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Related Concept Videos

Poisson Probability Distribution01:09

Poisson Probability Distribution

A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
The...
Basic Continuous Time Signals01:22

Basic Continuous Time Signals

Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
Basic Discrete Time Signals01:16

Basic Discrete Time Signals

The unit step sequence is defined as 1 for zero and positive values of the integer n. This sequence can be graphically displayed using a set of eight sample points, showing a step function starting from n=0 and remaining constant thereafter.
The unit impulse or sample sequence is mathematically expressed as zero for all n values except at n=0, where it is one. The unit impulse sequence, denoted by δ(n), is the first difference of the unit step sequence, while the unit step sequence u(n) is the...
Hazard Rate01:11

Hazard Rate

The hazard rate, also known as the hazard function or failure rate, is a statistical measure used to describe the instantaneous rate at which an event occurs, given that the event has not yet happened. From a probabilistic perspective, it represents the likelihood that a subject will experience the event in a very small time interval, conditional on surviving up to the beginning of that interval. In terms of frequency, the hazard rate can be viewed as the ratio of the number of events to the...
Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
In the...

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Related Experiment Video

Updated: Jun 14, 2026

Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy
08:25

Continuous Measurement of Biological Noise in Escherichia Coli Using Time-lapse Microscopy

Published on: April 27, 2021

Mean level signal crossing rate for an arbitrary stochastic process.

Harold T Yura1, Steen G Hanson

  • 1Electronics and Photonics Laboratory, The Aerospace Corporation, Los Angeles, California 90009, USA.

Journal of the Optical Society of America. A, Optics, Image Science, and Vision
|April 3, 2010
PubMed
Summary

A new analytical method simplifies calculating mean signal level crossing rates for optical applications. This technique transforms distributions, yielding accurate results confirmed by simulations for various probability functions.

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Last Updated: Jun 14, 2026

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Published on: April 27, 2021

Area of Science:

  • Optics and Photonics
  • Signal Processing
  • Probability Theory

Background:

  • The mean signal level crossing rate is crucial for analyzing signal behavior in optical systems.
  • Existing methods for calculating this rate for diverse probability density functions can be complex or limited.

Purpose of the Study:

  • To introduce a novel analytical method for determining the mean signal level crossing rate.
  • To provide accurate analytical expressions for distributions where they were previously unavailable.

Main Methods:

  • A unique transformation is employed to convert various probability distributions into a normal probability distribution.
  • The method's results are validated through numerical simulations.

Main Results:

  • The method successfully derives analytical expressions for the mean level crossing rate for uniform, gamma-gamma, and Rice-Nakagami distributions.
  • Analytical results generally align with numerical simulations, confirming the method's efficacy.

Conclusions:

  • The developed analytical method offers a simplified and effective approach to calculating mean signal level crossing rates.
  • The study presents new analytical results for previously intractable distributions, enhancing optical signal analysis.