Digital Commons Network^™

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 …

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 …

Real Time Grid Connected Der Inverter Control Fault Ride Through Model In Accordance With Ieee 1547–2018 Standard, Mark Joseph Booge

Masters Theses

Distributed Energy Resources (DERs) include inverter-based power generation units, which are increasing in demand due to the rising consumption of energy, need for improved grid stability, and other environmental factors. DERs can either supply or supplement the grid using different control strategies depending on the grid’s needs. IEEE standard 1547-2018 dictates the interconnection and operation requirements of grid-connected DERs under nominal and abnormal voltage conditions. Although inverter ride-through operation has been widely studied, the effects of the standard’s time-based requirements within a distribution grid system require further investigation.

This study investigates how the IEEE 1547-2018 time-based ride-through requirements affect a …

High-Density Power Conversion: Analyzing High-Frequency Transformer Scaling Laws And Grid-Filter Efficacy In Integrated Charger Inverters, Thilina Lakshan Dissanayakage

Masters Theses

This thesis investigates frequency scaling in high-frequency transformer (HFT) used in Dual Active Bridge (DAB) converters and evaluates different LCL filter design methodologies for grid-connected applications. The study explores the impact of operating frequency on transformer size reduction, efficiency, and overall performance by analyzing HFT designs at 50 kHz, 100 kHz, and 400 kHz. It is expected that increasing the switching frequency enables significant reduction in transformer volume, while introducing trade-offs in efficiency due to increased core and copper losses. In parallel, the thesis examines and compares three LCL filter design approaches under identical operating conditions using a 1 kW …

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 …

Analysis And Design Of A 2-Bit Millimeter-Wave Reconfigurable Intelligent Surface And Investigation Of Fss-Assisted Angular Stability, Max Spalek

Master's Theses

Millimeter-Wave (mm-wave) technology utilizes high frequency spectrum and can deliver multi-gigabit speeds, ultra-low latency, and massive bandwidth capacity. However, this technology is rather new and many technical challenges remain. On the other hand, Reconfigurable Intelligent Surfaces (RIS) have emerged as promising structures for controllable electromagnetic wavefront manipulation in wireless communication systems. This thesis presents the design of a 2-bit RIS unit-cell element operating throughout the 30–34 GHz mm-wave band, where the structure was progressively refined from an initial resonating geometry toward a grounded reflective RIS configuration. The proposed structure utilizes PIN-diode switching states to produce four discrete reflected phase states …

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 …

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 …

Computer Vision Methods For Detecting Counterfeit Usd Bills, Tyler W. Jones

Master's Theses

This thesis addresses the global challenge of counterfeit paper currency by proposing a classical computer vision framework for distinguishing genuine United States Dollar (USD) bills from counterfeit ones using image data. In contrast to existing approaches that rely on a large number of easily reproducible visual features, this work prioritizes the detection of a single, robust security feature: the ultraviolet (UV) reactive security strip embedded in genuine USD bills of denominations $5 and above. By focusing on a feature that is inherently difficult to replicate, the proposed method reduces the likelihood of counterfeit bills being misclassified as genuine.

The system …

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.

Design And Characterization Of A Pulsed Electric Field Chamber For Microalgae Lysis, Connor Strutin

Electrical Engineering

Microalgae are a promising renewable source of biofuel due to their high lipid content and rapid growth rates; however, algae cells must be electroporated (lysed) to release these lipids. The system developed in this project applies short (40 μs–10 ms), high-intensity (up to 33 kV/cm) pulsed electric fields (PEF) across cell membranes to enable electroporation.

Our lab-scale PEF chamber generates uniform, high-intensity electric fields using parallel-plate electrodes with sub-millimeter spacing. The system is designed to efficiently lyse microalgae cells for lipid extraction and biofuel production.

Maximum Power Point Tracking For Photovoltaic Cells, Rodrigo Menchaca-Ramirez, Jesus Martinez, Steven Rodriguez

Electrical Engineering

Unlike an ideal DC Power Supply, a solar panel does not operate with linear I-V Characteristics. The voltage and current characteristics of a solar panel change based on irradiance, temperature, weather conditions, and load demand. Because of these factors, solar panels do not always operate at their Maximum Power Point, which can lead to significant power losses. Maximum Power Point Tracking is used to adjust the panel operating point so that more usable power can be delivered to the load.This project developed a Maximum Power Point Tracking system using an STM32 Nucleo-L4A6ZG microcontroller and an LT8705A DC-DC converter stage. 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 …

Alford Loop Antennas For Ultra-High Energy Neutrino Detection, John K. Kimura

Master's Theses

The neutrino is a fundamental particle of matter emitted by high energy phenomena, such as supernovae and black holes. Detecting neutrinos can give scientists information regarding the frequency of high-energy cosmic events, as well as point them towards specific high-energy events. Ultra-high energy (UHE) neutrino interactions in Greenland ice result in electromagnetic pulses between 200MHz and 800MHz. The Radio Neutrino Observatory - Greenland (RNO-G) seeks to observe the resultant electromagnetic waves. The Alford Loop antenna is proposed to detect these emissions, because of its dipole-like radiation pattern, horizontal polarization, and adequate dimensions to fit in a 10" diameter ice borehole. …

Practical Multimodal Wearable Sensing For Functional Upper Extremity Primitive Classification With Application To Stroke Rehabilitation, Nicholas Weiss

Master's Theses

Stroke often causes long-term weakness and impaired motor control in the upper extremity (UE), making everyday tasks such as reaching, grasping, and moving objects more difficult. Restoring functional arm use is therefore a central goal of post-stroke rehabilitation. Measuring affected arm use continuously and objectively is important because isolated clinical assessments may not fully capture how the affected arm is used during therapy or daily life. Wearable sensors offer a promising approach for monitoring, but raw sensor signals are difficult to interpret directly. Functional movement primitives address this issue by describing UE behavior as smaller, task-agnostic movement units.

This thesis …

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, …

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 …

Autonomous Exploration Of An Environment With Static Obstacles Using A Ppo Agent, Brandon Knight

Theses

Research in autonomous exploration has created many effective algorithms that have been tested and proven to work in many different virtual and physical environments. Many optimizations have also been developed to reduce computational effort and increase exploration speed.

However, despite optimizations, these algorithms can still require considerable computational effort and time to explore even small environments. To obtain further improvements in computation and exploration speed, a reinforcement learning agent using actor-critic style proximal policy optimization (PPO) is trained to explore various environments efficiently, then compared to an algorithm using contemporary exploration methods.

Testing is performed in virtual environments with ideal …

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 …

Enabling Ml/Ai In 6g And Future Wireless Communication With Privacy Preservation, Mec Offloading And Quantum Computing, Changshi Zhou

Dissertations

The forthcoming sixth-generation (6G) and future wireless networks are envisioned to support an unprecedented range of services, delivering ultra-low latency, massive connectivity, and intelligent real-time responsiveness. These capabilities will enable emerging applications such as extended reality (XR), autonomous vehicles (AVs), industrial robotics, and the Internet of Things (IoT) to reach their full potential. Achieving this vision requires the integration of enabling technologies such as artificial intelligence and machine learning (AI/ML) and quantum computing, which are poised to play central roles in shaping the landscape of wireless communication systems.

In AI-native, data-driven, and computing-centric 6G networks, ML models will be deeply …

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 …

Developing A Framework For Mooc Dropout: The Role Of Utilitarian And Hedonic Values In Student Retention, Bipllab Roy, Mave D'Souza, Madhura Laghane

Northeast Journal of Complex Systems (NEJCS)

Massive Open Online Courses (MOOCs) have expanded access to higher education but continue to face persistently high dropout rates, raising concerns about their long‑term effectiveness and sustainability. This study develops and empirically tests a structural framework that links utilitarian values (perceived usefulness, certificate value, time flexibility), hedonic values (enjoyment, variety and novelty, personal interest alignment), and individual characteristics (goal orientation, self‑efficacy, motivation type) to MOOC student retention. Data were collected through a structured questionnaire administered to 200 MOOC learners from Christ University, Lavasa Campus, and analyzed using Structural Equation Modelling (SEM) in AMOS. The results show that goal orientation, self‑efficacy …

Modulating Electronic Structure With Linearly Fused Pyrazine Units For High-Voltage And Stable Zinc-Organic Batteries Cathode, Min-Jian Zhao, Li-Bin Zhang, Jin-Tao Wang, Kun Ding, Hai-Mei Liu, Yong-Gang Wang

Journal of Electrochemistry

High-voltage n-type organic cathode materials are critical for constructing zinc-organic batteries (ZOBs) with high energy density and long cycle life. However, the intrinsically unfavorable electronic structures and relatively high LUMO energy levels of most n-type materials often lead to sluggish kinetics, high solubility, and suboptimal discharge voltages (< 0.8 V). Here, we design a small molecule, quinoxalino[2’,3’:5,6]pyrazino[2,3-f][1,10]phenanthroline (DPQP), as a ZOB cathode by introducing locally electron-deficient motifs into the conjugated backbone of aromatic compounds. The linearly fused pyrazine units extending the pyrazine–benzene framework effectively optimize the electronic structure, thereby significantly enhancing the discharge voltage. Meanwhile, the expanded π-conjugated plane suppresses dissolution and accelerates charge-transfer kinetics. Benefiting from these features, the DPQP electrode exhibits an exceptional increase in average operating voltage from 0.61 V to 1.07 V (vs. Zn²⁺/Zn) at 0.1 A·g^–1, with an overpotential of only 140 mV. Notably, no discernible voltage decay occurs as the current density increases, indicating rapid and highly reversible redox kinetics. Furthermore, the DPQP cathode delivers outstanding cycling stability, maintaining over 2000 h of continuous operation at 0.1 A·g^{–1 …}

(Co,Ni,Mn,Cu,Zn)O High-Entropy Oxide Nanotubes As Efficient Bifunctional Electrocatalyst For Oxygen Evolution And Hydrazine Oxidation Reactions, Pan-Yan Chen, Wan-Wan Wu, Heng Bian, Wei-Wei Li, Xin-Sheng Zhao, Lu Wei

Journal of Electrochemistry

High-entropy oxides (HEOs) present significant scientific challenges in both design and synthesis due to their multielement and high-entropy nature, which involves complex combinations of multiple metal cations and oxygen anions, typically arranged in equimolar ratios to achieve structural stability. Herein, one-dimensional (Co,Ni,Mn,Cu,Zn)O high-entropy oxide nanotubes (HEO-NTs) are fabricated by means of a gradient electrospinning strategy with a tailored polyvinyl alcohol (PVA) molecular weight distribution and controlled pyrolysis. Benefiting from the HEO features and the synergistic effect of multicomponent sites, the as-synthesized (Co,Ni,Mn,Cu,Zn)O HEO-NTs exhibit exceptional bifunctional electrocatalytic activity for the oxygen evolution and hydrazine oxidation reactions (OER/HzOR). This study offers …

Exploring Maximal Length Cellular Automata To Generate Primitive Polynomials In Gf(2), Sumit Adak, Subhrajit Deb, Anurag Ghosh, Angshuman Roy, Souvik Roy

Northeast Journal of Complex Systems (NEJCS)

We present a simple method that uses cellular automata (CAs) to find primitive polynomials over GF(2). We used maximal length CAs as tools to generate primitive polynomials. It is usually very difficult to find maximal length CAs or primitive polynomials since they require exponential time, and there is no linear time method. However, in our work, given an n-size specific sequence of CA with reasonable probability, our technique computes a cycle of length at most 2^n-1 (maximal length) in O(n) time. The characteristic polynomials of synthesized maximal length CAs are claimed to be primitive since it was previously established that …

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 …