Defense Notices

Recent years have seen tremendous developments in the field of computer vision and its extensive applications. The fundamental task, image classification, benefiting from deep convolutional neural networks (CNN)'s extraordinary ability to extract deep semantic information from input data, has become the backbone for many other computer vision tasks, like object detection and segmentation. A modern detection usually has bounding-box regression and class prediction with a pre-trained classification model as the backbone. The architecture is proven to produce good results, however, improvements can be made with closer inspections. A detector takes a pre-trained CNN from the classification task and selects the final bounding boxes from multiple proposed regional candidates by a process called non-maximum suppression (NMS), which picks the best candidates by ranking their classification confidence scores. The localization evaluation is absent in the entire process. Another issue is the classification uses one-hot encoding to label the ground truth, resulting in an equal penalty for misclassifications between any two classes without considering the inherent relations between the classes. Ultimately, the realms of 2D image classification and 3D point cloud classification represent distinct avenues of research, each relying on significantly different architectures. Given the unique characteristics of these data types, it is not feasible to employ models interchangeably between them.

My research aims to address the following issues. (1) We proposed the first location-aware detection framework for single-shot detectors that can be integrated into any single-shot detectors. It boosts detection performance by calibrating the ranking process in NMS with localization scores. (2) To more effectively back-propagate gradients, we designed a super-class guided architecture that consists of a superclass branch (SCB) and a finer class branch (FCB). To further increase the effectiveness, the features from SCB with high-level information are fed to FCB to guide finer class predictions. (3) Recent works have shown 3D point cloud models are extremely vulnerable under adversarial attacks, which poses a serious threat to many critical applications like autonomous driving and robotic controls. To gap the domain difference in 3D and 2D classification and to increase the robustness of CNN models on 3D point cloud models, we propose a family of robust structured declarative classifiers for point cloud classification. We experimented with various 3D-to-2D mapping algorithm, bridging the gap between 2D and 3D classification. Furthermore, we empirically validate the internal constrained optimization mechanism effectively defend adversarial attacks through implicit gradients.

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.

Scattering by nanoparticles is an exciting branch of physics to control and manipulate light. More specifically, there have been fascinating developments regarding light scattering by sub-wavelength particles, including high-index dielectric and metal particles for their applications in optical resonance phenomena, detecting the fluorescence of molecules, enhancing Raman scattering, transferring the energy to the higher order modes, sensing, and photodetector technologies. This research area has recently gained renewed attention with the study of near-field effects at the nanoscale in advanced regimes of operation, including nonlinear effects and the time-varying parametric modulation of local material properties. When the particle size is comparable to or slightly bigger than the incident wavelength, Mie solutions to Maxwell's equations describe these electromagnetic scattering problems. The addition and excitation of nonlinear effects in these high-indexed sub-wavelength dielectric and plasmonic particles holds promise to improve the existing performance of the system or provide additional features directed toward novel applications. This dissertation explores Mie scattering from dielectric and plasmonic particles in the presence of nonlinear effects, more specifically second and third order nonlinear effects. For numerical analysis, an in-house Rigorous Coupled Analysis (RCWA) method has been developed in a Matlab environment and validated based on designing metasurfaces and comparing them with established results. For dielectrics, this dissertation presents a numerical study of the linear and nonlinear diffraction and focusing properties of dielectric metasurfaces consisting of silicon microcylinder arrays resting on a silicon substrate. Upon diffraction, such structures lead to the formation of near-field intensity profiles reminiscent of photonic nanojets and propagate similarly. The results indicate that the Kerr nonlinear effect i.e. third order nonlinear effect enhances light concentration throughout the generated photonic jet with an increase in the intensity of about 20% compared to the linear regime for the power levels considered in this work. The transverse beamwidth remains subwavelength in all cases, and the nonlinear effect reduces the full width. On the other hand, plasmonic structures give rise to localized surface plasmons and excitations of the conduction electrons within metallic nanostructures. These aren't propagating but instead confined to the vicinity of the nanostructure, interacting with the electromagnetic field. These modes emerge from the scattering between small conductive nanoparticles with an oscillating electromagnetic field. This dissertation introduces a novel mechanism to transfer energy from excited dipolar mode to such higher-order subradiant localized mode. Recent advancements in time-varying structures that help relax photon energy conservation constraints and a newly proposed plasmonic parametric resonance pave the way for this work. With the help of the second-order nonlinear wave mixing process and parametric modulation of the dielectric permittivity in a medium surrounding metal particles, we have introduced a way to accomplish the otherwise nearly impossible task to selectively couple energy into specific high order modes of a nanostructures. This work further shows that the oscillating mode amplitude reaches a steady state, and the steady state establishes the ideal modulation conditions that enhance the amplitude of the high-order mode.

Metagenomics is the study of microbial genomes from one common environment. Metagenomic data is directly derived from all microorganisms present in the environmental samples, in- including those inaccessible through conventional methods like laboratory cultures. Thus it offers an unbiased view of microbial communities, enabling researchers to explore not only the taxonomic composition (identifying which microorganisms are present) but also the community’s metabolic functions.

The metagenomic data consists of a huge number of fragmented DNA sequences from diverse microorganisms with different abundance. These characteristics pose challenges to analysis and impede practical applications. Firstly, the development of an efficient detection tool for a specific target from metagenomic data is confronted by the challenge of daunting data size. Secondly, the accuracy of the detection tool is also challenged by the incompleteness of metagenomic data. Thirdly, numerous analysis tools are designed for individual detection targets, and many detection targets are contained within the data, there is a need for comprehensive and scalable integration of existing resources.

In this dissertation, we conducted the computational microbiome analysis at different levels: (1) We first developed an assembly graph-based ncRNA searching tool, named DRAGoM, to im- improve the detection quality in metagenomic data. (2) We then developed an automatic detection model, named SNAIL, to automatically detect names of bioinformatic resources from biomedical literature for comprehensive and scalable organizing resources. We also developed a method to automatically annotate sentences for training SNAIL, which not only benefits the performance of SNAIL but also allows it to be trained on both manual and machine-annotated data, thus minimizing the need for extensive manual data labeling efforts. (3) We applied different analyzing tools to metagenomic datasets from a series of clinical studies and developed models to predict therapeutic benefits from immunotherapy in non-small-cell lung cancer patients using human gut microbiome signatures.

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.

The pursuit of autonomy in cyber-physical systems (CPS) presents a challenging task of real-time interaction with the physical world, prompting extensive research in this domain. Recent advancements in artificial intelligence (AI), particularly the introduction of deep neural networks (DNNs), have significantly enhanced CPS autonomy, notably boosting perception capabilities. 

CPS perception aims to discern, classify, and track the objects of interest in the operational environment, a task considerably challenging for computers in three-dimensional (3D) space. For this task of detecting objects, leveraging lidar sensors and processing their readings with deep neural networks (DNN) has become popular due to their excellent performance. 

However, in systems like self-driving cars and drones, object detection must be both accurate and timely, posing a challenge due to the high computational demand of lidar object detection DNNs. Furthermore, lidar object detection DNNs lack the capability to dynamically reduce their execution time by compromising accuracy (i.e. anytime computing). This adaptability is crucial since deadline constraints can change based on the operational environment and the internal status of the system.  

Prior research aimed at anytime computing for object detection DNNs using camera images are not applicable when considered to lidar-based detection due to architectural differences. Addressing this challenge, this thesis focuses on proposing novel techniques, such as Anytime-Lidar and VALO (Versatile Anytime Lidar Object Detection). These innovations aim to enable lidar-based object detection DNNs to make effective tradeoffs between latency and accuracy. Finally, the thesis aims to integrate the proposed anytime object detection techniques into unmanned aerial vehicles and introduce a system-level scheduler capable of managing multiple anytime computation capable tasks.  

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.

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. 

This paper introduces a novel approach to manage collections of artifacts through smart contract access control, rooted in on-chain role-based property-level access control. This smart contract facilitates the lifecycle of these artifacts including allowing for the creation,  modification, removal, and historical auditing of the artifacts through both direct and suggested actions. This method introduces a collection object designed to store role privileges concerning state object properties. User roles are defined within an on-chain entity that maps users' signed identities to roles across different collections, enabling a single user to assume varying roles in distinct collections. Unlike existing key-level endorsement mechanisms, this approach offers finer-grained privileges by defining them on a per-property basis, not at the key level. The outcome is a more flexible and fine-grained access control system seamlessly integrated into the smart contract itself, empowering administrators to manage access with precision and adaptability across diverse organizational contexts.  This has the added benefit of allowing for the auditing of not only the history of the artifacts, but also for the permissions granted to the users.  

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.