Electrical and Computer Engineering Commons^™

Advancing 4h-Silicon Carbide Power Mosfets Through Three-Dimensional Technology Computer Aided Design Optimization, Skylar A. Deboer

Electronic Theses & Dissertations (2024 - present)

This research contributes to the advancement of 1.2 kV 4H-Silicon Carbide (SiC) Metal-Oxide-Semiconductor Field-Effect Transistors (MOSFETs) through the development and implementation of sophisticated three-dimensional Technology Computer-Aided Design (3D TCAD) methodologies. These advanced simulation techniques enable comprehensive evaluation of innovative unit cell architectures that remain inaccessible to conventional two-dimensional (2D) TCAD approaches. By leveraging these 3D simulation capabilities, this work facilitates significant improvements in both the performance and reliability of next-generation wide-bandgap power devices.

Power devices convert electrical energy between different forms throughout the power grid making them essential in applications ranging from personal electronics, electric vehicles, renewable energy systems, industrial …

Vaim-Cff: A Variational Autoencoder Inverse Mapper Solution To Compton Form Factor Extraction From Deeply Virtual Compton Scattering, Manal Almaeen, Tareq Alghamdi, Brandon Kriesten, Douglas Adams, Yaohang Li, Huey-Wen Lin, Simonetta Liuti

Computer Science Faculty Publications

We develop a new methodology for extracting Compton form factors (CFFs) from deeply virtual exclusive reactions such as the unpolarized DVCS cross section using a specialized inverse problem solver, a variational autoencoder inverse mapper (VAIM). The VAIM-CFF framework not only allows us access to a fitted solution set possibly containing multiple solutions in the extraction of all 8 CFFs from a single cross section measurement, but also accesses the lost information contained in the forward mapping from CFFs to cross section. We investigate various assumptions and their effects on the predicted CFFs such as cross section organization, number of extracted …

S²Il: Structurally Stable Incremental Learning, S. Balasubramanian, P. Yedu Krishna, Talasu Sai Sriram, M. Sai Subramaniam, Manepalli Pranav Phanindra Sai, Ravi Mukkamala

Computer Science Faculty Publications

Feature Distillation (FD) strategies are proven to be effective in mitigating Catastrophic Forgetting (CF) seen in Class Incremental Learning (CIL). However, current FD approaches enforce strict alignment of feature magnitudes and directions across incremental steps, limiting the model’s ability to adapt to new knowledge. In this paper, we propose Structurally Stable Incremental Learning (S²IL), a FD method for CIL that mitigates forgetting by focusing on preserving the overall spatial patterns of features which promote flexible (plasticity) yet stable representations that preserve old knowledge (stability). We also demonstrate that our proposed method S²IL achieves strong incremental accuracy and outperforms other FD …

Energy-Based Deep Incomplete Multi-View Clustering, Ziyu Wang, Yiming Du, Rui Ning, Lusi Li

Computer Science Faculty Publications

Incomplete multi-view clustering (IMVC) deals with real-world scenarios where certain views are partially missing, posing significant challenges to effective clustering. Most existing IMVC approaches face a trade-off: imputation-free methods suffer from information bias and imbalance, while full-imputation methods risk introducing and propagating noise. To overcome these limitations, we propose Energy-Based Deep Incomplete Multi-View Clustering (Energy-DIMC), a novel selective-imputation framework that leverages energy-based models (EBMs) to guide reliable imputations and robust clustering. EBMs assess data compatibility by assigning lower energy to more coherent structures, effectively modeling complex inter-view and inter-sample dependencies. Inspired by EBMs, Energy-DIMC integrates four key components: 1) a …

Integrated Sensing And Covert Communications With Ris Adaptive And Non-Adaptive Modes, Jia Zhang, Dengfeng Zhang, Ke Liu, Jiande Sun, Min Li, Shihao Yan

Research outputs 2022 to 2026

In this work, we consider an integrated sensing and covert communication (ISACC) system with a finite blocklength L aided by a reconfigurable intelligent surface (RIS) with N elements. Specifically, with the aid of RIS, a transmitter Alice is to sense the potential existence of a target, and she is also probabilistically trying to send information to a receiver Bob covertly (i.e., trying to hide her transmissions from a warden Willie). Meanwhile, Willie is to detect whether Alice is sensing the target only or conducting ISACC. We consider two RIS operation modes, i.e., a non-adaptive mode, where RIS employs a common …

Mxenes In Biosensing: Enhancing Sensitivity And Flexibility - A Review Of Properties, Applications, And Future Directions, Ali Mohammad Amani, Lobat Tayebi, Ehsan Vafa, Alireza Jahanbin, Milad Abbasi, Ahmed Vaez, Hesam Kamyab, Lalitha Gnanasekaran, Shreeshivadasan Chelliapan

Electrical & Computer Engineering Faculty Publications

MXenes are a novel type of nanostructured material that has received a lot of attention for their potential applications in bioanalysis owing to their unique features. These materials, made from transition metal nitrides, carbides, or carbonitrides, have a number of advantages, including high hydrophilicity, a large surface area, strong metallic conductivity, superior ion transport capabilities, biocompatibility, and low diffusion barriers. Their surfaces are easily manipulated, making them more adaptable for a variety of applications, including biosensing. The outstanding properties of MXenes have attracted researchers of different fields, including renewable energy, fuel cells, supercapacitors, electronics, and catalysis. In the context of …

A Comprehensive Exploration Of 6g Wireless Communication Technologies, Md Nurul Absar Siddiky, Muhammad Enayetur Rahman, Md. Shahriar Uzzal, H. M. Dipu Kabir

Electrical & Computer Engineering Faculty Publications

As the telecommunications landscape braces for the post-5G era, this paper embarks on delineating the foundational pillars and pioneering visions that define the trajectory toward 6G wireless communication systems. Recognizing the insatiable demand for higher data rates, enhanced connectivity, and broader network coverage, we unravel the evolution from the existing 5G infrastructure to the nascent 6G framework, setting the stage for transformative advancements anticipated in the 2030s. Our discourse navigates through the intricate architecture of 6G, highlighting the paradigm shifts toward superconvergence, non-IP-based networking protocols, and information-centric networks, all underpinned by a robust 360-degree cybersecurity and privacy-by-engineering design. Delving into …

Detecting Anomalous Srf Cavity Behavior With Unsupervised Learning, Hal Ferguson, Jiang Li, Adam Carpenter, Chris Tennant, Dillon Thomas, Dennis Turner

Electrical & Computer Engineering Faculty Publications

We present an unsupervised learning framework for detecting anomalous superconducting radio-frequency (SRF) cavity behavior at the Continuous Electron Beam Accelerator Facility (CEBAF), emphasizing its initial performance and effectiveness. Key to the system’s success was the development of data acquisition systems (DAQs) that capture fast-sampled, information-rich signals, essential for detecting transient effects. The approach involves creating daily cavity-specific models using principal component analysis to handle variations in rf signal behavior and mitigate performance degradation from data drift. This unsupervised method eliminates the need for expensive labeling by continuously updating models with recent data. Deployed and operational for 3 months before a …

Data-Driven Gradient Optimization For Field Emission Management In A Superconducting Radio-Frequency Linac, S. Goldenberg, K. Ahammed, A. Carpenter, J. Li, R. Suleiman, C. Tennant

Electrical & Computer Engineering Faculty Publications

Field emission can cause significant problems in superconducting radio-frequency linear accelerators (linacs). When cavity gradients are pushed higher, radiation levels within the linacs may rise exponentially, causing degradation of many nearby systems. This research aims to utilize machine learning with uncertainty quantification to predict radiation levels at multiple locations throughout the linacs and ultimately optimize cavity gradients to reduce field emission-induced radiation while maintaining the total linac energy gain necessary for the experimental physics program. The optimized solutions show over 40% reductions for both neutron and gamma radiation from the standard operational settings.

Land Target Detection Algorithm In Remote Sensing Images Based On Deep Learning, Wenyi Hu, Xiaomeng Jiang, Jiawei Tian, Shitong Ye, Shan Liu

Electrical & Computer Engineering Faculty Publications

Remote sensing technology plays a crucial role across various sectors, such as meteorological monitoring, city planning, and natural resource exploration. A critical aspect of remote sensing image analysis is land target detection, which involves identifying and classifying land-based objects within satellite or aerial imagery. However, despite advancements in both traditional detection methods and deep-learning-based approaches, detecting land targets remains challenging, especially when dealing with small and rotated objects that are difficult to distinguish. To address these challenges, this study introduces an enhanced model, YOLOv5s-CACSD, which builds upon the YOLOv5s framework. Our model integrates the channel attention (CA) mechanism, CARAFE, and …

Detecting Sars-Cov-2 In Ct Scans Using Vision Transformer And Graph Neural Network, Kamorudeen Amuda, Almustapha Wakili, Tomilade Amoo, Lukman Agbetu, Qianlong Wang, Jinjuan Feng

Electrical & Computer Engineering Faculty Publications

The COVID-19 pandemic has presented significant challenges to global healthcare, bringing out the urgent need for reliable diagnostic tools. Computed Tomography (CT) scans have proven instrumental in detecting COVID-19-induced lung abnormalities. This study introduces Convolutional Neural Network, Graph Neural Network, and Vision Transformer (ViTGNN), an advanced hybrid model designed to enhance SARS-CoV-2 detection by combining Graph Neural Networks (GNNs) for feature extraction with Vision Transformers (ViTs) for classification. Using the strength of CNN and GNN to capture complex relational structures and the ViT capacity to classify global contexts, ViTGNN achieves a comprehensive representation of CT scan data. The model was …

The Impact Of Chromium Ion Implantation On Ald Lead Chalcogenide Thin Films, Haifeng Cong, Charlotte Poterie, Jean Francois Barbot, Helmut Baumgart

Electrical & Computer Engineering Faculty Publications

Inherently the synthesis of semiconducting materials by Atomic Layer Deposition ALD produces only intrinsic undoped films which require the introduction of small amounts of impurities for doping to change them into extrinsic semiconductors. Apart from various in-situ diffusion doping techniques like delta doping during the ALD process, post deposition doping by ion implantation affords the best control of dose and doping profile. The present study investigates the impact of 180 keV Cr⁺ ion implantation on the properties of semiconducting ALD lead chalcogenide thin films to improve their thermoelectric figure of merit. The implantation was accomplished with 180 keV Chromium …

A Proximal Policy Optimization-Based Controller For Enhanced Power Sharing In Microgrids, Seyedmohammad Hasheminasab, Armin Lotfy, Mohamad Alzayed, Hicham Chaoui

Electrical & Computer Engineering Faculty Publications

This paper introduces a Proximal Policy Optimization (PPO)-based virtual impedance (VI) controller to enhance both power sharing and system response under disturbances in inverter-interfaced microgrids. Traditional droop control methods often face challenges due to variations in feeder impedance, which degrade performance. The proposed controller continuously updates its policy based on changes in the operating environment. The control problem is modeled as a Markov Decision Process (MDP), in which the state and action spaces are explicitly defined, and a carefully designed reward function, satisfying system criteria and constraints, guides the learning process toward achieving the desired transient and steady-state performance. By …

A Novel Intelligent Thermal Feedback Framework For Electric Motor Protection In Embedded Robotic Systems, Mohamed Shili, Salah Hammedi, Hicham Chaoui, Khaled Nouri

Electrical & Computer Engineering Faculty Publications

As robotic systems advance in autonomy and sophistication while being used in uncertain environments, the challenge of building reliable and robust electric motors that are embedded into robotic systems has never been a more important engineering problem. Thermal distress caused by extended operation or excessive loading can negatively affect a motor’s performance and efficiency and lead to catastrophic hardware failure. This paper proposes a novel intelligent control framework that includes real-time thermal feedback for hybrid electric motors that are embedded into robotic systems. The framework relies on adaptive control techniques and lightweight machine learning techniques to estimate internal motor temperatures …

Energy-Aware Sensor Fusion Architecture For Autonomous Channel Robot Navigation In Constrained Environments, Mohamed Shili, Hicham Chaoui, Khaled Nouri

Electrical & Computer Engineering Faculty Publications

Navigating autonomous robots in confined channels is inherently challenging due to limited space, dynamic obstacles, and energy constraints. Existing sensor fusion strategies often consume excessive power because all sensors remain active regardless of environmental conditions. This paper presents an energy-aware adaptive sensor fusion framework for channel robots that deploys RGB cameras, laser range finders, and IMU sensors according to environmental complexity. Sensor data are fused using an adaptive Extended Kalman Filter (EKF), which selectively integrates multi-sensor information to maintain high navigation accuracy while minimizing energy consumption. An energy management module dynamically adjusts sensor activation and computational load, enabling significant reductions …

Flux-Weakening Control Methods For Permanent Magnet Synchronous Machines In Electric Vehicles At High Speed, Samer Alwaqfi, Mohamad Alzayed, Hicham Chaoui

Electrical & Computer Engineering Faculty Publications

Permanent magnet synchronous motors (PMSMs) are widely favored by manufacturers for use in electric vehicles (EVs) because of their many benefits, which include high power density at high speeds, ruggedness, potential for high efficiency, and reduced control complexity. However, since the Back Electromotive Force (EMF) increases proportionally with the motor’s rotational speed, it must be carefully controlled at high speeds. Flux-weakening (FW) control is required to avoid excessive electromagnetic flux beyond the power source and inverter’s voltage restrictions. This paper aims to compare various FW control strategies and analyze their effectiveness in maximizing the speed of PMSMs in EV applications …

Novel Micro-And Nanoformulations Of Paclitaxel For Targeting Metastatic Breast, Non-Small Cell Lung, And Pancreatic Cancers, Lobat Tayebi, Mehran Alavi

Electrical & Computer Engineering Faculty Publications

Nonlinear pharmacokinetics resulting from high lipophilic and low oral bioavailability, and hypersensitivity reactions and hyperlipidemia caused by formulation by Cremophor EL have limited clinical effectiveness of paclitaxel (Taxol). In this way, there is the critical necessity of innovative drug delivery systems (DDSs) to mitigate severe side effects and overcome clinical limitations of paclitaxel. In recent years, various micro- and nanoformulations, specifically polymeric nanoparticles (NPs) and lipid NPs, have been presented and approved by the Food and Drug Administration (FDA). In addition, other nanoformulations, such as polymeric nanoparticles (NPs), micelles, liposomes, and mesoporous silica nanoparticles, have shown promising results in vitro …

Advances In Battery Modeling And Management Systems: A Comprehensive Review Of Techniques, Challenges, And Future Perspectives, Seyed Saeed Madani, Yasmin Shabeer, Ananthu Shibu Nair, Michael Fowler, Satyam Panchal, Carlos Ziebert, Hicham Chaoui, Shi Xue Dou, Khay See, Saad Mekhilef, Françios Allard

Electrical & Computer Engineering Faculty Publications

Energy storage systems (ESSs) and electric vehicle (EV) batteries depend on battery management systems (BMSs) for their longevity, safety, and effectiveness. Battery modeling is crucial to the operation of BMSs, as it enhances temperature control, fault detection, and state estimation, thereby maximizing efficiency and preventing malfunctions. This paper thoroughly examines the most recent advancements in battery and BMS modeling, including data-driven, thermal, and electrochemical methods. Advanced modeling approaches are explored, including physics-based models that incorporate mechanical stress and aging effects, as well as artificial intelligence (AI)-driven state estimation. New technologies that facilitate data-driven decision-making, real-time monitoring, and simplified systems include …

Quantifying Trapped Magnetic Vortex Losses In Niobium Resonators At Mk Temperatures, D. Bafia, B. Abdisatarov, R. Pilipenko, Y. Lu, G. Eremeev, A. Romanenko, A. Grassellino

Electrical & Computer Engineering Faculty Publications

Trapped magnetic vortices in niobium introduce microwave losses that degrade the performance of superconducting resonators. While such losses have been extensively studied above 1 K, we report here their direct quantification in the millikelvin and low-photon regime relevant to quantum devices. Using a high-quality factor 3D niobium cavity cooled through its superconducting transition in controlled magnetic fields, we isolate vortex-induced losses and find the resistive component of the sensitivity to trapped flux S to be approximately 2 n Ω/mG at 10 mK and 6 GHz. The decay rate is initially dominated by two-level system (TLS) losses from the native niobium …

The Effect Of Electrode Geometry On Excited Species Production In Atmospheric Pressure Air–Hydrogen Streamer Discharge, Shirshak Kumar Dhali, Stuart Reyes

Electrical & Computer Engineering Faculty Publications

When a gas is overvolted at or near atmospheric pressure, it results in a streamer discharge formation. Electrode geometries exert significant impact on the electrical breakdown of gases by altering the spatial profile of the electric field. In many applications the efficient generation of radicals is critical and is determined by the characteristics of the streamer discharge. We examine the effect of electrode geometry on the streamer characteristics and the production of radicals. This is performed for three different electrode geometries: plane–plane, pin–plane, and pin–pin. A two-dimensional rotationally symmetric fluid model is used for the streamer discharge simulation in the …

Enhancing Channel Data Savings And Information Transfer Efficiency In Ultrasound Imaging, Sai Konda, Hicham Chaoui

Electrical & Computer Engineering Faculty Publications

Ultrasound is a popular imaging technique mainly due to its non-invasive nature. And so, it is being used in a variety of applications. Due to plane wave imaging technique in ultrasound, frame rate of ultrasound imaging has the potential for being very high. Due to which, many channel data frames are being generated within a few seconds. As a result, tasks such as storing data frames and transferring them from front end ultrasonic system to processing computers are presenting significant challenges. Our current research work minimized these issues. We proposed and implemented: (a) Data encoding technique - We combined every …

Quantum Efficiency, Electron Spin Polarization And Lifetime Study For Gaas Based Truncated Nanocone Array Photocathodes, Md. Aziz Ar Rahman, Greg Blume, Md Abdullah Mamun, Shukui Zhang, Hani Elsayed-Ali

Electrical & Computer Engineering Faculty Publications

[First slide] Direct band-gap photocathode (Eg = 1.42 eV).

Valence band maximum (VBM) is doubly degenerate, Max. electron spin polarization (ESP) ~35-40%.

Strained-supperlattice (SSL) removed the VBM degeneracy, ESP ~85-90% with QE of ~1% at 780 nm.

Requires UHV: Lifetime ~200 C with ~200 μA beam current.

Ion-back bombardment degrades the lifetime.

Improvement in charge lifetime required for JLAB polarized positron source program: 1mA-10mA beam current for 2 weeks ~ 10³10⁴C.

Design Of An Improved Robust Fractional-Order Pid Controller For Buck–Boost Converter Using Snake Optimization Algorithm, Seyyed Morteza Ghamari, Hasan Molaee, Mehrdad Ghahramani, Daryoush Habibi, Asma Aziz

Research outputs 2022 to 2026

With the increasing complexity of modern power systems, effective control of DC–DC converters has become crucial to ensure stability and efficiency. This paper focuses on optimizing the parameters of a known fractional-order proportional–integral–derivative (FOPID) controller for the control of a DC–DC buck–boost converter. The control of a DC–DC buck–boost converter is achieved using aFOPID approach. The gains of this technique have been enhanced utilizing the snake optimization (SO) algorithm. This converter exhibits unfavourable behaviour due to its non-minimum structure, necessitating a well-regulated controller to guarantee stability. The fractional concept is suggested here to enhance the dynamics of the classical PID …

Work In Progress: Using Internships As Means For Indirect Assessment Of Abet Criteria 3 “1-7” Student Outcomes, Robert Bass

Electrical and Computer Engineering Faculty Publications and Presentations

The Electrical & Computer Engineering (ECE) Department at Portland State University (PSU) has developed a Power Engineering Internship (PEI) program that provides engineering career development pathways within the electric utility industry. The PEI is supported by several U.S. Department of Energy grants that aim to develop quality career opportunities and develop a future electric utility workforce that represents the nation’s diverse populations. The PSU ECE Department intends to use surveys of internship participants as assessment tools for its ABET accreditation process, in particular, the ABET Criteria 3 Student Outcomes (SOs). SOs relate to the knowledge, skills, and behaviors that students …

Estimating Electric Power Consumption Of Grid-Enabled Residential Heat Pump Water Heaters, Dana Paresa, Robert Bass

Electrical and Computer Engineering Faculty Publications and Presentations

We present two methods for estimating compressor electric power consumption of residential heat pump water heaters. Power is calculated using data from voltage and current instrumentation, which add costs and potential failure points to the unit. The two methods presented herein use thermal instrumentation or derived data that are typically included within modern residential heat pump water heaters. One method uses data from lower and upper tank temperature sensors. The other method uses an attribute that is commonly available within grid-enabled flexible load heat pump water heaters. For both methods, fit equations were derived, for which coefficients are tuned to …

Polarized Photocathode R&D At Bnl And Spin Consideration For The Eic Preinjector, Jyoti Biswas, Erdong Wang, Omer Rahman, John Skaritka, Adam Masters, Sylvain Marsillac, Tai-De Li

Electrical & Computer Engineering Faculty Publications

Superlattice GaAs photocathodes are vital for producing polarized electron beams for the Electron-Ion Collider (EIC) at Brookhaven National Laboratory. The electron pre-injector at the EIC requires a 7 nC bunch with at least 85% spin polarization from a GaAs-based superlattice cathode. The doping density of the very surface layer of the cathode needs to be optimized to extract a high bunch charge beam from the high-voltage DC gun. The polarization axis of the emitted beam is longitudinal, and it will be rotated to transverse direction using two Wien filters, each rotating the spin by 45 degrees. In this paper, we …

Potential Of Lidar And Hyperspectral Sensing For Overcoming Challenges In Current Maritime Ballast Tank Corrosion Inspection, Sergio Pallas Enguita, Jiajun Jiang, Chung-Hao Chen, Samuel Kovacic, Richard Lebel

Electrical & Computer Engineering Faculty Publications

Corrosion in maritime ballast tanks is a major driver of maintenance costs and operational risks for maritime assets. Inspections are hampered by complex geometries, hazardous conditions, and the limitations of conventional methods, particularly visual assessment, which struggles with subjectivity, accessibility, and early detection, especially under coatings. This paper critically examines these challenges and explores the potential of Light Detection and Ranging (LiDAR) and Hyperspectral Imaging (HSI) to form the basis of improved inspection approaches. We discuss LiDAR’s utility for accurate 3D mapping and providing a spatial framework and HSI’s potential for objective material identification and surface characterization based on spectral …

Time-Marching Quantum Algorithm For Simulation Of Nonlinear Lorenz Dynamics, Efstratios Koukoutsis, George Vahala, Min Soe, Kyriakos Hizanidis, Linda Vahala, Abhay K. Ram

Electrical & Computer Engineering Faculty Publications

Simulating nonlinear classical dynamics on a quantum computer is an inherently challenging task due to the linear operator formulation of quantum mechanics. In this work, we provide a systematic approach to alleviate this difficulty by developing an explicit quantum algorithm that implements the time evolution of a second-order time-discretized version of the Lorenz model. The Lorenz model is a celebrated system of nonlinear ordinary differential equations that has been extensively studied in the contexts of climate science, fluid dynamics, and chaos theory. Our algorithm possesses a recursive structure and requires only a linear number of copies of the initial state …

Cellax Nanoparticles: Taxane Nanoformulations For Solid Tumor Targeting, Lobat Tayebi, Mehran Alavi

Electrical & Computer Engineering Faculty Publications

Nanoparticles (NPs), specifically polymer-modified NPs, have illustrated unique therapeutic advantages compared to bulk materials. Cellax NPs have provided a promising approach to cancer therapy by improving drug delivery, targeting the tumor microenvironment, and potentially overcoming drug resistance, all while reducing overall toxicity compared to traditional taxane treatment. By the flash nanoprecipitation (FNP) method, docetaxel and cabazitaxel have been formulated with polyethylene glycol (PEG) modified-acetylated carboxymethylcellulose (CMC) polymer to increase biocompatibility, bioavailability, specific targeting, and modulate the tumor microenvironment. However, there are some challenges and clinical limitations related to this formulation, encompassing optimum targeted delivery to tumors, overcoming biological barriers, such …

Green Bank Chime/Frb Outriggers Commissioning And Analog System Development, Kholoud Sharif Tag Alkhatem Khairy

Graduate Theses, Dissertations, and Problem Reports (ETD)

The main objective of this thesis is to document the development and commissioning of the Canadian Hydrogen Intensity Mapping Experiment. (CHIME) outrigger at the Green Bank Observatory. (GBO) in Green Bank, WV. This novel cylindrical wide-field radio transient telescope is currently operating in conjunction with CHIME. The CHIME outrigger at GBO aims to contribute significantly to the field of radio astronomy, with implications for both Fast Radio Burst. (FRB) science and broader astronomical research. The construction and commissioning of the CHIME outrigger at the GBO mark a pivotal step forward in pursuing high-precision wide-field detection and localization of radio transients, …