Electrical and Computer Engineering Commons^™

Hands-On Microgrid Education: Using Programmable Dc-Dc Converters To Teach Power & Energy Systems, Alejandra Zuniga

College of Engineering Summer Undergraduate Research Program

This project will develop an analog computing circuit that can accelerate power system simulations used for grid interconnection studies. The project will leverage analog computing to create a specialized circuit capable of simulating large-scale power networks with detailed models of power electronics-based loads, such as those found in data centers and manufacturing plants. A software application programming interface will be developed to integrate this circuit with a desktop computer, where simulations can be run by the user. The project team will also work with industry partners and utilities to evaluate the feasibility of the proposed technology for conducting real-world grid …

Developing A Low-Temperature Pathway For The Synthesis Of Two-Dimensional Ws2 Nanosheets, Akhil Potdar

Holster Scholar Projects

Since their discovery in 2004, two-dimensional (2D) materials have attracted great attention due to their unique mechanical, electrical, and chemical properties. However, their integration into devices is limited by the high temperatures required for crystalline growth, which prevents the use of flexible and biocompatible substrates like polymers for biomedical and next-generation electronic devices. This project aims to therefore develop a low-temperature synthesis process for two-dimensional tungsten disulfide (WS₂), a material particularly promising due to its tunable bandgap and biocompatibility. We propose that by first depositing an intermediate tungsten oxide film (WOx) via Hollow Cathode Plasma-Assisted Atomic Layer Deposition (HCP-ALD) and …

Foundations For Multi-Bit-Per-Cell Phase Change Memory Modeling Gst Crossbar Arrays, Sashah Wilson-Thompson

Holster Scholar Projects

This project builds a simulation foundation for selective cell heating in a phase-change memory (PCM) crossbar using Ge2Sb2Te5 (GST) as the active material. Using COMSOL Multiphysics® a 3D modeling software, couples Electric Currents, Electric Circuits, Heat Transfer in Solids, and Electromagnetic Heating for the simulation. A parameterized Tungsten (W)/GST-Amorphous/GST-Crystalline(phases) /W embedded in Silica Dioxide (SiO2) and surrounded in Silica Nitride (Si3N4) is validated at the single-cell level and scaled to small GST crossbars A terminal voltage (V_active/V_inactive, or 0 V if unselected) is applied through MOSFET and diode selector elements at the ends of each word line and bit line. …

A Risk-Averse Data-Driven Distributionally Robust Optimization Method For Transmission Power Systems Under Uncertainty, Mehrdad Ghahramani, Daryoush Habibi, Asma Aziz

Research outputs 2022 to 2026

The increasing penetration of renewable energy sources and the consequent rise in forecast uncertainty have underscored the need for robust operational strategies in transmission power systems. This paper introduces a risk-averse, data-driven distributionally robust optimization framework that integrates unit commitment and power flow constraints to enhance both reliability and operational security. Leveraging advanced forecasting techniques implemented via gradient boosting and enriched with cyclical and lag-based time features, the proposed methodology forecasts renewable generation and demand profiles. Uncertainty is quantified through a quantile-based analysis of forecasting residuals, which forms the basis for constructing data-driven ambiguity sets using Wasserstein balls. The framework …

Ge0.95sn0.05 On Si Avalanche Photodiode With Spectral Response Cutoff At 2.14 Μm, Justin Michael Rudie, Xiaoxin Wang, Rajesh Kumar, Grey Abernathy, Sylvester Amoah, Steven Akwabli, Hryhorii Stanchu, Perry C. Grant, Bao-Hua Li, Wei Du, Jifeng Liu, Shui-Qing Yu

Electrical Engineering and Computer Science Faculty Publications and Presentations

GeSn-based avalanche photodiode operating in shortwave infrared wavelength was demonstrated in this work. A separate absorption and charge multiplication structure was employed to take advantage of long wavelength absorption in GeSn and low impact ionization ratio of Si. Due to lattice mismatch between Si and GeSn that would degrade GeSn material quality if with direct growth, a 240-nm-thick Ge buffer was utilized, which simultaneously allows for the transporting photo generated electrons from the GeSn absorber to Si multiplication layer. Spectral response showed the cutoff wavelength beyond 2.1 mu m at room temperature. Dark current and capacitance-voltage measurements indicated a punch-through …

Insect-Foundation: A Foundation Model And Large Multimodal Dataset For Vision-Language Insect Understanding, Thanh-Dat Truong, Hoang-Quan Nguyen, Xuan-Bac Nguyen, Ashley Dowling, Xin Li, Khoa Luu

Electrical Engineering and Computer Science Faculty Publications and Presentations

Multimodal conversational generative AI has shown impressive capabilities in various vision and language understanding through learning massive text-image data. However, current conversational models still lack knowledge about visual insects since they are often trained on the general knowledge of vision-language data. Meanwhile, understanding insects is a fundamental problem in precision agriculture, helping to promote sustainable development in agriculture. Therefore, this paper proposes a novel multimodal conversational model, Insect-LLaVA, to promote visual understanding in insect-domain knowledge. In particular, we first introduce a new large-scale Multimodal Insect Dataset with Visual Insect Instruction Data that enables the capability of learning the multimodal foundation …

Resilience Engineering Via Bifurcation And Ecological Network Analysis: Demonstrated In An Electric Power Case Study, Rogelio Gracia Otalvaro

Doctoral Dissertations and Master's Theses

Modern systems are increasingly complex, interconnected cyber-physical systems that combine digital controls with physical infrastructure. This integration, along with the constant introduction of new technologies and actors into the network, enables reliable operation but introduces vulnerabilities to unexpected and varied disruptions and cascading failures, making resilience a critical concern. Traditional risk management and resilience assessment methods often struggle with the nonlinearity and dynamic behavior of these systems. This dissertation proposes a novel approach combining Bifurcation Analysis (BA) and Ecological Network Analysis (ENA) to enhance the understanding and improvement of system resilience. BA, a mathematical method from dynamical systems theory, is …

Research On The Construction Of Domain Knowledge Graph For Assisted Decision Making And Its Scenario-Oriented Application, Hao Xu, Linlin Ge, Yan Zhang, Sanhong Deng

Journal of Scientific Information Research

[Purpose/significance] This research constructed a domain knowledge graph and its scenario-oriented application framework for decision support at four levels: the data foundation layer, the key technology layer, the domain knowledge graph construction layer, and the scenario-oriented application layer. This framework aims to provide systematic support for knowledge discovery.

[Method/process] Based on the construction of a domain knowledge graph and its scenario-oriented application framework for decision support, this research focuses on the improvement of models and performance evaluation for fine-grained entity and relationship extraction at the discourse level within texts. The optimal model is selected to construct a domain knowledge graph. …

Integrating Demand Forecasting And Deep Reinforcement Learning For Real-Time Electric Vehicle Charging Price Optimization, Monowar Mahmud, Tarek Abedin, Md Mahfuzur Rahman, Shamiul Ashraf Shoishob, Tiong Sieh Kiong, Mohammad Nur-E-Alam

Research outputs 2022 to 2026

The rapid growth of electric vehicles (EVs) demands efficient, grid-friendly charging systems. This study introduces a dynamic pricing framework combining short-term demand forecasting and deep reinforcement learning. Using Adaptive Charging Network (ACN) data, XGBoost predicts charging demand accurately (R² = 0.84, MAE = 0.45 kW). Compared to a uniform rate applied to all charging usage, set at 0.15 USD/kWh across all hours, with no adjustment for system demand conditions or time-of-day, the optimized strategy enhanced total daily revenue by 133 % and diminished load variance by 72.37 %. The PPO agent also surpassed traditional Time-of-Use and demand-based pricing models …

Design Of A Robust Adaptive Cascade Fractional-Order Proportional–Integral–Derivative Controller Enhanced By Reinforcement Learning Algorithm For Speed Regulation Of Brushless Dc Motor In Electric Vehicles, Seyyed Morteza Ghamari, Mehrdad Ghahramani, Daryoush Habibi, Asma Aziz

Research outputs 2022 to 2026

Brushless DC (BLDC) motors are commonly used in electric vehicles (EVs) because of their efficiency, small size and great torque-speed performance. These motors have a few benefits such as low maintenance, increased reliability and power density. Nevertheless, BLDC motors are highly nonlinear and their dynamics are very complicated, in particular, under changing load and supply conditions. The above features require the design of strong and adaptable control methods that can ensure performance over a broad spectrum of disturbances and uncertainties. In order to overcome these issues, this paper uses a Fractional-Order Proportional-Integral-Derivative (FOPID) controller that offers better control precision, better …

Construction, Optimization, And Characterization Of An Undergraduate Cold Cathode Table-Top Electron Accelerator For Radiation Physics Education, Caitlin Balmer, Mason Skeath, Mehran M. Zaini, Peter Zencak, Erin M. Craig

Journal of the Symposium of University Research and Creative Expression

Project Mentor(s): Mehran Zaini, PhD; Peter Zencak

Rising cancer cases spurred advancements in radiation therapy modalities, including electron accelerators. In this report, descriptive and diagnostic analysis was utilized to develop and characterize a functional, low energy cold cathode table-top electron accelerator for radiation physics experimentation in Central Washington University’s (CWU) undergraduate radiation lab. The device features a tungsten cathode (TC), brass anode, and copper Faraday Cup (FC) in a vacuum, enclosed by blue-tinted polyvinyl chloride (PVC). TC electron emission was facilitated by applied electric fields from input voltages of 1000 V to 5000 V. FC collected electron current in the …

Generative Ai: Another Chapter Of Human-Machine Communication, Seungahn Nah, Patric R. Spence

Human-Machine Communication

This editorial introduces a special issue of Human-Machine Communication that explores how generative AI reshapes the communicative relationship between humans and machines. It highlights emerging research on technology use, education, interpersonal dynamics, and trust in AI-generated content, emphasizing that generative AI’s significance lies not in novelty but in the social negotiations it provokes around meaning, authority, and credibility.

Bridging Materials And Energy Storage Mechanisms In Zn-I2 Batteries, Rong-Qi Liu, Wen-Shuo Shang, Jin-Tao Zhang

Journal of Electrochemistry

Zinc-iodine (Zn-I₂) batteries have emerged as a compelling candidate for large-scale energy storage, driven by the growing demand for safe, cost-effective, and sustainable alternatives to conventional systems. Benefiting from the inherent advantages of aqueous electrolytes and zinc metal anodes, including high ionic conductivity, low flammability, natural abundance, and high volumetric capacity, Zn-I₂ batteries offer significant potential for grid-level deployment. This review provides a comprehensive overview of recent progress in three critical domains: positive-electrode engineering, zinc anode stabilization, and in situ characterization methods. On the cathode side, anchoring iodine to conductive matrices effectively mitigates polyiodide shuttling and enhances …

Significantly Enhanced Oxygen Reduction Reaction Activity In Co-N-C Catalysts Through Synergistic Boron Doping, Chang Lan, Jing-Sen Bai, Xin Guan, Shuo Wang, Nan-Shu Zhang, Yu-Qing Cheng, Jin-Jing Tao, Yu-Yi Chu, Mei-Ling Xiao, Chang-Peng Liu, Wei Xing

Journal of Electrochemistry

The weak adsorption energy of oxygen-containing intermediates on Co center leads to a considerable performance disparity between Co-N-C and costly Pt benchmark in catalyzing oxygen reduction reaction (ORR). In this work, we strategically engineer the active site structure of Co-N-C via B substitution, which is accomplished by the pyrolysis of ammonium borate. During this process, the in-situ generated NH₃ gas plays a critical role in creating surface defects and boron atoms substituting nitrogen atoms in the carbon structure. The well-designed CoB₁N₃ active site endows Co with higher charge density and stronger adsorption energy toward oxygen species, …

Series Reports From Professor Wei’S Group Of Chongqing University: Advancements In Electrochemical Energy Conversions (2/4): Report 2: High-Performance Water Splitting Electrocatalysts, Ling Zhang, Wang-Yang Wu, Qiu-Yue Hu, Shi-Dan Yang, Li Li, Rui-Jin Liao, Zi-Dong Wei

Journal of Electrochemistry

The unavailability of high-performance and cost-effective electrocatalysts has impeded the large-scale deployment of alkaline water electrolyzers. Professor Zidong Wei’s group has focused on resolving critical challenges in industrial alkaline electrolysis, particularly elucidating hydrogen and oxygen evolution reaction (HER/OER) mechanisms while addressing the persistent activity-stability trade-off. This review summarizes their decade-long progress in developing advanced electrodes, analyzing the origins of sluggish alkaline HER kinetics and OER stability limitations. Professor Wei proposes a unifying “12345 Principle” as an optimization framework. For HER electrocatalysts, they have identified that metal/metal oxide interfaces create synergistic “chimney effect” and “local electric field enhancement effect”, enhancing selective …

Local Electric Fields Coupled With Cl− Fixation Strategy For Improving Seawater Oxygen Reduction Reaction Performance, Yu-Rong Liu, Miao Zhang, Yan-Hui Yu, Ya-Lin Liu, Jing Li, Xiao-Dong Shi, Zhen-Ye Kang, Dao-Xiong Wu, Peng Rao, Ying Liang, Xin-Long Tian

Journal of Electrochemistry

Development of robust electrocatalyst for oxygen reduction reaction (ORR) in a seawater electrolyte is the key to realize seawater electrolyte-based zinc-air batteries (SZABs). Herein, constructing a local electric field coupled with chloride ions (Cl⁻) fixation strategy in dual single-atom catalysts (DSACs) was proposed, and the resultant catalyst delivered considerable ORR performance in a seawater electrolyte, with a high half-wave potential (E_1/2) of 0.868 V and a good maximum power density (Pmax) of 182 mW·cm⁻² in the assembled SZABs, much higher than those of the Pt/C catalyst (E_1/2: 0.846 V; Pmax: 150 mW·cm^{−2 …}

Pendant Micro-Droplet Evaporation Fabricates Fiber-Optic Mof Gas Sensor In Seconds, Abhishek Prakash Hungund, Bohong Zhang, Narasimman Subramaniyam, Thomas Spudich, Ryan O'Malley, Farhan Mumtaz, Rex E. Gerald, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

The development of photonic-based gas sensors using metal–organic frameworks (MOFs) and other microporous solids is often a multistep, complex process, typically involving MOF synthesis, purification, and attachment of microcrystals to an optical fiber end face. This study introduces a one-step method that integrates MOF synthesis and sensor head fabrication directly onto the fiber end face, forming an extrinsic Fabry–Perot interferometer (EFPI) with a thin film of MOF microcrystals. The resulting film, only 3–10-μm-thick, enhances sensor response by enabling rapid gas detection within seconds. Utilizing a pendant micro-droplet evaporation technique, this method forms a microporous MOF layer in situ, allowing unreacted …

Magnesium Sulfate Attack Of Alite Paste And Mitigation By Surface Carbonation: Monitoring And Comparison Using Novel Portable Fiber-Optic Raman Probe, Bohong Zhang, Gao Deng, Hongyan Ma, Jie Huang

Electrical and Computer Engineering Faculty Research & Creative Works

Sulfate attack on cement matrix is still a "confused world" especially when magnesium sulfate (MgSO₄) is the sulfate source. Accurate assessment of sulfate attack is essential for evaluating the structural integrity and durability of concrete in relevant environments. This study presents a portable fiber-optic Raman probe approach with 125 μm spatial resolution, designed for depth-resolved sulfate ingress monitoring in tricalcium silicate (C₃S, Alite) pastes. The probe is also used to evaluate the effectiveness of surface carbonation in mitigating sulfate attack. The results demonstrate a strong correlation between sulfate penetration depth and Raman spectral intensity ratios of sulfate-related vibrational …

Improvement Of Lithium-Metal Electrode All-Solid-State Batteries Performance By Shot Peening And Magnetron Sputtering, Atsuro Okumura, Manabu Kodama

15th International Conference on Shot Peening

To enable fast charging in lithium-metal anode all-solid-state batteries, suppressing lithium dendrite formation at the solid electrolyte (SE) interface is critical. Increasing fracture toughness via shot peening (SP) and improving interfacial contact with Au sputtering can inhibit dendrite growth. However, conventional sputtering may reduce toughness due to localized thermal damage. This study investigated magnetron sputtering as a low-damage, plasma-based Au deposition method. SEs with and without SP were fabricated and coated via normal and magnetron sputtering. Critical current density (CCD) and fracture toughness were evaluated. Without SP, CCD improvement was limited regardless of sputtering method due to poor bonding. With …

Experimental Study Of Energy Conversion Efficiency Improvement Of Photovoltaic (Pv) Module Using Hybrid Cooling System, Olufisayo O. Babalola, Olatunji W. Olademeji, Joseph B. Samson

Al-Bahir

The elevation of the photovoltaic module operating temperature resulting in diminution of its energy conversion efficiency is one of the key limitations to its application. A decrease of power delivered performance by 0.4-0.5% per 1 rise over its Standard Test Condition (STC) accounted for the overheating of the PV module. This study evaluates the energy conversion efficiency improvement of a PV module using hybrid cooling system. An hourly segmented hybrid cooling system made up of aluminum fins as passive cooling segment and helical structured copper tubules for water conduction as active cooling segment helps to improve the energy conversion efficiency …

Robust Fault Detection And Classification In Power Systems Via Physics-Informed And Data-Driven Learning, Biswash Basnet, Varsha Sen

Graduate Student Scholarship

Electrical faults in power transmission systems can severely affect grid stability, equipment safety, and operational reliability. Traditional protection schemes, particularly distance relays, depend on apparent impedance computation that changes with error, creating a risk of misclassification. The results from relay overreach, underreach, or complete maloperation due to CT/PT saturation lead to developing problems in high impedance situations. These limitations highlight the need for adaptive, data-driven alternatives. This paper proposes an intelligent fault detection and classification model based on supervised machine learning techniques that overcome these challenges. The system’s robustness was validated under different training sizes and Gaussian noise levels, demonstrating …

An Event-Based Time-Incremented Snn Architecture Supporting Energy-Efficient Device Classification, David L. Weathers, Michael A. Temple, Brett J. Borghetti

Faculty Publications

Recent advances in Radio Frequency (RF)-based device classification have shown promise in enabling secure and efficient wireless communications. However, the energy efficiency and low-latency processing capabilities of neuromorphic computing have yet to be fully leveraged in this domain. This paper is a first step toward enabling an end-to-end neuromorphic system for RF device classification, specifically supporting development of a neuromorphic classifier that enforces temporal causality without requiring non-neuromorphic classifier pre-training. This Spiking Neural Network (SNN) classifier streamlines the development of an end-to-end neuromorphic device classification system, further expanding the energy efficiency gains of neuromorphic processing to the realm of RF …

Enhancement Of The Received Signal Strength In Smart Grid Communication Systems, Doaa Talaat Elsherbiny, Mona Mohamed Shokair Prof., Mohamed Shalaby, Salah Elden Khamis, Sameh A. Napoleon

Journal of Engineering Research

Smart grids are networks that contain intelligence from the generation stage to the distribution stage. These grids can actually have excellent power efficiency and automated control thanks to intelligence. Within smart grids, the communication mechanism is a crucial area of study. There should be a dependable communication system in the smart grid. A comprehensive mathematical model for a communication system within smart grids is derived in this work. Additionally, the simulation findings validate the mathematical model that was derived. Additionally, experiments are being conducted to apply polar convolutional parallel code PCPC to enhance the performance of the suggested communication system. …

Interference Management For Device-To-Device Communications In Heterogeneous Cellular Networks Using Deep Reinforcement Learningdevice-To-Device Communication; Mmwave Communication; Spectrum Resource Allocation; Deep Reinforcement Learning; Hcns, Suzan Mohamed Shukry

Journal of Engineering Research

Integrating Device-to-Device (D2D) communication into Heterogeneous Cellular Networks (HCNs) augmented with Millimeter Wave (mmWave) technology presents a compelling approach to fulfill the escalating demands for ultra-high data throughput in next-generation wireless systems. Although these advancements significantly improve data transmission efficiency and network scalability, the coexistence of D2D and cellular users within a shared spectral environment triggers considerable interference, complicating network coordination. To mitigate this, the interference scenario is modeled as a unified optimization task involving mode selection and resource allocation, aiming to enhance the aggregate system throughput while adhering to strict SINR constraints for both communication tiers. To tackle this …

Design And Performance Analysis Of A Frequency-Reconfigurable Rectangular Patch Antenna With Dual-Slot Defected Ground Structure For Gsm Band, Sara Abdelbaset, Ashraf Khalaf, Amr Hussien, Ahmed A. Kabeel

Journal of Engineering Research

In this paper, an innovative frequency-tunable rectangular patch antenna is presented, featuring interlaced circular and U-shaped narrow slots, combined with a rectangular-shaped defected ground structure (DGS). This design is specifically developed to produce radiation patterns similar to those of traditional dipole antennas. The integration of the rectangular DGS with three RF varactor diodes, along with two equivalent microstrip conductors, enables a tunable operating frequency band that ranges from 1.08 GHz to 1.7 GHz, achieving a total bandwidth of 0.62 GHz. The DC biasing network connected to the three RF varactor diodes is utilized to finely tune and adjust the resonance …

Developing Workflows For Passive Acoustic Detection Of Bedload Transport, Quinn Morgan

Dissertations and Theses

Bedload transport is defined as the amount of sediment, including gravel and rocks, traveling down stream. Monitoring bedload transport is important for river safety, hydrological studies and conservation efforts. Existing methods of directly measuring bedload transport (or bedload flux) involve lowering a collection device into a river and measuring the sediment collected; which can be expensive and time consuming. Hydroacoustic sensors, such as hydrophones, have had success tracking bedload flux remotely. This works by measuring the relatively high frequency of sediment impacts to map onto total bedload transported. No perfected method for detection of sediment generated noise (SGN) currently exists. …

Underwater Acoustic Integrated Sensing And Communication: A Spatio-Temporal Freshness For Intelligent Resource Prioritization, Ananya Hazarika, Mehdi Rahmati

Electrical and Computer Engineering Faculty Publications

Underwater acoustic communication faces significant challenges including limited bandwidth, high propagation delays, severe multipath fading, and stringent energy constraints. While integrated sensing and communication (ISAC) has shown promise in radio frequency systems, its adaptation to underwater environments remains challenging due to the unique acoustic channel characteristics and the inadequacy of traditional delay-based performance metrics that fail to capture the spatio-temporal value of information in dynamic underwater scenarios. This paper presents a comprehensive underwater ISAC framework centered on a novel Spatio-Temporal Information-Theoretic Freshness metric that fundamentally transforms resource allocation from delay minimization to value maximization. Unlike conventional approaches that treat all …

Detecting Electrical Submersible Pump (Esp) Failures And Estimating Run Life Using Artificial Neural Networks, Mostafa Ahmed Sobhy

Theses and Dissertations

Electric Submersible Pumps (ESPs) are one of the important artificial lift methods for sustaining production in mature and high-water-cut wells; but may suffer frequent failures due to mechanical, electrical, hydraulic, chemical, and operational failures. These failures can yield substantial deferred production and intervention costs. Plenty of ESP installations are fitted with downhole sensors. Yet, it is observed that the current industry practice underutilizes the wealth of available sensor and operational data and lacks standardized, explainable failure-type identification and classification.

In this thesis, a comprehensive Machine Learning (ML) and Deep Learning (DL) framework was introduced for ESPs that simultaneously estimates remaining …

Building Multimodal Knowledge Graphs: Automation For Enterprise Integration, Ritvik Garimella, Hong Yung Yip, Revathy Venkataramanan, Amit P. Sheth

Publications

As enterprises increasingly aim to incorporate artificial intelligence into their workflows to tackle complex, multimodal tasks, the demand for intelligent, robust, and trustworthy systems is paramount. While multimodal large language models offer initial capabilities for processing diverse data streams, their dependence on embedding-based representations limit their effectiveness in delivering semantically grounded explanations and reasoning, as well as qualities essential for enterprise-grade applications. Neurosymbolic approaches provide a promising alternative by enabling traceable, context-aware decision making. However, constructing enterprise-level multimodal knowledge graphs (MMKGs) that enable neurosymbolic approaches remains largely impractical. Although prior efforts have explored MMKG construction, they fall short in addressing …

Optimizing Beer Fermentation Through Intelligent Control, Azizbek Nodirbekovich Yusupbekov, Mirjalol Yusupov

Chemical Technology, Control and Management

This paper presents an intelligent control approach for optimizing the beer fermentation process using fuzzy logic and adaptive neuro-fuzzy inference systems. By incorporating multivariable inputs—temperature error and pH deviation—the proposed system effectively handles the nonlinear dynamics and biological variability inherent in fermentation. Simulation results demonstrate improved control accuracy, responsiveness, and robustness compared to conventional methods, making the approach suitable for integration in modern brewery automation systems.