Related Experiment Video
Updated: Feb 7, 2026
07:27
Quantitative Analysis of Random Migration of Cells Using Time-lapse Video Microscopy
Published on: May 13, 2012
17.3K
Enhancing early-time diffusion through beam collimation in pulse propagation in sparse discrete random media.
Optics Letters
|August 2, 2018
Summary
This study analyzes pulsed beam propagation through random media, crucial for free-space optical communication. It identifies an "early-time diffusion" (ETD) component that enables high-rate data transfer, outperforming previous models.
Area of Science:
- Physics
- Optical Engineering
- Applied Mathematics
Background:
- Free-space optical communication faces limitations due to atmospheric obscurants affecting signal propagation.
- The radiative transfer equation (RTE) models light interaction with random media, but solutions for pulsed beams are complex.
Purpose of the Study:
- To solve the time-dependent RTE for pulsed collimated beams in random media.
- To analyze the contributions of early-time diffusion (ETD) and late-time diffusion (LTD) to signal propagation.
- To assess the potential of ETD for high-rate data transfer in optical communication.
Main Methods:
- Solving the RTE in the spherical-harmonics basis with angular momentum truncation.
- Analyzing time-resolved radiance, distinguishing between coherent, ETD, and LTD components.
- Comparing ETD/LTD signal ratios for collimated beams versus omnidirectional sources.
Main Results:
- Confirmed convergence of the RTE solution with increasing angular momentum truncation.
- Identified both LTD and ETD components in the time-resolved radiance.
- Observed significantly higher ETD to LTD signal ratios for collimated beams compared to omnidirectional sources.
Conclusions:
- The ETD component offers a promising avenue for high-rate data transfer in free-space optical communication.
- Collimated beams exhibit enhanced ETD, increasing their utility for communication and imaging applications.
- The findings advance the understanding of light propagation in random media for practical applications.
More Related Videos
Related Concept Videos
00:59Propagation of Uncertainty from Random Error
2.0K
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...
2.0K
01:26Discrete-time Fourier transform
1.1K
The Discrete-Time Fourier Transform (DTFT) is an essential mathematical tool for analyzing discrete-time signals, converting them from the time domain to the frequency domain. This transformation allows for examining the frequency components of discrete signals, providing insights into their spectral characteristics. In the DTFT, the continuous integral used in the continuous-time Fourier transform is replaced by a summation to accommodate the discrete nature of the signal.
One of the notable...
One of the notable...
1.1K
01:16Basic Discrete Time Signals
731
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...
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...
731
01:20Discrete-Time Fourier Series
711
The Discrete-Time Fourier Series (DTFS) is a fundamental concept in signal processing, serving as the discrete-time counterpart to the continuous-time Fourier series. It allows for the representation and analysis of discrete-time periodic signals in terms of their frequency components. Unlike its continuous counterpart, which utilizes integrals, the calculation of DTFS expansion coefficients involves summations due to the discrete nature of the signal.
For a discrete-time periodic signal x[n]...
For a discrete-time periodic signal x[n]...
711
01:12Diffusion
219.9K
Diffusion is the passive movement of substances down their concentration gradients—requiring no expenditure of cellular energy. Substances, such as molecules or ions, diffuse from an area of high concentration to an area of low concentration in the cytosol or across membranes. Eventually, the concentration will even out, with the substance moving randomly but causing no net change in concentration. Such a state is called dynamic equilibrium, which is essential for maintaining overall...
219.9K
01:24BIBO stability of continuous and discrete -time systems
941
System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
941