&lt;title&gt;Direct ultrashort-pulse retrieval in frequency-resolved optical gating using wavelets and a neural network&lt;/title&gt;

Krumbuegel, Marco A.; Fittinghoff, D. N.; DeLong, Kenneth W.; Trebino, Rick

doi:10.1117/12.239707

Cited by 3 publications

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“…13 In FROG, a nonlinear-optical autocorrelation signal is frequency-resolved by a spectrometer to produce a spectrogram (FROG trace) of the complex pulse field, which visually displays the pulse frequency vs. time and uniquely determines the electric field E(t). 13 In FROG, a nonlinear-optical autocorrelation signal is frequency-resolved by a spectrometer to produce a spectrogram (FROG trace) of the complex pulse field, which visually displays the pulse frequency vs. time and uniquely determines the electric field E(t).…”

Section: Introductionmentioning

confidence: 99%

<title>Ultrashort-pulse retrieval using frequency-resolved optical gating and an artificial neural network</title>

Krumbuegel¹,

DeLong²,

Fittinghoff³

et al. 1996

SPIE Proceedings

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Frequency-resolved optical gating (FROG) is a technique that allows the determination of the intensity and phase of ultrashort laser pulses. In FROG, a spectrogram of the pulse, the so-called FROG trace, is produced, from which the intensity and phase is then retrieved using an iterative algorithm. This algorithm performs well for all types of pulses, but it sometimes requires more than a minute to converge, and more rapid retrieval is important for many applications. It is therefore desirable to have a non-iterative computational method capable of inverting the function that relates the pulse intensity and phase to its FROG trace. In previous work, we showed that a neural network can retrieve simple pulses rapidly and directly. This original approach involved feature extraction by computing the lowest-order integral moments of the FROG trace, making it particularly sensitive to the presence of additive noise. Using parallel-processing hardware, we are now able to use FROG traces of limited size (32x32 pixel) without any feature extraction as input for a neural net. In addition, FROG traces of 64x64 pixel size, typical for experimental data, can be used in conjunction with a more noise-insensitive feature extraction method.

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Section: Introductionmentioning

confidence: 99%