Electrical and Computer Engineering Commons^™

A Reliable And Efficient Detection Pipeline For Rodent Ultrasonic Vocalizations, Sabah Shahnoor Anis, Devin Mark Kellis, Kris Ford Kaigler, Marlene A. Wilson, Christian O'Reilly

Faculty Publications

Analyzing ultrasonic vocalizations (USVs) is crucial for understanding rodents' affective states and social behaviors, but the manual analysis is time-consuming and prone to errors. Automated USV detection systems have been developed to address these challenges. Yet, these systems often rely on machine learning and fail to generalize effectively to new datasets. To tackle these shortcomings, we introduce ContourUSV, an efficient automated system for detecting USVs from audio recordings. Our pipeline includes spectrogram generation, cleaning, pre-processing, contour detection, post-processing, and evaluation against manual annotations. To ensure robustness and reliability, we compared ContourUSV with three state-of-the-art systems using an existing open-access USV …

An Effective Genetic Algorithm For Mixed Precision, Wanyu Zhang, Yu Shang, Min Tsao, Yiwei Li, Xiaoyu Song

Electrical and Computer Engineering Faculty Publications and Presentations

The precision of floating-point numbers is a critical task in high-performance computing. Many scientific applications rely on floating-point arithmetic, but excessive precision can lead to unnecessary computational overhead. Reducing precision may introduce unacceptable errors. Addressing this trade-off is essential for optimizing performance while ensuring numerical accuracy. In this paper, we present a genetic algorithm-based approach for tuning the precision of floating-point computations. Our method leverages algorithmic differentiation and first-order Taylor series approximation to assess the impact of precision variations efficiently. We employ stochastic partitioning algorithms with multiple precision combinations that meet the error requirements. Moreover, we present a genetic heuristic …

From Adversarial Attacks To Robust Classifiers - A Study In Social Media Spam Detection - Black Box & White Box, Jonathan Jose Penaloza Rumie

Undergraduate Theses

Adversarial attacks pose a significant threat to the reliability of machine learning-based spam detection systems in social media. This undergraduate thesis, "From Adversarial Attacks to Robust Classifiers: A Study in Social Media Spam Detection – Black Box & White Box," systematically examines the impact of both black-box and white-box adversarial attacks on a range of spam classifiers, including Logistic Regression, Decision Trees, Random Forests, K-Nearest Neighbors, Bagging, Gradient Boosting, and Support Vector Machines. Leveraging a novel dataset derived from Twitter spam messages and enhanced with adversarial perturbations such as synonym replacement and character-level modifications, this study evaluates classifier performance under …

31 - Shaped Adversarial Patches, Huong Quach

Undergraduate Research Symposium

In recent years, the development and deployment of computer vision models have become widespread, with applications ranging from autonomous vehicles to security systems. Among these, object detection algorithms like YOLO are particularly significant due to their real-time performance and accuracy in identifying and localizing objects within an image. However, the robustness of these models is increasingly challenged by adversarial attacks, which are deliberate manipulations designed to deceive the model's predictions.

In this paper, I present an approach to advancing the deception capabilities of adversarial patches, specifically targeting YOLO-based person detectors. The objective is to design and implement shaped adversarial patches …

Cascaded Hydropower Plant Optimization Using A Bi-Level Mixed Integer Model, Ahmad Heidari, Rui Bo

Miners Solving for Tomorrow Research Conference

No abstract provided.

On The Robustness Of Adaptive Resonance Theory Neural Networks, Shane Cairns, Leonardo Enzo Brito Da Silva, Sasha Petrenko, Donald C. Wunsch

Miners Solving for Tomorrow Research Conference

No abstract provided.

Training Ai Models For Automatic Melanoma Detection, Victoria Wegley, Jason Hagerty, R. Joe Stanley

Miners Solving for Tomorrow Research Conference

No abstract provided.

Impulse, Spring 2025, Jill Fier, Micayla Standish, Jerome J. Lohr College Of Engineering

Impulse (Jerome J. Lohr College of Engineering Publication)

2 | Faculty, Staff Honored
4 | The State of Lithium
6 | 100 Years of Ag and Biosystems Engineering
8 | Major Milestones
12 | Distinguished Engineer Geldert-Murphy
14 | Distinguished Engineering Heitkamp
16 | By the Numbers
18 | Space Grant Consortium at SDSU
20 | Healthcare Systems Engineering Program
22 | Ridgway Three-Peat
23 | Aerospace Club Launches
24 | Power and Energy Scholarships
25 | More Take Part in Nasa Challenges
26 | Weber Recognized as Student Leader
30 | Wild Hare Racing Ready
32| Sebern’s Success as Sole Operator
34 | Meink: From Prairie to …

Design And Characterization Of Radiation-Hardened Gaas Hbt-Based Optical Transceivers, Collin Burt

Electrical and Computer Engineering ETDs

Optical transceivers are desirable for their galvanic isolation and natural immunity to electromagnetic interference compared with direct electrical connections. This is true in normal operating environments as well as radiation environments. This work realizes and characterizes GaAs HBT-based optical transceivers across temperature and a range of radiation environments including high dose rate, total ionizing dose, neutron, and heavy ion. The transceiver designs are discussed and specific environmental design considerations are highlighted. Final results for each environment are provided and compared with existing transceivers.

Smart Home Hvac Digital Twin Ml Meta-Model For Electric Power Distribution Systems With Solar Pv And Cta-2045 Controls, Rosemary E. Alden, Evan S. Jones, Steven B. Poore, Dan M. Ionel

Electrical and Computer Engineering Graduate Research

Building modeling, specifically heating, ventilation, and air conditioning (HVAC) load and equivalent energy storage calculations, represent a key focus for decarbonization of buildings and smart grid controls. In this paper, an ultra-fast one minute resolution Hybrid Machine Learning Model (HMLM) is proposed as part of a novel contribution in the field of residential physics-based smart home surrogate modeling. Emulation of white box models, or digital twins, with editable parameters through machine learning (ML) meta-modeling serves as an alternative to wide-spread experimental big data collection. The HMLM employs combined k-means clustering with multiple linear regression (MLR) to emulate minutely HVAC power …

Antenna Multi-Band Enhancement By Employing Negative Impedance Converters, Kevin Pepin

Doctoral Dissertations and Master's Theses

This thesis presents the implementation of a Negative Impedance Converter (NIC) integrated with a planar monopole antenna, originally centered at 2.9 GHz, to create a multi-band network. The network exhibits resonances at frequencies of 0.9 GHz, 1.5 GHz and 1.9 GHz. The work highlights the design process of pairing a NIC with a capacitive, single-band antenna, addressing key challenges such as overcoming the limitations associated with low self-resonant frequency (SRF) inductors. The design process included the use of measurement-based component models and radio-frequency simulations using Keysight Advanced Design Systems (ADS). The developed NIC emulates a -0.99 pF capacitor using two …

Addressing Reliability Challenges In Generation Capacity Planning Under High Penetration Of Renewable Energy Resources And Storage Solutions: A Review, Taraneh Ghanbarzadeh, Daryoush Habibi, Asma Aziz

Research outputs 2022 to 2026

This study offers a comprehensive survey of generation capacity planning from a reliability perspective, considering the influence of renewable resources and energy storage systems. The uncertainties surrounding intermittent resources raise concerns about power system reliability. This has led to the proposal of new assessment and optimization frameworks in generation expansion planning, as evidenced by numerous reports and original research works. In addition to the challenge concerning power system reliability, phenomena such as Dunkelflaute require thorough consideration when planning problems. Furthermore, energy storage systems have emerged as a promising solution to address these challenges. Consequently, numerous researchers have worked on methods …

Clinical Use Of Sit2stand Ai Application For Kinematic Analysis In Prosthesis Users., Samerial Brown

Posters - 2025

Biomechanical analysis is a tool to evaluate prosthetic and orthotic patient's. These tools offer the clinician capability of understanding the mechanism of injury, gait deviation or prosthesis problem. Video based analysis require expensive hardware, software, and training which sometimes costs $40-100,000.

The recent advent of artificial intelligence (AI) has opened up the possibility of acquiring high speed human motion video analysis using low-cost hardware and open-source machine learning algorithms. Still, free assessments like the Sit2Stand test is a current clinical outcome measure which assesses ability of a patient to stand and sit as fast as possible 5x. The faster the …

Long-Term Techno-Economic Analysis Considering System Strength And Reliability Shortfalls Of Electric Vehicle-To-Grid-Systems Installations Integrated With Renewable Energy Generators Using Hybrid Gru-Classical Optimization Method, Md Ohirul Qays, Iftekhar Ahmad, Daryoush Habibi, Paul Moses

Research outputs 2022 to 2026

Higher penetration of Electric Vehicle-to-Grid charging stations (EV2GCSs) in power grids integrated with renewable energy generators (REGs) result in system-instabilities raising the risk of blackout issues. Although techno-economic feasibility of EV2GCSs has been identified within the literature addressing power outage and financial concerns as a contemporary solution, no long-term techno-economic analysis has been investigated considering system strength and reliability shortfalls before the EV2GCSs installation. Therefore, this research aims to evaluate the long-term (2025–2045) techno-economic analysis of EV2GCSs into REGs-integrated grid systems by accounting for system strength and reliability factors. A novel optimization framework is developed, combining Gated Recurrent Units (GRU) …

Amplification In Mems Rlc Circuits For Enhanced Sensitivity And Mems Applications, Mutaz Mohd Hamdi Al Fayad

Durham School of Architectural Engineering and Construction: Dissertations, Theses, and Student Research

Micro-electro-mechanical systems (MEMS) have garnered significant attention due to their unique characteristics, including small size, high sensitivity, and cost-effective mass production. While electrostatic actuation offers low power consumption, it requires high voltage to move the MEMS structure, posing a challenge for applications such as RF switches and MEMS resonator-based sensors. Reducing the required input voltage for electrostatic MEMS remains a key research focus.

This thesis investigates voltage amplification in electrostatic MEMS through integration with resonant RLC circuits. By leveraging resonance, optimized configurations are designed to maximize voltage gain and enhance the signal-to-noise ratio. Through theoretical modeling and simulations using MATLAB …

Analytical Dispatch Strategies For Pumped Storage Hydro: A Conditional Dynamic Programming Approach To Discontinuous Multi-Period Optimization Problems, Jian Liu, Jianwen Zhang, Zaiwu Gong, Donald C. Wunsch, Rui Bo

Electrical and Computer Engineering Faculty Research & Creative Works

The increasing integration of renewable energy sources like wind and solar poses significant challenges to secure and stable grid operation. Energy storage systems, particularly pumped storage hydro (PSH), play a crucial role in balancing power supply and demand. Traditional analytical studies of PSH economic dispatch problems often assume zero lower bounds for generating and pumping rates to simplify analysis and derive analytical solutions for multi-period optimization problems. However, the inherent mechanical design constraints of PSH require non-zero minimum flow rates for efficient operation. We analyze two scenarios, merchants having PSH only and merchants having both PSH and wind farms. In …

Transforming Optical Vernier Effect Into Coherent Microwave Interference Towards Highly Sensitive Optical Fiber Sensing, Ruimin Jie, Jie Huang, Chen Zhu

Electrical and Computer Engineering Faculty Research & Creative Works

The optical Vernier effect has garnered significant research attention and found widespread applications in enhancing the measurement sensitivity of optical fiber interferometric sensors. Typically, Vernier sensor interrogation involves measuring its optical spectrum across a wide wavelength range using a high-precision spectrometer. This process is further complicated by the intricate signal processing required for accurately extracting the Vernier envelope, which can inadvertently introduce errors that compromise sensing performance. In this work, we introduce a novel approach to interrogating Vernier sensors based on a coherent microwave interference-assisted measurement technique. Instead of measuring the optical spectrum, we acquire the frequency response of the …

Streamer Discharge Simulation For Plasma-Assisted Combustion, Stuart Jairo Reyes

Electrical & Computer Engineering Theses & Dissertations

A common and successful method to achieve atmospheric pressure fuel-air plasma-assisted combustion is through repetitive ns pulsed discharges and dielectric-barrier discharge. The transient phase in these discharges is dominated by transport influenced by strong space charges produced by ionization fronts, this can be best represented by the streamer model. The function of non-thermal plasma in these discharges is to excite the species in the fuel-air mixture to produce radicals which accelerate the chemical conversion reactions which directly lead to temperature rise, ultimately culminating in ignition. Therefore, the characterization of the streamer and its energy partitioning is essential to developing a …

Enhancing Iot Security Using Lightweight Machine Learning Algorithms: A Comprehensive Approach Using Ensemble Learning, Feature Selection, And Federated Transfer Learning, Khawlah Harahsheh

Electrical & Computer Engineering Theses & Dissertations

The rapid expansion of the Internet of Things (IoT) has introduced significant security vulnerabilities due to the resource-constrained nature of IoT devices and their exposure to cyber threats. Traditional security solutions are often infeasible due to the high computational and storage demands they impose. This dissertation presents a lightweight, AI-driven security framework that enhances IoT network resilience by integrating feature selection, ensemble learning, and federated transfer learning while maintaining data privacy and minimizing computational overhead.

The proposed framework consists of three primary components: Feature Selection for Intrusion Detection, which optimizes performance by reducing redundant data and improving detection accuracy with …

Development Of Laser-Induced Graphene-Based Bioanode Incorporating Thylakoid For Harvesting Energy, Amit Sarode

Biomedical Engineering Theses & Dissertations

Thylakoid-Based Biofuel Cells (TBFCs) present significant potential as renewable power sources; however, their development is impeded by challenges including delicate thylakoid membranes, limited electron transport efficiency, stability and dependence on expensive mediators. This study aimed to address these challenges by fabricating a novel photo-driven bioanode through the integration of Laser-Induced Graphene (LIG), Nb₄C₃T_x MXene, and thylakoid membranes. The fabrication process involved laser engraving to generate porous LIG electrodes, followed by MXene drop casting and thylakoid immobilization to enhance electrochemical performance and surface area. Morphological characterization supported that MXene incorporation increased active sites and surface roughness, …

Understanding The Breadth And Impact Of The Ias [Presidents Message], Ayman El-Refaie

Electrical and Computer Engineering Faculty Research and Publications

No abstract provided.

Heat-Pipe-Based Thermal Management System Design For A 250 Kw Gan-Based Integrated Modular Motor Drive, Seyed Iman Hosseini Sabzevari, Salar Koushan, Armin Ebrahimian, Towhid Islam Chowdhury, Nathan Weise, Ayman El-Refaie

Electrical and Computer Engineering Faculty Research and Publications

Integrated modular motor drive (IMMD) is an effective approach for realizing high-efficiency, high-power-density, and fault-tolerant electric machines. However, designing an efficient thermal management system (TMS) for the motor drive becomes a challenge, particularly due to space constraints. This article presents the design of a TMS based on 3-mm heat pipes for a 250-kW IMMD intended for aviation applications. The power electronics module is simulated using PLECS software where an electrothermal analysis is conducted. A simplified thermal resistance model of the system is developed to estimate the die junction temperature of gallium nitride (GaN) semiconductors. The performance of the proposed TMS …

Swvl: A Custom Ai-Powered Face Tracking Camera Gimbal, Alexander J. Anderson-Mcleod, Jakub Jerzmanowski, Michael Laitarovsky, Trevor Allison, Jagger Tanner

Senior Theses

In response to the growing demand for smarter, more responsive face tracking cameras in the post-pandemic world, our team designed SWVL, a custom AI-powered face tracking gimbal meant to address the limitations commonly encountered by the commercial models currently on the market. These commercially available gimbals come with several issues, such as frequently losing track of the person in the frame and requiring manual resets, which we sought to fix with our implementation. We designed a system with fully custom hardware and software including a 3D printed dual-axis camera gimbal driven by stepper motors, a control PCB based around an …

Design And Fabrication Of Compact Mimo Array Antenna With Tapered Feed Line For 5g Applications, F. Taher, Mohamed Fathy Abo Sree, Hesham A. Mohamed, Hussein Hamed Ghouz, Sarah Yehia Abdel Fatah

All Works

This paper presents the design, fabrication, measurement, and evaluation of two multiple input multiple output antenna elements (MIMOs) optimized for performance in the 5G frequency band. The MIMO antennas are integrated onto Roger/Druoid 5880 substrates with a dielectric constant of 2.2, housed within a compact volume measuring 20×24.14×0.79mm3. Initially, a thorough parametric study is conducted using the CST Electromagnetic (EM) Simulator to analyze and optimize the antenna's performance according to 5G application requirements. Subsequently, in the antenna design phase, calculations are made for the envelope correlation coefficient (ECC > 0.01dB) and diversity gain (DG > 9.9dB), yielding favorable outcomes. Fabrication and measurement …

Seabed Characterization Using Ambient Sound For A Range-Dependent Track In The New England Mud Patcha, Martin Siderius, Stan E. Dosso, Brian Granger

Electrical and Computer Engineering Faculty Publications and Presentations

Wind-generated, ocean ambient sound data were used to characterize seabed properties along a track in the New England Mud Patch. A 15-m vertical array, consisting of 16 hydrophones, collected ambient sound data across the 50–5000 Hz frequency band. The array drifted for 1 h, covering a 1.7 km track. Seabed characterization was performed using beamforming techniques, which limited the analysis to the 400–700 Hz band. Passive fathometer processing was applied to estimate the water–seabed interface and sub-bottom layering. Additionally, the data were used to estimate the power reflection coefficient, which was then used as input for a trans-dimensional geoacoustic inversion. …

Vessel Trajectory Prediction With Recurrent Neural Networks: An Evaluation Of Datasets, Features, And Architectures, Isaac Slaughter, Jagir Laxmichand Charla, Martin Siderius, John Lipor

Electrical and Computer Engineering Faculty Publications and Presentations

Maritime situational awareness tasks such as port management, collision avoidance, and search-and-rescue missions rely on accurate knowledge of vessel locations. The availability of historical vessel trajectory data through the Automatic Identification System (AIS) has enabled the development of prediction methods, with a recent focus on trajectory prediction via recurrent neural networks (RNNs) and other deep learning architectures. While these methods have shown promising performance benefits over kinematic and clustering-based models, comparing among RNN-based models remains difficult due to variations in evaluation datasets, region sizes, vessel types, and numerous other design choices. As a result, it is not clear whether recent …

On The Provenance Of Software Systems: Automating Software Traceability With Knowledge Graph And Large Language Model Synergy, Tyler Procko

Doctoral Dissertations and Master's Theses

The present dissertation delineates a system that enables those engaged in software development to automatically generate and maintain project life cycle provenance. All projects are implemented and made manifest with the development of artifacts, e.g., papers, code files, etc. Tools exist to accelerate artifact creation, but little focus is paid to the processes that produce them. In terms of Ontology, or, from Ancient Greek, the study of being, the two most basic entities in reality are Continuant and Occurrent, or, roughly, “Artifact” and “Process”. This dissertation posits that for any created artifact, its process of creation, i.e., its life …

Developing Fixed-Bias Langmuir Probes For Multi-Point Constellation Deployments, Henry Carter Valentine

Doctoral Dissertations and Master's Theses

Since their initial development in the early 20th century, electrostatic Langmuir probes have proved invaluable in terrestrial and interplanetary ionospheric sounding applications. When deployed aboard rocket and satellite platforms, these probes yield high-cadence, in-situ measurements of key plasma parameters such as electron density, ion density, and electron temperature. Furthermore, the efficacy of Langmuir probes in characterizing the three-dimensional structure and dynamics of ionospheric plasmas can be augmented by the technique of multi-payload deployments. In this work, we discuss the design, development, and analysis of fixed-bias Langmuir probes constructed for two multi-point science campaigns: the Mars-bound, dual-satellite Escape and Plasma Acceleration …

Smart Traffic Intersections: Leveraging Isac And Millimeter-Waves For Advanced Vehicle Platooning, Mohammed Risal Thadathil

Theses

The rapid advancement of self-driving cars is reshaping the transportation industry and accelerating the development of smart cities. Vehicle platooning, a key capability of autonomous vehicles, has the potential to enhance traffic efficiency, reduce congestion, and improve safety at intersections. However, maintaining platoon cohesion, minimizing latency, and optimizing traffic signal interactions remain significant challenges. This study addresses these issues by leveraging Integrated Sensing and Communication (ISAC) technology with millimeter-Waves (mmWaves) signals to optimize platooning performance at traffic signal intersections.

The research identifies gaps in existing Vehicle-to-Everything (V2X) communication frameworks, particularly in managing platoon movements in urban traffic intersections. To bridge …

Practical Experiments Conducted On Determining The Air Velocity At The Inlet And Outlet Of The Intensive Separator And Processing Of The Obtained Results, Ilxom Zapirovich Abbazov, Kudrat Sherzadovich Khodjayev, Bobir Nabijon Ugli Sharopov, Rashid Kaldybaev

Technical science and innovation

In the primary processing of cotton raw materials, the means of storing cotton, transporting it from the gins to the departments, and transferring the product between departments are pneumatic conveying devices. Separators are devices for separating cotton from the air and cotton mixture in such pneumatic conveying devices. Separators are used in two types of conditions, namely, in a mobile or stationary state. The device for separating cotton from air consists of three main working parts: they are the parts for feeding cotton into the working chamber, separating it from the air flow, and removing cotton and air. These parts, …