arXiv subjects

Luat T. Vuong

Publications and source records attributed to Luat T. Vuong.

14 recordsLinked to original sources

arXiv2026

Spatiotemporal Tracking of Persistent, Localized Speckles in Turbulent Atmospheric Propagation

Light propagation through turbulence produces speckles, whose ensemble behavior is typically characterized by snapshot intensity statistics. Here, we track the spatiotemporal evolution of individual speckles and quantify fragmentation, localization, and persistence under different diffraction and turbulence scales. Beam fragmentation coincides with complete spatial decorrelation defined by the magnitude-squared coherence. Fragmentation occurs closer to the source for larger beams, which indicates that smaller beams are more robust to decoherence. Subsequently, speckles are both spatially localized and persistent over distances significantly longer than their associated Rayleigh length. The combination of localization and persistence impacts the statistics of light relevant to their long-distance signaling and sensing.

physics.optics↗

arXiv2026

Spatiotemporal Tracking of Optical Speckles in Turbulent Atmospheric Propagation

The speckle fields produced by optical beam propagation through atmospheric turbulence are typically described using ensemble-averaged intensity and coherence statistics, which obscure the speckle-level dynamics. Here, we investigate the spatiotemporal evolution of speckles generated during Gaussian beam propagation through turbulence by explicitly tracking them as discrete resolvable substructures. We quantify object-level persistence, transverse extent, and trajectories as functions of propagation distance, turbulence strength, and source-plane beam width. Under fixed detection criteria, we observe that a subset of these speckles exhibits measurable width statistics and persistence distributions whose form depends on turbulence strength and source-plane beam size. This object-level framework remains well-defined within the regime of strong turbulence and provides a complementary approach to characterizing turbulent propagation beyond conventional ensemble-averaged metrics.

physics.optics↗

arXiv2026

Learning from Translation: Seasonal Errors and Feature Importance of the ERA5 Turbulence Predictions

Turbulence is a phenomena that is {\it locally} and statistically characterized by measurements, but it is caused by {\it nonlocal} energy cascades associated with the environment. The presence of turbulence coincides with fluctuations in the refractive index, which impact optical sensing, imaging, and signaling applications. Here, we study the machine learning models that predict near-surface optical turbulence strength $C_n^2$, derived from anemometer-based surface flux measurements through Monin-Obukhov similarity theory, using ERA5 reanalysis data as model inputs. We evaluate the model's ability to perform temporal extrapolation by training on one year of co-located $C_n^2$ observations and ERA5 data, and applying the model to ERA5 data from other years at the same site to reconstruct a multi-year time series. We compare the predictions across Southern California and New York. In spite of varying weather and terrain, the ML models show consistent performance and seasonal behavior across training years. All models show greater correlation, faster convergence, and lower prediction errors in the summer. However, some ERA5 features drive predictions in New York but not California and vice versa, and such feature dependence depends on the season. Seasonal error and feature trends suggest that turbulence is affected by atmospheric composition or other seasonal environmental considerations that are not currently monitored by ERA5. We find, regardless of terrain, the primary feature of importance to turbulence prediction is solar radiation, which underlines the central role of radiative energy transfer in driving atmospheric turbulence. We point toward physics-informed ML translation and feature selection as tools for improving the generalizability of data-driven models.

physics.optics↗

arXiv2025

Spectrum Selective Interfaces and Materials towards Non-photothermal Saltwater Evaporation: Demonstration with a White Ceramic Wick

Most solar desalination efforts are photothermal: they evaporate water with ``black'' materials that absorb as much sunlight as possible. Such ``brine-boiling'' methods are limited by the high thermal mass of water, i.e., its capacity to store and release heat. Here, we study the light-enhanced evaporation by a hard, white, aluminum nitride wick, and propose a route to selectively target salt-water bonds instead of bulk heating via deep-UV interactions. Through experiments and analyses that isolate the effects of light absorption and heating in aluminum nitride, we provide experimental evidence of a light-driven, spectrum-selective path to non-photothermal saltwater evaporation. Leverage of these light-matter interactions in white ceramic wicks may achieve low-cost, low-energy desalination, reduce the heat island effects of traditional solar technologies, and contribute to future cooling technologies where drought is also a concern.

cond-mat.mtrl-sci↗

arXiv2024

Polarimetric compressed sensing with hollow, self-assembled diffractive films

Sensing light's polarization and wavefront direction enables surface curvature assessment, material identification, shadow differentiation, and improved image quality in turbid environments. Traditional polarization cameras utilize multiple sensor measurements per pixel and polarization-filtering optics, which result in reduced image resolution. We propose a nanophotonic pipeline that enables compressive sensing and reduces the sampling requirements with a low-refractive-index, self-assembled optical encoder. These nanostructures scatter light into lattice modes, which encode the wavefront direction and the polarization ellipticity in the linearly-polarized components of the diffracted, interference patterns. Combining optical encoders with a neural network, the system predicts pointing and polarization when the interference patterns are adequately sampled. A comparison of ``ordered'' and ``random'' optical encoders shows that the latter both blurs the interference patterns and achieves higher resolution. Our work centers on the unexpected modulation and spatial multiplexing of incident light polarization by self-assembled hollow nanocavity arrays as a class of materials distinct from traditional metasurfaces that will not only enable encoding for polarization and optical computing but also for compressed sensing and imaging.

physics.optics↗

arXiv2024

Optical demultiplexing of fractal-structured beams in turbulent atmospheric environments

When information is spatially repeated in self-similar fractal beam patterns, only a portion of the diffracted beam is needed to reconstruct the kernel data. What is unique to a fractal-encoding scheme is that the image demultiplexing process can be, to a first approximation, easily performed optically. In prior work, we experimentally and numerically study fractal-encoded optical beams and their mid- and far-field propagation without added turbulence. Here, we present preliminary simulations of fractal-encoded beams with high turbulence ($C_n^2 \geq 10^{-14}$ m$^{-2/3}$) where we achieve respectable bit error rates of $10^{-3}$. These results are impressive given that: data with low fractal orders is shown, simple threshold-algorithms are used (i.e., no machine learning), and only a third of the beam, off-axis, is needed. More robust channel encoding is associated with increased fractal orders, larger collection areas, and higher kernel singular value decomposition entropy.

physics.optics↗

arXiv2022

Singular Value Decomposition and Entropy Dimension of Fractals

We analyze the singular value decomposition (SVD) and SVD entropy of Cantor fractals produced by the Kronecker product. Our primary results show that SVD entropy is a measure of image ``complexity dimension" that is invariant under the number of Kronecker-product self-iterations (i.e., fractal order). SVD entropy is therefore similar to the fractal Hausdorff complexity dimension but suitable for characterizing fractal wave phenomena. Our field-based normalization (Renyi entropy index = 1) illustrates the uncommon step-shaped and cluster-patterned distributions of the fractal singular values and their SVD entropy. As a modal measure of complexity, SVD entropy has uses for a variety of wireless communication, free-space optical, and remote sensing applications.

cond-mat.stat-mech↗

arXiv2020

Small-brain neural networks rapidly solve inverse problems with vortex Fourier encoders

We introduce a vortex phase transform with a lenslet-array to accompany shallow, dense, ``small-brain'' neural networks for high-speed and low-light imaging. Our single-shot ptychographic approach exploits the coherent diffraction, compact representation, and edge enhancement of Fourier-tranformed spiral-phase gradients. With vortex spatial encoding, a small brain is trained to deconvolve images at rates 5-20 times faster than those achieved with random encoding schemes, where greater advantages are gained in the presence of noise. Once trained, the small brain reconstructs an object from intensity-only data, solving an inverse mapping without performing iterations on each image and without deep-learning schemes. With this hybrid, optical-digital, vortex Fourier encoded, small-brain scheme, we reconstruct MNIST Fashion objects illuminated with low-light flux (5 nJ/cm$^2$) at a rate of several thousand frames per second on a 15 W central processing unit, two orders of magnitude faster than convolutional neural networks.

eess.IV↗

arXiv2018

Long-range Self-assembly via the Mutual Lorentz Force of Plasmon Radiation

Long-range interactions often proceed as a sequence of hopping through intermediate, statistically-favored events. Here, we identify a widely-overlooked mechanism for the mechanical motion of particles that arises from the Lorentz force and plasmon radiation. Even if the radiation is weak, the nonconservative Lorentz force produces stable locations perpendicular to the plasmon oscillation; over time, distinct patterns emerge. Experimentally, linearly-polarized light leads to the formation of 80-nm Au nanoparticles, perpendicularly-aligned, with lengths that are orders of magnitude greater than their plasmon near-field interaction. There is a critical intensity threshold and optimal concentration for observing self-assembly.

cond-mat.mes-hall↗

arXiv2017

Structure in scientific networks: towards predictions of research dynamism

Certain areas of scientific research flourish while others lose advocates and attention. We are interested in whether structural patterns within citation networks correspond to the growth or decline of the research areas to which those networks belong. We focus on three topic areas within optical physics as a set of cases; those areas have developed along different trajectories: one continues to expand rapidly; another is on the wane after an earlier peak; the final area has re-emerged after a short waning period. These three areas have substantial overlaps in the types of equipment they use and general methodology; at the same time, their citation networks are largely independent of each other. For each of our three areas, we map the citation networks of the top-100 most-cited papers, published pre-1999. In order to quantify the structures of the selected articles' citation networks, we use a modified version of weak tie theory in tandem with entropy measures. Although the fortunes of a given research area are most obviously the result of accumulated innovations and impasses, our preliminary study provides evidence that these citation networks' emergent structures reflect those developments and may shape evolving conversations in the scholarly literature.

cs.SI↗

arXiv2015

Robustness via Diffractal Architectures

When plane waves diffract through fractal-patterned apertures, the resulting far-field profiles or diffractals also exhibit iterated, self-similar features. Here we show that this specific architecture enables robust signal processing and spatial multiplexing: arbitrary parts of a diffractal contain sufficient information to recreate the entire original sparse signal.

physics.optics↗

arXiv2015

Spin-polarized Voltages on a 2D Self-assembled Plasmonic Crystal

The Photon Drag Effect (PDE) is a nonlinear process akin to optical rectification in which the momentum of light is transferred to charged carriers and converted to a DC voltage. Here, we experimentally demonstrate the spin-polarized voltage, associated with the transference of light's spin angular momenta to the linear momenta of charges, with visible-light illumination on a nanovoid self-assembled plasmonic crystal surface. Numerical calculations show that the gradient force, generally considered independent of polarization, is responsible for the majority of the momentum transfer. The PDE in this achiral system represents a distinct spin-orbit interaction that produces asymmetric hotspots whose locations change with circular polarization handedness. Our results significantly advance our understanding of the PDE and demonstrate realistic potential for scalable plasmonic materials that utilize PDE.

physics.optics↗

arXiv2013

Ultra-Low-Intensity Magneto-Optical and Mechanical Effects in Metal Nanocolloids

We theoretically and numerically investigate the linear and nonlinear magneto-optical contributions to the refractive index of metal nanospheres. The analysis is in good agreement with the experimental extinction spectra of gold nanocolloid solutions, with threshold magnetic fields less than 1 mT when illuminated with light intensities less than 1 W/cm^2. Plasmonic current loops and vortex power flows provide a new framework for the dynamical interaction between material magnetization, light polarization and nano-surfaces. The photo-induced magneto-optical interaction of non-magnetic metal nanocolloids imparts significant, non-negligible torque forces in nanofluids.

physics.optics↗

arXiv2006

Self-Focusing Dynamics of Coupled Optical Beams

We theoretically and experimentally investigate the mutual collapse dynamics of two spatially separated optical beams in a Kerr medium. Depending on the initial power, beam separation, and the relative phase, we observe repulsion or attraction, which in the latter case reveals a sharp transition to a single collapsing beam. This transition to fusion of the beams is accompanied by an increase in the collapse distance, indicating the effect of the nonlinear coupling on the individual collapse dynamics. Our results shed light on the basic nonlinear interaction between self-focused beams and provide a mechanism to control the collapse dynamics of such beams.

physics.optics↗