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  1. Free, publicly-accessible full text available January 1, 2024
  2. Ceramics are brittle due in large part to the limited availability of energy dissipation pathways when they are subjected to an impact load. The primary avenue for improving the material reliability and energy-absorption capability is to create new energy dissipation mechanisms that can be used to replace or minimize the kinetic energy associated with the debris shattering. In this paper, a computational framework is developed to investigate the relationship between phase composition and energy dissipation pathways in polymer derived ceramic (PDC) composites by accounting for the key processing parameters and deformation/failure mechanisms. It is found that the phase composition thatmore »promotes both the Mullins effect and the ligament bridging mechanism can significantly improve the structural integrity of the composite material. A fundamental understanding of how to redistribute the impact energy dissipation in a controllable path would hold great promise for fabricating PDC composites with tailored properties.« less
    Free, publicly-accessible full text available July 1, 2023
  3. Developing suitable approximate models for analyzing and simulating complex nonlinear systems is practically important. This paper aims at exploring the skill of a rich class of nonlinear stochastic models, known as the conditional Gaussian nonlinear system (CGNS), as both a cheap surrogate model and a fast preconditioner for facilitating many computationally challenging tasks. The CGNS preserves the underlying physics to a large extent and can reproduce intermittency, extreme events, and other non-Gaussian features in many complex systems arising from practical applications. Three interrelated topics are studied. First, the closed analytic formulas of solving the conditional statistics provide an efficient andmore »accurate data assimilation scheme. It is shown that the data assimilation skill of a suitable CGNS approximate forecast model outweighs that by applying an ensemble method even to the perfect model with strong nonlinearity, where the latter suffers from filter divergence. Second, the CGNS allows the development of a fast algorithm for simultaneously estimating the parameters and the unobserved variables with uncertainty quantification in the presence of only partial observations. Utilizing an appropriate CGNS as a preconditioner significantly reduces the computational cost in accurately estimating the parameters in the original complex system. Finally, the CGNS advances rapid and statistically accurate algorithms for computing the probability density function and sampling the trajectories of the unobserved state variables. These fast algorithms facilitate the development of an efficient and accurate data-driven method for predicting the linear response of the original system with respect to parameter perturbations based on a suitable CGNS preconditioner.« less
    Free, publicly-accessible full text available May 17, 2023
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  6. In this article, real-time jamming detection against unmanned aerial vehicles (UAVs) is proposed via the integration of a software-defined radio (SDR) with an on-board Raspberry Pi processor. The SDR is utilized for capturing and forwarding the radio frequency signals to a receiver module hosted in the processor. This module extracts signal features characterized by orthogonal frequency division multiplexing (OFDM) parameters, energy parameters, and signal-to-noise ratio (SNR) parameters. Upon feature extraction, the aforementioned module exploits a machine learning (ML) classifier for detecting and classifying four jamming types; namely, barrage, single-tone, successive-pulse, and protocol-aware. The resulting configuration yielded in an overall detectionmore »rate (DR) of 93% and a false alarm rate (FAR) of 1.1%, which are in proximity to their counterparts obtained during the validation stage of the receiver module.« less
    Free, publicly-accessible full text available May 19, 2023
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