Frequency noise suppression in a diode laser locked to a travelling wave resonator incorporating a Brewster prism

2008 ◽  
Vol 281 (6) ◽  
pp. 1668-1670 ◽  
Author(s):  
V. Grishina ◽  
J. Winterflood
Keyword(s):  
Diode Laser ◽  
Noise Suppression ◽  
Travelling Wave ◽  
Frequency Noise ◽  
Wave Resonator

scholarly journals Stabilizing diode laser to 1 Hz-level Allan deviation with atomic spectroscopy for Rb four-level active optical frequency standard

2019 ◽  
Vol 125 (11) ◽  
Author(s):  
Pengyuan Chang ◽  
Shengnan Zhang ◽  
Haosen Shang ◽  
Jingbiao Chen
Keyword(s):  
Diode Laser ◽  
Noise Suppression ◽  
Laser System ◽  
Frequency Standard ◽  
Atomic Spectroscopy ◽  
Frequency Noise ◽  
Optical Frequency ◽  
Transition Line ◽  
Allan Deviation ◽  
Optical Frequency Standard

Abstract We achieve a compact ultra-stable 420 nm blue diode laser system by immediately stabilizing the laser on the hyperfine transition line of Rb atom. The Allan deviation of the residual error signal reaches 1 Hz-level Allan deviation within 1 s averaging time, and the fractional frequency Allan deviation is $$1.4\times 10^{-15}/\sqrt{\tau }$$1.4×10-15/τ, which shows the best result of frequency-stabilized lasers based on the atomic spectroscopy without Pound–Drever–Hall (PDH) system. The signal-to-noise ratio of the atomic spectroscopy is evaluated to be 3,000,000 from the Allan deviation formula, which is the highest record, to the best of our knowledge. The frequency noise suppression characterization is demonstrated and the maximal noise suppression can be near 40 dB at 6 Hz. As a good candidate of pumping source, the ultra-stable 420 nm diode laser is successfully used in our Rb four-level active optical frequency standard system. The method can be easily extended to other wavelengths ultra-stable lasers with a Allan deviation of $$10^{-15}$$10-15 level retaining an atomic reference with low cost and low complexity while in the absence of an expensive and complicated PDH system.

scholarly journals Imaging Noise Suppression: Fourth-Order Partial Differential Equations and Travelling Wave Solutions

Mathematics ◽  
2020 ◽  
Vol 8 (11) ◽  
pp. 2019
Author(s):  
Sameerah Jamal
Keyword(s):  
Fourth Order ◽  
Noise Suppression ◽  
Linear Equations ◽  
Travelling Wave ◽  
Order Partial Differential Equation ◽  
Travelling Wave Solutions ◽  
Partial Differential ◽  
Total Variation Norm ◽  
Wave Solutions ◽  
Variation Norm

In this paper, we discuss travelling wave solutions for image smoothing based on a fourth-order partial differential equation. One of the recurring issues of digital imaging is the amount of noise. One solution to this is to minimise the total variation norm of the image, thus giving rise to non-linear equations. We investigate the variational properties of the Lagrange functionals associated with these minimisation problems.

Desert low-frequency noise suppression by using adaptive DnCNNs based on the determination of high-order statistic

2019 ◽  
Vol 219 (2) ◽  
pp. 1281-1299 ◽  
Author(s):  
X T Dong ◽  
Y Li ◽  
B J Yang
Keyword(s):  
Order Statistic ◽  
Seismic Data ◽  
Noise Suppression ◽  
Random Noise ◽  
Low Frequency ◽  
High Order ◽  
Frequency Noise ◽  
Low Frequency Noise ◽  
High Order Statistic ◽  
Adaptive Noise

SUMMARY The importance of low-frequency seismic data has been already recognized by geophysicists. However, there are still a number of obstacles that must be overcome for events recovery and noise suppression in low-frequency seismic data. The most difficult one is how to increase the signal-to-noise ratio (SNR) at low frequencies. Desert seismic data are a kind of typical low-frequency seismic data. In desert seismic data, the energy of low-frequency noise (including surface wave and random noise) is strong, which largely reduces the SNR of desert seismic data. Moreover, the low-frequency noise is non-stationary and non-Gaussian. In addition, compared with seismic data in other regions, the spectrum overlaps between effective signals and noise is more serious in desert seismic data. These all bring enormous difficulties to the denoising of desert seismic data and subsequent exploration work including geological structure interpretation and forecast of reservoir fluid. In order to solve this technological issue, feed-forward denoising convolutional neural networks (DnCNNs) are introduced into desert seismic data denoising. The local perception and weight sharing of DnCNNs make it very suitable for signal processing. However, this network is initially used to suppress Gaussian white noise in noisy image. For the sake of making DnCNNs suitable for desert seismic data denoising, comprehensive corrections including network parameter optimization and adaptive noise set construction are made to DnCNNs. On the one hand, through the optimization of denoising parameters, the most suitable network parameters (convolution kernel、patch size and network depth) for desert seismic denoising are selected; on the other hand, based on the judgement of high-order statistic, the low-frequency noise of processed desert seismic data is used to construct the adaptive noise set, so as to achieve the adaptive and automatic noise reduction. Several synthetic and actual data examples with different levels of noise demonstrate the effectiveness and robustness of the adaptive DnCNNs in suppressing low-frequency noise and preserving effective signals.

A neural signal detection amplifier with low-frequency noise suppression

2009 ◽  
Author(s):  
Takeshi Yoshida ◽  
Yoshihiro Masui ◽  
Ryoji Eki ◽  
Atsushi Iwata ◽  
Masayuki Yoshida ◽  
...  
Keyword(s):  
Signal Detection ◽  
Noise Suppression ◽  
Low Frequency ◽  
Frequency Noise ◽  
Low Frequency Noise ◽  
Neural Signal

scholarly journals Frequency noise suppression of optical injection-locked quantum cascade lasers

2018 ◽  
Vol 26 (12) ◽  
pp. 15167 ◽  
Author(s):  
Xing-Guang Wang ◽  
Bin-Bin Zhao ◽  
Frédéric Grillot ◽  
Cheng Wang
Keyword(s):  
Noise Suppression ◽  
Quantum Cascade Lasers ◽  
Optical Injection ◽  
Frequency Noise ◽  
Quantum Cascade ◽  
Cascade Lasers
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