In low-light conditions, it is known that Poisson noise and quantization noise become dominant sources of noise. While intensity difference is usually measured by Euclidean distance, it often breaks down due to an unnegligible amount of uncertainty in observations caused by noise. In this paper, we develop a new noise model based upon Poisson noise and quantization noise. We then propose a new ...

Aperture Uncertainty One of the key concerns when performing IF sampling is that of aperture jitter or aperture uncertainty. The terms aperture jitter and aperture uncertainty are frequently interchanged in text. In this application, they have the same meaning. Aperture uncertainty is the sample-tosample variation in the encode process. Aperture uncertainty has three residual effects: the first...

Deep neural network (DNN) models have achieved state-of-the-art predictive accuracy in a wide range of applications. However, it remains challenging task to accurately quantify the uncertainty DNN predictions, especially those continuous outcomes. To this end, we propose Bayesian deep noise (B-DeepNoise), which generalizes standard DNNs by extending random variable from output layer all hidden ...

This paper describes a system noise assessment of conceptual supersonic aircraft called the NASA 55t Supersonic Technology Concept Aeroplane (STCA), its prediction uncertainty, and related validation tests. A landing takeoff (LTO) standard for is needed to realize future aircraft, impact due introduction should be analyzed develop standard. System assessments uncertainty analyses using Monte Ca...

It is well known that classical states of light exhibit shot noise, characteristic of independent or uncorrelated particles. For phase estimation problems, this leads to a shot-noise limited uncertainty of 1/sqrt[N], where N is the number of particles detected. It is also well known that the shot-noise limit is not fundamental: squeezed states and entangled states can be used for sub-shot-noise...