Related Experiment Video
Updated: Apr 27, 2026

12:34
Methods for Measuring the Orientation and Rotation Rate of 3D-printed Particles in Turbulence
Published on: June 24, 2016
9.8K
Random bits, true and unbiased, from atmospheric turbulence
Davide G Marangon1, Giuseppe Vallone1, Paolo Villoresi1
1Department of Information Engineering, University of Padova, via Gradenigo 6/B, Padova, Italy.
Scientific Reports
|July 1, 2014
Summary
Researchers generated genuine random numbers using atmospheric turbulence and laser beams. An algorithm extracts randomness from images without post-processing, passing rigorous tests for secure communications and simulations.
Area of Science:
- Physics
- Information Science
- Optical Engineering
Background:
- Genuine random numbers are crucial for secure communications, simulations, and information science.
- Physical processes with inherent unpredictability offer a source for true random number generation.
- Atmospheric turbulence effects on light propagation present a potential avenue for randomness extraction.
Purpose of the Study:
- To investigate the use of optical propagation in strong atmospheric turbulence for generating genuine random numbers.
- To develop and validate an algorithm for extracting randomness from laser beam images without post-processing.
Main Methods:
- Observing a laser beam after a 143 km free-space path through atmospheric turbulence.
- Developing a novel algorithm to extract randomness directly from receiver beam images.
- Subjecting the generated numbers to rigorous, selective randomness tests.
Main Results:
- Successfully generated numbers qualifying as genuine random numbers.
- The developed algorithm effectively extracts randomness without requiring post-processing.
- Demonstrated the algorithm's potential for generalization to other physical processes.
Conclusions:
- Optical propagation in atmospheric turbulence is a viable method for generating genuine random numbers.
- The developed image-based extraction algorithm is efficient and requires no post-processing.
- The methodology shows promise for diverse applications in secure communications and scientific simulations.
More Related Videos
Related Concept Videos
Random Error
8.2K
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
8.2K
Bias
6.2K
Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
6.2K
Random and Systematic Errors
11.2K
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
11.2K
Random and Systematic Errors
972
972
Random Variables
14.7K
A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
14.7K
Uncertainty in Measurement: Accuracy and Precision
93.8K
Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.
93.8K

