Defense Notices

Stimming refers to repetitive actions or behaviors used to regulate sensory input or express feelings. Children with developmental disorders like autism (ASD) frequently perform stimming. This includes arm flapping, head banging, finger flicking, spinning, etc. This is exhibited by 80-90% of children with Autism, which is seen in 1 among 36 children in the US. Head banging is one of these self-stimulatory habits that can be harmful. If these behaviors are automatically identified and notified using live video monitoring, parents and other caregivers can better watch over and assist children with ASD.
Classifying these actions is important to recognize harmful stimming, so this study focuses on developing a deep learning-based approach for stimming action recognition. We implemented and evaluated four models leveraging three deep learning architectures based on Convolutional Neural Networks (CNNs), Autoencoders, and Vision Transformers. For the first time in this area, we use skeletal joints extracted from video sequences. Previous works relied solely on raw RGB videos, vulnerable to lighting and environmental changes. This research explores Deep Learning based skeletal action recognition and data processing techniques for a small unstructured dataset that consists of 89 home recorded videos collected from publicly available sources like YouTube. Our robust data cleaning and pre-processing techniques helped the integration of skeletal data in stimming action recognition, which performed better than state-of-the-art with a classification accuracy of up to 87%
In addition to using traditional deep learning models like CNNs for action recognition, this study is among the first to apply data-hungry models like Vision Transformers (ViTs) and Autoencoders for stimming action recognition on the dataset. The results prove that using skeletal data reduces the processing time and significantly improves action recognition, promising a real-time approach for video monitoring applications. This research advances the development of automated systems that can assist caregivers in more efficiently tracking stimming activities.

Just as the internet-of-things leverages machine learning over a vast amount of data produced by an innumerable number of sensors, the Internet of Catalysis program uses similar strategies with catalysis research. One application of the Internet of Catalysis strategy is treating research papers as datapoints, rich with text, figures, and tables. Prior research within the program focused on machine learning models applied strictly over text.

This project is the first step of the program in creating a machine learning model from the images of catalysis research papers. Specifically, this project creates a dataset of faradaic efficiency graphs using transfer learning from pretrained models. The project utilizes FasterRCNN_ResNet50_FPN, LayoutLMv3SequenceClassification, and computer vision techniques to recognize figures, extract all graphs, then classify the faradaic efficiency graphs.

Downstream of this project, researchers will create a graph reading model to integrate with large language models. This could potentially lead to a multimodal model capable of fully learning from images, tables, and texts of catalysis research papers. Such a model could then guide experimentation on reaction conditions, catalysts, and production.

Significant advances in information and networking technologies have transformed Cyber-Physical Systems (CPS) into networked cyber-physical systems (NCPS). A noteworthy example of such systems is smart grid networks, which include distributed energy resources (DERs), renewable generation, and the widespread adoption of Electric Vehicles (EVs). Such complex NCPS require intelligent and autonomous control solutions. For example, the increasing number of EVs introduces significant sources of demand and user behavior uncertainty that can jeopardize grid stability during peak hours. Traditional model-based demand-supply controls fail to accurately model and capture the complex nature of smart grid systems in the presence of different uncertainties and as the system size grows. To address these challenges, data-driven approaches have emerged as an effective solution for informed decision-making, predictive modeling, and adaptive control to enhance the resiliency of NCPS in uncertain environments.

As a powerful data-driven approach, Multi-Agent Reinforcement Learning (MARL) enables agents to learn and adapt in dynamic and uncertain environments. However, MARL techniques introduce complexities related to communication, coordination, and synchronization among agents. In this PhD research, we investigate autonomous control for smart grid decision networks using MARL. First, we examine the issue of imperfect state information, which frequently arises due to the inherent uncertainties and limitations in observing the system state.

Second, we focus on the cooperative behavior of agents in distributed MARL frameworks, particularly under the central training with decentralized execution (CTDE) paradigm. We provide theoretical results and variance analysis for stochastic and deterministic cooperative MARL algorithms, including Multi-Agent Deep Deterministic Policy Gradient (MADDPG), Multi-Agent Proximal Policy Optimization (MAPPO), and Dueling MAPPO. These analyses highlight how coordinated learning can improve system-wide decision-making in uncertain and dynamic environments like EV networks.

Third, we address the scalability challenge in large-scale NCPS by introducing a hierarchical MARL framework based on a cluster-based architecture. This framework organizes agents into coordinated subgroups, improving scalability while preserving local coordination. We conduct a detailed variance analysis of this approach to demonstrate its effectiveness in reducing communication overhead and learning complexity. This analysis establishes a theoretical foundation for scalable and efficient control in large-scale smart grid applications.

This project presents the development of a custom Convolutional Neural Network (CNN) designed to classify object states—such as sliced, diced, or peeled—in cooking environments. Recognizing fine-grained object states is essential for context-aware manipulation but remains challenging due to visual similarity between states and a limited dataset. To address these challenges, I built a lightweight CNN from scratch, deliberately avoiding pretrained models to maintain domain specificity and efficiency. The model was enhanced through data augmentation and optimized dropout layers, with additional experiments incorporating batch normalization, Inception modules, and residual connections. While these advanced techniques offered incremental improvements during experimentation, the final model—a combination of data augmentation, dropout, and batch normalization—achieved ~60% validation accuracy and demonstrated stable generalization. This work highlights the trade-offs between model complexity and performance in constrained environments and contributes toward real-time state recognition with potential applications in assistive technologies.

The growing dependence on electronic systems in consumer and mission critical domains requires engineers who understand the inner workings of digital hardware. Yet many students bypass hardware electives, viewing them as abstract, mathematics heavy, and less attractive than software courses. Escalating workforce shortages in the semiconductor industry and the recent global chip‑supply crisis highlight the urgent need for graduates who can bridge hardware knowledge gaps across engineering sectors. In this thesis, I have developed FPGA‑based games, embedded in inclusive curricular modules, which can make hardware concepts accessible while fostering interest, self‑efficacy, and positive outcome expectations in hardware engineering. A design‑based research methodology guided three implementation cycles: a pilot with seven diverse high‑school learners, a multiweek residential summer camp with high‑school students, and a fifteen‑week multidisciplinary elective enrolling early undergraduate engineering students. The learning experiences targeted binary arithmetic, combinational and sequential logic, state‑machine design, and hardware‑software co‑design. Learners also moved through the full digital‑design flow, HDL coding, functional simulation, synthesis, place‑and‑route, and on‑board verification. In addition, learners explored timing analysis, register‑transfer‑level abstractions, and simple processor datapaths to connect low‑level circuits with system‑level behavior. Mixed‑method evidence was gathered through pre‑ and post‑content quizzes, validated surveys of self‑efficacy and outcome expectations, focus groups, classroom observations, and gameplay analytics. Paired‑sample statistics showed reliable gains in hardware‑concept mastery, self‑efficacy, and outcome expectations. This work contributes a replicable framework for translating foundational hardware topics into modular, game‑based learning activities, empirical evidence of their effectiveness across secondary and early‑college contexts, and design principles for educators who seek to integrate equitable, hands‑on hardware experiences into existing curricula.

Although the Center for Remote Sensing of Ice Sheets (CReSIS) performs several radar calibration steps to produce Operation IceBridge (OIB) radar depth sounder data products, these datasets are not radiometrically calibrated and the swath array processing uses ideal (rather than measured [calibrated]) steering vectors. Any errors in the steering vectors, which describe the response of the radar as a function of arrival angle, will lead to errors in positioning and backscatter that subsequently affect estimates of basal conditions, ice thickness, and radar attenuation. Scientific applications that estimate physical characteristics of surface and subsurface targets from the backscatter are limited with the current data because it is not absolutely calibrated. Moreover, changes in instrument hardware and processing methods for OIB over the last decade affect the quality of inter-seasonal comparisons. Recent methods which interpret basal conditions and calculate radar attenuation using CReSIS OIB 2D radar depth sounder echograms are forced to use relative scattering power, rather than absolute methods.

As an active target calibration is not possible for past field seasons, a method that uses natural targets will be developed. Unsaturated natural target returns from smooth sea-ice leads or lakes are imaged in many datasets and have known scattering responses. The proposed method forms a system of linear equations with the recorded scattering signatures from these known targets, scattering signatures from crossing flight paths, and the radiometric correction terms. A least squares solution to optimize the radiometric correction terms is calculated, which minimizes the error function representing the mismatch in expected and measured scattering. The new correction terms will be used to correct the remaining mission data. The radar depth sounder data from all OIB campaigns can be reprocessed to produce absolutely calibrated echograms for the Arctic and Antarctic. A software simulator will be developed to study calibration errors and verify the calibration software. The software for processing natural targets and crossovers will be made available in CReSIS’s open-source polar radar software toolbox. The OIB data will be reprocessed with new calibration terms, providing to the data user community a complete set of radiometrically calibrated radar echograms for the CReSIS OIB radar depth sounder for the first time.

With the increasing congestion of the usable RF spectrum, it is increasingly necessary for communication and radar systems to share the same frequencies without disturbing one another. To accomplish this, research has focused on designing a class of non-repeating radar waveforms that appear as noise at the receiver of uncooperative systems, but the peak power from high-power pulsed systems can still overwhelm nearby in-band systems. Therefore, to minimize peak power while maximizing the total energy on target, radar systems must transition to operating at a 100% duty cycle, which inherently requires Simultaneous Transmit and Receive (STAR) operation.

One inherent difficulty when operating monostatic STAR systems is the direct path coupling interference that can saturate a number of components in the radar’s receive chain, which makes digital processing methods that remove this interference ineffective. This thesis proposes a method to reduce the self-interference between the radar’s transmitter in receiver prior to the receiver’s sensitive components to increase the power that the radar can transmit at. By using a combination of tests that manipulate the timing, phase, and magnitude of a secondary waveform that is injected into the radar just before the receiver, upwards of 35.0 dB of self-interference cancellation is achieved for radar waveforms with bandwidths of up to 100 MHz at both S-band and X-band in both simulation and open-air testing.

Recent advancements in optical fiber technology have proven to be invaluable in a variety of fields, extending far beyond high-speed communications. These innovations enable optical fiber sensing, which plays a critical role across diverse applications, from medical diagnostics to infrastructure monitoring and automotive systems. This research focuses on leveraging commercially available coherent optical transceiver systems to develop novel measurement techniques for characterizing optical fiber properties. Specifically, our goal is to leverage a digitally chirped frequency-modulated continuous wave (FMCW) to extract detailed information about optical fiber characteristics, as well as target range. Through this approach, we aim to enable more accurate and fast assessments of fiber performance and integrity, while exploring the potential for utilizing existing optical communication networks to enhance fiber characterization capabilities. This goal is investigated through three distinct projects: (1) fiber type characterization based on intensity-modulated electrostriction response, (2) self-homodyne coherent Light Detection and Ranging (LiDAR) system for target range and velocity detection, and (3) birefringence measurements using a coherent Polarization-sensitive Optical Frequency Domain Reflectometer (OFDR) system.

Electrostriction in an optical fiber is introduced by interaction between the forward propagated optical signal and the acoustic standing waves in the radial direction resonating between the center of the core and the cladding circumference of the fiber. The response of electrostriction is dependent on fiber parameters, especially the mode field radius. We demonstrated a novel technique of identifying fiber types through the measurement of intensity modulation induced electrostriction response. As the spectral envelope of electrostriction induced propagation loss is anti-symmetrical, the signal to noise ratio can be significantly increased by subtracting the measured spectrum from its complex conjugate. We show that if the field distribution of the fiber propagation mode is Gaussian, the envelope of the electrostriction-induced loss spectrum closely follows a Maxwellian distribution whose shape can be specified by a single parameter determined by the mode field radius.         

We also present a self-homodyne FMCW LiDAR system based on a coherent receiver. By using the same linearly chirped waveform for both the LiDAR signal and the local oscillator, the self-homodyne coherent receiver performs frequency de-chirping directly in the photodiodes, significantly simplifying signal processing. As a result, the required receiver bandwidth is much lower than the chirping bandwidth of the signal. Multi-target detection is demonstrated experimentally, and while only amplitude modulation is required in the LiDAR transmitter, the phase-diversity coherent receiver enables simultaneous detection of both range and velocity for each target, along with the sign of the target’s velocity.

In addition, we demonstrate a polarization-sensitive OFDR system utilizing a commercially available digital coherent optical transceiver to generate a linear frequency chirp via carrier-suppressed single-sideband modulation. This method ensures linearity in chirping and phase continuity of the optical carrier. The coherent homodyne receiver, incorporating both polarization and phase diversity, recovers the state of polarization (SOP) of the backscattered optical signal along the fiber, mixing with an identically chirped local oscillator. With a spatial resolution of approximately , a chirping bandwidth, and a measurement time, this system enables precise birefringence measurements. By employing three mutually orthogonal SOPs of the launched optical signal, we can measure birefringence vectors along the fiber, providing not only the magnitude of birefringence but also the direction of any external pressure applied to the fiber.

Binary similarity is a fundamental technique that enables software analysis practitioners to compare machine-level code at scale and with fine granularity. With application in software reverse engineering, vulnerability research, malware attribution and more, state-of-the-art binary similarity tools have undergone thorough research and development to account for variations in compilers, optimizations, machine architectures, and even obfuscations. And, although these tools aim to compare and detect binary-level code segments generated from similar or identical source code, no preexisting work has investigated the effects of source languages other than C and C++. This thesis addresses this research gap by presenting a thorough investigation of SOTA binary similarity tools when applied to modern compiled languages, Rust and Golang.

To adequately evaluate the capabilities of the available binary similarity approaches, this work includes three distinct tools - BSim, a new component of the Ghidra Software Reverse Engineering Framework, which utilizes a clustering based similarity mechanism; BinDiff, an industry-recognized tool using graph-based comparisons; and jTrans, a BERT-based model fine-tuned to the binary similarity task. First, to enable this work, we introduce a new dataset of Rust and Golang binaries compiled from leading open-source projects in the Homebrew and Arch Linux repositories. Comprised of 800 binaries and over 1 million functions, this dataset was built to represent a broad range of implementation styles, application diversity, and source language features. Next, the main investigation of this thesis is presented wherein we asses each approach's ability to accurately report semantically equivalent functions compiled from the same source code. Results across the three tools reveal a systematic degradation of precision when comparing binaries produced by Rust and Go rather than those produced by C and C++. Finally, we provide a technical demonstration which highlights the implications of these results and discuss near- and long-term solutions to more adequately equip binary analysis practitioners.  
 

Deep learning is widely used in healthcare, finance, and other critical domains, raising concerns about system trustworthiness. However, deep learning models and data still face three types of critical attacks: model theft, identity impersonation, and abuse of AI-generated content (AIGC). To address model theft, homomorphic encryption has been explored for privacy-preserving inference, but it remains highly inefficient. To counter identity impersonation, prior work focuses on detection, disruption, and tracing—yet fails to protect source and target images simultaneously. To prevent AIGC abuse, methods like evaluation, watermarking, and machine unlearning exist, but text-driven image editing remains largely unprotected.

This report addresses the above challenges through three key designs. First, to enable privacy-preserving inference while accelerating homomorphic encryption, we propose PrivDNN, which selectively encrypts the most critical model parameters, significantly reducing encrypted operations. We design a selection score to evaluate neuron importance and use a greedy algorithm to iteratively secure the most impactful neurons. Across four models and datasets, PrivDNN reduces encrypted operations by 85%–98%, and cuts inference time and memory usage by over 97% while preserving accuracy and privacy. Second, to counter identity impersonation in deepfake face-swapping, where both the source and target can be exploited, we introduce PhantomSeal, which embeds invisible perturbations to encode a hidden “cloak” identity. When used as a target, the resulting content displays visible artifacts; when used as a source, the generated deepfake is altered to resemble the cloak identity. Evaluations across two generations of deepfake face-swapping show that PhantomSeal reduces attack success from 97% to 0.8%, with 95% of outputs recognized as the cloak identity, providing robust protection against manipulation. Third, to prevent AIGC abuse, we construct a comprehensive dataset, perform large-scale human evaluation, and establish a benchmark for detecting AI-generated artwork to better understand abuse risks in AI-generated content. Building on this direction, we propose Protecting Copyright against Image Editing (PCIE) to address copyright infringement in text-driven image editing. PCIE embeds an invisible copyright mark into the original image, which transforms into a visible watermark after text-driven editing to automatically reveal ownership upon unauthorized modification.