Digital Commons Network^™

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 …

Autonomous Rover Solar And Battery Management System: Team B, Karla Jazmin Lira Gonzalez, Paige Ambriz, Kunj Shah, Elhom Delasa Sheykhi

Electrical Engineering

This project focused on the software and printed circuit board (PCB) integration for Poly1Rover’s battery management system (BMS). Poly1Rover is a student organization dedicated to building a low-cost autonomous rover designed to operate on Mars. Since solar availability, temperature, and other conditions on Mars vary, the rover requires a reliable battery management system that accurately monitors battery health and status. This team’s work included the communication and control side of the BMS. The subsystem was designed around the Renesas ISL94202 battery monitor and the MSP430 controller. Battery information was read from the ISL94202 registers through I2C communication, converted from raw …

Design And Evaluation Of An Open-Source Ac/Dc Power Converter For Microgrid Applications, Ryan J. Rayos

Master's Theses

This thesis presents the design, implementation, and evaluation of an open-source bidirectional power conversion platform intended for microgrid applications. As distributed energy resources, battery storage systems, and hybrid AC/DC architectures continue to increase in adoption, there is a growing demand for flexible, low-cost, and programmable power electronic platforms capable of interfacing between DC and AC subsystems. Existing commercial solutions are often proprietary and expensive, limiting their accessibility for educational, research, and rapid prototyping applications. This work expands upon the existing open-source Atinverter platform by increasing its operating voltage capability, integrating bidirectional power flow, and implementing telemetry systems suitable for microgrid …

Integration Of Robotic Arm And Conveyor With Programmable Logic Controller, Drake A. Small

Master's Theses

To meet new curriculum demands brought on by Cal Poly's upcoming switch to semesters, a new, cross-disciplinary lab module was developed for EE 435 (Industrial Power Control and Automation). The module emphasizes career-applicable skills, preparing students for the field of controls engineering within the manufacturing industry. These skills include robotic control, computer networking and configuration, and embedded systems. The work focused on integrating a collaborative robot arm to pick-and-place boxes on a conveyor. A myCobot 320 Pi and an Ultimation Powered Roller MDR Conveyor were integrated with the existing PLC system using Modbus RTU and EtherNet/IP communication protocols, respectively. A …

A Performance-Weighted Environmental Assessment Of Ultra-High-Volume Fly Ash Substitution In Portland Cement Concrete, Youngguk Seo, M.A. Karim, Teddy Tzvetkov, Joshua Hardy

Faculty Articles

Fly ash substitution for cement in Portland cement concrete (PCC) has been regarded as a sustainable solution, but its widespread application remains constrained by concerns over mechanical performance and durability of PCC, especially at higher replacement rates. This study evaluates PCC mixes incorporating fly ash Type C (FA-C) or Type F (FA-F) across cement replacement rates from 10% to 90%, tracking fresh-state workability, compressive strength, and surface electrical resistivity at 7, 14, and 28 curing days. A process-based life cycle assessment (LCA) with the TRACI 2.1 method quantified global warming potential (GWP, kg CO₂/m³) under a …

Evaluation Of Laser Doppler Flowmetry And Transillumination Laser Speckle Contrast Imaging To Measure Blood Flow Change In Ischemia Mouse Hindlimb Models, Simon Park

Master's Theses

Peripheral Artery Disease (PAD), caused by plaque buildup and narrowing of the arteries, reduces blood flow to the extremities. Promoting collateral arteriogenesis can help redirect blood flow. However, simple growth of collaterals may not be enough to reverse PAD. Instead, the ability for the collaterals to vasodilate and control blood flow is important, meaning assessing collateral function is important for the arteriogenesis assesment. Although microscopy techniques can measure vasodilation, contrast limitations in larger vessels such as collaterals make blood flow measurements difficult. Techniques such as ultrasound or magnetic resonance imaging (MRI) can measure blood flow but have limitations with cost …

Polysaber: A Custom Reactive Lightsaber Soundboard, Pedro B. Medeiros

Computer Engineering

The PolySaber project was developed as a custom reactive lightsaber control system built as a fully custom PCB design. The purpose of the project was to create a lower-cost and more customizable alternative to commercially available lightsaber soundboards while simultaneously providing hands-on experience in PCB design, embedded systems development, and hardware integration. Commercial lightsaber soundboards are expensive, proprietary, and difficult for hobbyists to customize. The PolySaber project addresses this by creating a modifiable hardware platform built around the ESP32 microcontroller. The system supports programmable firmware, RGB NeoPixel blade control, motion sensing, reactive swing and clash effects, onboard audio amplification, and …

A Near-Field Communication (Nfc) Multi-Sensor Node With Optimized Read Range And Adaptive Power Management For Remote Monitoring, Rishin Patra, Hilary Scott Nkimbeng Cho, Jin W. Choi

Michigan Tech Publications

This paper presents the design of a batteryless near-field communication (NFC) multi-sensor node with an integrated adaptive power-management system for sensing applications. The work focuses on harvesting energy from a 13.56 MHz NFC field to power an ultra-low power sensing platform. The design consists of the TI RF430FRL152H, an integrated NFC transponder with an embedded MSP430 microcontroller core and ferroelectric random-access memory (FRAM) non-volatile memory. The system combines an ISO/IEC 15693 NFC front end, a tuned loop antenna for optimized power harvesting, and multiple analog and digital sensor interfaces, and a firmware architecture for intermittent harvested energy operation. The aforementioned …

Foundation For A Digital Beamforming System Using Software Defined Radio, Daniel Cruz Guerrero, Shiron Bendrihem, Benjamin Tucker

Electrical Engineering

This report demonstrates a basis for the foundation of a software-defined radio (SDR)-based digital beam forming system. Specifically, a discussion of time, frequency, and phase synchronization is had in the absence of costly hardware like an OctoClock. Furthermore, this report digs deep into the design process and device characterization of a 4-way Wilkinson Power Divider, which is a necessary hardware component for phase calibration. SDR synchronization and calibration are critical in any digital beamforming system.

Rc-Nf: Robot-Conditioned Normalizing Flow For Real-Time Anomaly Detection In Robotic Manipulation, Shijie Zhou, Bin Zhu, Jiarui Yang, Xiangyu Zhao, Jingjing Chen, Yu-Gang Jiang

Research Collection School Of Computing and Information Systems

Recent advances in Vision-Language-Action (VLA) models have enabled robots to execute increasingly complex tasks. However, VLA models trained through imitation learning struggle to operate reliably in dynamic environments and often fail under Out-of-Distribution (OOD) conditions. To address this issue, we propose Robot-Conditioned Normalizing Flow(RC-NF), a real-time monitoring model for robotic anomaly detection and intervention that ensures the robot's state and the object's motion trajectory align with the task. RC-NF decouples the processing of task-aware robot and object states within the normalizing flow. It requires only positive samples for unsupervised training and calculates accurate robotic anomaly scores during inference through the …

Design And Parametric Study Of A Mems-Based Reservoir Computer For Reinforcement Learning, Andrew P. Carr

Master's Theses

Single-node reservoir computing (RC) is a hardware-efficient approach to machine learning, leveraging the dynamics of physical systems. In this work, two reinforcement learning algorithms, Q-learning and Proximal Policy Optimization (PPO), are applied to a simulated micro-electro-mechanical system (MEMS)-based reservoir computer to solve both discrete and continuous control tasks. MEMS-based reservoirs are low-power, compact, and their natural frequencies (kHz to MHz) pair well with real-time control loops. To explore the relationship between reservoir dynamics and learning performance, a parametric study is conducted on two reservoir hyperparameters, reservoir size and neuron separation, using CartPole-v1 and MountainCar-v0. The RC successfully learns multiple tasks …

Rf Fingerprinting: Neural Networks For Device Identification, Pranav Chainani, Maxwell Gertner, Genevieve Patmore

Electrical and Computer Engineering Senior Theses

Conventional cybersecurity protocols authenticate devices using digital credentials that can be stolen, copied, or extracted from compromised hardware. Radio frequency (RF) fingerprinting offers a complementary physical-layer authentication mechanism that binds device identity to the unforgeable manufacturing variations present in every transmitter’s analog hardware. This thesis explores the application of convolutional neural networks (CNNs) to RF fingerprinting, focusing on the identification of nominally identical IoT transmitters from raw I/Q samples of the LoRa preamble’s turn-on transient.

We developed an end-to-end system consisting of a modular data collection testbench using a USRP B210 software-defined radio, a 1D CNN trained directly on raw …

Rf Target Identification With Custom Radar Systems, Francis Chau, Alfred Galindez, Austin Petersen

Electrical and Computer Engineering Senior Theses

This project presents the design, construction, and testing of a low-cost Frequency- Modulated Continuous Wave (FMCW) radar system inspired by the MIT Coffee-Can Radar. The system was developed as a modular educational radar platform intended to demonstrate fundamental radar concepts while incorporating updated RF components, custom printed circuit board designs, and improved system-level testing. The radar architecture includes a triangle-wave modulator, voltage-controlled oscillator, attenuator, power amplifier, RF splitter, transmit and receive antennas, low-noise amplifier, mixer, SMA interconnects, and video amplifier.

The project focused on validating individual subsystems, characterizing the chirped RF output, tuning the antennas near the 2.4 GHz operating …

Santa Clara Radio Astronomy Project (Scrap) V, Alejandro Hernandez, Agustin Garcia

Electrical and Computer Engineering Senior Theses

Santa Clara Radio Astronomy Project (SCRAP) V aims to develop an entirely operational, real-time platform software for an affordable radio telescope that can operate autonomously. This thesis describes the design and implementation process of the fifth generation project through concentrating on the following three key areas: the creation of the live data acquisition and visualization dashboard, thorough verification of the hardware chain received by us, and outdoor system protection from weather factors. The live data dashboard that was created in the MATLAB App Designer completely solved the architecture issues faced with its predecessor being the Python version of the system, …

Skinclusive Ai: Towards Equitable Skin Cancer Detection For Deployment On Edge Devices, Joseph Galicinao

Master's Theses

Skin cancer is one of the most prevalent cancers worldwide, yet existing deep-learning models exhibit significant racial disparities because many widely used datasets are heavily skewed toward lighter skin tones. In addition, many approaches are not designed for deployment on resource-constrained devices, which limits accessibility. This work presents a comprehensive evaluation of classical machine learning and deep-learning based models for binary skin lesion classification, identifying the Swin-Tiny transformer architecture as the most effective backbone. To address bias, we curate a skin-tone balanced dataset, and introduce fairness-aware training through adversarial training, and joint distribution oversampling, to improve performance across protected attributes. …

Development Of A Multi-Mode Switching-Inverter Laboratory Module For An Introductory Power Electronics Course, Darcy Eliasi

Master's Theses

DC–AC converters, or inverters, are essential power‑electronic interfaces widely used in photovoltaic systems, microgrids, energy‑storage installations, and numerous other modern electrical applications. This thesis presents the design, construction, and testing of an improved laboratory module developed as a student learning tool for demonstrating four fundamental inverter switching techniques: simple square wave (SSWI), modified square wave (MSWI), bipolar PWM, and unipolar PWM. Building upon the previous module, the improved design centers on three key enhancements. These include the selection of a more suitable wave‑generation solution, a multiplexer‑based mode‑selection architecture that reduces user complexity and eliminates conflicting signal configurations, and the integration …

Efficient Mathematical Modeling And Synthesis Of Realistic Musical Instrument Sounds, Andrew Chookaszian

Master's Theses

This thesis develops and evaluates a compact parametric additive synthesis model for isolated musical instrument tones. The method analyzes a single-note recording, estimates its fundamental frequency, extracts harmonic amplitude and frequency behavior, and stores the sound as a reduced set of interpretable parameters. These parameters include the note duration, pitch, per-harmonic amplitude envelopes, phase information, and amplitude- and frequency-modulation vibrato parameters. The stored model is then used to resynthesize the tone without directly using the original audio waveform.

The model was evaluated using synthetic signals, real instrument samples, objective error metrics, storage comparisons, pitch and duration modification tests, and listening …

Space Quacker Advanced Development (Squad): Lora Modulation On A Leo Cubesat Mission For Evaluation Of 916 Mhz Uplink, Samantha Brunton

Master's Theses

The LoRa Modulation format was developed by SEMTECH in 2018 and has revolutionized terrestrial Internet of Things (IoT) networks. LoRa (Long Range) has been successfully demonstrated as a long-range, low-data-rate communication modulation format and packet protocol for low-power terrestrial applications. LoRa offers low power consumption, low cost, robust signal sensitivity, and long communication range. Recently, interest has grown in using LoRa communication in Low Earth Orbit (LEO) satellite systems, especially for global IoT connectivity. Previous studies have investigated the theoretical feasibility and simulated the performance of LoRa satellite communication. Companies such as Lacuna Space have demonstrated the practical potential of …

Evaluating The Cost-Benefit Tradeoffs Of Simt Control Mechanisms In Resource-Constrained Gpus, Nikolas Tambornini

Master's Theses

This thesis conducts a hardware-level analysis of the cost–benefit tradeoffs associated with increasingly complex SIMT control mechanisms in a resource-constrained GPU core. Four design implementations are evaluated: a Base Tiny GPU without warp scheduling capability; a Warp Scheduler using round-robin warp selection; a Branch Divergence implementation incorporating a dedicated divergence stack and post-dominator reconvergence mechanism; and a Dynamic Warp Allocation implementation that replaces the static warp structure with a runtime warp manager, regrouping threads by current program counter to recover SIMD lane utilization during active divergence. Each design is evaluated using two complementary measurements: functional simulation implemented using the CocoTB …

Evaluation Of The Effect Of Vibration On Signal Reflection In Coaxial Cable Connectors For Vibration Sensing In Aircraft Structures And Systems, Saidanvar Esanjonovich Valiev, Anthony C. Okafor, Jeremiah J. Rittenhouse, Daniel S. Stutts, Jie Huang

Mechanical and Aerospace Engineering Faculty Research & Creative Works

This paper investigates the effects of vibration on signal reflection (S11) in aerospace data transmission line (ADTL) and commercial data transmission line (DTL) connectors for their alternative use as vibration sensors. The impact of vibration on the S11 signal was investigated on five ADTL and four DTL connectors at six vibration frequencies (20 Hz, 40 Hz, 80 Hz, 160 Hz, 320 Hz, and 640 Hz) and four vibration accelerations (0.5G, 1 G, 2 G, and 4G). The experiment was conducted using a split-plot design with a cable type assigned as the main-plot factor, with vibration frequency and acceleration as subplot …

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 …

Retinomorphic Mid-Wave Infrared In-Sensor Processing Engine: Device-To-Architecture Co-Design, Hemalatha Nagaraju

Theses

Conventional frame-based CMOS image sensors acquire full-frame pixel data at discrete time intervals, resulting in substantial spatial redundancy and loss of temporal information between frames. The repeated conversion and transfer of redundant pixel data increases bandwidth and power consumption in machine vision systems. Retinomorphic sensing architectures address these limitations by enabling programmable, analog-domain processing directly at the sensor interface. A compact behavioral model of the PbSe device is developed in HSPICE based on calibrated TCAD simulation data to capture gate-controlled photocurrent modulation under varying illumination and gate bias conditions. Error analysis is performed to quantify the deviation between TCAD-generated photocurrent …

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 …

Decision Making At Triage Classification Using Svm With Smote Technique, Mehanas Shahul, Pushpalatha Kp

Northeast Journal of Complex Systems (NEJCS)

The efficient functioning of triage gates in overcrowded emergency departments (EDs) occurs in the context of the complex adaptive system (CAS) framework, where diverse system elements – patients, medical personnel, resources, patients’ inflow patterns, and patients themselves – simultaneously and dynamically influence the decision process. This study addresses the automated incorporation of machine learning triage algorithms as part of the system triage process to support automated classified risk-level recognition based on a limited set of vital signs. Patients are dynamically subsumed under high and low-risk categories enhanced by sensitivity, which enables optimal diagnosis and triage response to the critical clinician …

Uncovering Discrete States From Multimodal Psychophysiological Data Using Gaussian Latent Dirichlet Allocation (Glda), Congyu Wu, Aaron Fisher, David Schnyer

Northeast Journal of Complex Systems (NEJCS)

In this article we explore and validate the utility of an unsupervised probabilistic model, Gaussian Latent Dirichlet Allocation (GLDA), for discovering discrete states from repeated, multimodal psychophysiological samples collected from multiple individuals. Psychology and medical research heavily involves measuring potentially related but individually inconclusive variables from a cohort of participants to derive diagnosis, necessitating clustering analysis for state identification. Traditional probabilistic clustering models such as Gaussian Mixture Model (GMM) assume a global mixture of component distributions, which may not be realistic for observations from different patients. The GLDA model borrows the individual-specific mixture structure from a popular topic model Latent …

Asymmetric Opinion Formation Of Emotional Excitable Agents, Irene Ferri, Emanuele Cozzo, Aleix Nicolás-Olivé, Albert Díaz-Guilera, Luce Prignano

Northeast Journal of Complex Systems (NEJCS)

The bounded confidence model represents a widely adopted framework for modeling opinion dynamics wherein actors have a continuous-valued opinion and interact and approach their positions in the opinion space only if their opinions are within a specified confidence threshold. Here, we propose a novel framework where the confidence bound is determined by a decreasing function of their emotional arousal, an additional independent variable distinct from the opinion value. Additionally, our framework accounts for agents' ability to broadcast messages, with interactions influencing the timing of each other's message emissions. Our findings underscore the significant role of synchronization in shaping consensus formation. …

Visualization And Marker-Less Tracking Of User-Defined Pre-Processed Mri Articulator Data Using Deep Learning, Michael De George

Student Theses

This thesis presents a comprehensive framework for the automated tracking and visualization of articulatory movements based on magnetic resonance imaging (MRI) data. A well-known data analysis tool for markerless pose estimation, known as DeepLabCut, is investigated for this purpose. The performance of this tool is enhanced through the design and implementation of a pre-processor. DeepLabCut is a markerless pose estimation toolbox based on deep learning, which overcomes the issue of making manual annotations frame-by-frame. Limitations from manually marking the MRI images are addressed by implementing transfer learning with convolutional neural networks to achieve accurate, user-defined articulator tracking without markers. Current …

The Impact Of Optimization Approximation Algorithms On The Performance Of The Bht-Qaoa, Ali Al-Bayaty, Marek Perkowski

Electrical and Computer Engineering Faculty Publications and Presentations

This article investigates the performance impact of five classical optimization approximation algorithms on our previously introduced quantum search algorithm, termed the Boolean–Hamiltonians Transform for Quantum Approximate Optimization Algorithm (BHT-QAOA), to effectively search for all best-approximated solutions for Boolean-based problems. These optimization approximation algorithms are BFGS, L-BFGS-B, SLSQP, COBYLA, and COBYQA. Their performance impact is evaluated and compared using two proposed performance metrics—(i) the final number of function evaluations (the lower numbers denote the best optimization approximation algorithms) and (ii) the final quality of qubit measurements (the higher values indicate all best-approximated solutions were found for a problem). Arbitrary classical Boolean …

Evaluation Of Intracavity Electromagnetic Field Probes And Sources: Implications For Shielding Effectiveness, Joseph Anthony Ferreri

Electrical and Computer Engineering ETDs

To measure the electric field in a reverberant cavity, a small, minimally invasive probe is required. Common solutions include electrically small surface mounted monopole antennas, B-dots, and D-dots. To obtain an accurate field measurement with a particular probe, it is necessary to characterize it to compensate for its ability to convert electric field into voltage which requires a gauge factor known as effective height. The characterization process is straight forward in open space on a ground plane but requires more insight when in situ in a reverberant cavity. This work adapts ground plane probe characterization methods for cavity measurements, facilitating …

Self-Supervised Spoofing Detection, David S. Choi

Electrical and Computer Engineering ETDs

Global Navigation Satellite Systems (GNSS) are vulnerable to spoofing attacks that can mislead receivers with counterfeit signals. Traditional detection techniques, such as antenna-based, encryption based, and signal processing approaches, often face limitations in adaptability, computational cost, or reliance on predefined thresholds. Supervised machine learning models, while powerful, require large labeled datasets and struggle to generalize to unseen spoofing scenarios. In this work, we propose a self-supervised spoofing detection framework based on Adaptive Sparse Gaussian Processes (ASGP). The method predicts incoming GNSS features using past observations and identifies spoofing as anomalous deviations in the prediction residuals. Unlike supervised approaches, ASGP adapts …