Engineering Commons^™

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. …

Ai-Powered Accessibility Tracker For Inclusive Public Spaces, Yenny Ma, Kevin Beltran

College of Engineering Summer Undergraduate Research Program

This research project will develop and evaluate a smartphone-based, AI-powered system to crowdsource and analyze accessibility features and barriers in public spaces. Using computer vision and geospatial mapping, the system will identify and categorize issues such as uneven sidewalks, missing or inadequate curb ramps, damaged tactile paving, obstructive overhangs, and the absence of visual or auditory wayfinding cues. The overarching goal is to generate a dynamic, real-time accessibility map that empowers individuals with diverse mobility, sensory, and cognitive needs to navigate public spaces more safely and confidently. The project will integrate technologies and methods from applied machine learning, mobile computer …

Impulse, Fall 2025, Jill Fier, Sierra Brown, Jerome J. Lohr College Of Engineering

Impulse (Jerome J. Lohr College of Engineering Publication)

2 | Faculty News
3 | SDSU Vet Hua to Head Civil Engineering
6 | Ad Lunam — To the Moon
10 | Building A Safety Culture
11 | Department News
12 | Surface Mount Technology Kickoff
14 | Construction Management Student, Volleyball Player Building Success Piece by Piece
16 | Student Awards, Honors
18 | Student Competition Results
20 | NASA Win Opens Doors for New Product Development Order
22 | SDSU Engineering Student Tabbed as Noblereach Fellow
26 | Knabach Award Recipient from Apprentice Lineman to Company President
28 | Metzger Create Faculty Endowment for SDSU Engineering
30 …

Inductorless Cascaded Low-Power Dc-Dc Converter: Optimizing Performance Metrics Through Machine Learning Techniques, Ahmed Khaled, Sameh O. Abdellatif

Electrical Engineering

This study presents a groundbreaking methodology for optimizing the operational efficiency of a three-stage boost DC-DC cascaded converter through the application of a Random Forest(RF) machine learning algorithm. A novel figure of merit is meticulously formulated to quantitatively evaluate the converter’s performance, focusing on critical metrics such as power conversion efficiency, output DC ripple levels, and response time. The Random Forest model is trained on a comprehensive dataset encompassing a wide range of resistive and capacitive design parameters, with the figure of merit serving as the output indicator. Rigorous simulations and analyses demonstrate that the integration of LM741 operational amplifiers …

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 …

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. …

High Torque Density Dual-Stator Vernier Motors With Flux Concentrating Rotors, Esmaeil Mohammadi, Ali Mohammadi, Mohammad Amin Jalali Kondelaji, Pedram Asef, Ion G. Boldea, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

Axial Flux Permanent Magnet (AFPM) machines are increasingly being studied for low-speed, direct-drive applications due to their compact structure and high torque output. This study proposes two novel dual-stator AFPM vernier machine topologies: a spoke-type rotor configuration and a back-to-back Halbach array rotor. Both designs employ dual outer stators with 12 double-layer concentrated windings, and high-polarity rotor configurations to enhance flux concentration. A three-dimensional finite element model, which was previously validated by a laboratory prototype motor was utilized to evaluate the electromagnetic characteristics of the proposed topologies. These characteristics include torque density, airgap flux distribution, and harmonic content. Comparative results …

Multi-Phase Wireless Power Transfer With High Power Density Inductive Coils For Electric Drone Charging, Lucas A. Gastineau, Donovin D. Lewis, Omer Onar, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

Wireless charging of unmanned ground vehicles and aircraft has been proposed to increase charging reliability and security, allow for autonomous functionality, and either reduce battery size or increase continuous flight time. This paper proposes a three-phase Litz wire primary and a two-phase PCB secondary for high secondary-side power density considering misalignment tolerances, surface and volumetric power density, and coil sizing. Electromagnetic 3D finite element analysis (FEA) simulations are conducted to study variation in mutual inductance and coupling coefficient with different secondary coil sizes and number of turns, horizontal and vertical misalignment between the primary and secondary, and a combination of …

Nonlinear Design Scaling Of Electric Machines Based On Hybrid De And Meta-Modeling Application To Synchronous Motors With Combined Pm Stator And Reluctance Rotor Excitation, Oluwaseun A. Badewa, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

This paper presents an innovative method for nonlinear scaling of electric machines by integrating machine learning (ML)-based meta-modeling with a differential evolution (DE) algorithm. The technique is applied to high-performance combined-excitation synchronous electric motors which exhibit highly nonlinear characteristics, making performance scaling challenging. The proposed approach employs an ML meta-model trained on data obtained from finite element analysis (FEA), utilizing an experimentally validated model for nonlinear scaling and performance prediction at different power ratings. The accuracy of the meta-model in capturing the nonlinear relationships between design parameters and motor performance is first assessed using metrics such as R-squared (R2) and …

Multi Electric Machines With Series And Parallel Electromechanical Combinations For Aircraft, David R. Stewart, Donovin D. Lewis, Matin Vatani, Diego A. Lopez-Guerrero, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

High-performance electric propulsion systems require fault tolerant, power dense, electric machines capable of maintaining high efficiency across a dynamic range of operation. To address these inherently conflicting requirements, multi-motor architectures employing electromechanically coupled modular configurations have been proposed to enhance system efficiency, fault tolerance, and redundancy. This paper investigates four mechanically coupled configurations for a coreless axial flux permanent magnet (CAFPM) motor unit integrating series, parallel, and hybrid architectures with differential and gearbox coupling. Performance and optimal sizing for motors in each configuration are determined through 3D finite element analysis (FEA). To assess fault tolerance and system redundancy, Markov chain …

Design Optimization And Scaling Of Coreless Afpm Machines Using Hybrid Fea-Based Differential Evolution And Machine Learning, Matin Vatani, David R. Stewart, Donovin D. Lewis, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

This paper presents a machine learning (ML) based design framework for the fast and accurate optimization of coreless axial flux permanent magnet (AFPM) machines. Although the absence of magnetic cores eliminates material nonlinearity, the design process remains highly nonlinear due to the complex influence of geometric parameters. To overcome the computational challenges of finite element analysis (FEA)-based optimization, a series of multi-objective differential evolution (MODE) optimizations were conducted across various machine sizes at constant power output. The resulting design data was used to train an artificial neural network (ANN), enabling rapid prediction of machine performance without the need for repeated …

Coreless Axial Flux Permanent Magnet Machines With Concentrated Coils And Various Pole/Coil Combinations, Matin Vatani, Spencer M. Goode-Kulchar, John F. Eastham, Xiaoze Pei, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

This paper comprehensively analyzes coreless stator axial flux permanent magnet (AFPM) machines by investigating rotor magnetic fields, stator winding factors, and 2D/3D finite element analysis (FEA) simulations. The torque production theory in coreless AFPM machines is studied with detailed derivations for flux density and current density. The impact of rotor permanent magnet (PM) width is examined for both surface-mounted and Halbach array configurations, followed by a discussion of its influence on the air-gap harmonic spectrum. The effect of stator coil side width is analyzed through a detailed winding factor study across various pole-to-coil ratios and a discussion on the trade-off …

Voltage And Reactive Power Combined Control Of Utility Devices And Smart Inverters On A Distribution Grid With Solar Pv, Steven B. Poore, Rosemary E. Alden, Evan S. Jones, Thomas Morstyn, Aron Patrick, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

With adoption of distributed energy resources (DERs) expected in future grids, voltage regulation methods need to be reevaluated and improved to ensure their effectiveness under the high volatility of renewable generation. A multi-timescale cluster-based method is proposed to optimize and disperse operation of voltage controlling utility devices including capacitor banks (CBs) and load tap changers (LTCs) while allowing faster response time with customer-owned smart inverters (SIs) in-between switching operations. The proposed method is tested on a digital twin (DT) of a very large utility distribution grid with 2,018 nodes and 8.65MW peak load to evaluate its effectiveness in future grid …

Torque-Speed Characteristic Estimation Based On Gaussian Processes And Adaptive Sampling Strategy For Permanent Magnet Synchronous Machines, Marcelo D. Silva, Pedram Asef, Oluwaseun A. Badewa, Rosemary E. Alden, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

Internal Permanent Magnet Synchronous Machines (IPMs) are widely used and typically optimized to meet specific performance requirements. Parameters such as base speed, maximum torque, and maximum speed commonly define the torque- speed characteristic of a given design. This study introduces a novel machine learning approach for statistically estimating the torque-speed characteristics of IPMs using Gaussian Process Regression (GPR), which models predictions as random variables. By leveraging uncertainty quantification, the study explores sampling strategies that enable the construction of a high-precision meta-model with minimal error and uncertainty. The proposed adaptive sampling strategy, combined with GPR, accurately estimates torque-speed characteristics and associated …

Degradation Minimization Of Utility-Scale Li-Ion Bess Through Operational Optimization Employing An Equivalent Circuit Model, Kwabena A. Kyeremeh, Grant M. Fischer, Donovin D. Lewis, Aron Patrick, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

The increasing deployment of utility-scale battery energy storage systems (BESS) necessitates effective strategies for supporting grid services while minimizing degradation that may compromise system longevity. High charge/discharge rates (C-rate) and imbalanced operation of multi-unit BESS configurations may accelerate degradation. This paper proposes a degradation-aware operational optimization based on Model Predictive Control (MPC) for coordinating multiple BESS units under physical and operational constraints. The multi-objective optimization model imposes penalties on C-rate magnitude, operational state-of-charge (SoC) disparity, and battery internal resistance modeled using an equivalent circuit model. A case study conducted for a fleet of BESS units over a one-week load profile …

Csc36000 - Modern Distributed Computing Assignment, Saptarashmi Bandyopadhyay

Open Educational Resources

This assignment covers standard performance metrics for Distributed Systems and the basics of Multiprocessing for CSC36000 - Modern Distributed Computing at the City College of New York CUNY. It is an interactive coding assignment intended to be executed in a Python notebook.

Safety-Critical Formation Control Of Non-Holonomic Multi-Robot Systems In Communication-Limited Environments, Logan Beard

College of Engineering Summer Undergraduate Research Program

This project explores advanced methodologies for distributed planning, formation control, and coordination in heterogenous multi-agent robotic systems consisting of small mobile robots and nano unmanned aerial vehicles (UAVs). Leveraging decentralized control theories and optimization techniques, the research aims to develop robust algorithms for real-time collaborative task execution, efficient formation maintenance, and adaptive coordination strategies. Python and ROS will be used extensively to simulate, validate, and experimentally deploy distributed robotics algorithms. The student researcher significantly contributes to the theoretical and practical advancement of multi-agent system technologies.

Eggbeater Antenna Design 915 Mhz, Anshul Deshmukh

College of Engineering Summer Undergraduate Research Program

The Sal-E cube sat mission that is planned for launch in 2026 includes a 902-928 MHz receiving module called the Space Quacker Advanced Development (SQUAD) module. The SQUAD module uses the LoRa modulation format for unlicensed uplink to the satellite using the 902-928 MHz Industrial, Scientific, and Medical (ISM) band. The goal of the SQUAD module is to demonstrate the link robustness of the LoRa communication standard to a low earth orbit (LEO) satellite using this ISM band. To demonstrate this communication link, a 902-928 MHz uplink ground station needs to be established at Cal Poly. The goal of this …

Analysis And Evaluation Of Backtracking Settings For Energy Yield Optimization At The Cal Poly Solar Farm, Kayla Go-Oco

College of Engineering Summer Undergraduate Research Program

The Cal Poly Solar farm has been built as a single axis tracking facility with two different types of panels. Both conventional single cell solar panels and twin cell solar cells have been used in its construction. Twin Cell panels typically perform better than their conventional counterparts when shaded by other panels in the row in front of them in a fixed tilt system. However neither module performs well when even a small portion is shaded. This project will access the system API to access data for the field and process to determine the energy yield improvements that can be …

Building A Smart Transportation Network To Prevent Multi-Vehicle Collisions During Sudden Slowdowns, Leo Huang, Patrick Zhao

College of Engineering Summer Undergraduate Research Program

This proposed SURP project aims to design and evaluate a smart transportation network capable of preventing multiple-vehicle collisions due to sudden slowdowns in traffic. This project will simulate abrupt braking scenarios and implement adaptive vehicle-to-vehicle (V2V) communication protocols. By enhancing real-time awareness and responsiveness among vehicles, the system will reduce pileup risks and improve road safety.

Modeling An Energy Management System For Residential Hybrid Ac/Dc Power Networks, Theodor Buerchner, Giovanni Malone, Alex Maldonado

College of Engineering Summer Undergraduate Research Program

In pursuit of supporting the global efforts in reducing carbon footprint and reliance on fossil fuels, this project seeks to continue the development of a hybrid AC/DC house prototype at Cal Poly State University. To enhance the power flow to DC loads, a dedicated 48 V DC bus will be constructed to replace the impractical multiple DC buses in the previous system. This iteration will also add a key feature that enables users to monitor real-time AC and DC powers. Another new functionality will involve the provision of a mix of latching and non-latching relays to switch between sources, thus …

Building Pathways To Computer Science Careers For Latinx Students Through Multilingual Collaborative Block-Based Programming, Cis Garcia, Noemi Corona Calvario

College of Engineering Summer Undergraduate Research Program

The underrepresentation of Latinx students in computer science highlights the need for innovative and inclusive educational approaches. This project addresses challenges such as limited access to educational resources and the demand for multilingual learning tools by developing a co-located, collaborative, game-based programming environment. Designed for use on phones, tablets, and laptops, this tool supports English, Spanish, and Mixtec, facilitating broader engagement. By promoting peer collaboration and interactive learning, our approach challenges traditional notions of solitary programming and reinforces the idea that expertise is shared, fostering an inclusive and equitable learning environment.

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 …

Data Center Developments For Flexible Generation Dispatch, Advanced Infrastructure, And Ultra-Fast Digital Twins, Grant M. Fischer, Rosemary E. Alden, Donovin D. Lewis, Aron Patrick, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

The rapid advancement and widespread integration of artificial intelligence (AI) is driving demand for unprecedented deployment of power-intensive computational infrastructure, including multi-megawatt data centers with the potential for facilities with gigawatt-scale capacity in the near future. In this paper, load growth projections for the US are reviewed, and an example energy dispatch solution considering a mixed energy portfolio with flexible, renewable, distributed, and load-based generation is employed. The brief technology review included in the paper covers aspects of electric power, cooling, and computational infrastructures. The concept of a data center digital twin for transient load, grid interaction, and hybrid energy …

Spectral Modeling Of Electromagnetic Radiation From Power Converters, Regan A. Varner

Theses and Dissertations

Switching power converters are increasingly utilized in contemporary electronics. They are becoming more compact and operate at higher frequencies, which contributes to radiated electromagnetic interference (EMI). This EMI is important to quantify for electromagnetic compatibility of the converters with other nearby systems. In this thesis, analytical models have been developed for both ideal and non-ideal switching waveforms, employing piecewise functions to describe time-domain waveforms, with their corresponding frequency spectra derived and compared to empirical spectra. The results demonstrate that the comparisons with measured frequency data exhibit agreement with the analytical models up to approximately 25 MHz. Additionally, our analysis indicates …

Towards Robust Bmocz Schemes For Non-Coherent Wireless Communication, Anthony Joseph Perre

Theses and Dissertations

Non-coherent communication has emerged as a promising approach to address several challenges at the physical layer. A particular scheme called binary modulation on conjugate-reciprocal zeros (BMOCZ) uses the zeros (i.e, roots) of a polynomial to convey information bits. In this thesis, we strive to improve the reliability of BMOCZ in various scenarios. In particular, we utilize machine learning (ML) methods to determine the parameters for BMOCZ and improve upon the decoding performance. Moreover, we introduce a smooshed BMOCZ variant to combat typical impairments encountered within wireless communications, including timing offsets (TOs) for noncoherent orthogonal frequency division multiplexing (OFDM). Through numerical …

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 …

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 …

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 …

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 …