Computer and Systems Architecture Commons^™

Preserving Structural Alias Information Across The Mlir-To-Llvm Lowering Boundary, Shravan Sheth

Master's Theses

Machine learning training and inference workloads run at massive scale, where the efficiency of generated machine code directly affects throughput and energy consumption. The compilers that lower model specifications to hardware instructions rely on optimization passes that can only exploit information visible in the representations they operate on. MLIR, the intermediate representation framework underlying production machine learning compilers such as IREE and Triton, is designed so that each abstraction level can encode semantics that lower levels cannot represent. One example is memref.subview, which partitions a buffer into typed regions with explicit offsets and sizes, making structural non-overlap provable from the …

Xylem: A Comparative Analysis Of Gpu Dispatch Pipelines For Large-Scale, Procedural Environments, Srinivas Sundararaman

Master's Theses

The real-time rendering of large-scale, procedural scenes presents a significant performance challenge for traditional CPU-bound rendering pipelines. The high volume of draw calls and the need for complex culling and level-of-detail management create bottlenecks that limit scene complexity and visual fidelity. This thesis investigates the evolution of GPU-driven rendering paradigms via the Xylem renderer within NVIDIA’s Donut rendering framework as a solution to these challenges.

A comprehensive benchmarking framework is developed to implement and quantitatively analyze three distinct rendering strategies for a procedurally generated, parameterizable, large-scale forest scene. The evaluated pipelines include: (1) traditional instanced rendering, (2) compute-driven indirect rendering …

Autonomous Vision-Based Litter Collection Rover, Dante Michael Benedetti, Benjamin Scott Tavares, Nathan Heil

Electrical Engineering

This report documents the design, implementation, and testing of an autonomous litter-collection rover developed as a Senior Project Design Lab (EE 460/463/464) at California Polytechnic State University. The rover integrates autonomy, computer vision, embedded real-time control, mecanum-wheel omnidirectional mobility, and a two-degree-of-freedom robotic arm to detect, approach, and collect small ground-level litter such as bottles, wrappers, and paper fragments.

The system uses a two-layer compute architecture: an NVIDIA Jetson Orin Nano running ROS 2 for perception, SLAM, and path planning, paired with an STM32L4A6ZG microcontroller for real-time motor control and odometry. The robot is built on a multi-level aluminum frame …

Solving Linearly Constrained Quadratic Programs Using Field Programmable Analog Arrays, Tyler Wynn, Alonzo Arroyo

Electrical Engineering

This paper presents a field-programmable analog array (FPAA) implementation for solving linearly constrained quadratic programs (LCQPs) directly in the analog domain. The solver is based on a continuous-time primal-dual control architecture with integral action, anti-windup compensation, and a piecewise-linear nonlinearity for enforcing affine inequality constraints. A switched-capacitor implementation using three AN231E04 FPAAs is developed, and coefficient scaling methods are introduced to keep internal and output signals within the voltage limits of the hardware. A global scaling factor is used to reduce internal signal excursions, while solution-space scaling is shown to modify the implemented optimization coefficients and alter the local closed-loop …

Teaching Multi-Core Architecture: Design And Implementation Of A Cache-Coherent Cpu For Undergraduate Education, Isaac R. Lake

Master's Theses

Modern computing has relied on multicore processors for high performance for nearly two decades, yet undergraduate computer engineering curricula often provide limited exposure to parallel hardware architectures and their design challenges. This thesis presents the design of CPE 433, a course that extends the pipelined and cached OTTER CPU developed in CPE 233 and CPE 333 into a multicore processor capable of running parallel workloads. The thesis documents the architectural changes required to adapt the verified single-core design into a multicore system, including MMIO-based interrupt support, a shared cache hierarchy with cache-coherence mechanisms, and clock-gated modules. It further presents a …

Evaluating Design Choices For Gpu-Accelerated Finite-Difference Time-Domain Simulation On Resource-Constrained Devices, Joel Manesh

Master's Theses

The Finite-Difference Time-Domain (FDTD) method is a numerical technique for solving partial differential equations. It was first developed to solve Maxwell’s equations for electromagnetic wave propagation and has since been extended to model other physical phenomena governed by wave propagation. Because the FDTD method is data-parallel, it is well-suited for GPU acceleration; modern FDTD-based simulations run offline on large GPU clusters. There is, however, very little research on running FDTD on resource-constrained embedded GPUs, which are increasingly popular for real-time applications.

This thesis explores a CUDA-based FDTD solver for the 3D wave equation on the Nvidia Jetson Orin Nano. This …

Concrete And Masonry Code (Beams), Michael Alexander Simas Rocha

Architectural Engineering

This report only covers the beam design of the code that was written. The end goal of this project is to write code for concrete and masonry following what was taught in the lecture classes. This project is acting as a foundation for the end goal of a program that can compete with SAP2000, ETABS, RISA, and others. One final goal is to have the print out have the look and feel of having been done by hand. While coding; several factors were recorded including time spent coding, debugging, and then to do three problems by hand vs how long …

Gps Tracking Smart Dash Cam Capstone Review, Simrah Saleem

University Honors Theses

This thesis details our design, implementation, and collaborative development of an intelligent vehicle logging system built on a Raspberry Pi 5. Unlike standard consumer dash cams that act as closed "black boxes," our system uses a dual-camera stereo vision setup integrated with centimeter-level accuracy. While we successfully built a functional Proof of Concept capable of event-triggered recording, dual-monitor visualization, and smart detection and recognition, this paper focuses on our engineering journey and the real-world challenges we faced. Using an Agile framework, we split into three specialized sub-teams to handle hardware, database, and interface design in parallel. This structure created unique …

Holistic Dram Enhancements: From Intrinsic In-Memory Operations To Robust Security Mechanisms, Ranyang Zhou

Dissertations

Dynamic Random-Access Memory (DRAM) is both the performance bottleneck and a critical security boundary of modern computing systems. Its physical properties make it an attractive substrate for near-data computation—yet those same properties expose it to disturbance-based hardware attacks. This dissertation argues that these two dimensions are not independent: the architectural choices that make DRAM efficient also reshape its threat landscape. Addressing both requires a unified approach to memory architecture and security co-design.

The first part of this dissertation attacks the memory wall through four processing-in-DRAM (PIM) frameworks. ReD-LUT and LT-PIM unify lookup-table arithmetic with charge-sharing logic, achieving up to 37.8x …

Deep Learning Approaches For Ctenophore Identification And Tracking, Anagha Bharadwaj

Theses

Ctenophores are translucent marine organisms with nearly invisible tentacles and pose significant challenges due to their transparent morphology and ambiguous structural features. This research addresses the classification and tracking of these organisms and evaluates the performance of current computer vision models under sparse-data environments.

A dataset from the NJIT Life History Lab consisting of microscopic laboratory videos and photographs of different growth stages is used to train and assess a number of convolutional neural network designs, including VGG16, ResNet, BioCLIP2, YOLO, and DeepLabCut. Additionally, a web-based interface is developed to evaluate expert-labeled ground truth with the model's performance.

The findings …

Wake-On-Anomaly Federated Architecture For Privacy-Preserving Poultry Health Early Warning, Mahmoud Aziz Louati

Masters Theses

Highly pathogenic avian influenza outbreaks, respiratory disease, heat stress, and silent equipment failures share one operational reality: they are detected too late because today’s poultry-health workflow is reactive, manual, and dependent on producers volunteering commercially sensitive data. This thesis presents a wake-on-anomaly federated architecture that addresses both the detection-latency problem and the privacy–adoption deadlock that has so far prevented cross-farm collaboration. The architecture is organized in two tiers. Tier 1 is a lightweight LSTM autoencoder that continuously screens four routine telemetry channels (water, feed, house temperature, activity proxy) and emits a per-window reconstruction-error score. A debounced k-of-m trigger with cooldown …

Native Wayland Compositing On Apple Ecosystems: Assessing The Feasibility Of “Wawona” Compositor, Alex Spaulding

2026 Symposium

The Wayland display protocol is the modern standard for Linux window management, emphasizing security, performance, and simplicity. Expanding this ecosystem to macOS, iOS, and Android introduces technical hurdles due to proprietary windowing systems and divergent hardware APIs. This research evaluates the feasibility of developing a native Wayland Compositor for Apple and Android, given the closed nature of these ecosystems.

“Wawona” bridges this gap by architecting a native Wayland Compositor capable of executing unmodified Linux applications. The methodology involves implementing the Wayland protocol stack into native abstractions leveraging Metal, Android’s graphics pipeline, and CoreAnimation.

Investigating Creative Possibility Using Automated Composition, Kian Drees

Undergraduate Honors Theses

As a constructive method, Johann Joseph Fux’s theory of counterpoint defines a space of musical possibility for contrapuntal composition. I developed a computational method for generating melodies to systematically investigate selected properties of this space. My program recursively generates a tree of all possible cantus firmus melodies of a specified length or all possible first species counterpoints on a given cantus firmus, starting with an empty root node and adding child nodes representing possible musical notes at each step until the specified length is reached. I then investigated several properties of the generated melodies, such as the approximate relationship between …

Improving Human Visual Search: Enhancing Lung Cancer Nodule Detection In Medical Images, Christopher Khajira

Masters Theses

Human visual search involves the identification of relevant signals within information-rich environments, which is a fundamental problem in visual perception. While detection accuracy and response time are commonly used to evaluate performance in visual search, these measures do not reveal the underlying cognitive and computational structure that produces observable behavior. A key challenge lies in distinguishing between competing processing architectures, particularly in complex visual domains where different models can produce similar behavioral outcomes. This study addresses this challenge by developing a computational experimental framework for analyzing visual search behavior using System Factorial Technology (SFT). The experimental framework integrates naturalistic medical …

Exploration Of Real-Time Power Electronic Simulation On Amd Npu, Shouyu Du

All Theses

The rapid electrification of the automotive and data center sectors has created a critical demand for high-fidelity, real-time simulation of complex power electronic systems. While Hardware- in-the-Loop (HIL) simulation on Field-Programmable Gate Arrays (FPGAs) is the current industry standard, it presents significant challenges regarding development complexity and memory limita- tions. This dissertation investigates the feasibility of utilizing Neural Processing Units (NPUs)— specifically the AMD AI Engine (AIE) XDNA2 architecture—as a novel platform for real-time, deterministic circuit simulation. This study establishes a comprehensive automated modeling framework based on graph theory and Massarini’s method to derive linear state-space representations for arbitrary circuit …

A Full System Co-Simulation Platform For Evaluating Edge Machine Learning Inference Using Compute-In-Memory, Belsen Lee

Electrical Engineering and Computer Science (MS) Theses

We present a full-system co-simulation platform for evaluating embedded machine learning (ML) inference using compute-in-memory (CIM). CIM architectures aim to reduce data movement overhead by performing matrix operations in memory, but end-to-end benefits depend on system-level integration costs that are difficult to assess with isolated hardware models alone. To address this gap, we develop an integrated RISC-V QEMU-SystemC co-simulation environment that allows standard embedded Linux to interact with a transaction-level CIM accelerator model via memory-mapped I/O, direct memory access (DMA), and interrupts. To evaluate performance, we benchmark an MNIST image inference workload and a synthetic fully connected neural network, comparing …

Design And Verification Of The Multi-Slit Solar Explorer Camera Field Programmable Gate Arrays, Jordan M. Johnson

All Graduate Reports and Creative Projects, Fall 2023 to Present

The Multi-Slit Solar Explorer, or MUSE, is a NASA mission that will take images of the Sun to study solar flares and the solar corona. The mission will provide insight into the mechanisms behind space weather. The mission consists of two cameras: the Spectrograph (SG), and the Context Imager (CI). The Utah State University Space Dynamics Laboratory is providing both cameras for the mission.

This report describes a part of the design and verification process for a central component on these cameras known as the Field Programmable Gate Arrays (FPGAs). These FPGAs are programmed to acquire, handle, and send images …

A Novel Approach To Creativity Assessment: Forced Pairwise Ranking With Large Language Models, Phillip R. Gregory Jr

Master's Theses

Assessing creativity at scale remains a persistent challenge in cognitive science, as human raters are costly, slow, and often inconsistent in their judgments. This thesis introduced a novel framework for automated scientific creativity assessment using forced pairwise ranking, in which fine-tuned large language models compared response pairs and determined which was more creative. Five empirical studies were conducted using Llama-2-7B and Llama-2-13B models adapted via LoRA fine-tuning and benchmarked against human scored responses from the Scientific Creative Thinking Test. A regression baseline achieved Pearson �� = .74 on the test set, matching the human inter-rater ceiling reported in the literature. …

Robust Hardware Trojan Detection Leveraging Dual‑Domain Features And Stacked Ensemble Learning, Sefatun-Noor Puspa

All Theses

In Cyber-physical systems rely on sensors, communication, and computing, all powered by integrated circuits (ICs). These ICs are vulnerable to malicious hardware attacks, with hardware Trojans being one of the stealthiest threats. Trojans are malicious implants in the circuitry, which are often inserted during design or fabrication stages. This stealthy addition remains dormant until triggered and might cause functional disruptions or sensitive information leakage once triggered. Traditional IC validation methods, such as functional testing and logic analysis, usually fail to capture these subtle anomalies because hardware Trojans are intentionally designed to mimic normal circuit behavior. They often remain dormant under …

Post-Vote Tampering In Nigerian Elections And The Role Of Blockchain-Enabled Electoral Systems, Ransome Chukwubuikem Enechukwu

Electronic Theses and Dissertations

Post-vote tampering during the collation and transmission of election results remains a persistent challenge in Nigerian elections, enabling manipulation of already-cast votes and weakening public trust in electoral outcomes. Existing technological interventions, including biometric voter accreditation and digital result transmission systems, improve voter authentication but do not adequately secure the post-vote result collation process. This thesis proposes a blockchain-enabled framework designed to protect the integrity of election results during the collation and transmission stages. Using a Design Science Research methodology, the study develops a permissioned blockchain framework based on Hyperledger Fabric that records polling-unit results as immutable ledger entries and …

Conditioning Hierarchical Diffusion Transformers On Rf Circuit Parameters, Ethan Morton

McKelvey School of Engineering Graduate Student Theses & Dissertations

As modern ML techniques have become increasingly advanced, they have begun to be integrated into wireless RF systems for classification, identification, and spectrum management. Deep Neural Networks (DNNs) enable RF system operators and designers to design more flexible systems with greater robustness to errors and attacks. However, neural networks require significant amounts of properly annotated data to train. Current data labeling methods lack the ability to obtain reliable true labels for circuit properties such as carrier frequency offset (CFO), power amplifier (PA) non-linearity, and in-phase/quadrature (IQ) imbalance. This thesis investigates the efficacy of a novel architecture, RF-Diffusion, for generating high-quality …

Uav-Based Sensor Integration For In-Situ Lake Water Quality Monitoring, Alec R. Campbell

Biological and Agricultural Engineering Undergraduate Honors Theses

Surface water monitoring is often constrained by limited spatial and temporal coverage due to the labor-intensive nature of traditional sampling methods, particularly in environments that are difficult to access or pose safety risks. Unmanned aerial vehicles (UAVs) offer a promising solution by enabling more frequent, spatially distributed, and cost-effective data collection. This study presented the design, development, and field evaluation of a UAV-based system for real-time, in-situ water quality monitoring. The system integrated multiple sensors, including oxidation-reduction potential (ORP), RGB spectrometry, pH, electrical conductivity (EC), dissolved oxygen (DO), and a multispectral spectrometer within a UAV platform.

Field testing was conducted …

Reward Representation Learning, Gregory M. Hyde

Dartmouth College Ph.D Dissertations

The \emph{Markov decision process} (MDP) has long served as the canonical model for sequential decision-making. However, it assumes that the reward function is Markov with respect to a given state representation---an assumption that often does not hold in practice. Instead, agents typically only perceive streams of observations and actions and must infer the latent structure according to which reward unfolds over time. From this perspective, reward prediction is initially non-Markov, reflecting a mismatch between the agent's current representation and the underlying structure of the environment.

In this thesis, we advance the view that reward is not simply a signal to …

Using Ali-4 Z-Implication In Controller Design, Shamil Azer Ahmadov

Chemical Technology, Control and Management

One of the widely used theories in the information processing is Professor Zadeh's fuzzy logic theory. Fuzzy implications form the basis of this theory. When processing information using fuzzy implication, the chosen judgment method and the type of implication affect the result. Referring to the review of the relevant literature on fuzzy implications, it can be noted that there are still unresolved problems and issues. For example, fuzzy implications cannot be used in processing imperfect information or information based on probabilistic and fuzzy uncertainty. Existing fuzzy implications face limitations in practical applications. Fuzzy implications only take into account inaccuracy, but …

Shelter Portal: Qr-Based Service Tracking For Low-Barrier Shelters, Kate Steele, Colton Knopik, Michael Fischer

2026 Symposium

Shelter Portal is a web-based service tracking application developed for low-barrier shelters, including Catholic Charities’ House of Charity and Rising Strong programs. Many shelters still rely on manual headcounts and estimated meal totals, which are labor-intensive, error-prone, and insufficient for tracking individual service use over time. This limits operational visibility and makes it difficult to generate reliable reports, identify usage trends, and support external reporting requirements. Shelter Portal addresses this problem by providing a more accurate and privacy-conscious way to document shelter services.

The system was designed as a kiosk and web-based platform that uses scannable QR code cards to …

Escaping Isolation: An Analysis Of Virtual Machine And Container Breakout Vulnerabilities, Felix Iov

Cybersecurity Undergraduate Research Showcase

Cloud computing providers rely on multi-tenant architectures to maximize resource efficiency. This infrastructure depends on virtualization, which provides isolation between clients. This comes primarily in the form of Virtual Machines (VMs) and Containers. However, “breakout attacks” or “escapes” are a critical threat where attackers bypass these isolation layers to gain unauthorized access to the host system and neighboring environments. This paper surveys virtualization escape threats and analyzes three case studies: a runc container escape (Leaky Vessels), a VMware ESXi VM escape (VSOCKPuppet), and an NVIDIA GPU container escape (NVIDIAScape). Each demonstrates different attack surfaces, including file descriptor misuse, kernel driver …

Reducing Systemic Bias In Behavioral Targeting Using Explainable Ai: The Harmonia Complex Systems Approach, Ruchira Deokar, Preethi Nanjundan, Jossy P. George, Carlos Gershenson

Northeast Journal of Complex Systems (NEJCS)

Behavioral targeting is a key part of the modern advertising web's algorithmic engine. However, it is unclear whether optimization processes worsen bias, promote unchecked spread in filter bubbles or lower overall users' trust levels. This paper introduces HARMONIA (Holistic Adaptive Regulatory Model for Optimizing Non-transparent Intelligent Advertising), a comprehensive, data-driven Explainable Artificial Intelligence (XAI) framework aimed at transforming behavioral targeting via transparency, interpretability, and adaptive ethical regulation. This paper conducted a comprehensive Explorative Data Analysis (EDA) on the public Criteo Display Advertising Dataset, which contains over 45 million records, to identify patterns in high-dimensional user-ad interaction space. This analysis uncovered …

Ai-Scm Cmm: A Capability Maturity Model For Artificial Intelligence Integration In Supply Chain Management, Lordt Becklines, Omar F. El-Gayar, Patti Brooks, Insu Park

Annual Research Symposium

Artificial intelligence is increasingly deployed in supply chain management, yet many organizations struggle to align adoption efforts with process readiness, data quality, governance, and workforce capabilities, and they still lack validated supply chain specific roadmap for assessing readiness, sequencing investments, and reducing implementation risk. This study develops and evaluates a Capability Maturity Model for Artificial Intelligence Integration in Supply Chain Management to address that gap. Using a design science research approach, the study synthesizes prior literature and practitioner knowledge to define maturity dimensions, capability indicators, and staged progression levels for AI integration in supply chain contexts. The artifact and assessment …

Large-Scale File Fragment Classification Via Multi-View Learning, Samuel Hildebrand

LSU Master's Theses

File reassembly is one of the most fundamental tasks in digital forensics, enabling recovery of data from potentially damaged storage media even when file system metadata is unavailable. This thesis reviews more than two decades of work in the realm of file carving, with a particular focus on fragmented file carving, which remains a focus of research, and file fragment classification, a principal component of fragmented file carving. This thesis serves a literature review of both file carving and fragmented file carving, surveys the massive amounts of data needed for the task of fragment classification and the datasets that serve …

A Data-Driven Framework For Modeling Car-Following Behavior Using Conditional Transfer Entropy And Dynamic Mode Decomposition, Poorendra Ramlall

Student Research Symposium (SRS)

Accurate modeling of car-following behavior is essential for understanding traffic dynamics and enabling predictive control in intelligent transportation systems. This study presents a novel data-driven framework that combines information-theoretic input selection via conditional transfer entropy (CTE) with dynamic mode decomposition with control (DMDc) for identifying and forecasting car-following dynamics. In the first step, CTE is employed to identify the specific vehicles that exert directional influence on a given subject vehicle, thereby systematically determining the relevant control inputs for modeling its behavior. In the second step, DMDc is applied to estimate and predict the dynamics by reconstructing the closed-form expression of …