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Related Concept Videos

Random Sampling Method01:09

Random Sampling Method

Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
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Related Experiment Video

Updated: Jun 11, 2026

Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters
14:58

Optical Scatter Microscopy Based on Two-Dimensional Gabor Filters

Published on: June 2, 2010

Optical random number generator based on photoevent locations.

A J Martino, G M Morris

    Applied Optics
    |June 29, 2010
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an optical random number generator using a photon-counting detector. Its performance approaches that of pseudorandom number generators, offering a novel approach to random number generation.

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    A Photonic System for Generating Unconditional Polarization-Entangled Photons Based on Multiple Quantum Interference

    Published on: September 5, 2019

    Area of Science:

    • Physics
    • Computer Science
    • Optics

    Background:

    • Random number generation is crucial for various applications.
    • Traditional pseudorandom number generators have limitations.
    • Optical methods offer a potential alternative for true randomness.

    Purpose of the Study:

    • To develop and evaluate an optical random number generator (ORNG).
    • To utilize a 2-D position-sensitive photon-counting detector for random number generation.
    • To assess the performance of the ORNG using different control objects.

    Main Methods:

    • Employed a 2-D position-sensitive photon-counting detector.
    • Used photoevent locations from detected photons to generate random numbers.
    • Imaged control objects (film and video monitor) onto the detector.
    • Implemented an iterative calibration procedure to determine object brightness distribution.

    Main Results:

    • The spatial distribution of photoevent locations was successfully used for random number generation.
    • Performance evaluation showed the ORNG approached the quality of pseudorandom number generators.
    • Both film and video monitor control objects yielded comparable results.

    Conclusions:

    • The developed optical random number generator demonstrates promising performance.
    • The use of a photon-counting detector and control object imaging is effective.
    • This optical approach offers a viable alternative for generating high-quality random numbers.