Related Experiment Video
Updated: Nov 24, 2025

06:25
Time Multiplexing Super Resolving Technique for Imaging from a Moving Platform
Published on: February 12, 2014
8.7K
Resampling the transmission matrix in an aberration-corrected Bessel mode basis
Optics Express
|December 28, 2020
Summary
Researchers accurately measured light transport statistics using Bessel modes to overcome limitations of conventional methods. This approach faithfully recovers key properties of light propagation through air.
Area of Science:
- Optics and Photonics
- Wave Phenomena
Background:
- The optical transmission matrix (TM) provides insights into light transport through scattering media.
- Accurate TM characterization relies on orthogonal and complete measurement bases.
- Conventional methods face challenges from sampling effects and optical aberrations.
Purpose of the Study:
- To investigate light transport statistics through a sample.
- To develop an experimental method for accurate TM measurement.
- To recover singular values, eigenvalues, and eigenmodes of light propagation.
Main Methods:
- Utilized a basis of Bessel modes of the first kind for TM measurement.
- Employed an experimental setup to probe light propagation through a finite thickness of air.
- Focused on overcoming sampling effects and optical aberrations inherent in traditional techniques.
Main Results:
- Successfully recovered singular values and eigenvalues of the TM.
- Identified the eigenmodes of light propagation.
- Demonstrated the fidelity of Bessel modes in capturing TM properties.
Conclusions:
- Bessel modes provide an accurate basis for optical transmission matrix measurements.
- This method overcomes limitations of conventional techniques for characterizing light transport.
- The study successfully recovered key optical properties of light propagation through air.
Related Concept Videos
Upsampling
470
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
470
Reconstruction of Signal using Interpolation
561
Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
561
Aliasing
400
Accurate signal sampling and reconstruction are crucial in various signal-processing applications. A time-domain signal's spectrum can be revealed using its Fourier transform. When this signal is sampled at a specific frequency, it results in multiple scaled replicas of the original spectrum in the frequency domain. The spacing of these replicas is determined by the sampling frequency.
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
If the sampling frequency is below the Nyquist rate, these replicas overlap, preventing the original...
400
Bandpass Sampling
370
In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2....
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2....
370
Sampling Theorem
1.0K
In signal processing, the analysis of continuous-time signals, denoted as x(t), often involves sampling techniques to convert these signals into discrete-time signals. This process is essential for digital representation and manipulation. A critical component in sampling is the train of impulses, characterized by the sampling interval and the sampling frequency. The relationship between these parameters and the original signal's properties dictates the success of the sampling process.
1.0K
Transmission-Line Differential Equations
556
Transmission lines are essential components of electrical power systems. They are characterized by the distributed nature of resistance (R), inductance (L), and capacitance (C) per unit length. To analyze these lines, differential equations are employed to model the variations in voltage and current along the line.
Line Section Model
A circuit representing a line section of length Δx helps in understanding the transmission line parameters. The voltage V(x) and current i(x) are measured from...
Line Section Model
A circuit representing a line section of length Δx helps in understanding the transmission line parameters. The voltage V(x) and current i(x) are measured from...
556

