Defense Notices

AMD’s latest generation of adaptive system-on-chip (SoC) devices, the Versal product family, offers enhanced processing capabilities that are attractive to researchers and system designers. However, these capabilities introduce a significant knowledge barrier, limiting the practical benefits of Versal devices compared to more mature platforms from AMD, Intel, and other industry vendors. This project addresses this challenge through two primary deliverables: a software framework and a comprehensive user manual targeting Versal development. The software framework, named RSL Versal Core, provides a framework for users unfamiliar with Versal devices by selectively abstracting away more complex design components. Using a small set of commands, users can synthesize a programmable logic (PL) design, compile a Linux operating system for the onboard Arm processor with PL communication support, and program supported development boards. Following initial setup, the framework also supports extended software and firmware development for specific project needs. The accompanying user manual documents both RSL Versal Core and broader Versal development concepts. It guides users through reproducing and customizing the framework outputs manually and introduces key architectural and design principles useful for effective Versal-based system development. Together, these deliverables enable new developers to rapidly gain proficiency with Versal platforms and enable implementation of digital signal processing (DSP) concepts.

Due to rapid advances in high-speed analog-to-digital conversion and software-defined architectures, modern radar systems increasingly shift signal generation and conditioning into the digital domain. These architectures enable high-fidelity signal capture and provide substantial flexibility in waveform synthesis and signal processing that was previously impractical in analog implementations. Despite these advances, however, achievable radar performance remains fundamentally constrained by the physical transmit hardware through which the signal is ultimately realized. Nonlinear amplification, finite bandwidth, and memory effects introduce distortion that creates a significant gap between idealized waveform design and the waveform that is physically radiated.

To address this limitation, this work proposes a system-aware radar waveform design framework that couples data-driven system identification with deterministic optimization to generate waveforms tailored to the underlying transmit hardware. A complex baseband memory polynomial model is developed to characterize nonlinear transmit-chain behavior using loopback measurements, where $\ell_1$-regularized LASSO estimation is employed to improve robustness against ill-conditioning and feature redundancy. Under this architecture, a generalized integrated sidelobe level (GISL) objective is reformulated using logarithmic scalarization to produce a numerically stable and Pareto-tunable optimization criterion capable of balancing output energy and sidelobe suppression. Additionally, efficient vectorized gradient expressions are derived using Wirtinger calculus and implemented using gradient-based descent and the limited-memory BFGS algorithm for practical high-dimensional waveform synthesis.

To validate the framework, a comprehensive hardware-in-the-loop testbench was developed supporting direct model identification and experimental evaluation of optimized waveform performance. Simulation and experimental results demonstrate that continuous-phase FM waveforms exhibit strong inherent robustness to nonlinear distortion, while phase-coded waveforms with large instantaneous phase discontinuities show significantly greater sensitivity to transmit-chain impairments. Across both waveform classes, the proposed framework achieves substantial improvements in output power efficiency and pulse compression performance relative to system-agnostic waveform design. These results demonstrate that transmitter constraints must be treated as fundamental design variables rather than secondary effects and establish system-aware optimization as a practical framework for next-generation radar waveform synthesis.

Due to the ever-growing demand for access to the finite resources of the electromagnetic spectrum, significant effort has been directed toward improving spectrum utilization. This has become a particular challenge in radar transmission design, where waveform diversity techniques have emerged as a promising solution despite the accompanying implementation complexity. Diverse signals are inherently non-repeating and pose unique challenges in comparison to traditional radar waveforms. Software defined radios (SDRs) allow for traditional RF components and signal processing to be implemented and controlled in software rather than hardware, providing a platform for testing experimental radar algorithms. This thesis presents a real-time parallel implementation of five previously developed distinct waveform-diverse radar signals for use in a coherent SDR system. The implemented waveforms include stochastic waveform generation (StoWGe), multi-user radar communication (MURC), phase-attached radar communication (PARC), pseudo-random optimized frequency modulation (PRO-FM), and waveform recycling. To enable real-time generation at maximum SDR data rates, these waveforms are implemented using digital synthesis techniques via GPU parallel processing. This approach alleviates CPU resource limitations by offloading computationally intensive waveform generation tasks to the GPU, enabling continuous high-throughput operation. A custom asynchronous transmit and receive architecture is developed to integrate these GPU-accelerated waveforms with UHD-based SDR hardware. The system leverages a multithreaded framework approach that can sustain coherent and synchronized radar operation. To validate the system, a series of loopback testing across all waveforms and a variety of parameters is completed to confirm the execution of the generate-transmit-receive chain.

The RF spectrum is a precious, finite resource with ever-increasing demand. Consequently, the mandate to be a "good spectral neighbor" is in direct conflict with the requirements for high-performance sensing where correlation error is fundamentally limited. As such, matched-filter radar performance is often sidelobe-limited with estimation error being constrained by the time-bandwidth (TB) of the collective emission. The methods developed here seek to bridge this gap between idealized radar performance and practical utility via waveform design.    

Estimation error becomes more complex when employing pulse-agility. In doing so, range-sidelobe modulation (RSM) spreads energy across Doppler, rendering traditional methods ineffective. To address this, the gradient-based complementary-FM framework was developed to produce complementary sidelobe cancellation (CSC) after coherently combining subsets within a pulse-agile emission. In contrast to the majority of complementary signals, explored via phase-coding, these Comp-FM waveform subsets achieve CSC while preserving hardware-compatibility since they are FM (though design distortion is never completely avoided). Although Comp-FM addressed practicality via hardware amenability, CSC was localized to zero-Doppler. This work expands the Comp-FM notion to a Doppler-generalized (DG) framework, extending the cancellation condition to an arbitrary span. The same framework can likewise be employed to jointly optimize an entire coherent processing interval (CPI) to minimize RSM within the radar point-spread-function (PSF), thereby generalizing the notion of complementarity and introducing the potential for cognitive operation if sufficient scattering knowledge is available a-priori.          

Sensing with a single emitter is limited by self-inflicted error alone (e.g., clutter, sidelobes), while MIMO systems must additionally contend with the cross-responses from emitters operating concurrently (e.g., simultaneously, spatially proximate, in a shared spectrum), further degrading radar sensitivity. Now, total correlation error is dictated by the overlapping TB (i.e., how coincident are the signals) and number of operating emitters, compounding difficulty to estimate if left unaddressed. As such, the determination of "orthogonal waveforms" comprises a large portion of MIMO literature, though remains a phenomenological misnomer for pulsed emissions. Here, the notion of complementary-FM is applied to a multi-emitter context in which transmitter-amenable quasi-orthogonal subsets, occupying the same spectral band, are produced via a similar gradient-based approach. To further practicalize these MIMO-Comp-FM waveform subsets, the same "DG" approach described above, addressing the otherwise-default Doppler-induced degradation of complementary signals, is applied. In doing so, Doppler-independent separability and complementarity greatly improves estimation sensitivity for multi-emitter systems. 

This MIMO-Comp-FM framework is developed for standard matched filter processing. Coupling this framework with a "DG" form of the previously explored MIMO-MiCRFt is also investigated, illustrating the added benefit of pairing optimized subsets with similarly calibrated processing. 

Each of these methods is developed to address unique and increasingly complex sources of estimation error. All approaches are initially developed and evaluated via simulated analysis where ground-truth is known. Then, despite hardware-induced distortion being unavoidable, the MIMO-Comp-FM framework is confirmed via loopback measurements to preserve the majority of CSC that was observed in simulation. Finally, open-air demonstration of each approach validates practical utility on a radar system.

Metagenomic sequencing has become a central paradigm for studying complex microbial communities and their interactions with the host, with emerging applications in clinical prediction and disease modeling. In this work, we first investigate two representative application scenarios: predicting immune checkpoint inhibitor response in non-small cell lung cancer using gut microbial signatures, and characterizing host–microbiome interactions in neonatal systems. The proposed reference-free neural network captures both compositional and functional signals without reliance on reference genomes, while the neonatal study demonstrates how environmental and genetic factors reshape microbial communities and how probiotic intervention can mitigate pathogen-induced immune activation.

These studies highlight both the promise and the inherent difficulty of metagenomic analysis: transforming raw sequencing data into clinically actionable insights remains an algorithmically fragmented and computationally intensive process. This challenge arises from two key limitations: the lack of a unified algorithmic foundation for sequence alignment and the absence of systematic approaches for selecting and organizing analytical tools. Motivated by these challenges, we present a unified computational framework for metagenomic analysis that integrates complementary algorithmic and systems-level solutions.

First, to resolve fragmentation at the alignment level, we develop the Versatile Alignment Toolkit (VAT), a unified algorithmic system for biological sequence alignment across diverse applications. VAT introduces an asymmetric multi-view k-mer indexing scheme that integrates multiple seeding strategies within a single architecture and enables dynamic seed-length adjustment via longest common prefix (LCP)–based inference without re-indexing. A flexible seed-chaining mechanism further supports diverse alignment scenarios, including collinear, rearranged, and split alignments. Combined with a hardware-efficient in-register bitonic sorting algorithm and dynamic index-loading strategy, VAT achieves high efficiency and broad applicability across read mapping, homology search, and whole-genome alignment. Second, to address the challenge of tool selection and pipeline construction, we develop SNAIL, a natural language processing system for automated recognition of bioinformatics tools from large-scale and rapidly growing scientific literature. By integrating XGBoost and Transformer-based models such as SciBERT, SNAIL enables structured extraction of analytical tools and supports automated, reproducible pipeline construction.

Together, this work establishes a unified framework that is grounded in real-world applications and addresses key bottlenecks in metagenomic analysis, enabling more efficient, scalable, and clinically actionable workflows.