Engineering Commons^™

Design And Control Of A Stroke Therapy Device, Hugh Elliott

Open Access Master's Theses

Stroke is a leading cause of physical disability around the world, and the likelihood of stroke increases as people live longer. The current number of physiotherapists is insufficient to meet the increasing demand for their services. As a result, there has been a focus on developing robotic devices that function similarly to traditional therapy, enabling multiple patients to be seen simultaneously. While many devices have been created and tested, most are expensive, complex, and require trained personnel for supervision, thereby limiting their outreach. This thesis presents the design and control of a low-cost stroke therapy device designed to promote upper …

Dynamic Beamforming And Array Shape Estimation, Nicholas Ryan Costick

Open Access Master's Theses

The accurate estimation of towed sonar array shapes during complex maneuvers is a critical challenge affecting beamforming and target localization performance. When underwater arrays experience sharp turns or rapid movements, sensor positions become difficult to track precisely, negatively impacting the reliability of beamforming methods. This thesis addresses the issue of dynamic array shape uncertainty, motivated by operational challenges faced by the Navy.

A maximum-likelihood estimation (MLE) method is developed to simultaneously estimate the array shape and field directionality (spatial spectrum) during maneuvers. The proposed solution expands upon previous research, specifically the dynamic spatial spectrum estimation techniques described by Rogers and …

Heart Rate And Electrodermal Activity And Their Relationship With Performance In High Fatigue Environments, Brian Dunbar

Open Access Master's Theses

Confidential material has been removed.

Optimal Subspace Estimation For Linear Nested Arrays: Applications And Performance Metrics, Brendan Dunn

Open Access Master's Theses

OSE (Optimal Subspace Estimation) is an algorithm that obtains an estimated subspace from structured data observed in noise. While OSE is able to obtain an accurate subspace estimate with a small number of snapshots, it has not been demonstrated how this affects the performance of many applications that leverage OSE. One such application is beamforming; Using an OSE-based beamformer, this thesis will apply common performance metrics to measure how much of an advantage an accurate subspace estimate provides. This algorithm will be compared against other widely used beamformers such as MPDR and DMR to benchmark its efficacy. Mismatch is then …

Improving System-Level Outcomes Via Artificial Intelligence Decision Support In Kidney Utilization, Casey I. Canfield, Cihan H. Dagli, Daniel Burton Shank, Krista Lentine, Mark Schnitzler, Henry Randall, V. Sriram Siddhardh Nadendla, Brendon Cummiskey

Engineering Management and Systems Engineering Faculty Research & Creative Works

Transplantation provides patients suffering from end-stage kidney disease a better quality of life and long-term survival. However, over 20% of deceased donor kidneys are not utilized and never transplanted. While this is sometimes medically appropriate, this also reflects missed opportunities. We are designing Artificial Intelligence decision support for the kidney offer process to support both demand at the transplant center and supply at the organ procurement organization. This includes (1) developing deep learning models, (2) evaluating the effect of explainable interfaces, (3) improving fairness in the model output, (4) identifying factors that influence adoption decisions, and (5) conducting a randomized …

Electricity Theft Detection With An Adaptive Deep Learning Architecture, Mohammed Sleiman, Cihan Dagli, Rui Bo

Engineering Management and Systems Engineering Faculty Research & Creative Works

Electricity theft presents a significant challenge to the power industry. This paper demonstrates an adaptive deep framework integrating dimensionality reduction, graph modeling, attention mechanisms, and dynamic feature refinement for improving theft detection. Principal Component Analysis squeezes consumption data while an Autoencoder extracts latent representations and denoises the input. A Gated Graph Convolutional Neural Network uses k-Nearest Neighbors to model local relationships, while Transformers capture long range global dependencies. Neural Ordinary Differential Equations then refine features over continuous time, improving adaptability to complex patterns. The framework achieves 94.01% accuracy with stratified 5-fold cross validation. However, class imbalance challenges the minority class …

Cyber Forensics With Deep Learning Recurrent Neural Networks, Pfautch Ric, Dagli Cihan, Ashiku Lirim

Engineering Management and Systems Engineering Faculty Research & Creative Works

Detection of anomalies and anti-patterns is essential for adaptive systems with the ability to perform without foreknowledge. Some problems require both classification and regression along with sensitivity tuning and explainability. Some have highly dimensional datasets that are time dependent. This research offers results for Long-Short-Term Memory (LSTM) and Gated Recurrent Units (GRU) algorithms using the BETH dataset. It unpacks metadata attributes and stages a unique approach via Abstract-Feature Analysis (AFA), hyper parameter tuning, and Principal Component Analysis (PCA) within the RNN model. By removing foreknowledge, this research offers insights into RNN anomaly detection performance when an event absent in training …

Large-Range And High-Sensitivity Displacement Sensing Based On Extrinsic Fabry-Perot Interferometer Assisted Microwave Photonic Filter, Shiyu Li, Ruimin Jie, Osamah Alsalman, Jie Huang, Chen Zhu

Electrical and Computer Engineering Faculty Research & Creative Works

Displacement is a pivotal physical parameter, and advancements in displacement sensor technology have enabled the creation of a diverse array of physical and mechanical sensors through seamless integration with mechanical transducers. In this study, we introduce a displacement sensing technique leveraging an extrinsic Fabry-Perot interferometer (EFPI) assisted microwave photonic filter. By translating displacement-induced variations in the EFPI's optical reflection into peak frequency shifts within its frequency response, we achieve large-dynamic-range displacement measurements with outstanding signal quality and demodulation ease. Proof-of-concept demonstrations showcase a substantial 5 mm range with a remarkable sensitivity of 1.148 GHz/mm, achieved using a basic single-mode fiber-based …

Annotated 3d Point Cloud Dataset For Traffic Management In Simulated Urban Intersections, Elham Binshaflout, Chaima Zaghouani, Nawfal Guefrachi, Charalampos Antoniadis, Hakim Ghazzai, Ahmad Alsharoa, Gianluca Setti

Electrical and Computer Engineering Faculty Research & Creative Works

Ensuring accurate traffic perception and road safety in complex urban environments remains a significant challenge. Advanced traffic monitoring increasingly relies on deep learning, which requires large data volumes. However, existing datasets are often limited to CCTV video footage or focus on dynamic scenarios captured by sensors mounted on ego vehicles. This narrow perspective reduces the effectiveness of comprehensive traffic monitoring, particularly for LiDAR sensors, which typically capture only the vehicle's viewpoint and miss critical areas such as intersections and pedestrian crossings. To address these limitations, we propose a holistic strategy for rapid data collection in urban settings using simulated 3D …

Multi-Dimensional Iot-Based Energy Management Approach For Smart Homes: A Unified Model For Comfort And Energy Efficiency, Muhammad Ans, Teodoro Montanaro, Ilaria Sergi, Ahmad Alsharoa, Miriam Pezzuto, Luigi Patrono

Electrical and Computer Engineering Faculty Research & Creative Works

As smart home technologies evolve, achieving energy-efficient indoor climate management while maintaining comfort and air quality is a growing priority. This paper introduces a novel optimization framework for smart buildings that minimizes energy costs and dynamically manages indoor environmental conditions, specifically temperature, CO₂ concentration, and illuminance. Unlike conventional systems, our model incorporates dynamic constraints that respond to day-night comfort requirements and leverage real-time variations in electricity prices and environmental conditions. By optimally controlling the power levels of air conditioning, air purification, and lighting systems, the framework ensures indoor comfort while significantly reducing operational costs.A nonlinear optimization approach with dynamic …

Enhancing Fiber Optic Interferometric Sensing With Microwave Photonics-Based Dispersion Fourier Transform And Integrated Magnitude–Phase Analysis, Chen Zhu, Ruimin Jie, Chenxi Huang, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

Fiber optic inline interferometers are widely used for high-precision sensing due to their sensitivity, compactness, and immunity to electromagnetic interference. Traditional optical spectral analysis methods suffer from limited dynamic range due to free spectral range (FSR) constraints, while microwave photonic filtering (MPF) techniques based on dispersion Fourier transform (DFT) provide an alternative by mapping optical signals into the radio frequency (RF) domain. However, conventional passband frequency tracking in MPF systems has limited sensitivity, and the recently demonstrated phase-based methods, though highly sensitive, are constrained by phase wrapping beyond 2π. In this work, we propose and experimentally demonstrate an integrated magnitude–phase …

An Entropy-Bounded, General, Model-Based Framework For Lossy Compression Of Sensor Data, Steven Thompson, Maciej Zawodniok

Electrical and Computer Engineering Faculty Research & Creative Works

In many industries, digital twinning has become an indispensable element of advanced technologies. However, digital twins are heavily reliant on extensive Internet of Things (IoT) sensor measurement data to function effectively. Consequently, data mining has become a lucrative endeavor, akin to gold rushes in the XIX century. However, the substantial volume of collected data often stresses the storage capacities for smaller to medium-sized enterprises, necessitating efficient compression techniques. Error-bound lossy compression offers substantial data reduction advantages, but introduces distortion that, when uncontrolled, can adversely affect analysis. This paper proposes an information optimization scheme that employs information entropy as a comprehensive …

Deep Learning For Uav Classification: Impact Of Noise And Multipath Fading In Rf Signals, Prajoy Podder, Maciej Jan Zawodniok, Sanjay Kumar Madria

Electrical and Computer Engineering Faculty Research & Creative Works

The increasing presence of unmanned aerial vehicles (UAVs) raises serious security concerns, particularly regarding unauthorized drone operations. Recent U.S. security statistics report a sharp rise in unauthorized UAV activities, with the Federal Aviation Administration (FAA) receiving over 100 monthly reports of illegal drone operations near airports. In 2024 alone, Dedrone records 1.19 million unauthorized drone flights across major U.S. cities, highlighting the need for robust UAV detection and classification systems. In this work, a lightweight Convolutional Neural Network (CNN) model is proposed for RF-based UAV classification under noisy and multipath fading conditions. The proposed CNN consists of multiple convolutional blocks, …

Design And Optimization Of A Miniaturized Spiral Antenna For Ultra-Wideband Applications, Mckennan Starkey, Cody Goins, Victor Khilkevich, Daryl Beetner

Electrical and Computer Engineering Faculty Research & Creative Works

Ultra-wideband communication in the frequency range from 3.1 to 10.6 GHz is popular for short range wireless networks. Designing a wideband antenna that covers the entire 7.5 GHz band is challenging, particularly if the antenna should be small, unobtrusive, and should effectively communicate with another antenna as their positions and orientations change. A miniaturized wideband circularly polarized Archimedean spiral antenna is developed in this study which is wideband and circularly polarized. Miniaturization and optimization techniques are introduced to enable an antenna size of $30 \text{mm} \times 30 \text{mm}$ and a thickness less than 0.5 mm.

A Non-Destructive And Simple Setup Method For Dielectric Liquid Characterization In A Wide Frequency Range With Djordjevic-Sarkar Model, Reza Vahdani, Reza Asadi, Seyedmehdi Mousavi, Xiaoning Ye, Donghyun Kim

Electrical and Computer Engineering Faculty Research & Creative Works

This paper introduces a non-destructive and simple setup method for characterizing dielectric liquids over a broad frequency range (up to 30 GHz) using the Djordjevic-Sarkar model. By employing a differential microstrip line and comparing scattering parameters in air-filled and liquid-immersed scenarios, the proposed method achieves precise dielectric constant (DK) and dissipation factor (Df) extraction. 2 Liquid samples (PAO4 and DC-15) were tested using this method. Validation against the cavity resonance method demonstrates a strong agreement for the extracted DK values, with a relative error of less than 1.5 %, indicating high accuracy. However, the method is less sensitive to Df, …

Mitigating Optical Module Emi Using Common- And Differential-Mode Filters, Shivali Singh, Rakshith Kumar Gopalaiah, Di Li, Mokshit Tejasvi, Shipra Shipra, Victor Khilkevich

Electrical and Computer Engineering Faculty Research & Creative Works

A strategy to mitigate electromagnetic interference in quad/octal small form-factor pluggable interfaces using a common-mode/differential-mode filter is presented in this research. Differential source imbalance produces significant common- and differential-mode noise in the differential pseudorandom binary sequence signal. EMI associated with this noise can be reduced by incorporating a band stop filter onto the differential line on a printed circuit board. Two kinds of filters were tested: common mode and common/differential mode. It was demonstrated that the application of a common-mode/differential-mode filter is more advantageous than a common-mode filter and allows to achieve the suppression of the total radiated power by …

Impact Of Voltage Regulator Modules On Power Distribution Network Impedance, Hanyu Zhang, Zhiping Yang, Alvis Hsu, Ryan Hou, Chulsoon Hwang

Electrical and Computer Engineering Faculty Research & Creative Works

The voltage regulator module (VRM) impacts the power distribution network (PDN) impedance in the low frequency range. The effect of VRM is essential for reliable PDN analysis and simulation. However, the existing VRM models for power integrity (PI) simulation lack an in-depth understanding of the effect of the feedback control in the VRM. In this paper, the impact of the VRM control loop on the PDN impedance is investigated. The relationship between the VRM output impedance and the PDN impedance at the IC input pin is derived. The output impedance of a VRM is analyzed using small signal analysis. Eventually, …

Data Representation And Preprocessing Effects On S-Parameter Modeling Of High-Speed Channels Using Machine Learning, Hyunwook Park, Davit Kharshiladze, Yifan Ding, Ling Zhang, Natalia Bondarenko, Hanqin Ye, Kaushal Sanjay Mhalgi, Brice Achkir, Chulsoon Hwang

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, the effects of data representations and preprocessing on machine learning based S-parameter modeling of high-speed channels are investigated. Using a transformer network as a base model, two S-parameter representations in real/imaginary and magnitude/phase are compared and studied. Considering S-parameter data distributions, various preprocessing techniques including MinMax normalization, standardization, robust scaling, power transformation, and quantile transformation are compared and analyzed to improve accuracy. Moreover, the accuracy results are compared depending on the electrical length of target channels.

Obstacles And Mitigations For An Accurate Low Impedance, Low Frequency Measurement, Faye Squires, Yifan Ding, Matthew Doyle, Matteo Cocchini, Samuel Connor, Francesco De Paulis, Albert E. Ruehli, Chulsoon Hwang, Lijun Jiang

Electrical and Computer Engineering Faculty Research & Creative Works

The two-port shunt configuration is often heralded as the gold standard for low-impedance measurements. However, this measurement method is not without its own issues. The shunt configuration inherently creates a ground loop between the measurement device's reference plane and the reference of the device under test (DUT). Additionally, probes often must be oriented in such a way that allows inductive coupling to occur. This work uses microprobes to measure the shunt impedance of a non-ideal short to explore the limitations of this measurement method in terms of both frequency and impedance. It highlights the importance of ground loop isolation and …

Multi-Objective Inverse Optimization Of High-Speed Interconnects Using Cascaded Deep Neural Network, Yicheng Zhang, Ling Zhang, Hyunwook Park, Bo Pu, Xiao Ding Cai, Chulsoon Hwang, Bidyut Sen, Jun Fan, Er Ping Li, James L. Drewniak

Electrical and Computer Engineering Faculty Research & Creative Works

This paper proposes a novel multi-objective inverse optimization method for high-speed interconnects based on a cascaded deep neural network (DNN) structure, which can efficiently optimize characteristic impedance, insertion loss, and far-end crosstalk (FEXT) simultaneously. Parameter optimization for high-speed interconnects is essential to the signal integrity and electrical performance of complex designs such as multilayer printed circuit boards (PCBs) and chiplets. Conventional optimization approaches often rely on numerous optimization iterations, which is highly time-consuming, especially in high dimensional parameter spaces. This paper proposes a novel DNN based method by cascading an inverse-prediction network and a forward-prediction network to achieve multi-objective optimization …

Differential Via Modeling Using Multilayer Perceptron-Sequential (Mlp-Seq) Neural Network, Hyunwook Park, Shruti Sawant, Bandi Sathvika, Arun Chada, Soumya Singh, Seema Pk, Taein Shin, Haeseok Suh, Junyong Park, Bhyrav Mutnury, Donghyun Kim

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, an encoder-decoder structured multi-layer perceptron-sequential (MLP-SEQ) networks are proposed to model high-speed differential vias for estimating differential insertion loss (IL) and return loss (RL). Sequential neural networks including recurrent neural network (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU) are introduced as the decoder NN to treat frequency responses as sequences. The proposed models are validated by finite element method (FEM) simulation results. The accuracy and training times of MLP-RNN, MLP-LSTM, and MLP-GRU models are compared and analyzed. Based on the MLP-LSTM model, various design of experiments (DoEs) are conducted to enhance the reproducibility and …

Coupling Path Analysis Of Data Center Ssd Storage Systems Based On Visualization Technique, Xiangrui Su, Haran Manoharan, Jihun Kim, Lalit Kumar, Heewon Kang, Chunghyun Ryu, Chulsoon Hwang

Electrical and Computer Engineering Faculty Research & Creative Works

Electrostatic discharge (ESD) is a major source of electromagnetic interference, capable of causing damage, malfunctions, or disruptions in electronic devices. As a result, ESD immunity testing is a critical component of electromagnetic compatibility (EMC) standards. In this study, the radiation emitted from the ESD gun body over 2 GHz is characterized and modeled using Huygens' principle. The equivalent field source was validated across three different environments, demonstrating accuracy with errors of less than 10 dB. A coupling path visualization technique was then employed to identify critical coupling paths, providing guidance for the strategic placement of absorbers. Simulation results showed that …

Characterization And Full-Wave Modeling Of Corona Discharge Induced Coupling To Touchscreen Displays, Zhekun Peng, Shubhankar Marathe, Javad Meiguni, Ali Foudazi, Jianchi Zhou, Li Shen, Viswa Pilla, Cheung Wei Lam, Donghyun Kim, David Pommerenke, Daryl G. Beetner

Electrical and Computer Engineering Faculty Research & Creative Works

An electrostatic discharge (ESD) to the glass of a touchscreen display can damage capacitive touch sensor circuitry and traces. The ESD creates a sparkless corona discharge on the glass surface, which couples to sense patches on the other side of the glass. Currents induced on sense traces during the corona discharge are measured in an evaluation setup, which resembles the touchscreen design. Waveforms for current induced at different distances from the discharge position are recorded and compared. The induced currents are analyzed by their peak value and the total charge or energy delivered to the sense trace for different discharge …

Ethics Vs.. Regulation: Converging Frameworks For Trustworthy Human-Centered Ai In Biomedical Research, Tayo Obafemi-Ajayi, Tiffani J. Bright, Emily F. Wong, Donald Wunsch, Joan Peckham, Jason H. Moore

Electrical and Computer Engineering Faculty Research & Creative Works

The accelerating impact of AI in biomedical research is driving significant advances in precision medicine. As these systems increasingly shape health outcomes, the imperative to develop trustworthy, reliable, and ethically grounded AI becomes more pressing, particularly in addressing concerns related to data integrity, patient safety, and equitable outcomes. While the potential of AI to transform biomedical research is clear, its responsible integration depends on more than technological capability. Ensuring that these systems are aligned with societal values requires a dual commitment: the operationalization of ethical principles throughout the AI life cycle and the establishment of robust regulatory mechanisms. Ethics provides …

Spin Mechanisms In Gd-Doped Gan Implanted With Oxygen Carbon At Room Temperature, Vishal Saravade, Amirhossein Ghods, Chuanle Zhou, Ian Ferguson

Electrical and Computer Engineering Faculty Research & Creative Works

Gadolinium-doped gallium nitride implanted with oxygen and carbon show carrier-mediated spin mechanisms at room temperature. As-grown Gd-doped GaN grown by metal-organic chemical vapor deposition using a tris(cyclopentadienyl) gadolinium precursor shows Ordinary Hall Effect and no ferromagnetism at room temperature. Upon O or C implantation in Gd-doped GaN, Anomalous Hall Effect that is indicative of carrier-mediated spin and ferromagnetism is observed. A good crystal quality is maintained even after implantation. O and C favor interstitial sites and occupy deep-level acceptor-type states in Gd-doped GaN. Room-temperature spin and ferromagnetism that is induced by gadolinium in Gd-doped GaN is activated by O and …

Sift Feature-Based Relative Altitude Estimation Enhanced With Siamese Network, Shirin Nasr-Esfahani, S. Jagannathan

Electrical and Computer Engineering Faculty Research & Creative Works

In GPS-denied environments or when GPS signals are unreliable or unavailable, alternative methods of accurate localization with coordinate generation become critical. To address localization, the scale-invariant feature transform (SIFT) algorithm, along with its numerous adaptations, is extensively utilized in computer vision and remote sensing for matching image features to identify objects and perform localization. This article presents a novel approach for estimating the relative altitude of unmanned aerial vehicles (UAVs) using SIFT features' scale (size), omitting the need for additional data like camera intrinsic parameters, as well as extensive image datasets are also required for training. Furthermore, the approach enhances …

Quality Factor Estimation Of Additively Manufactured Frequency Selective Surfaces Via Active Microwave Thermography, Logan M. Wilcox, Alexander Hook, Emma T. Bohannon, Doyle T. Motes, Cody Morrow, Kristen M. Donnell

Electrical and Computer Engineering Faculty Research & Creative Works

Additive manufacturing (AM) has increased in popularity in recent years due to improvements in efficiency over traditional manufacturing methods. Furthermore, the availability of electrically conductive fused deposition modeling (FDM) filaments has opened the door to the manufacture of conductive structures, including frequency selective surfaces (FSSs). Traditionally, the performance of an FSS is verified experimentally through electromagnetic (EM) measurement of the FSS's reflection and/or transmission response. However, this verification approach is not ideal for rapid, in situ inspections due to the time-consuming measurement process, expensive and complicated equipment, and difficult to interpret results. To this end, active microwave thermography (AMT), a …

Enhanced Multidimensional Harmonic Retrieval In Mimo Wireless Channel Sounding, Yanming Zhang, Wenchao Xu, A. Long Jin, Tianquan Tang, Min Li, Peifeng Ma, Lijun Jiang, Steven Gao

Electrical and Computer Engineering Faculty Research & Creative Works

This paper introduces a recursive parallel dynamic mode decomposition (RPDMD) scheme tailored for multidimensional harmonic retrieval (MHR), specifically applied to MIMO wireless channel sounding. The RPDMD algorithm is devised to address the complexities inherent in multidimensional scenarios, leveraging the dynamic mode decomposition (DMD) framework within a recursive parallel structure. Initially, the observed tensorial multidimensional harmonic data is transformed into a two-dimensional matrix format along the r-th dimension. Subsequently, DMD dissects this matrix data into eigenvalues and their associated modes. The real and imaginary components of the DMD eigenvalues yield damping factors and frequencies in the r-th dimension, respectively. Furthermore, recursive …

A Low-Frequency-Stable Higher-Order Isogeometric Discretization Of The Augmented Electric Field Integral Equation, Maximilian Nolte, Riccardo Torchio, Sebastian Schöps, Jürgen Dölz, Felix Wolf, Albert E. Ruehli

Electrical and Computer Engineering Faculty Research & Creative Works

This contribution investigates the connection between Iso geometric analysis (IGA) and integral equation (IE) methods for full-wave electromagnetic problems up to the low-frequency limit. The proposed spline-based IE method allows for an exact representation of the model geometry described in terms of nonuniform rational B-splines (NURBS) without meshing. This is particularly useful when high accuracy is required or when meshing is cumbersome, for instance, during the optimization of electric components. The augmented electric field IE (EFIE) is adopted, and the deflation method is applied, so the low-frequency breakdown is avoided. The extension to higher-order basis functions is analyzed and the …

Component Level Em Emission Assessment And Management For Rf Desensitization, Xiangrui Su, Wenchang Huang, Junghee Cho, Joonki Paek, Chulsoon Hwang

Electrical and Computer Engineering Faculty Research & Creative Works

Radio frequency (RF) desensitization is a common issue caused by high-speed components in modern electronic devices. Consequently, numerous studies have focused on characterizing and quantifying electromagnetic (EM) emission sources by using near field scanning to detect EM noise sources. However, before near field scanning, no predetermined threshold is available to quickly assess whether an EM noise source will pose RF desensitization risks in the receiving antenna. This article presents a method to optimize near field scanning settings through EM emission management analysis. Drawing on experience from numerous EM emission studies, we introduce an EM emission management procedure for two common …