Computer Engineering Commons^™

Applying Large Language Models For Surgical Case Length Prediction, Adhitya Ramamurthi, Bhabishya Neupane, Priya Deshpande, Ryan Hanson, Srujan Vegesna, Deborah Cray, Bradley H. Crotty, Melek Somai, Kellie R. Brown, Sachin S. Pawar, Bradley Taylor, Anai N. Kothari

Electrical and Computer Engineering Faculty Research and Publications

Importance Accurate prediction of surgical case duration is critical for operating room (OR) management, as inefficient scheduling can lead to reduced patient and surgeon satisfaction while incurring considerable financial costs.

Objective To evaluate the feasibility and accuracy of large language models (LLMs) in predicting surgical case length using unstructured clinical data compared to existing estimation methods.

Design, Setting, and Participants This was a retrospective study analyzing elective surgical cases performed between January 2017 and December 2023 at a single academic medical center and affiliated community hospital ORs. Analysis included 125493 eligible surgical cases, with 1950 used for LLM fine-tuning and …

Network Intelligence For Next-Generation Wireless Networks: Advancing Distribution And Coordination, Yonatan Melese Worku

Electrical and Computer Engineering ETDs

Next-generation wireless networks, encompassing 6G and beyond, face rigorous demands for ultra-low latency, ubiquitous connectivity, exceptionally high data rates, and robust security, necessitating innovative approaches to resource optimization and network protection. This dissertation proposes a pioneering framework that synergizes advanced methodologies—deep reinforcement learning, deep learning, blockchain, and multi-agent systems—to address these challenges. Distributed architectures, underpinned by AI-driven multi-agent systems, form the backbone of this framework, enabling seamless integration and intelligent orchestration across diverse domains. The research advances IoT-based systems leveraging machine learning for resource efficiency in healthcare applications, develops reinforcement learning-driven frameworks to optimize energy and coverage for Unmanned Aerial …

Optimization And Model Averaging Of Histogram-Based Place Cell Firing Rate Maps Using The Point Process Framework, Murat Okatan

Turkish Journal of Electrical Engineering and Computer Sciences

The firing rate of hippocampal place cells depends on the spatial position of the organism in an environment. This position dependence is often quantified by constructing spike-in-location and time-in-location histograms, the ratio of which yields a firing rate map. The purpose of this study is to present a new method for optimizing the spatial resolution of histogram-based firing rate maps. It is pointed out that histogram-based firing rate maps are conditional intensity functions of inhomogeneous Poisson process models of neural spike trains, and, as such, they can be optimized through model selection within the point process framework. The point process …

Isar Imaging Of Drone Swarms At 77 Ghz, Remzi̇ye Büşra Çoruk, Ali̇ Kara, Eli̇f Aydin

Turkish Journal of Electrical Engineering and Computer Sciences

The proliferation of easily available, internet-purchased drones, coupled with the emergence of coordinated drone swarms, poses a significant security threat for airspace. Detecting these swarms is crucial to prevent potential accidents, criminal misuse, and airspace disruptions. This paper proposes a novel inverse synthetic aperture radar (ISAR) imaging technique for high-resolution reconstruction of drone swarms at 77 GHz millimeter wave (mmWave) frequency, offering a valuable tool for military and defense anti-drone systems. The key parameters affecting down-range and cross-range resolution (0.05 m), ultimately enabling the generation of detailed ISAR images are discussed. Here, we create diverse scenarios encompassing various swarm formations, …

Adaptive Backstepping Control With Real-Time Fuzzy Logic Parameter Selection Of A Field- Oriented Control-Based Permanent Magnet Synchronous Motor Driver, Fati̇h Bayir, Erkan Zergeroğlu

Turkish Journal of Electrical Engineering and Computer Sciences

This study proposes an adaptive backstepping control approach integrated with a real-time fuzzy logic parameter selection algorithm to enhance the robustness and stability of a permanent magnet synchronous motor (PMSM) controller under parametric uncertainties and external disturbances. Although backstepping control performs well under varying disturbances, it must be supported by an adaptive control algorithm to effectively handle both variable disturbances and parameter uncertainties. Moreover, because the fixed parameters of the adaptive backstepping controller limit the dynamic performance of the velocity tracking loop, this study incorporates fuzzy logic control—a soft computing algorithm capable of real-time parameter adjustment—to achieve more robust outcomes. …

Excitation Of Synchronous Machine By Contactless Power Transfer - Review From The Perspective Of Electric Vehicles, Erhan Tuncel, Emi̇n Yildiriz

Turkish Journal of Electrical Engineering and Computer Sciences

Electrically excited synchronous machines (EESMs) are one of the best choices for propulsion motor appli cation in electric vehicles (EVs) due to their wide torque-speed characteristics. Moreover, the air gap flux density can be easily controlled by varying the excitation current. Despite these advantages, it is difficult to transfer the current required by the rotating excitation winding into the motor under conventional methods, so it is not widely used in EVs. In this study, the emerging literature on contactless power transfer methods is reviewed for applicability to an EESM that can operate as an EV propulsion motor. Design criteria such …

Rescon: Residual Consistency For Real-World Super-Resolution, Erdi̇ Saritaş, Hazim Kemal Ekenel

Turkish Journal of Electrical Engineering and Computer Sciences

Real-world super-resolution is a highly challenging problem in the field of computer vision. Besides enhancing image resolution and improving visual details, information loss due to complex real-world degradations is desired to be restored. One of the primary hardness of this problem is finding sufficiently large paired datasets for training. Researchers have developed techniques that generate synthetic low-resolution pairs using high-resolution images with a generative adversarial network-based degradation generator to address this issue. In these approaches, the degradation generator is trained by utilizing real-world low-resolution images as the target domain, generating a degraded low-resolution counterpart of the high-resolution input. However, in …

Magnetic Macro Pendulum Design And Real-Time Control Application: Simulation And Experiment, Hüseyi̇n Yildiz, Serdar Yilmaz, Yasemi̇n Poyraz Koçak, Erol Uzal

Turkish Journal of Electrical Engineering and Computer Sciences

Over the last decade, the number of studies in the field of magnetic micro robots has significantly increased due to expectations of performing microsurgery, drug delivery, and similar medical procedures. Magnetic micro robots have advantages over other types of micro robots in terms of having independent designs for rotor and stator structures. Magnetic micro robots can be controlled by magnetic fields and can be programmed to move in certain directions and to perform various functions. This paper implements the computer-aided real-time control of a single-arm micro-pendulum structure to (eventually) perform cell manipulation tasks. The mechanical structure, mathematical model, control circuit …

Explicit Bounds And Parallel Algorithms For Counting Multiply Gleeful Numbers, Sara Moore, Jonathan P. Sorenson

Computer Science and Software Engineering

Let k ≥ 1 be an integer. A positive integer n is k-\textit{gleeful} if n can be represented as the sum of kth powers of consecutive primes. For example, 35=2³+3³ is a 3-gleeful number, and 195=5²+7²+11² is 2-gleeful. In this paper, we present some new results on k-gleeful numbers for k > 1.

First, we extend previous analytical work. For given values of x and k, we give explicit upper and lower bounds on the number of k-gleeful representations of integers n ≤ x.

Second, we describe and analyze two new, efficient parallel …

Algorithms For Combined Regular Synthesis Of Controller Parameters In Control Systems For Dynamic Objects, Khusan Zakirovich Igamberdiev, Latafat Abbas Gizi Gardashova, Yulduz Mukhtarkhodjayevna Abdurakhmanova, Uktam Farkhodovich Mamirov

Technical science and innovation

Nonlinear control system that includes m-dimensional input control signal and extended (n+s) - dimensional state vector, the last s components of which form a vector of unknown parameters θ satisfying a general difference equation is being considered. The quality criterion is determined by the loss function. The optimal control must satisfy the Bellman equation with respect to the optimal loss function. To be defined an approximate solution that preserves an active use of information. For this purpose, the system is linearized in accordance to the nominal trajectory. This problem is seen as incorrectly stated. The values of the preliminary data …

Statistical Monitoring Of Hard Faults In Digital Systems, Dany Akshay Deep Isukapalli

Electrical Engineering Theses and Dissertations

Achieving a high test coverage is crucial for helping to ensure that integrated circuits are working correctly and are non-defective. Although scan-based structural tests are used throughout the industry, high-level functional tests may be needed to detect some defects— especially those that are environmentally sensitive. Unfortunately, the character of functional test makes it difficult to obtain high coverage, and it is even hard to estimate coverage because fault simulation times of large circuits are long. As a result, some method is required for predicting the ability of a functional test that has not been fault simulated to detect defects. In …

Performance Enhancement In Rewound Industrial Retrofit Solutions For Five- And Six-Phase Permanent Magnet Assisted Synchronous Reluctance Machines, Kotb B. Tawfiq, Ayman M. El-Refaie, Peter Sergeant, Hatem Zeineldin, Ahmed Al-Durra, Ehab F. El-Sadaany

Electrical and Computer Engineering Faculty Research and Publications

This paper investigates upgrading aging three-phase Permanent Magnet Assisted Synchronous Reluctance Machines (PMaSynRMs) into multiphase configurations—specifically six- and five-phase windings—without modifying the existing stator or rotor laminations. This retrofit supports circular economic principles by extending machine life and reducing material waste and cost. Four configurations are examined: the original three-phase winding, asymmetrical six-phase winding, symmetrical six-phase winding, and five-phase winding. The feasibility of rewinding existing three-phase stators is explored for different slot/pole combinations. Balanced rewound five-phase windings are feasible only when the stator's slot/pole ratio is greater than or equal to 9. Both symmetrical and asymmetrical rewound six-phase windings are …

Analysis Of Vision Transformers And Domain Adaptation In Long-Range Facial Recognition, Zachary Michael Swanson

Department of Electrical and Computer Engineering: Dissertations, Theses, and Student Research

Atmospheric turbulence presents a significant barrier to long-range facial recognition, introducing severe geometric distortions and blur that degrade image quality. This thesis investigates deep learning approaches for mitigating these effects, with a focus on transformer based architectures and domain adaptation strategies.

An in-depth benchmarking study was performed using convolutional neural networks (CNNs) and vision transformers (ViTs) on the Husker BRIAR Research Collection from up to 500m (HBRC-500) face dataset. The results demonstrated that vision transformers, particularly hierarchical vision transformers like the shifted-window (Swin) transformer, outperform CNN-based models at long distances due to their ability to model global spatial relationships and …

The Importance Of The Analog-To-Digital Converter In The Measurement System, Aliev Ravshan, Anvar Djalilov

Chemical Technology, Control and Management

At the moment, many scientific researches are being conducted all over the world on the economical use of water and energy resources. Most of the scientific research works are aimed at improving measurement techniques and technologies, that is, increasing their accuracy. With this in mind, a high-precision analog-to-digital converter due to its unique metrological and technical characteristics was studied in this research paper. As a result of the study, it became clear that the use of a small-sized, high-precision sigma-delta analog-to-digital converter in modern measuring technology has a positive effect on its accurate and efficient operation.

Multi-Modal Covid-19 Detection Using Cough Sounds And Medical Information With Attention-Enhanced Deep Learning, Mohamed Talaat Saidahmed, Reda Elbasiony, Marwa Reda Bastwesy, Asmaa Aly Hagar

Journal of Engineering Research

The COVID-19 pandemic has highlighted the need for fast, non-invasive, and cost-effective diagnostic tools. Cough sounds, as a prominent symptom of respiratory diseases, present a promising modality for automated COVID-19 detection. In this study, we propose a novel multi-modal deep learning framework for COVID-19 detection that leverages cough sounds and patient-specific medical information. Our approach extracts two types of acoustic features—Mel-Frequency Cepstral Coefficients (MFCCs) and Mel spectrograms—and integrates them with clinical metadata to improve diagnostic ac-curacy. The MFCC branch employs 1D convolutional layers followed by Efficient Channel Attention mechanism. The Mel spectrogram branch utilizes ResNet-50 combined with ECA to capture …

Dynamic Resource Allocation For Wireless Networks And Radar Systems Via Deep Reinforcement Learning, Ziyang Lu

Dissertations - ALL

The rapid advancement of wireless communication technologies and the proliferation of smart devices have led to increasingly complex and dynamic network environments. These developments have posed significant challenges to traditional radio resource management (RRM) techniques, which often struggle with scalability, adaptability, and real-time decision-making. In response to these limitations, this dissertation explores the application of advanced machine learning, particularly deep reinforcement learning (DRL), to develop intelligent, adaptive, and data-efficient solutions for resource management in wireless networks and radar systems. We begin by addressing joint channel access and power control in wireless interference networks using centralized, distributed, and federated multi-agent DRL …

From Assembly Lines To The Open Road: Predicting Rare Events In Autonomous Systems, Ruwan Wickramarachchi

Publications

In the age of embodied AI and smart automation, autonomous agents are increasingly deployed in high-stakes, real-world environments. Ensuring the robustness and resilience of these systems in the face of rare but critical failure events is essential for their safe and reliable operation. Accurate forecasting of such rare events is particularly crucial, as a single overlooked anomaly can lead to catastrophic consequences. In manufacturing, for instance, unplanned downtime due to rare failures costs industries over \$50 billion annually, with sectors like automotive losing more than \$2 million per hour—even with preventive maintenance systems in place.

However, the extreme rarity and …

How’S It Growing? Tools For Observing Snow And Sea Ice In A Changing Arctic Ocean, Ian Alexander Raphael

Dartmouth College Ph.D Dissertations

September Arctic sea ice extent has diminished by roughly 50% in the 45 years since satellite observations began. The Arctic Ocean may experience ice-free summers within the next decade, with implications for habitat, resource extraction, geopolitics, and local and global climate change. To predict how Arctic sea ice will change in the future, we need to understand its behavior in the present. In situ sea ice mass balance measurements (snow accumulation, ice growth, snow and ice surface melt, and bottom melt) are essential for studying the processes driving rapid changes in the ice pack, and for validating remote sensing measurements …

Development Of An Embedded Iot Board For Real-Time Floor Estimation Of Autonomous Robots, Carter J. Sorensen

Electronic Theses and Dissertations

As service robots become more prevalent in multi-story environments such as hospitals, hotels, and laboratories, accurate floor-level detection is critical to ensuring operational reliability. Consider a robot tasked with delivering medical samples in a multi-story laboratory. Without accurate feedback, a robot exiting on the wrong floor could introduce delays, disrupt workflows, or compromise sample integrity. Internet of Things (IoT) technologies offer a way to address these risks by providing real-time error detection and corrective capability. However, current IoT-based floor estimation systems often require invasive modifications to building infrastructure—particularly elevator control panels. These approaches introduce challenges related to cost, liability, backward …

An Advanced Hexacopter For Autonomous Exploration Of Mars: Attitude Control And Navigation Strategies, Laura Sopegno

Electronic Theses and Dissertations

Mars exploration has recently witnessed major interest within the scientific community. Unmanned robotic platforms offer reliable solutions to acquire and collect data and information from the Red Planet. Particularly, rovers, landers, and orbiters have significantly shaped planetary exploration on the Moon and Mars, contributing significantly to past missions while also highlighting limitations in their capacity to cover diverse terrains over wide ranges. Given current advances in Unmanned Aircraft Systems (UASs), Unmanned Aerial Vehicles (UAVs) offer promising alternatives for future scientific missions.

It is argued that hexacopters, with their relatively compact design and redundancy, present a promising …

Online Hyperparameter Tuning For Llm Optimization, Ethan Lin, Nathan Yu, Jeromy Chang

Computer Science and Engineering Senior Theses

Large Language Models (LLMs) are becoming increasingly popular in modern society. However, despite their popularity, the deployment of LLMs in real-world scenarios is extremely challenging due to substantial computational costs and memory constraints. Edge devices, like smartphones and IoT devices, lack resources needed to run these models locally, instead offloading computations for cloud computing. Cloud computing requires users to send their data over the internet leading to numerous privacy and security concerns. In some domains, such as health and finances, sending such sensitive information is not an option. Existing solutions to compress or increase inference speed include Small Language Models …

Secure Your Hardware With Randomization And Redundancy, Dennis Cao, Joseph Khamisy, Sumeet Upadhya

Computer Science and Engineering Senior Theses

Differential Fault Analysis (DFA) is a potent hardware attack that threatens cryptographic security by injecting faults into a cipher implementation to reveal secret keys. This project aims to mitigate DFA attacks on the Advanced Encryption Standard (AES) by implementing targeted countermeasures in an embedded AES-128 encryption core. Two key techniques are explored: Randomization and Triple Modular Redundancy (TMR). The randomization approach introduces unpredictability into the encryption process, which involves inserting dummy rounds, artificial noise, and random delays, to disrupt an attacker’s timing and analysis, while TMR provides redundancy by replicating critical rounds of computation and using majority voting to correct …

Spy Bot, Nathanael Bruce Farris

Computer Engineering

This project set out to develop a compact, mobile-controlled robotic platform designed for remote video surveillance and control via a smartphone. Using a Raspberry Pi as the core controller, the robot integrates a Flask-based server, HTML/JavaScript-based user interface, and an infrared-capable camera to provide real-time video streaming and directional control through a web browser. Key design challenges included managing power delivery, integrating motor control with live video, and modernizing web technologies to ensure smooth communication between client and server. The final system demonstrates a responsive and portable proof of concept that highlights the potential for future upgrades, such as night …

A Turtlebot3 Hardware Testbed For Distributed Kalman Filter Localization, Dmitri Dobrynin, Indigo T. Garcia

Electrical Engineering

This report presents the preliminary design for a distributed localization framework for a multi-robot system. Many robotics research papers provide simulations of proposed algorithms in regards to formation control and task allocation. However, it is often that these proposals are without hardware experiments, being limited only to simulation. The objective of this framework is to provide a hardware implementation of a distributed Kalman filtering algorithm for multi-agent localization, as well as provide grounds for future multi-agent experiments. The framework is implemented on a swarm of three Turtlebot3 mobile robots. The robots can accurately localize themselves with respect to other agents …

Distributed Formation Control Of Nonholonomic Mobile Robots: Safety-Critical Leader-Follower Approach With Obstacle Avoidance And Dynamic Reconfiguration, Kelvin C. Villago

Master's Theses

Networked control systems for multi-agent robotics have emerged as a critical paradigm for executing complex coordinated tasks in diverse environments. While formation control serves as the backbone of such systems, real-world deployment introduces significant challenges including communication constraints, environmental obstacles, and the need for adaptive reconfiguration. This research addresses these challenges by developing a novel unified framework that seamlessly integrates obstacle avoidance algorithms with dynamic formation reconfiguration capabilities, specifically designed for communication-limited networked control architectures. The proposed framework represents a significant advancement over existing approaches by simultaneously handling both static and dynamic obstacles while maintaining system cohesion under communication constraints. …

Open Source Asic Design Curriculum, Francisco Wilken

Master's Theses

The ever-growing importance of Application-Specific Integrated Circuits (ASICs) in a high-compute world necessitates that college graduates entering the workforce are well prepared to design them. This thesis details the design of novel ASIC curriculum, using open source tools to teach at the undergraduate level. By moving to a higher level of abstraction than classical transistor-focused coursework, chip design material can be made accessible earlier in an undergraduate degree. Additionally, open source tools provide a powerful, free, and portable platform for students to create their own designs, solving assignments focused on industry readiness. Finally, this thesis studies the results and challenges …

Reverse Engineering Bring Up And Profiling Of Multi-Fpga Systems, Henry A. Evans

Master's Theses

FPGAs have long been used for prototyping and verifying high-speed digital designs in industry and in academic research. As ASIC designs have grown in complexity and size, prototyping those designs on FPGAs has required multiple FPGAs that sometimes span multiple servers. Western Digital donated multiple FPGA-based systems to Cal Poly in 2023. These servers contain multiple high-end AMD FPGAs that are ideal for prototyping large high-speed digital designs, however the full documentation on how to use the servers and how the servers work was not provided. The servers did not come with any information on how to program the FPGAs, …

Autonomous Mapping Rover, Garrett Jones, Timothy Kyle Chu, Eugenio Caruso Pasos

Electrical Engineering

This report details the design, implementation, and evaluation of "Rovero," an autonomous mapping rover developed as a senior project. Rovero integrates state-of-the-art technologies, including ROS2 for communication, SLAM algorithms for mapping, and sensor fusion for accurate navigation. The project aimed to achieve robust autonomous operation in indoor environments, leveraging a combination of LiDAR, IMU, and encoder data for real-time decision-making. Key challenges addressed include path planning, obstacle avoidance, and integration of multiple sensor inputs. Results demonstrate successful mapping capabilities and efficient navigation performance.

Framework For Multi-Agent Coordination And Distributed Localization In Micro-Uavs, Minwoo Park, Nikolas Tambornini

Electrical Engineering

Micro-UAVs (unmanned aerial vehicles) due to their inexpensive nature and compact form factor have shown an increase in prevalence throughout a multitude of applications including, but not limited to: search and rescue, military reconnaissance, and agriculture monitoring. However, for a majority of these high impact applications, a swarm of micro-UAVs are required and furthermore mandate that they are able to cooperatively and autonomously coordinate with each other. For long, controlling and communicating between a user and a singular micro-UAV has been a well known and solved problem, however the same can't be said for swarms of micro-UAVs. This project seeks …

Gamified Gait Rehabilitation Via Real-Time Biofeedback And Adaptive Hip-Exoskeleton Control, Mariya Huzaifa Tohfafarosh

Theses

Gait impairments arise from systemic diseases, age-related degeneration, musculoskeletal dysfunctions, or neurological conditions. While traditional rehabilitation can be effective, they often face challenges such as high costs, inaccessibility, and low patient engagement. To address these challenges, my work introduces a virtual reality-based rehabilitation (VRBR) system, integrating real-time motion and electromyographic (EMG) muscle activation feedback with a gamified virtual environment for enhanced adaptability and engagement. The system includes a custom-designed hip-exoskeleton that provides adaptive spring-like assistance or resistance, supporting both mobility-impaired users and strength training. Assistance levels can be tuned to match the user's progress. Additionally, a custom pressure insole was …