Publication

Harmonic Holographic Microscopy Using Nanoparticles as Probes for Three-Dimensional Cell Imaging

Demetri Psaltis, Ye Pu, Ye Pu, Rachel Grange, Chia-Lung Hsieh
2009
Conference Papers

Abstract

We demonstrate the three-dimensional imaging capability of harmonic holographic microscopy by using the second harmonic generation from BaTiO3 nanoparticles as the signal. Three-dimensional distributions of the BaTiO3 nanoparticles in biological cells are recorded without scanning. (C) 2009 Optical Society of America

Official source

https://infoscience.epfl.ch/entities/publication/e51c75af-abcf-443d-875c-598d61a8ec61

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Demetri Psaltis, Ye Pu, Ye Pu, Rachel Grange, Chia-Lung Hsieh
2009
Conference Papers

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/e51c75af-abcf-443d-875c-598d61a8ec61

About this result

Related concepts (14)

Multivariate normal distribution

In probability theory and statistics, the multivariate normal distribution, multivariate Gaussian distribution, or joint normal distribution is a generalization of the one-dimensional (univariate) normal distribution to higher dimensions. One definition is that a random vector is said to be k-variate normally distributed if every linear combination of its k components has a univariate normal distribution. Its importance derives mainly from the multivariate central limit theorem.

Complex normal distribution

In probability theory, the family of complex normal distributions, denoted or , characterizes complex random variables whose real and imaginary parts are jointly normal. The complex normal family has three parameters: location parameter μ, covariance matrix , and the relation matrix . The standard complex normal is the univariate distribution with , , and . An important subclass of complex normal family is called the circularly-symmetric (central) complex normal and corresponds to the case of zero relation matrix and zero mean: and .

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In probability and statistics, a mixture distribution is the probability distribution of a random variable that is derived from a collection of other random variables as follows: first, a random variable is selected by chance from the collection according to given probabilities of selection, and then the value of the selected random variable is realized. The underlying random variables may be random real numbers, or they may be random vectors (each having the same dimension), in which case the mixture distribution is a multivariate distribution.

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