Pulsed light optical rangefinder

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We consider the problem of defining and fitting models of autoregressive time series of probability distributions on a compact interval of Double-struck capital R. An order-1 autoregressive model in this context is to be understood as a Markov chain, where ...

Over the past decade, quantum photonics platforms aiming at harnessing the fundamental properties of single particles, such as quantum superposition and quantum entanglement, have flourished. In this context, single-photon emitters capable of operating at ...

We consider fluid flows, governed by the Navier-Stokes equations, subject to a steady symmetry-breaking bifurcation and forced by a weak noise acting on a slow timescale. By generalizing the multiple-scale weakly nonlinear expansion technique employed in t ...

The space industry has experienced substantial growth in recent years, leading to rapid advancements in space exploration and space-based technologies. Consequently, the study of electronics and sensor performance in extreme environments has become crucial ...

Given two jointly distributed random variables (X,Y), a functional representation of X is a random variable Z independent of Y, and a deterministic function g(⋅,⋅) such that X=g(Y,Z). The problem of finding a minimum entropy functional representation is kn ...

Ultrafast ultrasound imaging, characterized by high frame rates, generates low-quality images. Convolutional neural networks (CNNs) have demonstrated great potential to enhance image quality without compromising the frame rate. However, CNNs have been most ...

This study presents a novel framework for evaluating the luminance measurement capabilities of High Dynamic Range (HDR) sensor cameras in indoor glare conditions. Results indicate that the practical usage range of the CSEM VIP camera is significantly lower ...

In this manuscript, we present a collective multigrid algorithm to solve efficiently the large saddle-point systems of equations that typically arise in PDE-constrained optimization under uncertainty, and develop a novel convergence analysis of collective ...

A kernel method for estimating a probability density function from an independent and identically distributed sample drawn from such density is presented. Our estimator is a linear combination of kernel functions, the coefficients of which are determined b ...

Decay of a particle into more particles is a ubiquitous phenomenon to interacting quantum systems, taking place in colliders, nuclear reactors or solids. In a nonlinear medium, even a single photon would decay by down-converting (splitting) into lower-freq ...

One major challenge in distributed learning is to efficiently learn for each client when the data across clients is heterogeneous or non iid (not independent or identically distributed). This provides a significant challenge as the data of the other client ...

Correct prediction of particle transport by surface waves is crucial in many practical applications such as search and rescue or salvage operations and pollution tracking and clean-up efforts. Recent results by Deike et al. (J. Fluid Mech., vol. 829, 2017, ...

Single-photon avalanche diodes (SPADs) are novel image sensors that record the arrival of individual photons at extremely high temporal resolution. In the past, they were only available as single pixels or small-format arrays, for various active imaging ap ...

We present a narrow-linewidth second-harmonic source based on a laser diode injection locked to an optically poled silicon nitride microresonator. The device is a compact, highly-coherent dual-wavelength source, with high conversion efficiency up to 280%/W ...

PET reconstruction algorithms have long relied on sinogram rebinning. However, as detectors grow smaller in a recent wave of cutting-edge scanners, individual sensors no longer accrue hundreds of photons. Instead, most detect a single photon or none at all ...

The crowdedness of the RF spectrum constitutes problems in communication, however it also accommodates large amount of useful information. This information can be used to determine existing signals and operating contexts. However, spectrum condition varies ...

We consider increasingly complex models of matrix denoising and dictionary learning in the Bayes-optimal setting, in the challenging regime where the matrices to infer have a rank growing linearly with the system size. This is in contrast with most existin ...

The discretization of robust quadratic optimal control problems under uncertainty using the finite element method and the stochastic collocation method leads to large saddle-point systems, which are fully coupled across the random realizations. Despite its ...

Distributionally robust chance constrained programs minimize a deterministic cost function subject to the satisfaction of one or more safety conditions with high probability, given that the probability distribution of the uncertain problem parameters affec ...

Since the birth of Information Theory, researchers have defined and exploited various information measures, as well as endowed them with operational meanings. Some were born as a "solution to a problem", like Shannon's Entropy and Mutual Information. Other ...

Pulsed light optical rangefinder

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On distributional autoregression and iterated transportation

Investigation of room-temperature single-photon emitters in GaN-based materials and GaN/AlN quantum dots

Noise-induced transitions past the onset of a steady symmetry-breaking bifurcation: The case of the sudden expansion

Single-Photon Avalanche Diode Image Sensors for Harsh Radiation Environments

Information Spectrum Converse for Minimum Entropy Couplings and Functional Representations

A KL Divergence-Based Loss for In Vivo Ultrafast Ultrasound Image Enhancement with Deep Learning

Performance investigation of cameras using HDR sensors for daylight glare evaluations

A multigrid method for PDE-constrained optimization with uncertain inputs

Density estimation in RKHS with application to Korobov spaces in high dimensions

Down-conversion of a single photon as a probe of many-body localization

Personalized Privacy-Preserving Distributed Learning on Heterogeneous Data

Stochastic particle transport by deep-water irregular breaking waves

Burst Vision Using Single-Photon Cameras

Self-injection Locked Second-Harmonic Generation in Optically Poled Silicon Nitride Microresonators

PET REBINNING WITH REGULARIZED DENSITY SPLINES

Intelligent RF System for Ultra Low Power Spectrum Sensing with Machine Learning

Statistical limits of dictionary learning: Random matrix theory and the spectral replica method

Preconditioners for robust optimal control problems under uncertainty

On Approximations of Data-Driven Chance Constrained Programs over Wasserstein Balls

A Functional Perspective on Information Measures