Application with Wavelet Detection to Signal Process of SINS

2012 ◽  
Vol 571 ◽  
pp. 671-675
Author(s):  
Xiang Yuan Huang ◽  
Xia Qing Tang ◽  
Li Bi Guo ◽  
Xu Wei Cheng
Keyword(s):  
High Frequency ◽  
Simulation Experiment ◽  
Detection Method ◽  
Low Frequency ◽  
Frequency Noise ◽  
Wavelet Filter ◽  
Initial Alignment ◽  
Signal Process ◽  
Signal Characteristic ◽  
Alignment Process

Aimed at disturbance caused from motor running and personnel ambulation during initial alignment process of SINS, a new signal detection method of disturbance based on wavelet analysis is brought out. Through analyzing original signal characteristic of FOG and the data with wavelet filter on disturbance base, finds out wavelet filter just have effectiveness to high frequency noise. Then T&L signal detecting law is introduced, and builds T&L signal with high frequency part of wavelet decomposing to estimates interfere time and then resample. Offline simulation experiment results indicate the method can eliminate low frequency disturbance effectively and has certain apply value.

scholarly journals Small Floating Target Detection Method Based on Chaotic Long Short-Term Memory Network

2021 ◽  
Vol 9 (6) ◽  
pp. 651
Author(s):  
Yan Yan ◽  
Hongyan Xing
Keyword(s):  
High Frequency ◽  
Short Term Memory ◽  
Detection Method ◽  
Low Frequency ◽  
Sea Clutter ◽  
Short Term ◽  
Term Memory ◽  
Memory Network ◽  
Long Short Term Memory ◽  
Small Targets

In order for the detection ability of floating small targets in sea clutter to be improved, on the basis of the complete ensemble empirical mode decomposition (CEEMD) algorithm, the high-frequency parts and low-frequency parts are determined by the energy proportion of the intrinsic mode function (IMF); the high-frequency part is denoised by wavelet packet transform (WPT), whereas the denoised high-frequency IMFs and low-frequency IMFs reconstruct the pure sea clutter signal together. According to the chaotic characteristics of sea clutter, we proposed an adaptive training timesteps strategy. The training timesteps of network were determined by the width of embedded window, and the chaotic long short-term memory network detection was designed. The sea clutter signals after denoising were predicted by chaotic long short-term memory (LSTM) network, and small target signals were detected from the prediction errors. The experimental results showed that the CEEMD-WPT algorithm was consistent with the target distribution characteristics of sea clutter, and the denoising performance was improved by 33.6% on average. The proposed chaotic long- and short-term memory network, which determines the training step length according to the width of embedded window, is a new detection method that can accurately detect small targets submerged in the background of sea clutter.

Aerodynamic noise from an asymmetric airfoil with perforated extension plates at the trailing edge

2020 ◽  
pp. 1475472X2097838
Author(s):  
CK Sumesh ◽  
TJS Jothi
Keyword(s):  
High Frequency ◽  
Sound Pressure ◽  
Pore Diameter ◽  
Low Frequency ◽  
Aerodynamic Noise ◽  
Frequency Noise ◽  
Low Frequency Noise ◽  
Trailing Edge ◽  
High Frequency Noise ◽  
Displacement Thickness

This paper investigates the noise emissions from NACA 6412 asymmetric airfoil with different perforated extension plates at the trailing edge. The length of the extension plate is 10 mm, and the pore diameters ( D) considered for the study are in the range of 0.689 to 1.665 mm. The experiments are carried out in the flow velocity ( U∞) range of 20 to 45 m/s, and geometric angles of attack ( αg) values of −10° to +10°. Perforated extensions have an overwhelming response in reducing the low frequency noise (<1.5 kHz), and a reduction of up to 6 dB is observed with an increase in the pore diameter. Contrastingly, the higher frequency noise (>4 kHz) is observed to increase with an increase in the pore diameter. The dominant reduction in the low frequency noise for perforated model airfoils is within the Strouhal number (based on the displacement thickness) of 0.11. The overall sound pressure levels of perforated model airfoils are observed to reduce by a maximum of 2 dB compared to the base airfoil. Finally, by varying the geometric angle of attack from −10° to +10°, the lower frequency noise is seen to increase, while the high frequency noise is observed to decrease.

Research on Improvement Algorithm of Image Edge Detection

2014 ◽  
Vol 539 ◽  
pp. 141-145
Author(s):  
Shui Li Zhang
Keyword(s):  
Cognitive Psychology ◽  
Edge Detection ◽  
High Frequency ◽  
Detection Method ◽  
Low Frequency ◽  
Frequency Information ◽  
Image Edge Detection ◽  
Improvement Algorithm ◽  
Image Edge ◽  
Edge Detection Method

This paper presents new theorems Stevens edge detection method based on cognitive psychology on. Firstly, based on the number of the image is decomposed into high-frequency and low-frequency information, and the high-frequency information extracted by subtracting the maximum number of images to the image after the filter, then the amount of high frequency information into psychological cognitive psychology based on Stevenss theorem. The algorithm suppression refined edge after the non-minimum, applications Pillar K-means algorithm to extract image edge. Experimental results show that: the brightness of the image is converted to the amount of psychological edge can better unify under different brightness values.

Paper 14: Acoustic Detection of Cavitation

1966 ◽  
Vol 181 (1) ◽  
pp. 1-8 ◽  
Author(s):  
I. S. Pearsall
Keyword(s):  
High Frequency ◽  
Critical Frequency ◽  
Low Frequency ◽  
Francis Turbine ◽  
Frequency Noise ◽  
Performance Tests ◽  
Acoustic Detection ◽  
Cavitation Noise ◽  
Further Development ◽  
Onset Of Cavitation

The onset of cavitation in a hydraulic machine can be determined visually and its effect on performance by performance tests. It would be convenient to have an alternative method that required neither transparent sections nor expensive tests. Initial tests have been made measuring noise over a frequency range of 20 c/s-20 kc/s in one-third octave bands, on a number of pumps and turbines. An accelerometer attached to the casing was used. The tests indicated that, generally, the onset of cavitation was accompanied by a rise in the high-frequency noise, whilst the low-frequency noise increased as the cavitation developed. A maximum of cavitation noise was reached before the efficiency and load fell off. In some cases difficulty was experienced because blade cavitation was drowned by noise caused by other cavitation, such as the vortex in a Francis turbine. It also appears that the noise following the onset of cavitation is at the frequency which is used as a critical frequency in accelerated erosion tests. Further development of techniques is required, but the initial tests are encouraging.

scholarly journals The Scaling LInear Macroweather model (SLIM): using scaling to forecast global scale macroweather from months to decades

2015 ◽  
Vol 6 (1) ◽  
pp. 489-545 ◽  
Author(s):  
S. Lovejoy ◽  
L. del Rio Amador ◽  
R. Hébert
Keyword(s):  
Stochastic Models ◽  
High Frequency ◽  
Low Frequency ◽  
Global Scale ◽  
Forecast Skill ◽  
Previous Attempt ◽  
Frequency Noise ◽  
Forecast Errors ◽  
System Memory ◽  
Two Parameter

Abstract. At scales of ≈ 10 days (the lifetime of planetary scale structures), there is a drastic transition from high frequency weather to low frequency macroweather. This scale is close to the predictability limits of deterministic atmospheric models; so that in GCM macroweather forecasts, the weather is a high frequency noise. But neither the GCM noise nor the GCM climate is fully realistic. In this paper we show how simple stochastic models can be developped that use empirical data to force the statistics and climate to be realistic so that even a two parameter model can outperform GCM's for annual global temperature forecasts. The key is to exploit the scaling of the dynamics and the enormous stochastic memories that it implies. Since macroweather intermittency is low, we propose using the simplest model based on fractional Gaussian noise (fGn): the Scaling LInear Macroweather model (SLIM). SLIM is based on a stochastic ordinary differential equations, differing from usual linear stochastic models (such as the Linear Inverse Modelling, LIM) in that it is of fractional rather than integer order. Whereas LIM implicitly assumes there is no low frequency memory, SLIM has a huge memory that can be exploited. Although the basic mathematical forecast problem for fGn has been solved, we approach the problem in an original manner notably using the method of innovations to obtain simpler results on forecast skill and on the size of the effective system memory. A key to successful forecasts of natural macroweather variability is to first remove the low frequency anthropogenic component. A previous attempt to use fGn for forecasts had poor results because this was not done. We validate our theory using hindcasts of global and Northern Hemisphere temperatures at monthly and annual resolutions. Several nondimensional measures of forecast skill – with no adjustable parameters – show excellent agreement with hindcasts and these show some skill even at decadal scales. We also compare our forecast errors with those of several GCM experiments (with and without initialization), and with other stochastic forecasts showing that even this simplest two parameter SLIM model is somewhat superior. In future, using a space–time (regionalized) generalization of SLIM we expect to be able to exploiting the system memory more extensively and obtain even more realistic forecasts.

Effects of Noise Frequency on Performance and Annoyance for Women and Men

1981 ◽  
Vol 52 (2) ◽  
pp. 435-441 ◽  
Author(s):  
Kelli F. Key ◽  
M. Carr Payne
Keyword(s):  
Sex Differences ◽  
Magnitude Estimation ◽  
High Frequency ◽  
Ambient Noise ◽  
Direct Relationship ◽  
Low Frequency ◽  
Frequency Noise ◽  
Complex Task ◽  
High Frequency Noise ◽  
Noise Frequency

Effects of noise frequencies on both performance on a complex psychomotor task and annoyance were investigated for men ( n = 30) and women ( n = 30). Each subject performed a complex psychomotor task for 50 min. in the presence of low frequency noise, high frequency noise, or ambient noise. Women and men learned the task at different rates. Little effect of noise was shown. Annoyance ratings were subsequently obtained from each subject for noises of various frequencies by the method of magnitude estimation. High frequency noises were more annoying than low frequency noises regardless of sex and immediate prior exposure to noise. Sex differences in annoyance did not occur. No direct relationship between learning to perform a complex task while exposed to noise and annoyance by that noise was demonstrated.

scholarly journals Extracting super-resolution details directly from a diffraction-blurred image or part of its frequency spectrum

2019 ◽  
Author(s):  
Edward Y Sheffield
Keyword(s):  
High Frequency ◽  
Simulation Experiment ◽  
Fourier Spectrum ◽  
Low Frequency ◽  
Super Resolution ◽  
Diffraction Limit ◽  
Full Information ◽  
High Frequency Part ◽  
Blurred Image ◽  
Blurred Images

It is usually believed that the low frequency part of a signal’s Fourier spectrum represents its profile, while the high frequency part represents its details. Conventional light microscopes filter out the high frequency parts of image signals, so that people cannot see the details of the samples (objects being imaged) in the blurred images. However, we find that in a certain “resolvable condition”, a signal’s low frequency and high frequency parts not only represent profile and details respectively. Actually, any one of them also contains the full information (including both profile and details) of the sample’s structure. Therefore, for samples with spatial frequency beyond diffraction-limit, even if the image’s high frequency part is filtered out by the microscope, it is still possible to extract the full information from the low frequency part. On the basis of the above findings, we propose the technique of Deconvolution Super-resolution (DeSu-re), including two methods. One method extracts the full information of the sample’s structure directly from the diffraction-blurred image, while the other extracts it directly from part of the observed image’s spectrum (e.g., low frequency part). Both theoretical analysis and simulation experiment support the above findings, and also verify the effectiveness of the proposed methods.

scholarly journals Seismic section image detail enhancement method based on bilateral texture filtering and adaptive enhancement of texture details

2020 ◽  
Vol 27 (2) ◽  
pp. 253-260
Author(s):  
Xiang-Yu Jia ◽  
Chang-Lei DongYe
Keyword(s):  
High Frequency ◽  
Structural Information ◽  
Low Frequency ◽  
Frequency Noise ◽  
Seismic Section ◽  
Multi Scale ◽  
Adaptive Enhancement ◽  
Enhancement Method ◽  
Frequency Components ◽  
Texture Filtering

Abstract. The seismic section image contains a wealth of texture detail information, which is important for the interpretation of the formation profile information. In order to enhance the texture detail of the image while keeping the structural information of the image intact, a multi-scale enhancement method based on wavelet transform is proposed. Firstly, the image is wavelet decomposed to obtain a low-frequency structural component and a series of high-frequency texture detail components. Secondly, bilateral texture filtering is performed on the low-frequency structural components to filter out high-frequency noise while maintaining the edges of the image; adaptive enhancement is performed on the high-frequency detail components to filter out low-frequency noise while enhancing detail. Finally, the processed high- and low-frequency components reconstructed by wavelets can obtain a seismic section image with enhanced detail. The method of this paper enhances the texture detail information in the image while preserving the edge of the image.

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