Defense Notices

This thesis focuses on the multiple processing techniques that can be applied to a single receive element co-located with a Frequency Diverse Array (FDA) transmission structure that illuminates a large volume to estimate the scattering characteristics of objects within the illuminated space in the range, Doppler, and spatial dimensions. FDA transmissions consist of a number of evenly spaced transmitting elements all of which are radiating a linear frequency modulated (LFM) waveform. The elements are configured into a Uniform Linear Array (ULA) and the waveform of each element is separated by a frequency spacing across the elements where the time duration of the chirp is inversely proportional to an integer multiple of the frequency spacing between elements. The complex transmission structure created by this arrangement of multiple transmitting elements can be received and processed by a single receive element. Furthermore, multiple receive processing techniques, each with their own advantages and disadvantages, can be applied to the data received from the single receive element to estimate the range, velocity, and spatial direction of targets in the illuminated volume relative to the co-located transmit array and receive element. Three different receive processing techniques that can be applied to FDA transmissions are explored. Two of these techniques are novel to this thesis, including the spatial matched filter processing technique for FDA transmission structures, and stretch processing using virtual array processing for FDA transmissions. Additionally, this thesis introduces a new type of FDA transmission structure referred to as ”slow-time” FDA.

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 Vehicle (EV). 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 the 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. Within this context, first, we examine the issue of imperfect state information, which frequently arises due to the inherent uncertainties and limitations in observing the system state. Secondly, we investigate the challenges associated with distributed MARL techniques, with a special focus on the central training distributed execution (CTDE) methods. Throughout this research, we highlight the significance of cooperation in MARL for achieving autonomous control in smart grid systems and other cyber-physical domains. Thirdly, we propose a novel robust MARL framework using a hierarchical structure. We perform an extensive analysis and evaluation of our proposed hierarchical MARL model for large-scale EV networks, thereby addressing the scalability and robustness challenges as the number of agents within a NCPS increases.

In response to the challenges posed by increasingly sophisticated criminal behaviour that strategically evades conventional identification methods, this research advocates for a paradigm shift in forensic practices. Departing from reliance on traditional biometric techniques such as DNA matching, eyewitness accounts, and fingerprint analysis, the study introduces a pioneering biometric approach centered on facial recognition systems. Addressing the limitations of established methods, the proposed methodology integrates two key components. Firstly, facial features are meticulously extracted using the Histogram of Oriented Gradients (HOG) methodology, providing a robust representation of individualized facial characteristics. Subsequently, a face recognition system is implemented, harnessing the power of the K-Nearest Neighbours machine learning classifier. This innovative dual-method approach aims to significantly enhance the accuracy and reliability of criminal identification, particularly in scenarios where conventional methods prove inadequate. By capitalizing on the inherent uniqueness of facial features, this research strives to introduce a formidable tool for forensic practitioners, offering a more effective means of addressing the evolving landscape of criminal tactics and safeguarding the integrity of justice systems. 

In the pursuit of unraveling the intricate mechanisms underlying the aging process, this study presents a sophisticated bioinformatics pipeline tailored for the analysis of MicroRNA (miRNA) data obtained from Oxford MinION instruments. MiRNAs, small non-coding RNA molecules, play pivotal roles in the regulation of gene expression and have emerged as key players in various biological processes, including aging. The utilization of Oxford MinION, a nanopore sequencing platform, allows for high-throughput and real-time sequencing of miRNAs, providing a comprehensive view of the miRNA landscape.

Our integrative bioinformatics pipeline encompasses raw data preprocessing, quality control, alignment, and quantification of miRNAs. Leveraging state-of-the-art algorithms, the pipeline enables the identification of differentially expressed miRNAs associated with the aging phenotype. Functional enrichment analysis is employed to elucidate the biological pathways influenced by these miRNAs, shedding light on the molecular intricacies of the aging process. Furthermore, network analysis is employed to unravel potential regulatory interactions among identified miRNAs and their target genes.

This comprehensive approach amalgamates cutting-edge sequencing technology with advanced bioinformatics methodologies, offering a robust framework for deciphering the complex regulatory networks governing aging. The findings derived from this pipeline not only enhance our understanding of the molecular basis of aging but also provide potential targets for therapeutic interventions aimed at modulating the aging process.

Legacy radar systems largely rely on repeated emission of a linear frequency modulated (LFM) or chirp waveform to ascertain scattering information from an environment. The prevalence of these chirp waveforms largely stems from their simplicity to generate, process, and the general robustness they provide towards hardware effects. However, this traditional design philosophy often lacks the flexibility and dimensionality needed to address the dynamic “complexification” of the modern radio frequency (RF) environment or achieve current operational requirements where unprecedented degrees of sensitivity, maneuverability, and adaptability are necessary.

Over the last couple of decades analog-to-digital and digital-to-analog technologies have advanced exponentially, resulting in tremendous design degrees of freedom and arbitrary waveform generation (AWG) capabilities that enable sophisticated design of emissions to better suit operational requirements. However, radar systems typically require high powered amplifiers (HPA) to contend with the two-way propagation. Thus, transmitter-amenable waveforms are effectively constrained to be both spectrally contained and constant amplitude, resulting in a non-convex NP-hard design problem.

While determining the global optimal waveform can be intractable for even modest time-bandwidth products (TB), locally optimal transmitter-amenable solutions that are “good enough” are often readily available. However, traditional matched filtering may not satisfy operational requirements for these sub-optimal emissions. Using knowledge of the transmitter-receiver chain, a discrete linear model can be formed to express the relationship between observed measurements and the complex scattering of the environment. This structured representation then enables more sophisticated least-square and adaptive estimation techniques to better satisfy operational needs, improve estimate fidelity, and extend dynamic range.

However, radar dimensionality can be enormous and brute force implementations of these techniques may have unwieldy computational burden on even cutting-edge hardware. Additionally, a discrete linear representation is fundamentally an approximation of the dynamic continuous physical reality and model errors may induce bias, create false detections, and limit dynamic range. As such, these structure-based approaches must be both computationally efficient and robust to reality.

Here several generalized discrete radar receive models and structure-based estimation schemes are introduced. Modifications and alternative solutions are then proposed to improve estimate fidelity, reduce computational complexity, and provide further robustness to model uncertainty.

Austere environments are defined by the US Military as those regularly experiencing significant environmental hazards, have limited access to reliable electricity, or require prolonged use of body armor or chemical protection equipment.  We propose that in modern society, this definition can extend also to telecommunications infrastructure, areas where an active adversary controls the telecommunications infrastructure and works against the people such as protest areas in Iran, Russia, and China or areas experiencing conflict and war such as Eastern Ukraine.  People in these austere environments need basic text communications and the ability to share simple media like low resolution pictures.  This communication is complicated by the adversaries’ capabilities as a potential nation-state actor. To address this, Low Earth Orbit satellite clusters, like Starlink, can be used to exfiltrate communications completely independent of local infrastructure.  This, however, creates another issue as these satellite ground terminals are not inherently designed to support many users over a large area.  Traditional means of extending this connectivity create both power and security concerns.  We propose that Bluetooth Mesh can be used to extend connectivity and provide communications. 

Bluetooth Mesh provides a low signal footprint to reduce the risk of detection, blends into existent signals within the 2.4ghz spectrum, has security aspects in the specification, and devices can utilize small batteries maintaining a covert form factor.  To realize this security enhancements must be made to both the provisioning process of the Bluetooth Mesh network and a key management scheme that ensures the regular and secure changing of keys either in response to an adversary’s action or as a prevention of an adversary’s action must be implemented.  We propose a provisioning process using whitelists on both provisioner and device and uses attestation for passwords allowing devices to be provisioned on deployment to protect the at-risk population and prevent BlueMirror attacks.  We also propose, implement, and measure the impact of an automated key exchange that meets the Bluetooth Mesh 3 phase specification.  Our experimentation, in a field environment, shows that Bluetooth Mesh has the throughput, reliability and security to meet the requirements of at-risk populations in austere environments. 

This ground-breaking project addresses a critical issue in crop production: precisely determining plant-available inorganic nitrogen (IN) in soil to optimize fertilization strategies. Current methodologies frequently struggle with the complexities of determining a soil's nitrogen content, resorting to approximations and labor-intensive soil testing procedures that can lead to the pitfalls of under or over-fertilization, endangering agricultural productivity. Recognizing the scarcity of historical inorganic nitrogen (IN) data, this solution employs a novel approach that employs Generative Adversarial Networks (GANs) to generate statistically similar inorganic nitrogen (IN) data. 

This synthetic data set works in tandem with data from the Decision Support System for Agrotechnology Transfer (DSSAT). To address the data's inherent time-series nature, we use the power of Long Short-Term Memory (LSTM) neural networks in our predictive model. The resulting model is a sophisticated and accurate tool that can provide reliable estimates without extensive soil testing. This not only ensures precision in nutrient management but is also a cost-effective and dependable solution for crop production optimization. 

Model Predictive Control (MPC) has emerged as a potent approach for controlling nonlinear systems in the robotics field and various other engineering domains. Its efficacy lies in its capacity to predictively optimize system behavior while accommodating state and input constraints. Although MPC typically relies on precise dynamic models to be effective, real-world dynamic systems often harbor uncertainties. Ignoring these uncertainties can lead to performance degradation or even failure in MPC.

Nonlinear latent force models, integrating latent uncertainties characterized as Gaussian processes, hold promise for effectively representing nonlinear uncertain systems. Specifically, these models incorporate the state-space representation of a Gaussian process into known nonlinear dynamics, providing the ability to simultaneously predict future states and uncertainties.

This thesis delves into the application of MPC to nonlinear latent force models, aiming to control nonlinear uncertain systems. We formulate a stochastic MPC problem and, to address the ensuing receding-horizon stochastic optimization problem, introduce a scenario-based approach for a deterministic approximation. The resulting scenario-based approach is assessed through simulation studies centered on the motion planning of an autonomous vehicle. The simulations demonstrate the controller's adeptness in managing constraints and consistently mitigating the effects of disturbances. This proposed approach holds promise for various robotics applications and beyond.

The advent of advanced natural language processing (NLP) techniques has revolutionized the way we handle textual data. This project presents the implementation of exploring contextual sentence understanding on the Quora Insincere Questions dataset using the pretrained BERT architecture. In this study, we explore the application of BERT, a bidirectional transformer model, for text classification tasks. The goal is to classify if a question contains hateful, disrespectful or toxic content. BERT represents the state-of-the-art in language representation models and has shown strong performance on various natural language processing tasks. In this project, the pretrained BERT base model is fine-tuned on a sample of the Quora dataset for next sentence prediction. Results show that with just 1% of the data (around 13,000 examples), the fine-tuned model achieves over 90% validation accuracy in identifying insincere questions after 4 epochs of training. This demonstrates the effectiveness of leveraging BERT for text classification tasks with minimal labeled data requirements. Being able to automatically detect toxic, hateful or disrespectful content is important to maintain healthy online discussions. However, the nuances of human language make this a challenging natural language processing problem. Insincere questions may contain offensive language, hate speech, or misinformation, making their identification crucial for maintaining a positive and safe online environment. In this project, we explore using the pretrained Bidirectional Encoder Representations from Transformers (BERT) model for next sentence prediction on the task of identifying insincere questions.

The research investigates the utilization of Synthetic Minority Oversampling Techniques (SMOTE) in the context of machine learning models applied to biomedical datasets, particularly focusing on mitigating demographic data disparities. The study is most relevant to underrepresented demographic data. The primary objective is to enhance the SMOTE methodology, traditionally designed for addressing class imbalances, to specifically tackle ethnic imbalances within feature representation. In contrast to conventional approaches that merely exclude race as a fundamental or additive factor without rectifying misrepresentation, this work advocates an innovative modification of the original SMOTE framework, emphasizing dataset augmentation based on participants' demographic backgrounds. The predominant aim of the project is to enhance and reshape the distribution to optimize model performance for unspecified demographic subgroups during training. However, the outcomes indicate that despite the application of feature balancing in this adapted SMOTE method, no statistically significant enhancement in accuracy was discerned. This observation implies that while rectifying imbalances is crucial, it may not independently suffice to overcome challenges associated with heterogeneity in species representation within machine learning models applied to biomedical databases. Consequently, further research endeavors are necessary to identify novel methodologies aimed at enhancing sampling accuracy and fairness within diverse populations.

Quantum computing presents an opportunity to accelerate machine learning (ML) tasks on quantum processors in a similar vein to existing classical accelerators, such as graphical processing units (GPUs). In the classical domain, convolutional neural networks (CNNs) effectively exploit data locality using the convolution operation to reduce the number of fully-connected operations in multi-layer perceptrons (MLPs). Preserving data locality enables the pruning of training parameters, which results in reduced memory requirements and shorter training time without compromising classification accuracy. However, contemporary quantum machine learning (QML) algorithms do not leverage the data locality of input features in classification workloads, particularly for multidimensional data. This work presents a multidimensional quantum convolutional classifier (MQCC) that adapts the CNN structure to a variational quantum algorithm (VQA). The proposed MQCC uses quantum implementations of multidimensional convolution, pooling based on the quantum Haar transform (QHT) and partial measurement, and fully-connected operations. Time-complexity analysis will be presented to demonstrate the speedup of the proposed techniques in comparison to classical convolution and pooling operations on modern CPUs and/or GPUs. Experimental work is conducted on state-of-the-art quantum simulators from IBM Quantum and Xanadu modeling noise-free and noisy quantum devices. High-resolution multidimensional images are used to demonstrate the correctness and scalability of the convolution and pooling operations. Furthermore, the proposed MQCC model is tested on a variety of common datasets against multiple configurations of related ML and QML techniques. Based on standard metrics such as log loss, classification accuracy, number of training parameters, circuit depth, and gate count, it will be shown that MQCC can deliver a faithful implementation of CNNs on quantum machines. Additionally, it will be shown that by exploiting data locality MQCC can achieve improved classification over contemporary QML methods. 

Software vulnerabilities, stemming from coding weaknesses and poor development practices, have become increasingly prevalent. These vulnerabilities could be exploited by attackers to pose risks to the confidentiality, integrity, and availability of software. To protect themselves, end-users of software may have an interest in knowing if the software they buy and use is secure from such attacks. Our work is motivated by this need to automatically assess and rate the security properties of binary software.

To increase user trust in third-party software, researchers have devised several techniques and tools to identify and mitigate coding weaknesses in binary software. Therefore, our first task in this work is to assess the current landscape and comprehend the capabilities and challenges faced by binary-level techniques aimed at detecting critical coding weaknesses in software binaries. We categorize the most important coding weaknesses in compiled programming languages, and conduct a comprehensive survey, exploration, and comparison of static techniques designed to locate these weaknesses in software binaries. Furthermore, we perform an independent assessments of the efficacy of open-source tools using standard benchmarks.

Next, we develop techniques to assess if secure coding principles were adopted during the generation of the software binary. Towards this goal, we first develop techniques to determine the high-level source language used to produce the binary. Then, we check the feasibility of detecting the use of secure coding best practices during code development. Finally, we check the feasibility of detecting the vulnerable regions of code in any binary executable. Our ultimate future goal is to employ all of our developed techniques to rate the security-quality of the given binary software.

With threat actors ever evolving, the need for secure communications continues to grow. By using non-traditional means as a way of a communication network, it is possible to securely communicate within a region using the bluetooth mesh protocol. The goal is to automatically place these mesh devices in a defined region in order to ensure the integrity and reliability of the network, while also ensuring the least number of devices are placed. By placing a provisioner node, the rest of the specified region populates with mesh nodes that act as relays, creating a network allowing users to communicate within. By utilizing Dijkstra’s algorithm, it is possible to calculate the Time to Live (TTL) between two given nodes in the network, which is an important metric as it directly affects how far apart two users can be within the region. When placing the nodes, a range for the nodes being used is specified and accounted for, which impacts the number of nodes needed within the region. Results show that when nodes are placed at coordinate points given by the generated map, users are able to communicate effectively across the specified region. In this project, a web interface is created in order to allow a user to specify the TTL, range, and the number of nodes to use, and proceeds to place each device within the region drawn by the user.

Large Language Models (LLMs) have become integral to natural language processing, involving initial broad pretraining on generic data followed by fine-tuning for specific tasks or domains. While advancements in Parameter Efficient Fine-Tuning (PEFT) techniques have made strides in reducing resource demands for LLM fine-tuning, they possess individual constraints. This project addresses the challenges posed by PEFT in the context of transformers architecture for sequence-to-sequence tasks, by integrating two pivotal techniques: Low-Rank Adaptation (LoRA) for computational efficiency and adaptive layers for task-specific customization. To overcome the limitations of LoRA, we introduce a simple yet effective hyper alignment adapter, that leverages a hypernetwork to generate decoder inputs based on encoder outputs, thereby serving as a crucial bridge to improve alignment between the encoder and the decoder. This fusion strikes a balance between the fine-tuning complexity and task performance, mitigating the individual drawbacks while improving the encoder-decoder alignment. As a result, we achieve more precise and contextually relevant sequence generation. The proposed solution improves the overall efficiency and effectiveness of LLMs in sequence-to-sequence tasks, leading to better alignment and more accurate output generation.

Having control over nano- and micro-sized objects' position inside a suspension is crucial in many applications such as: trapping and manipulating microscopic objects, sorting particles and living microorganisms, and building microscopic size 3D crystal structures and lattices. This control can be achieved by judiciously engineering optical forces and light-matter interactions inside colloidal suspensions that result in optical trapping. However, in the current techniques, to confine and transport particles in 3D, the use of high NA (Numerical Aperture) optics is a must. This in turn leads to several disadvantages such as alignment complications, narrow field of view, low stability values, and undesirable thermal effects. Hence, here we study a novel optical trapping method that we named asymmetric counter-propagating beams where optical forces are engineered to overcome the aforementioned limitations of existing methods. This novel system is significantly easier to align due to its utilization of much lower NA optics in combination with engineered beams which create a very flexible manipulating system. This new approach allows the trapping and manipulation of different shape objects, sizing from tens of nanometers to hundreds of micrometers by exploiting asymmetrical optical fields with high stability. In addition, this technique also allows for significantly larger particle trapping volumes. As a result, we can apply this method to trapping much larger particles and microorganisms that have never been trapped optically before as well as building 3D lattices and crystal structures of microscopic-sized particles. Finally, this novel approach allows for the integration of a variety of spectroscopy and microscopy techniques, such as light-sheet fluorescence microscopy, to extract time-sensitive information and acquire images with detailed features from trapped entities.