Related Experiment Video
Updated: Mar 28, 2026

11:15
A Guide to Structured Illumination TIRF Microscopy at High Speed with Multiple Colors
Published on: May 30, 2016
26.5K
One-dimensional rainbow technique using Fourier domain filtering
Optics Express
|December 25, 2015
Summary
This study introduces a new optical design for one-dimensional rainbow refractometry, enabling precise measurement of droplet size and refractive index. The method uses a spatial filter for accurate angular calibration, improving droplet analysis.
Area of Science:
- Optical Physics
- Fluid Dynamics
- Analytical Chemistry
Background:
- Rainbow refractometry measures droplet size and refractive index simultaneously.
- Accurate refractive index measurement relies on precise angular calibration of the rainbow scattering angle.
Purpose of the Study:
- To propose a novel optical design for one-dimensional rainbow refractometry.
- To enhance the accuracy of angular calibration for droplet analysis.
- To simultaneously measure the refractive index and size of n-heptane droplets.
Main Methods:
- Implemented a one-dimensional spatial filter in the Fourier domain for optical design.
- Developed a simple calibration method to correlate CCD pixel data with scattering angle.
- Applied both standard and global one-dimensional rainbow techniques using the new design.
Main Results:
- The proposed optical design accurately determines the relationship between scattering angle and CCD pixels.
- The method successfully measures the refractive index and size of n-heptane droplets.
- Perpendicularly incident light selection aligns with classical rainbow refractometry inversion algorithms.
Conclusions:
- The novel optical design offers accurate angular calibration for rainbow refractometry.
- This technique provides a reliable method for simultaneous measurement of droplet size and refractive index.
- The approach is validated by successful application to n-heptane droplets.
Related Concept Videos
Convergence of Fourier Series
521
The Fourier series is a powerful mathematical tool for representing periodic signals as an infinite sum of complex exponentials. In practice, this infinite series is truncated to a finite number of terms, yielding a partial sum. This truncation makes the approximation of the signal feasible but introduces certain challenges, particularly near discontinuities, known as the Gibbs phenomenon.
The Gibbs phenomenon refers to the persistent oscillations and overshoots that occur near discontinuities...
The Gibbs phenomenon refers to the persistent oscillations and overshoots that occur near discontinuities...
521
Discrete Fourier Transform
1.1K
The Discrete Fourier Transform (DFT) is a fundamental tool in signal processing, extending the discrete-time Fourier transform by evaluating discrete signals at uniformly spaced frequency intervals. This transformation converts a finite sequence of time-domain samples into frequency components, each representing complex sinusoids ordered by frequency. The DFT translates these sequences into the frequency domain, effectively indicating the magnitude and phase of each frequency component present...
1.1K
Basic signals of Fourier Transform
1.2K
The Fourier Transform is a pivotal mathematical tool in signal processing, enabling the transformation of time-domain signals into their frequency-domain representations. Among the numerous elements within this domain, certain functions like the sinc function, delta function, and exponential signals hold significant importance due to their unique properties and implications.
The sinc function, defined as sinc(x) = sin(πx)/(πx), is particularly notable for its symmetry and behavior at...
The sinc function, defined as sinc(x) = sin(πx)/(πx), is particularly notable for its symmetry and behavior at...
1.2K
Properties of Fourier Transform II
901
The Fourier Transform (FT) is an essential mathematical tool in signal processing, transforming a time-domain signal into its frequency-domain representation. This transformation elucidates the relationship between time and frequency domains through several properties, each revealing unique aspects of signal behavior.
The Frequency Shifting property of Fourier Transforms highlights that a shift in the frequency domain corresponds to a phase shift in the time domain. Mathematically, if x(t) has...
The Frequency Shifting property of Fourier Transforms highlights that a shift in the frequency domain corresponds to a phase shift in the time domain. Mathematically, if x(t) has...
901
Discrete-Time Fourier Series
850
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]...
850
Fast Fourier Transform
1.2K
The Fast Fourier Transform (FFT) is a computational algorithm designed to compute the Discrete Fourier Transform (DFT) efficiently. By breaking down the calculations into smaller, manageable sections, the FFT significantly reduces the computational complexity involved. Direct computation of an N-point DFT requires N2 complex multiplications, whereas the FFT algorithm needs only (N/2)log2N multiplications, offering a much faster performance.
The computational efficiency of the FFT becomes...
The computational efficiency of the FFT becomes...
1.2K

