scholarly journals Stability of pulsar rotational and orbital periods

2009 ◽  
Vol 5 (H15) ◽  
pp. 226-227
Author(s):  
Sergei Kopeikin
Keyword(s):  
General Relativity ◽  
Time Scale ◽  
Random Noise ◽  
Low Frequency ◽  
Frequency Noise ◽  
Tests Of General Relativity ◽  
Orbital Parameters ◽  
Pulsar Timing ◽  
Orbital Periods ◽  
The Stability

AbstractMillisecond and binary pulsars are the most stable astronomical standards of frequency. They can be applied to solving a number of problems in astronomy and time-keeping metrology including the search for a stochastic gravitational wave background in the early universe, testing general relativity, and establishing a new time-scale. The full exploration of pulsar properties requires that proper unbiased estimates of spin and orbital parameters of the pulsar be obtained. These estimates depend essentially on the random noise components present in pulsar timing residuals. The instrumental white noise has predictable statistical properties and makes no harm for interpretation of timing observations, while the astrophysical/geophyeical low-frequency noise corrupts them, thus, reducing the quality of tests of general relativity and decreasing the stability of the pulsar time scale.

scholarly journals Binary Pulsar Tests of General Relativity in the Presence of Low-Frequency Noise

2000 ◽  
Vol 177 ◽  
pp. 117-120
Author(s):  
Sergei M. Kopeikin ◽  
Vladimir A. Potapov
Keyword(s):  
Binary Stars ◽  
Strong Field ◽  
Low Frequency ◽  
Alternative Theories Of Gravity ◽  
Frequency Noise ◽  
Fundamental Limits ◽  
Tests Of General Relativity ◽  
Orbital Parameters ◽  
Theories Of Gravity ◽  
Timing Noise

AbstractThe influence of the low-frequency timing noise on the precision of measurements of the Keplerian and post-Keplerian orbital parameters in binary pulsars is studied. Fundamental limits on the accuracy of tests of alternative theories of gravity in the strong-field regime are established. The gravitational low-frequency timing noise formed by an ensemble of binary stars is briefly discussed.

scholarly journals Low Frequency Noise Remove from EEG Signal

2020 ◽  
Vol 9 (1) ◽  
pp. 1510-1513
Keyword(s):  
Human Brain ◽  
Random Noise ◽  
Low Frequency ◽  
Frequency Noise ◽  
Eeg Signal ◽  
Low Frequency Noise ◽  
Frequency Range ◽  
Average Value ◽  
Different Types ◽  
The Brain

The electrical activity of the brain recorded by EEG which used to detect different types of diseases and disorders of the human brain. There is contained a large amount of random noise present during EEG recording, such as artifacts and baseline changes. These noises affect the low -frequency range of the EEG signal. These artifacts hiding some valuable information during analyzing of the EEG signal. In this paper we used the FIR filter for removing low -frequency noise(<1Hz) from the EEG signal. The performance is measured by calculating the SNR and the RMSE. We obtained RMSE average value from the test is 0.08 and the SNR value at frequency(<1Hz) is 0.0190.

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.

Infrasound — Tutorial and Review: Part 1

1983 ◽  
Vol 2 (3) ◽  
pp. 1-31 ◽  
Author(s):  
O. Backteman ◽  
J. Köhler ◽  
L. Sjöberg
Keyword(s):  
Home Environment ◽  
Leisure Time ◽  
Random Noise ◽  
Low Frequency ◽  
Sound Level ◽  
Frequency Noise ◽  
Full Agreement ◽  
Normal People ◽  
Short Time ◽  
Frequency Area

Infrasound is everywhere. Even in an environment that is very quiet, infrasound levels of 40 dB(IL) (2–20 Hz) can be measured. Results from numerous investigations about the influence of infrasound on people and animals have been published during the last 20 years. What all these investigations have in common is that only short time exposures have been investigated i.e. infrasound exposures during minutes or hours. Discrete frequencies in the range of 4–16 Hz have often been used instead of low frequency random noise spectra. The results of these investigations vary considerably, which may be due to the fact that there has not been equal excitation. In certain experiments and reports the stimuli spectra are often not accounted for. If they are accounted for this is done inadequately. This is also true for the way the results have been treated. Some conclusions that have been made have to be seen as spectacular! Several scientists have pointed out that the results may be caused by stimulus in the audible range, not by infrasound. At the conference “Low frequency noise and hearing” (May 1980 in Aalborg, Denmark), where most of the leading scientists of the low frequency area participated, it was stated that there are people who seem to be more sensitive to low frequency disturbances than others. In general, infrasound should not be a direct problem for normal people, which these research results showed. There was full agreement that there is not enough research being done today on low frequency disturbances in the range of 20–100 Hz. These disturbances cannot be sufficiently indicated by measuring the sound level in dB(A). The participants seemed quite convinced that people are irritated by low frequency disturbances both during work, at home and during leisure time. This has not been sufficiently noted. Most machines in industries and in homes also have frequencies in the region of 50 Hz, e.g. transformers, compressors, diesel- and gas motors, electric motors and fans. The objective of this work has been to make a comprehensive review, especially in accounting for infrasound levels under normal working conditions and in home environment, both in Sweden and in the rest of the world. Due to the fact that the results from different reports are not uniform and often incomplete, a direct comparison of results is not possible.

scholarly journals A pulsar-based time-scale from the International Pulsar Timing Array

2019 ◽  
Vol 491 (4) ◽  
pp. 5951-5965 ◽  
Author(s):  
G Hobbs ◽  
L Guo ◽  
R N Caballero ◽  
W Coles ◽  
K J Lee ◽  
...  
Keyword(s):  
Time Scales ◽  
Time Scale ◽  
Limiting Factor ◽  
Radio Pulsars ◽  
Pulsar Timing ◽  
The Us ◽  
The Stability ◽  
Solar System Dynamics ◽  
Noise Models ◽  
Pulsar Timing Array

ABSTRACT We have constructed a new time-scale, TT(IPTA16), based on observations of radio pulsars presented in the first data release from the International Pulsar Timing Array (IPTA). We used two analysis techniques with independent estimates of the noise models for the pulsar observations and different algorithms for obtaining the pulsar time-scale. The two analyses agree within the estimated uncertainties and both agree with TT(BIPM17), a post-corrected time-scale produced by the Bureau International des Poids et Mesures (BIPM). We show that both methods could detect significant errors in TT(BIPM17) if they were present. We estimate the stability of the atomic clocks from which TT(BIPM17) is derived using observations of four rubidium fountain clocks at the US Naval Observatory. Comparing the power spectrum of TT(IPTA16) with that of these fountain clocks suggests that pulsar-based time-scales are unlikely to contribute to the stability of the best time-scales over the next decade, but they will remain a valuable independent check on atomic time-scales. We also find that the stability of the pulsar-based time-scale is likely to be limited by our knowledge of solar-system dynamics, and that errors in TT(BIPM17) will not be a limiting factor for the primary goal of the IPTA, which is to search for the signatures of nano-Hertz gravitational waves.

scholarly journals Desert seismic signal denoising by 2D compact variational mode decomposition

2019 ◽  
Vol 16 (6) ◽  
pp. 1048-1060 ◽  
Author(s):  
Yue Li ◽  
Linlin Li ◽  
Chao Zhang
Keyword(s):  
Seismic Data ◽  
Random Noise ◽  
Low Frequency ◽  
Seismic Signal ◽  
Frequency Noise ◽  
Variational Mode Decomposition ◽  
Support Functions ◽  
Noise Characteristics ◽  
Mode Decomposition ◽  
Effective Signal

Abstract Noise suppression and effective signal recovery are very important for seismic signal processing. The random noise in desert areas has complex characteristics due to the complex geographical environment; noise characteristics such as non-stationary, non-linear and low frequency. These make it difficult for conventional denoising methods to remove random noise in desert seismic records. To address the problem, this paper proposes a two-dimensional compact variational mode decomposition (2D-CVMD) algorithm for desert seismic noise attenuation. This model decomposes the complex desert seismic data into an finite number of intrinsic mode functions with specific directions and vibration characteristics. The algorithm introduces binary support functions, which can detect the edge region of the signal in each mode by penalizing the support function through the L1 and total variation (TV) norm. Finally, the signal can be reconstructed by the support functions and the decomposed modes. We apply the 2D-CVMD algorithm to synthetic and real seismic data. The results show that the 2D-CVMD algorithm can not only suppress desert low-frequency noise, but also recover the weak effective signal.

Independence of short time scale fluctuations of quasi-periodic oscillations and low-frequency noise in GX 5-1

1990 ◽  
Vol 361 ◽  
pp. 514 ◽  
Author(s):  
J. P. Norris ◽  
P. Hertz ◽  
K. S. Wood ◽  
B. A. Vaughan ◽  
P. F. Michelson ◽  
...  
Keyword(s):  
Time Scale ◽  
Low Frequency ◽  
Short Time Scale ◽  
Frequency Noise ◽  
Low Frequency Noise ◽  
Periodic Oscillations ◽  
Short Time
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