The Utilization of Gaussian Filter Method on Voice Record Frequency Noise

2020 ◽  
Author(s):  
Al-Khowarizmi ◽  
Halim Maulana
Keyword(s):  
Filter Method ◽  
Frequency Noise ◽  
Gaussian Filter

scholarly journals Stereo-PIV measurements of spatio-temporal turbulence correlations in an axisymmetric jet

2015 ◽  
Vol 778 ◽  
pp. 216-252 ◽  
Author(s):  
C. D. Pokora ◽  
J. J. McGuirk
Keyword(s):  
Fourth Order ◽  
Single Point ◽  
Aerodynamic Noise ◽  
Filter Method ◽  
Frequency Noise ◽  
Turbulent Structures ◽  
Validation Data ◽  
Piv Measurements ◽  
Temporal Correlations ◽  
Spatio Temporal

Stereoscopic three-component particle image velocimetry (3C-PIV) measurements have been made in a turbulent round jet to investigate the spatio-temporal correlations that are the origin of aerodynamic noise. Restricting attention to subsonic, isothermal jets, measurements were taken in a water flow experiment where, for the same Reynolds number and nozzle size, the shortest time scale of the dynamically important turbulent structures is more than an order of magnitude greater that in equivalent airflow experiments, greatly facilitating time-resolved PIV measurements. Results obtained (for a jet nozzle diameter and velocity of 40 mm and $1~\text{m}~\text{s}^{-1}$, giving $\mathit{Re}=4\times 10^{4}$) show that, on the basis of both single-point statistics and two-point quantities (correlation functions, integral length scales) the present incompressible flow data are in excellent agreement with published compressible, subsonic airflow measurements. The 3C-PIV data are first compared to higher-spatial-resolution 2C-PIV data and observed to be in good agreement, although some deterioration in quality for higher-order correlations caused by high-frequency noise in the 3C-PIV data is noted. A filter method to correct for this is proposed, based on proper orthogonal decomposition (POD) of the 3C-PIV data. The corrected data are then used to construct correlation maps at the second- and fourth-order level for all velocity components. The present data are in accordance with existing hot-wire measurements, but provide significantly more detailed information on correlation components than has previously been available. The measured relative magnitudes of various components of the two-point fourth-order turbulence correlation coefficient ($R_{ij,kl}$) – the fundamental building block for free shear flow aerodynamic noise sources – are presented and represent a valuable source of validation data for acoustic source modelling. The relationship between fourth-order and second-order velocity correlations is also examined, based on an assumption of a quasi-Gaussian nearly normal p.d.f. for the velocity fluctuations. The present results indicate that this approximation shows reasonable agreement for the measured relative magnitudes of several correlation components; however, areas of discrepancy are identified, indicating the need for work on alternative models such as the shell turbulence concept of Afsar (Eur. J. Mech. (B/Fluids), vol. 31, 2012, pp. 129–139).

Research on a Wavelet Filtering Method of the Fiber Optic Gyroscope Based on EMD

2013 ◽  
Vol 705 ◽  
pp. 253-257
Author(s):  
Bin Zhou ◽  
Wei Wang
Keyword(s):  
Fiber Optic ◽  
Filter Method ◽  
Frequency Noise ◽  
Wavelet Filter ◽  
Fiber Optic Gyro ◽  
Fiber Optic Gyroscope ◽  
Random Drift ◽  
Mode Decomposition ◽  
Bias Instability ◽  
Emd Method

Due to the weak nonstationary characteristics of high-precision fiber optic gyro output random drift. In order to reduce the random drift, this paper proposes a new nonlinear, nonstationary filter method—the Empirical mode decomposition (EMD) method. Firstly some key technologies were discussed, and then proposed a Wavelet filter method of the fiber optic gyroscope based on EMD. Using fiber optic gyro data conducted off-line simulation verification. The results indicated that Wavelet filter method based on EMD can effectively remove high frequency noise compared with the ordinary wavelet filter method, and can also effectively suppress other noise. The bias instability of Gyro output from 0.0061 ° / h reduced to 0.001 ° / h. Accuracy is improved obviously.

A study of a two phases medium in pipe vessel using gaussian filter method and k-NN classifier

2013 ◽  
Vol 25 (3) ◽  
pp. 635-641 ◽  
Author(s):  
Mohd Rizal Manan ◽  
Muhammad Naufal Mansor ◽  
Mohd Hafiz Fazalul Rahiman ◽  
Ruzairi Abdul Rahim ◽  
Sazali Yaacob
Keyword(s):  
Filter Method ◽  
Gaussian Filter ◽  
Two Phases

Mine Location Algorithm Based on Multiple Linear Regression

2011 ◽  
Vol 58-60 ◽  
pp. 1830-1835
Author(s):  
Xiao Ying Wang ◽  
Ying Ge Chen
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Regression Coefficient ◽  
Training Sample ◽  
Filter Method ◽  
Gaussian Filter ◽  
Location Error ◽  
Measuring Range ◽  
Mine Environment ◽  
Location Algorithm

This paper put forward a mine location algorithm based on multiple linear regression, which using only simple RSSI value to get a higher location accuracy under long narrow and sensitive mine environment. General RSSI measurement method and its drawbacks are discussed in the paper. In order to acquire smaller location error, we filtered some abnormal RSSI data through Gaussian filter method. And we deduced regression equation according to multiple linear regression principle. Combined with training sample, we got their regression parameter. We did relevant location experiment again in the same environment---40m long and narrow bomb shelter which may imitate mine tunnel to a great extent, which shows that the total errors are limited in 3m and 75% errors are less than 2m. What’s more, it can be extended to infinite measuring range with the same set regression coefficient in similar environment.

Noise Qualification in Bali Palm Leaf Image with Gaussian Filter Method

2020 ◽  
Vol 8 (3) ◽  
pp. 301
Author(s):  
Wayan Adhitya Prathama ◽  
I Gede Arta Wibawa
Keyword(s):  
Image Quality ◽  
Signal To Noise Ratio ◽  
Filter Method ◽  
Gaussian Filter ◽  
Signal To Noise ◽  
Filter Noise ◽  
Leaf Image ◽  
Palm Leaf ◽  
Better Than

In improving the quality of the image basically makes the appearance of an image better than before. One thing that is done in improving the image quality is noise qualification. This noise qualification aims to reduce the level of noise contained in a digital image. In this study, the image used is the image of Bali palm leaf. There are many methods that can be used to qualify for noise. One of them is the Gaussian filter. In this study, Gaussian Filter is used as a method to qualify the noise contained in the palm leaf image. The image quality after the noise qualification process is calculated using PSNR (Peak Signal to Noise Ratio). The higher the PSNR value obtained, the better the image quality. In this study, the PSNR value obtained in the palm leaf image after processing the noise qualification is 54.7625 db. Keywords: Gaussian Filter, Noise, PSNR, Bali Palm Leaf, Image

Decrease in Sub-harmonics Frequency Noise Induced by Rotor Eccentric Motion and its Induction Currents between Parallel Stator Circuits

2013 ◽  
Vol 133 (10) ◽  
pp. 995-1002 ◽  
Author(s):  
Masanori Arata ◽  
Motoyasu Mochizuki ◽  
Takashi Araki ◽  
Takashi Hanai ◽  
Masakatsu Matsubara
Keyword(s):  
Frequency Noise ◽  
Eccentric Motion
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