terminal Sliding Mode Control For Robotic Manipulators Based On Disturbance Observer, Xin Zhang, Xu Wang

Turkish Journal of Electrical Engineering and Computer Sciences

This paper presents an adaptive backstepping nonsingular fast terminal sliding mode controller integrated with a nonlinear disturbance observer to achieve precise trajectory tracking of robotic manipulators subject to model uncertainties and unknown time-varying disturbances. A dead-zone–based adaptive gain mechanism is introduced to dynamically adjust the control gain according to the deviation of the sliding surface, thereby enhancing robustness and reducing chattering. The proposed reaching law ensures fast, nonsingular, and adaptive convergence, suppressing high-frequency oscillations without compromising stability and the nonlinear disturbance observer enables real-time estimation and compensation of modeling errors, friction, and external disturbances for superior rejection. The semiglobal uniform …

Chaotic Artificial Bee Colony-Optimized Stacking Ensemble For Robust Multifault Diagnosis Of Wind Turbines, Veilraj Revathi, Solaimalai Jeyadevi, Madasamy Sudalaimani

Turkish Journal of Electrical Engineering and Computer Sciences

The complex electromechanical structure of wind turbines, along with harsh operating conditions, poses significant challenges for precise and robust fault diagnosis. To address this challenge, an ensemble multifault diagnostic framework based on an adaptive chaotic artificial bee colony (C-ABC)-optimized support vector machine (SVM) and gradient boosting machine (GBM) is proposed. In the proposed framework, data redundancy and overfitting are reduced through a two-stage hybrid filter-transformer-based feature reduction approach using ReliefF, followed by Principal Component Analysis. The chaos function of the proposed C-ABC maintains an adaptive balance between the exploration and exploitation phases, thereby preventing premature convergence, which is a common …

A Near Linear-Phase Analog Frequency Sampling Filter Design Framework Using A Second-Order Trust-Region Optimization Technique, Edreese Basharyar

UNLV Theses, Dissertations, Professional Papers, and Capstones

Analog frequency sampling filters (FSFs) provide an efficient means of realizing finite impulse response (FIR)-like behavior in continuous-time systems, but their practical implementation is constrained by the requirement for perfect pole-zero cancellation along the imaginary axis. Because exact cancellation is physically unattainable due to component variations, ideal linear-phase Type 1 analog FSFs exhibit uncancelled poles that result in system instability. To address this limitation, this thesis introduces a near-linear-phase design framework for Type 1 analog FSFs that achieves both stability and design flexibility through the inclusion of a damping constant, ρ, which shifts the poles into the left half of …

An Optimization Method For Near-Linear Phase Analog Frequency Sampling Filter Design, Leonardo Ledesma

UNLV Theses, Dissertations, Professional Papers, and Capstones

Analog frequency sampling filters (FSFs) realize a desired frequency response by interpolating a frequency response through a set of harmonically related frequency samples from the filter’s frequency response and are magnitude and phase coefficients used in the filters transfer function. FSFs can be designed to have exact linear phase which makes the FSF attractive for many applications. A FSF’s system transfer function (STF) shows that the filter can be implemented by a series connection of a comb filter and a parallel array of resonators. However, the FSF requires that the zeros created by the comb filter cancel the imaginary axis …

Model Based Control And Hil Verification Of An Integrated Battery Management System, Catalin Sabou

UNLV Theses, Dissertations, Professional Papers, and Capstones

The rapid advancement of electric vehicle technologies necessitates highly reliable Battery Management Systems (BMS); however, validating embedded supervisory logic presents a notable challenge. While physical pack testing is accurate, it is costly and hazardous for early stage software evaluation. This thesis presents the design, implementation, and rigorous validation of an integrated BMS developed for the Battery Workforce Challenge, bridging the gap between model based design and safe hardware execution. The core of this work is a model based supervisory controller, developed in MATLAB/Simulink and executed on an STM32G4 embedded target. To facilitate embedded validation while preserving a representative battery environment, …

Visual Interpretability Of Multimodal Tissue Perfusion Classification Using Grad-Cam And Saliency Maps, Metehan Zorluoglu

UNLV Theses, Dissertations, Professional Papers, and Capstones

Accurate identification of the tissue perfusion phase from hand images can aid doctors in decision-making with non-invasive techniques. The present study proposes a multimodal deep learning model for classifying the tissue perfusion phase using infrared, thermal, and visible spectrum images of the human hand. The proposed model consists of various preprocessing techniques such as manipulation, homography alignments, and masking. The significant contribution of this thesis is the interpretability analysis of deep learning models, achieved through the analysis of saliency maps and the Gradient-weighted Class Activation Mapping (Grad-CAM) methods. The purpose of this method is to find out how the convolutional …

A Descriptive Analysis Of Plant Leaf Disease Detection Using Machine Learning And Deep Learning Models: A Systematic Review, Arzoo Chamoli, Anuj Kumar

Turkish Journal of Electrical Engineering and Computer Sciences

Plant leaf disease detection (PLDD) is a growing active research area with burgeoning practical applications across various sectors such as agricultural monitoring, food security, and environmental conservation. Accurate segmentation and classification of plant leaf diseases remains a key challenge in the field of plant leaf disease prediction. The challenge demands automated methods for the plant disease identification because it needs to develop better crop management systems, which will boost agricultural production. In this article, we provide a systematic review of various machine learning (ML) and deep learning (DL) methods extensively used for PLDD. The review strategy follows a formal protocol, …

Multiobjective Optimization Framework For Renewable Energy Iot Networks Balancing Security, Energy Efficiency, And Communication Reliability, Muhammad Hjouj Btoush, Ashraf S. Mashaleh, Amjad Gawanmeh

Turkish Journal of Electrical Engineering and Computer Sciences

The deployment of Internet of things (IoT) networks powered by renewable energy sources presents unique challenges in balancing security requirements, energy efficiency, and communication reliability. This paper presents a comprehensive multiobjective optimization framework for secure renewable energy IoT nodes that addresses fundamental trade-offs between these competing objectives. We develop a mathematical model incorporating energy harvesting dynamics, security protocols, and communication performance metrics across various environmental scenarios. The proposed framework employs a modified NSGA-II algorithm to identify Pareto-optimal configurations for different deployment contexts. Through extensive simulation analysis, we demonstrate that hybrid energy sources (solar-wind combinations) with lightweight security protocols achieve optimal …

Predictive Current Control Approach For Grid-Integrated Multifunctional Converter Under Source And Load Disturbances, Ravi Kumar Majji, Tirumalasetty Chiranjeevi, Chilukoti Varaha Narasimha Raja, Nagulapati Kiran

Turkish Journal of Electrical Engineering and Computer Sciences

This paper discusses and presents a model predictive control (MPC)-based predictive current control technique for a solar photovoltaic (PV)-integrated grid system during dynamic operation. This control technique employs extension pq (EPQ) theory to estimate reference currents and utilizes an MPC framework for tracking reference currents. Various MATLAB/Simulink simulations were conducted for solar PV generation (source disturbances) and dynamic loading. The results of the OPAL-RT OP4510 real-time simulation are also presented. A multifunctional grid-integrated converter (MFGC) integrates solar active power into the utility grid while achieving unity power factor, reactive power compensation, current balancing, and harmonic suppression. EPQ optimizes mathematical calculations, …

Performance Analysis Of Video Coding For Machines With Vision Transformers, Vaishnavi Dhulipudi

Electronic Theses and Dissertations 2020 - Present

This thesis investigates the performance of Video Coding for Machines (VCM) with Vision Transformer based object detection models. While existing VCM studies and tool designs have largely been developed under CNN-based assumptions, recent advances in computer vision have shown the growing importance of transformer based models. Motivated by this shift, this work studies whether VCM compressed data remains suitable for Vision Transformer based inference in addition to conventional CNN-based task networks.

To address this problem, three representative transformer based object detection models were selected: DETR, SWIN, and YOLOS. These models were chosen to represent different architectural styles, namely a CNN …

Fault Location In Dc Microgrids Using Traveling Waves, Sajay Krishnan Paruthiyil

Electrical and Computer Engineering ETDs

In DC power systems, rapid fault location is crucial for maintaining reliable operation, particularly with the prevalence of DC-DC converters. This study investigates fault location techniques in DC systems utilizing Traveling Waves (TWs). Following data normalization, multi-resolution analysis employs discrete wavelet transform to capture high-frequency patterns of TW's wavelet coefficients. Parseval's theorem is utilized to quantify the energy of these coefficients. First, a curve-fitting technique is employed to estimate fault locations in DC microgrids. Then, two transfer learning approaches are proposed: first approach integrates Parseval energy curves into a Gaussian process estimator, while second employs feedforward neural network for fault …

Radio Frequency Resonate And Fire (Rf-Raf) Neurons Supporting Device Classification, David L. Weathers, Michael A. Temple, Brett J. Borghetti

Faculty Publications

Radio Frequency Fingerprinting (RFF) enables passive physical-layer device authentication by exploiting unintentional hardware variations in wireless transmitters. Neuromorphic implementations are attractive, given their potential for low-latency, energy-efficient inference capability under Size, Weight, and Power (SWaP) constraints at the edge. A new RFF capability is demonstrated here using recently introduced Radio Frequency Resonate-and-Fire (RF-RAF) neurons and eight WirelessHART devices. Performance is evaluated for RF-RAF-generated fingerprints against the established Gabor Transform (GTX) baseline using three classifier architectures: Random Forest (RndF), Convolutional Neural Network (CNN), and a Time-Incremented Spiking Neural Network (TI-SNN). The results show that RF-RAF fingerprints achieve an average classification accuracy …

Spad Camera Image Analysis, Pratheen Reddy Pininti

Electronic Theses and Dissertations 2020 - Present

I present a thorough noise characterization of the Canon MS-500, a Single-Photon Avalanche Diode (SPAD) camera system, tested under both lit and dark conditions. The camera outputs 10-bit digital number (DN) values produced by an internal processing pipeline whose design is not publicly documented. All analyses in this thesis therefore describe the camera’s DN output — the signal that any downstream detection, tracking, or classification system will actually receive — rather than the photon-counting statistics of the underlying SPAD array. All computations were performed on the native 10-bit data. Where a measured quantity has a known photon-counting analog, the relationship …

Testing And Qualification Of Low-Voltage Power Supplies For The Atlas Tile Hadronic Calorimeter Phase-Ii Upgrade, Justice A. Jones

2026 Spring Honors Capstones Projects

The High Luminosity upgrade of the Large Hadron Collider (HL-LHC) places increased thermal and operational demands on detector electronics, requiring highly reliable power systems. The ATLAS Tile Hadronic Calorimeter (TileCal) uses low-voltage power supply (LVPS) bricks to power front-end electronics, but these units operate in inaccessible regions, making failures difficult to repair. Therefore, rigorous qualification procedures are essential. This work focuses on improving LVPS reliability through a structured burn-in process. Each unit undergoes pre-burn-in electrical verification using a Single Test Stand (STS), followed by sustained operation under load and elevated temperature, and post-burn-in requalification. Standard cooling conditions limit temperatures to …

Riki&Dolphin: Real Time Data Transmission From The Bottom Of A Cave To A Website, Luca Tringali, Giacomo Canciani Dr., Tecla Tripari, Alexander Debenjak, Caterina Bearzotti

International Journal of Speleology

Coming from over 10 years of experience in cave monitoring in northeast Italy, Gruppo Speleologico Talpe del Carso, has designed Riki and Dolphin: customizable, low cost, and easy to assemble tools for getting real time data transmission from the bottom of a cave, even underwater, to a webserver. Their use has been tested to monitor air temperature inside the Abisso Bonetti Cave (Classical Karst, Italy), proving for the first time that a cave in Gorizian Karst can systematically be colder than the outdoor temperature even in winter, recording an internal temperature even lower than 0°C. The Dolphin device can be …

Largest 2-Regular Subgraphs In Complete S-Partite Graphs, Yiyang Jiang

McKelvey School of Engineering Graduate Student Theses & Dissertations

In this thesis, we focus on the class of complete $S$-partite graphs, for $S$ an undirected graph possibly with self-loops, and address the problem of finding largest $2$-regular subgraphs of these graphs, which can be formulated as an integer linear program. Roughly speaking, a complete $S$-partite graph is obtained by replacing every single node of $S$ with a number of nodes, preserving the edge/non-edge relations of $S$. Our motivation in studying largest $2$-regular subgraphs is rooted in the structural systems theory, particularly in the problem of finding largest subnetworks that can sustain controllability or asymptotic stability of the corresponding subsystems. …

Rgfc: Registry Grounded Function Calling With Stage-Decomposed Evaluation, Ritvik Garimella, Vedant Khandelwal, Anvi Kohli, Amit Sheth

Publications

Tool-calling evaluations often report whether the final call matches a reference answer, but this single outcome merges different failures: choosing a function outside the available registry, violating the selected schema, or filling arguments with values not supported by the user request. These failures matter more when models must choose from larger tool registries. We introduce RFCD, a registry-scaled function-calling benchmark built from BFCL, Glaive Function Calling v2, and Tool-Call-Data, with standardized JSON-schema registries ranging from 5 to 3,000 functions. We also introduce TAAG, a deterministic stage-decomposed evaluator for registry conformance, structural completeness, and argument grounding. Across six locally served sub-4B …

Leveraging Information Theory And Ecological Network Analysis To Monitor Communication Networks In A Student Aerospace Team, Christine Sessions

Doctoral Dissertations and Master's Theses

Effective communication is a critical component of successful collaboration in group projects and team settings. However, systematically tracking and analyzing team communications can be challenging, especially in complex, multi-member teams such as those found in the aerospace industry. This paper explores an information-theoretic approach that leverages encoding techniques and graph theory to analyze communication networks within a university’s multi-year, student-led cubesat design project (Project COMET). By utilizing Shannon Entropy as a measure of information flow, encoding communication patterns, and analyzing Ecological Network Analysis parameters, this research aims to understand how an Embry-Riddle Aeronautical University student project team evolves over the …

Data-Driven Physical-Layer Optimization For Rf And Optical Communications: Transmitarray Metasurfaces, Contextual-Bandit Mud, And Coherent Optical Links, Chengtao M. Xu

Doctoral Dissertations and Master's Theses

This dissertation studies three physical-layer optimization problems that share a common difficulty: the forward model, whether an electromagnetic solver or a statistical channel model, is either too expensive to evaluate at design scale or too unstable to hold across a single processing block. The first two problems are treated in their own right, as problems in antenna design and in multi-user detection over time-variant channels; the third carries the methodology onto a coherent optical link, where size, weight, and power constraints make a small-satellite deployment the natural target.

The antenna work targets a tri-layer pixelated unit cell fabricated on Avient …

Woodward Cuk Converter, Aditi Venkatesh, Jennifer Pereira

Honors Capstones

The purpose of this senior design project is to design and build a Cuk converter, a type of buck–boost converter. The project involves developing an analytical model and validating it through hardware testing. Cuk converters are important because they are widely used in everyday electronics, particularly in renewable energy systems such as solar power, power supplies, and electric vehicles. Their ability to increase, decrease, and reverse the polarity of voltage makes them flexible. The continuous input and output currents result in low ripple, reducing component stress and improving efficiency. To achieve these goals, simulations will be conducted using LTspice to …

Development Of Hybrid Self-Cleaning Coating Using Lanthanide-Based Light Spectral Response Material To Enhance Photovoltaic (Pv) Panel Efficiency In Operational Environment, Khishn Kumar Kandiah

Student Works (2020-2029)

The efficiency of photovoltaic (PV) panels is crucial, as solar energy has become an alternative energy source that meets today’s global energy demands. Hence, dust accumulation and spectral mismatch have major effects on the efficiency of PV panels, around 20 %, but in extreme situations, this can reach up to 64 %. Therefore, initiatives are underway to address these issues, including PV cooling systems, conventional cleaning methods, and various coatings to enhance efficiency. In this work, a novel coating is fabricated with high transparency, excellent self-cleaning properties, and good spectral-modifying properties. Two types of powder composites are employed in this …

Designing Enhanced Nonlinearity In Plasmonic Devices With Epsilon-Near-Zero Films, Kevin Tran Le

Electrical Engineering and Computer Science (MS) Theses

The growing demand for energy-efficient optical information processing motivates compact nonlinear photonic devices that can operate at low power. Silicon photonics is a mature platform for linear optical functions, but nonlinear operation remains challenging because of its weak Kerr response, two-photon absorption at telecommunication wavelengths, and limited compatibility with deeply subwavelength plasmonic confinement. This thesis computationally investigates epsilon-near-zero thin films integrated into plasmonic waveguide architectures as a route toward stronger light–matter interaction in compact nonlinear devices.

Two waveguide geometries are examined: a hybrid metal-insulator-metal plasmonic slab waveguide incorporating an ultrathin indium tin oxide epsilon-near-zero layer (5–50 nm), and a dielectric-loaded …

Design And Verification Of The Multi-Slit Solar Explorer Camera Field Programmable Gate Arrays, Jordan M. Johnson

All Graduate Reports and Creative Projects, Fall 2023 to Present

The Multi-Slit Solar Explorer, or MUSE, is a NASA mission that will take images of the Sun to study solar flares and the solar corona. The mission will provide insight into the mechanisms behind space weather. The mission consists of two cameras: the Spectrograph (SG), and the Context Imager (CI). The Utah State University Space Dynamics Laboratory is providing both cameras for the mission.

This report describes a part of the design and verification process for a central component on these cameras known as the Field Programmable Gate Arrays (FPGAs). These FPGAs are programmed to acquire, handle, and send images …

Small-Scale Analog Spiking Neural Network: Design And Simulation Of An Analog Deep Neural Network, Lucas Hogue

Electrical Engineering and Computer Science Undergraduate Honors Theses

A small-scale analog schematic of a spiking neural network (SNN) is designed and demonstrated through the analog design environment (ADE) within Cadence Virtuoso. An SNN is a type of neuromorphic system that utilizes components mimicking the function of the neurons and synapses found in biological neural networks. The SNN designed in this project is composed of four layers: one input layer, two hidden layers, and one output layer. The inclusion of more than one hidden layer classifies the network as “deep” and increases the network’s efficiency at handling data with increased complexity. The SNN categorizes inputs by producing a set …

​Autonomous Agentic Orchestration For Physics-Aware Scientific Discovery: An Integrative Multimodal Framework For 2d Material Characterization​, Sankalp Pandey

Electrical Engineering and Computer Science Undergraduate Honors Theses

The advancement of next-generation semiconductor and quantum technologies relies on the scalability of the fabrication of two-dimensional (2D) van der Waals heterostructures. However, this process is severely bottlenecked by characterization workflows. Optical microscopy provides high-throughput imaging of 2D material flakes, but lacks the explicit physical priors required for the discernment of sub-nanometer thickness variations, such as distinguishing monolayers from bilayers. The use of computer vision models to automate the localization and characterization process of the flakes was proposed. As a part of this effort, we develop QuantumFlake, an open-source framework to streamline the integration and deployment of computer vision models …

Domain-Specific Design On Fpga And Npu For Scientific Computing And On-Device Llm Inference, Miaoxiang Yu

All Dissertations

High-performance computing (HPC) is changing rapidly as scientific simulations and large language model (LLM) workloads push the need for higher performance under tight power and memory constraints. Conventional platforms such as CPUs and GPUs accelerate computation through instruction-driven parallelism, relying on multithreading, SIMD, and SIMT execution, but increasingly encounter scalability limits imposed by the power and memory walls. In contrast, Field-Programmable Gate Arrays (FPGAs) and Neural Processing Units (NPUs) offer a high-efficiency alternative through dataflow-oriented architectures that exploit deep pipelining and customized memory hierarchies to reduce data movement. However, the performance potential of these spatial accelerators remains largely unrealized when …

Utility-Scale Battery Energy Storage Systems - Field Pilot Unit Experience, Analysis, And Review Of Technological Advancements, Kwabena A. Kyeremeh, Shaun E. Lavin, Grant M. Fischer, Dan M. Ionel, Aron Patrick

Electrical and Computer Engineering Graduate Research

Utility-scale Battery Energy Storage Systems (BESS) are becoming increasingly important in modern power systems, providing services ranging from backup support to load balancing and peak shaving. The long-term operational performance and reliability of these systems with expanding deployment remain insufficiently documented. This paper provides an operational assessment of a 1MW/2MWh utility-scale BESS alongside a systematic review of technology developments across major subsystems. Operational metrics including round-trip efficiency (RTE), outage characteristics, and degradation patterns are examined to identify performance trends and the underlying causes of outages. In addition, testing and operational principles aligned with utility practice and standards are presented to …

An Energy- And Area- Efficient Two-Stage Latch Comparator Using Common-Mode Reset, Larry Yu

Electrical Engineering Theses and Dissertations

This thesis presents an energy-and-area-efficient two-stage comparator implemented in TSMC 65 nm CMOS that incorporates a Floating Inverter Amplifier (FIA) followed by a latch using a common-mode reset scheme. The FIA improves input sensitivity and reduces kickback noise by isolating the input nodes from large latch voltage swings. The proposed latch resets its output nodes to a common-mode voltage rather than one of its supply rails, thereby reducing switching energy during the reset phase. The comparator can resolve differential input voltages as small as 2 mV with a clock period of 500 ps, achieving 74.33 μV input-referred noise while consuming …

A Full System Co-Simulation Platform For Evaluating Edge Machine Learning Inference Using Compute-In-Memory, Belsen Lee

Electrical Engineering and Computer Science (MS) Theses

We present a full-system co-simulation platform for evaluating embedded machine learning (ML) inference using compute-in-memory (CIM). CIM architectures aim to reduce data movement overhead by performing matrix operations in memory, but end-to-end benefits depend on system-level integration costs that are difficult to assess with isolated hardware models alone. To address this gap, we develop an integrated RISC-V QEMU-SystemC co-simulation environment that allows standard embedded Linux to interact with a transaction-level CIM accelerator model via memory-mapped I/O, direct memory access (DMA), and interrupts. To evaluate performance, we benchmark an MNIST image inference workload and a synthetic fully connected neural network, comparing …

Central Pattern Generator Test Bench Report, Stephanie Wing-Yee Lee, Dustin Wong, Joseph Hernandez

Electrical Engineering

The project presents the design, fabrication, and implementation of a PCB test bench that interfaces with a custom VLSI chip designed to emulate the neural activity of a Central Pattern Generator (CPG). The test bench provides a precise and reliable platform for supporting signal operations and validating neuromorphic functionality. The design integrates both analog and digital circuitry to provide stable biasing, signal routing, and measurement access for chip characterization. The incorporation of op-amp buffers and adjustable potentiometers enables precise voltage tuning, resulting in controlled neuron-like behavior within the custom IC. The two-layer PCB supports modular interfacing with standard laboratory instruments …