Computer Engineering Commons^™

Boundary Value Problems Associated With Morse-Novikov Cohomology Groups Of Riemannian Manifolds With Boundary, Qusay S. A. Al-Zamil, Mohammed Y. Abass

Iraqi Journal for Computer Science and Mathematics

In this paper, we provide an affirmative response to the following question: if Morse-Novikov cohomology groups of M with non-empty boundary ∂M do not vanish, then what topological or analytical(geometrical) constraints can be enforced to ensure that the equation 𝖽θω=η is solvable for any non-trivialprescribed [η] in absolute Hθk(M) or relative Hθk(M,∂M) Morse-Novikov cohomology groups?. Where 𝖽θω=𝖽ω+θ∧ω and 0≠[θ]∈HdR1(M) for any ω∈ …

Retracted: A Novel Scheme To Optimize Lsb Steganography Based On A Logistic Chaotic Map And Genetic Algorithm, Dena Abu Laila, Ibrahim Moh'd Obeidt, Mohammad Aljaidi, Mohammed Amin Almaiah, Muneer Albourini, Qais Al-Na'amneh, Ghassan Samara, Rami Shehab, Khaled Momani

Iraqi Journal for Computer Science and Mathematics

Steganography has gained importance in various fields, including cloud security, internet banking, military applications, and medical imaging. Additionally, it is popular due to its many uses, becoming a hot research topic. The Least Significant Bit (LSB) approach is among the simplest ways to embed secret data in a cover image. We proposed a new scheme using LSB with the genetic algorithm GA to find the optimal solution permutation for embedding the pixel assortment of the image where data is to be concealed, and a chaotic genetic algorithm (CGA) to efficiently find the best crossover and mutation performance in the chromosomes. …

Hybrid Methods Of Two Conjugate Gradient Coefficients With Modified Condition For Solving Unconstrained Optimization, Yasir Salih, Sudradjat

Iraqi Journal for Computer Science and Mathematics

The conjugate gradient (CG) technique is an efficient method for solving nonlinear optimization problems, especially for large scale problems. This is due to simplicity, low memory requirement, and computational cost, which satisfy the sufficient descent condition. This paper presents a new modification of the PRP method which can be considered as a convex combination of PRP and DPRP methods. The performance of the new method is analyzed to ensure the descent property under exact line search. Numerical experiments indicate that this method has good convergence performance and is promising.

Wesearch - Scu’S Research Hub, Beatrice Hackman, Hilary Le, Meagan Vu, Nicholas Cheung

Computer Science and Engineering Senior Theses

Santa Clara University (SCU) hosts a flourishing undergraduate research program, but discovering research opportunities remains inefficient due to inconsistent information channels. Students rely on informal communication and disconnected departmental web pages to find research roles. In response, our senior design team developedWeSearch: a centralized, web-based platform that consolidates research opportunities across all SCU laboratories. It allows students to browse labs, projects, and apply directly to open positions. Faculty members can create lab profiles, post research openings, manage applications, and assign administrative roles to student assistants.

The platform was designed using Figma and implemented with React.js on the front-end. Firebase was …

Visualizing Sustainability Data With Ai, Matthew Van Dyke, Emily Chen, Hayden Le, Dorothy Xu

Computer Science and Engineering Senior Theses

EcoVisor is a prototype system for extracting, structuring, and visualizing sustainability related metrics from natural language articles. The project integrates a FastAPI backend with a Next.js frontend to transform raw text or article URLs into structured sustainability data, detect trends over time, and generate visual outputs for users. Key capabilities include automated scraping of web articles for sustainability semantics, sustainability related metric data extraction, post-processing of extracted values into cleaned segments and sentences using HTML parsing, grouping of metrics by category and year, generation of Chart.js visualizations, and production of concise “comic brief” summaries for accessible storytelling. EcoVisor aims to …

Accelerating Llm Inference With Smart Nic Tokenization And Caching, Sudarshan Mehta

Computer Science and Engineering Master's Theses

Tokenization is a critical preprocessing step in machine learning pipelines, particularly for generative AI models such as large language models (LLMs). Despite significant advances in training and inference technologies, tokenization continues to be a notable performance bottleneck due to its CPU-bound nature and limited acceleration support. This thesis proposes NetTokenizer, a system designed to offload tokenization from CPUs to network data planes. Utilizing software frameworks such as the Data Plane Development Kit (DPDK) and hardware-based SmartNIC solutions, NetTokenizer substantially improves inference efficiency. Experimental evaluations demonstrate up to a 95.9% reduction in tail latency and a 5.73x throughput increase compared to …

Neural Ranking In Sparse Data Environments, Xuyang Wu

Engineering Ph.D. Theses

Neural ranking models have become central to modern information retrieval (IR) systems, powering applications such as web search, product recommendation, and question answering. However, their effectiveness often hinges on access to abundant labeled supervision, a condition that is rarely met in real-world scenarios. In many domains, including e-commerce, healthcare, and legal search, labeled user interactions (e.g., clicks, purchases, or expert annotations) are sparse, especially for long-tail queries and new content. This data sparsity challenges the generalization, adaptability, and fairness of traditional ranking approaches.

This thesis presents a unified investigation of neural ranking under sparse supervision, introducing novel methods and analytical …

Tiny-Tlic: Lightweight Transformer Based Lossy Image Compression, Chen Li

Computer Science and Engineering Master's Theses

This thesis presents a comprehensive study on image compression, beginning with a review of both traditional and learning-based methods. Building upon the conventional Variational Autoencoder (VAE) framework, we explore the integration of Vision Transformer (ViT) architectures into the image compression pipeline to leverage their global modeling capabilities. Based on this, we propose Tiny-TLIC (Tiny Transformer-Based Lossy Image Compression), a lightweight yet effective model that introduces two novel components: Integrated Convolution and Self-Attention (ICSA) for enhanced feature representation, and a Multistage Context Model (MCM) for improved entropy estimation. To validate our approach, we train both a baseline Transformer-Based Image Compression (TIC) …

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 …

From Vigilance To Veracity: Hallucination Detection, Mitigation, And Safety Enhancement In Large Language Models, Sharanya Dasgupta

Master’s Dissertations

Human cognition, driven by complex neurochemical processes, oscillates between imagination and reality and learns to self-correct whenever such subtle drifts lead to hallucinations or unsafe associations. In recent years, large language models (LLMs) have garnered widespread attention due to their adeptness at generating innovative responses to the given prompts across a multitude of domains, yet they exhibit a critical limitation: the propensity to produce factually incorrect and potentially harmful content while preserving syntactic coherence and logical structure. In this work, we hypothesize that these deficiencies in LLMs originate from their internal representational dynamics. Our observations indicate that, during passage generation, …

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 …

Custom Data Acquisition System For The Cal Poly Racing Baja Team, Joe Keenan

Computer Engineering

The Cal Poly Racing Baja team relies on data to analyze and improve upon various vehicle systems in an off-road style vehicle. In order to accomplish this, some data logging or data acquisition (DAQ) is required to collect the data. This project explores the use of a controller area network with flexible data rate (CAN FD) bus along with a custom file format to store data on an SD card that can be made easily acceptable to engineers on the team.

Open-Source Cubesat Flight Software And Simulation, James Gruber

Computer Engineering

An open-source FreeRTOS-based flight software framework for CubeSats, with an example flight software package and simulation demonstrating real-time attitude control.

How Long Can I Transmit? A Mobility Aware Mmwave-Based Uav Communication Framework, Shawon Mitra

Master’s Dissertations

One primary focus of next generation wireless communication networks is the millimeterwave (mmWave) spectrum, typically considered in the 30 GHz to 300 GHz frequency range. Despite their promise of high data rates, mmWaves su↵ers from severe attenuation while passing through obstacles. Unmanned aerial vehicles (UAVs) have been proposed to o↵set this limitation on account of their additional degrees of freedom, which can be leveraged to provide line of sight (LoS) transmission paths. While some prior works have proposed analytical frameworks to compute the LoS probability for static ground users and a UAV, the same is lacking for mobile users on …

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. …

Optimizing Web Servers With Io_Uring, Mihika Nigam

Master's Theses

Modern web servers face unprecedented demands for high throughput and low latency [10] [11]. Yet, even the state-of-the-art optimizations often fail under heavy workloads on existing infrastructure. Despite advancements in hardware, communication between applications and the kernel remains a critical bottleneck.

Industry surveys reveal that over 50% of production servers still rely on traditional epoll-based architectures [5]. This research aims to investigate and characterize Linux’s new io_uring subsystem, which has the potential to overcome these challenges. Through controlled load testing of various existing architectures like event-driven, multi-process, and multi-threaded architectures (including other commercial servers), we demonstrate how io_uring can help …

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, …

Griddle: A Novel Hardware Based Matrix Multiplier Architecture, Seth Kiefer

Master's Theses

Matrix multiplication is a computational cornerstone in modern artificial intelligence and scientific computing, yet general-purpose processors struggle to perform these operations efficiently at scale. This thesis presents Griddle, a novel hardware architecture for matrix multiplication implemented on a Xilinx Artix-7 FPGA. Griddle focuses on flexibility and scalability by adopting a purely iterative approach that supports arbitrarily shaped input matrices without requiring padding or strict dimensional constraints. The architecture uses computational pipelines to execute a multiplication operation. Each pipe consists of a multiplication core and accumulation buffer that compute matrix products in parallel. The multiplication core contains a set of multiplier …

Conversational Social Robot, Julianna M. Christopoulos, Mackenzie Goldman, Cece E. Hujanen, Jared Hunter

Mechanical Engineering

Background: Social robots are used in various settings to reduce burden on human workers and expand opportunities for people in need of assistance.

Challenge: Design a humanoid robot head and torso capable of holding conversations and interacting with a user using a LLM and motion. Conversations are limited to discussing Cal Poly resources and opportunities with visitors to the Bently Research Center on campus.

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.

Farmnav-Uav: A Field-Ready Autonomous Uav For Tracking Static And Dynamic Trajectories In Agricultural Applications, Veera Venkata Ram Murali Krishna Rao Muvva

Department of Agricultural and Biological Systems Engineering: Dissertations, Theses, and Student Research

This study presents the design and development of a custom-built uncrewed aerial vehicle (UAV) tailored for precision agriculture. Unlike commercial UAVs, which are often constrained by proprietary systems and limited hardware customization, the proposed platform emphasizes modularity, upgradeability, and cost-effectiveness. The UAV is equipped with a Cube Blue flight controller for reliable low-level actuation and a Raspberry Pi 4 companion computer that executes a Model Predictive Control (MPC) algorithm for high-level trajectory optimization and stability enhancement.

Most conventional UAV autopilot systems rely on non-optimal control strategies, such as proportional–integral–derivative (PID) controllers, which can be inadequate for dynamic or resource-constrained environments. …

Deeppanorf: Deep Prior For Neural 3d Reconstruction From Sparse Panoramas, Edward Du

Master's Theses

Advances in neural field representations have led to a significant improvement in view synthesis quality. However, many current novel view synthesis methods rely on a dense set of input views, which can be impractical and inefficient in real-world applications. We propose DeepPanoRF, a novel method for 360◦ scene reconstruction from a sparse set of input equirectangular panoramas. Built upon K-Planes, a radiance field representation that encodes explicit features on orthogonal feature planes, our method does not directly learn feature grids. Instead, we parameterize the feature grids to enable sparse view reconstruction without pretraining or additional regularization. We implement a custom …

Adversarial Deep Reinforcement Learning For Tank Duel Simulation Using Lidar-Based Observations, Braedan Kennedy

Master's Theses

Previous research has demonstrated that reinforcement learning agents can learn to steer differential-drive robots around obstacles using 2D lidar scans as observations. However, these studies typically treat all range returns as undifferentiated obstacles—objects to avoid—without distinguishing between different object types. This thesis builds upon previous research by introducing an adversarial task in which an agent must interpret raw range readings to both avoid static obstacles and identify, pursue, and engage a hostile target.

To investigate this problem, this thesis introduces TankGame, a novel, lightweight 2D tank duel simulator. Each agent receives a 360° lidar scan, controls its motion via tread …

Fault Tolerant Dynamic Task Allocation For Heterogeneous Multi-Robot Systems, Jack R. Cline

Master's Theses

This research presents a novel approach to dynamic task allocation in heterogeneous multi-robot systems with integrated fault detection capabilities. As multiple industries are becoming more reliant on multi-robot systems for tasks, maintaining operational efficiency despite robot failures becomes critical. We propose a framework that combines optimization-based task allocation with a Kalman filter that estimates task progress for anomaly detection to identify unreliable agents and dynamically redistribute tasks. Observing values such as the normalized innovation squared (NIS), covariance, and progress rate, the algorithm can designate a robot as faulty. Embedding information about which robots are faulty in the task algorithm allows …

Sentry V3: Extending Context Switches On A Trusted Secure Coprocessor, Mark Kong

Master's Theses

Software correctness and integrity is only ensured through trust in the un- derlying hardware. However, modern computer systems are complex to design and secure. Thus, given the choice between performance and security, companies will often prioritize performance, resulting in vulnerable systems. This creates exploitable systems that must be patched retroactively because business value performance over security. One approach to this issue is to separate the root of security from the rest of the system to create a minimal trusted computing base. Trustguard is one instance of this. Trustguard implements a Containment Architecture with Ver- ified Output (CAVO) model which shows …

Design Principles For Robotic-Controlled 3d Printing Of Soft Tissue-Mimicking Materials, Karina Mealey

Master's Theses

Geometrically and physically accurate 3D models are emerging as essential tools for surgeons in pre-operative planning and training, but current methods of producing these models are not sufficient. Existing solutions have downsides such as high costs, long and tedious production times, high complexity resulting in a lack of scalability, or an inability to meet material requirements for accurately simulating human tissue.

The objective of this work is to develop principles for a low-cost, easy-to-use 3D printing system capable of prototyping with soft tissue-mimicking materials. To pursue this goal, a MyCobot280 robot arm was used as the mechanism to explore the …

Assessment Of The Imx7ulp Heterogeneous Soc For Use In A Next Generation Student Cubesat Obc, Lorenzo A. Pedroza

Master's Theses

CubeSats represent a rapidly evolving platform for space research, industry, and education, demanding increasingly sophisticated onboard computing solutions that balance performance, fault tolerance, size, weight, and power constraints. Although the design of the Cal Poly's CubeSat Lab's (PolySat) current On-Board Computer (OBC) keeps up with many of these factors, the demands of modern workloads like fine attitude determination will start to outpace available compute. While the selection of a more capable processor to address this would have traditionally resulted in increased energy usage, modern system-on-chip (SoC) architectures offer novel ways to trade available compute for power savings on-the-fly. With this …

A Fault-Tolerant, Multi-Heap Dynamic Memory Allocator For Freertos, Garrett E. O'Neill

Master's Theses

The use of dynamic memory allocation presents a significant challenge for embedded systems, particularly in applications that require high reliability. The software controlling these systems needs to perform critical operations within strict timing constraints, and memory management plays a critical role in a system’s ability to meet these constraints. Dynamic memory allocation is inherently non-deterministic: if a task requests memory, it is impossible to predict how long it will take for the memory to be allocated. If a critical task were to rely on dynamically allocated memory, its execution could become stalled leading to a missed deadline and system failure. …

Implementation Of Area-Based Aggregation On Multivariate Continuous Uncertain Data, Rahul Nair

Master's Theses

Uncertain data is incredibly widespread - from sensor data to AI-based learned information, there exists a need to associate information with a certain probability of its veracity. Traditional relational databases lack a built-in functionality to support uncertain data, instead assigning them boilerplate values. Probabilistic databases tackle this problem by assigning non-deterministic data with an associated, often discrete, probability. Variants of probabilistic databases, namely continuous uncertain databases, are used to better model data represented through ranges and distributions. This is especially applicable with sensor-based geographic data as most commercial equipment contains some inherent margin of error.

While uncertain and probabilistic databases …