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APEX stands for Additive System of ... is the speed value (aka sensitivity value): ... eliminated exposure calculator dials in favor of tabulated APEX values. However ...
The f-number (relative aperture) determines the depth of field, and the shutter speed (exposure time) determines the amount of motion blur, as illustrated by the two images at the right (and at long exposure times, as a second-order effect, the light-sensitive medium may exhibit reciprocity failure, which is a change of light sensitivity ...
The use of radiometric units is appropriate to characterize such sensitivity to invisible light. In sensitometric data, such as characteristic curves, the log exposure [4] is conventionally expressed as log 10 (H). Photographers more familiar with base-2 logarithmic scales (such as exposure values) can convert using log 2 (H) ≈ 3.32 log 10 (H).
A minimum detectable signal is a signal at the input of a system whose power allows it to be detected over the background electronic noise of the detector system. It can alternately be defined as a signal that produces a signal-to-noise ratio of a given value m at the output.
Nick Sirianni corrected the phrasing. A reporter asked the head coach how his Philadelphia Eagles responded after losing franchise quarterback Jalen Hurts to a concussion.. Hurts sustained a ...
Noise-equivalent power (NEP) is a measure of the sensitivity of a photodetector or detector system. It is defined as the signal power that gives a signal-to-noise ratio of one in a one hertz output bandwidth. [1] An output bandwidth of one hertz is equivalent to half a second of integration time. [2]
The 'worst-case' sensitivity or specificity must be calculated in order to avoid reliance on experiments with few results. For example, a particular test may easily show 100% sensitivity if tested against the gold standard four times, but a single additional test against the gold standard that gave a poor result would imply a sensitivity of ...
The adjoint state method is a numerical method for efficiently computing the gradient of a function or operator in a numerical optimization problem. [1] It has applications in geophysics, seismic imaging, photonics and more recently in neural networks.