The Impact Of Optimization Approximation Algorithms On The Performance Of The Bht-Qaoa,
2026
Portland State University
The Impact Of Optimization Approximation Algorithms On The Performance Of The Bht-Qaoa, Ali Al-Bayaty, Marek Perkowski
Electrical and Computer Engineering Faculty Publications and Presentations
This article investigates the performance impact of five classical optimization approximation algorithms on our previously introduced quantum search algorithm, termed the Boolean–Hamiltonians Transform for Quantum Approximate Optimization Algorithm (BHT-QAOA), to effectively search for all best-approximated solutions for Boolean-based problems. These optimization approximation algorithms are BFGS, L-BFGS-B, SLSQP, COBYLA, and COBYQA. Their performance impact is evaluated and compared using two proposed performance metrics—(i) the final number of function evaluations (the lower numbers denote the best optimization approximation algorithms) and (ii) the final quality of qubit measurements (the higher values indicate all best-approximated solutions were found for a problem). Arbitrary classical Boolean …
Evaluation Of Intracavity Electromagnetic Field Probes And Sources: Implications For Shielding Effectiveness,
2026
University of New Mexico
Evaluation Of Intracavity Electromagnetic Field Probes And Sources: Implications For Shielding Effectiveness, Joseph Anthony Ferreri
Electrical and Computer Engineering ETDs
To measure the electric field in a reverberant cavity, a small, minimally invasive probe is required. Common solutions include electrically small surface mounted monopole antennas, B-dots, and D-dots. To obtain an accurate field measurement with a particular probe, it is necessary to characterize it to compensate for its ability to convert electric field into voltage which requires a gauge factor known as effective height. The characterization process is straight forward in open space on a ground plane but requires more insight when in situ in a reverberant cavity. This work adapts ground plane probe characterization methods for cavity measurements, facilitating …
Novel Algorithmic Methods For Random Telegraph Noise Detection And Characterization In Electronic Devices,
2026
University of New Mexico
Novel Algorithmic Methods For Random Telegraph Noise Detection And Characterization In Electronic Devices, Victor Darie Pepel
Electrical and Computer Engineering ETDs
Random telegraph noise (RTN) produces discrete stochastic fluctuations in nanoscale semiconductor devices and increasingly limits performance and reliability as dimensions scale. This dissertation introduces three algorithmic contributions enabling automated and accurate RTN characterization across diverse devices and operating conditions. First, a computationally efficient histogram-based detection algorithm enables rapid identification of RTN in large focal plane array datasets for statistically robust defect analysis. Second, a frequency decomposition framework separates slow and fast RTN components, extending the range of extractable time constants and reducing estimation error in multi-trap signals obscured by background noise. Third, to address the lack of standardized RTN metrics, …
Applications And Comparisons Of Machine Learning Methods In Ultra-Fast Laser Control,
2026
University of New Mexico
Applications And Comparisons Of Machine Learning Methods In Ultra-Fast Laser Control, Aasma Aslam
Electrical and Computer Engineering ETDs
This dissertation demonstrates the applications and comparative analyses of machine learning methods in ultrafast laser control. By learning the relationship between the system’s input parameters and output pulse characteristics, the performance of a laser can be significantly improved. In this work, the results are presented in two stages by utilizing data from the femtosecond laser system. The first stage concerns two neural networks, named NN1 (fitrnet) and NN2 (feedforwardnet). The second stage, which extended with five different models, namely the linear regression (fitlm), the support vector machine (SVM), the Gaussian process regression (GPR), the boosted tree (fitrensemble), and LASSO (fitrlinear), …
Self-Supervised Spoofing Detection,
2026
University of New Mexico - Main Campus
Self-Supervised Spoofing Detection, David S. Choi
Electrical and Computer Engineering ETDs
Global Navigation Satellite Systems (GNSS) are vulnerable to spoofing attacks that can mislead receivers with counterfeit signals. Traditional detection techniques, such as antenna-based, encryption based, and signal processing approaches, often face limitations in adaptability, computational cost, or reliance on predefined thresholds. Supervised machine learning models, while powerful, require large labeled datasets and struggle to generalize to unseen spoofing scenarios. In this work, we propose a self-supervised spoofing detection framework based on Adaptive Sparse Gaussian Processes (ASGP). The method predicts incoming GNSS features using past observations and identifies spoofing as anomalous deviations in the prediction residuals. Unlike supervised approaches, ASGP adapts …
Quantum Control Protocols For Robust Quantum Computing,
2026
University of New Mexico
Quantum Control Protocols For Robust Quantum Computing, Leeseok Kim
Electrical and Computer Engineering ETDs
The fundamental goal of quantum computing is to precisely control quantum systems to perform meaningful tasks, including implementing high-fidelity quantum gates for reliable quantum computation and accurately simulating complex quantum many- body dynamics. In this dissertation, we develop improved quantum control protocols for three distinct objectives, quantum error suppression, quantum optimal control, and analog quantum algorithms, achieving performance beyond standard approaches. First, we introduce new dynamical decoupling protocols, including both determin- istic and randomized constructions, that can substantially outperform conventional deterministic sequences. We then extend the randomized approach to dynamically corrected gates. Second, we propose a randomized quantum optimal control …
Fault Tolerant Quantum Computing With Lower Overhead,
2026
University of New Mexico
Fault Tolerant Quantum Computing With Lower Overhead, Benjamin E. Anker
Electrical and Computer Engineering ETDs
Quantum computation promises asymptotic speedups over classical algorithms, but realizing these advantages requires overcoming the noisiness of quantum hardware. Although fault-tolerant error correction can allow for reliable quantum computation even using unreliable components, the resource overheads required can substantially erode the asymptotic performance gains. This dissertation focuses on constructing and optimizing fault-tolerant procedures with lower overhead than previous methods were capable of. We present new frameworks for fault-tolerant syndrome extraction using flag gadgets with exponentially reduced ancilla requirements, explicit measurement schedules that achieve asymptotically fewer measurements than stabilizer generators, and a general method for making arbitrary Clifford circuits fault tolerant. …
Alternative Positioning, Navigation, And Timing In Global Navigation Satellite System Denied Environments,
2026
University of New Mexico
Alternative Positioning, Navigation, And Timing In Global Navigation Satellite System Denied Environments, Joshua R. Atencio
Electrical and Computer Engineering ETDs
Global Navigation Satellite Systems (GNSS) provide the majority of critical positioning, navigation, and timing (PNT) services for civilian, commercial, and military applications. However, GNSS is vulnerable to service denial from spoofing and jamming from adversaries and environmental obstruction. These vulnerabilities highlight the need for resilient Alternative PNT (APNT) methods. This dissertation investigates APNT frameworks operating in GNSS denied environments. We develop coalition formation and matching theoretic models that allow users APNT services from anchor nodes under resource constraints and in adversarial or emergency conditions. The proposed frameworks optimize positioning accuracy, network utility, and system stability while accounting for geometric dilution …
Statistical And Spectral Theory For Spatially Correlated Random Aperiodic Antenna Arrays,
2026
University of New Mexico - Main Campus
Statistical And Spectral Theory For Spatially Correlated Random Aperiodic Antenna Arrays, Thomas Edward Christian Jr
Electrical and Computer Engineering ETDs
Aperiodic phased arrays enable beam steering, interference suppression, and spectrum efficiency for 6G, radar, biomedical imaging, and distributed sensing. Minimum inter-element spacing and keep out zones induce spatial correlation, violating the i.i.d. element-position assumption behind classical probabilistic random array theory. This dissertation develops a unified probabilistic spectral framework for correlated (non-i.i.d.) arrays. Second moment power pattern analysis incorporates the pair-correlation function and structure factor , recovering the i.i.d. limit when and accommodating unequal excitations. Side lobe and main lobe fields deviate from Rayleigh/Exponential and are modeled by weighted Nakagami and Gamma-mixture distributions, parameterized via Monte Carlo. The blue noise spectral …
Millimeter-Wave Antenna Gain Enhancement Through Stacked Planar Substrates,
2026
University of New Mexico
Millimeter-Wave Antenna Gain Enhancement Through Stacked Planar Substrates, Zachary Bergstedt
Electrical and Computer Engineering ETDs
This work presents a new wideband millimeter-wave (mmWave) and sub-terahertz antenna design with flexible directivity through the integration of stepped horn antennas and transverse substrate integrated waveguide (SIW) slots for radar and communication applications. The work gives a theoretical and analytical basis for this filter-inspired approach to improving bandwidth and directivity, and presents design and results for a standalone stepped horn, a Ka-band antenna with a solid stepped horn and SIW feed, and W-band antennas with empty SIW feeds and stepped horns manufactured out of multiple planar layers. The realized antennas show bandwidth up to 40% and gain up to …
Calibration Methodology, Diagnostic Performance, And Redesign Of The Current Monitors On The Z Machine At Sandia National Laboratories,
2026
University of New Mexico - Main Campus
Calibration Methodology, Diagnostic Performance, And Redesign Of The Current Monitors On The Z Machine At Sandia National Laboratories, Stacie Hernandez
Electrical and Computer Engineering ETDs
A proper evaluation of the current diagnostics fielded in the inner-MITL region of the Z facility in 3D simulation models had not been performed until now. The evaluation of the current monitors has brought insight to their performance in a new view that has led to discoveries. The B-dot probe was the current diagnostic-of-choice since before the refurbishment of the Z facility [1] and until the development of the Inductively Driven Transmission Line (IDTL) current diagnostic [2]. Experimental data has shown that the IDTL can produce cleaner signals and it is more robust than conventional B-dots. Simulation (modeled using COMSOL …
Hierarchical Safe Reinforcement-Learning Framework For Mission-Aware, Edge-Enabled Multi-Uav Iot Networks,
2026
University of New Mexico - Main Campus
Hierarchical Safe Reinforcement-Learning Framework For Mission-Aware, Edge-Enabled Multi-Uav Iot Networks, Abee F. Alazzwi
Electrical and Computer Engineering ETDs
This Ph.D. dissertation presents a unified Hierarchical Safe Reinforcement Learning (HSRL) framework for mission-aware, edge-enabled multi-UAV Internet of Things (IoT) networks. The work addresses the need for autonomous aerial infrastructures capable of delivering low-latency communication, scalable edge computation, and provably safe operation in dynamic environments. The dissertation develops three primary contributions. First, it formulates longhorizon drone base station placement and load balancing as a strategic actor–critic learning problem, enabling proactive adaptation to spatiotemporal demand variations. Second, it introduces a mission-aware multi-agent reinforcement learning controller for coordinated mobility, sensing, and computation offloading under latency and energy constraints. Third, it integrates a …
Classification Of Hif Detection In Nev Profile Using Wavelet Transform And Convolution Neural Network,
2026
TÜBİTAK
Classification Of Hif Detection In Nev Profile Using Wavelet Transform And Convolution Neural Network, Abdul Hafiz Kassim, Mohd Abdul Talib Mat Yusoh, Aster Smith Valentinie Wilson Nottelmarc, Ahmad Farid Abidin, Sim Sy Yi, Daw Saleh Sasi Mohammed
Turkish Journal of Electrical Engineering and Computer Sciences
High impedance faults (HIFs) present a critical challenge in power systems due to their subtle signal characteristics, which often remain undetected by conventional protection methods. These faults typically do not produce significant phase disturbances, making reliable detection difficult. However, analysis of the neutral-to-earth voltage (NEV) profile under fault conditions provides a promising alternative for fault identification. Existing approaches for detecting and classifying HIFs using NEV signals remain limited and may result in inaccurate maintenance decisions. This paper proposes a fault classification framework for multiple fault types, including HIF, three-phase fault, three-phase fault to ground, double line, double line to ground, …
Complex-Valued Convolutional Neural Network With Time-Frequency Representation For Electrocardiogram-Based Arrhythmia Detection, Kajeeth Kumar Gurusamy, Muthurajkumar Sannasy
Turkish Journal of Electrical Engineering and Computer Sciences
This research proposes an end-to-end procedure for arrhythmia detection based on electrocardiogram (ECG) signals using complex-valued convolutional neural network (CVCNN) incorporated with time-frequency representation. The proposed model leverages complex numbers to capture amplitude and phase information that enhances the ability of the model for detecting time-frequency variation in cardiac signals. First, signal preprocessing techniques---including normalization, wavelet denoising, and R-peak detection---are applied. Subsequently, the model extracts complex features from raw ECG data by employing the Hilbert transform to derive the analytic signal and the short-time Fourier transform (STFT) to generate a time–frequency representation. The proposed CVCNN framework effectively learns spatial-temporal features …
Accurate Diagnosis Of Diseases By A Novel Ai Pipeline Based On Feature Extraction, Feature Ranking, And Feature Selection From Medical Images, Tuğba Nur Bozkurt, Mehmet Emi̇n Yüksel
Turkish Journal of Electrical Engineering and Computer Sciences
The rapid growth of the global population has led to a substantial increase in the number of patients, while the availability of healthcare professionals has not expanded at a comparable rate. This imbalance highlights the urgent need for efficient and reliable computer-aided decision support systems that can reduce clinical workload while maintaining high diagnostic accuracy. In this study, a novel and systematically integrated artificial intelligence-based pipeline is proposed for medical image classification, combining statistical significance-driven feature ranking with evolutionary feature selection in a unified framework. The proposed pipeline consists of four sequential stages: feature extraction, ranking, selection, and classification. Features …
Adapting Independent Large-Scale Pretrained Models For Human Action Recognition,
2026
TÜBİTAK
Adapting Independent Large-Scale Pretrained Models For Human Action Recognition, Selen Pehli̇van
Turkish Journal of Electrical Engineering and Computer Sciences
Transferring knowledge from large-scale, independently pretrained image and text models to video understanding requires addressing several challenges, including maintaining generalization capabilities of models, integrating them into multimodal architectures, and fine-tuning with temporal dynamics. This study evaluates the effectiveness of parameter-efficient fine-tuning (PEFT) techniques in transferring pretrained knowledge from two independent models for video action recognition within a simple, streamlined multimodal fusion pipeline. Specifically, we adapt CLIP as the text branch and DINOv2 as the image branch, keeping both backbones frozen to preserve their pretrained robustness, while introducing lightweight, task-specific modules to adapt and fuse the branches with temporal dynamics. A …
Adaptive Backstepping Nonsingular Fast Terminal Sliding Mode Control For Robotic Manipulators Based On Disturbance Observer,
2026
TÜBİTAK
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,
2026
Unnamalai Institute of Technology
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,
2026
University of Nevada, Las Vegas
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,
2026
University of Nevada, Las Vegas
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 …
