Adaptive Mean Ridgelet Transform Filtering for Detecting Signal and Comparison of Algorithm's Implements

2015 ◽  
Vol 719-720 ◽  
pp. 1171-1176
Author(s):  
Guang Ping Zhu ◽  
Hui Sun
Keyword(s):  
Frequency Domain ◽  
Hough Transform ◽  
Power Distribution ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Simulation Experiments ◽  
Ridgelet Transform ◽  
Time Frequency ◽  
Low Snr ◽  
Mean Filter

For solving the problem which the performance of detection was reduced in the low signal to noise ratio (SNR) using Wigner-Ville Hough transform (WHT), the method of XWVD adaptive mean Ridgelet transform filtering (XWVD-M-FRIT) was proposed. In this method, due to the power distribution of signal is different from noise or reverberation in time-frequency domain, so designed adaptive axial mean filter, then using Ridgelet transform filtering to restrain noise or reverberation. At last, it is to detect the signal using Hough transform. The results of real and simulation experiments showed, compared with WHT, in the low SNR the new method is feasible to restrain noise or reverberation in time-frequency domain for improving the performance of signal detection. furthermore, the performance of varying implement of adaptive mean and Ridgelet transform filtering were compared.

The Technology of Line-Spectrum Enhancement Based on Image Processing

2012 ◽  
Vol 429 ◽  
pp. 308-312
Author(s):  
Dai Zhu Zhu ◽  
Wen Hua Huang
Keyword(s):  
Image Processing ◽  
Signal Processing ◽  
Data Analysis ◽  
Frequency Domain ◽  
Signal To Noise Ratio ◽  
Trial Data ◽  
Line Spectrum ◽  
Signal To Noise ◽  
Time Frequency ◽  
Low Snr

Time-frequency spectrogram analysis is a basic method in passive radar and sonar. It′s necessary to enhance the line-spectrum to improve the performance in low SNR(Signal to Noise Ratio) and strong interference presented that wider detecting range and long reacting time can be obtained. The traditional line-spectrum enhancing technology based on signal′s coherence can′t work well in very low SNR, and the performance will drop sharply when the line-spectrums are close in frequencies or the number of line-spectrums increases. A new method which combines image processing with signal processing is brought out to overcome these defects. It can work well when the SNR in frequency domain is close to 0dB,which is much lower than ALE and other traditional technology. The results derived from simulation and trial data analysis show that it′s stable and can be applied in the field with line-spectrums.

scholarly journals Detection of Polyphase Codes Radar Signals in Low SNR

2016 ◽  
Vol 2016 ◽  
pp. 1-6 ◽  
Author(s):  
Runlan Tian ◽  
Guoyi Zhang ◽  
Rui Zhou ◽  
Wei Dong
Keyword(s):  
Hough Transform ◽  
Signal To Noise Ratio ◽  
Superior Performance ◽  
Radar Signal ◽  
Time Frequency ◽  
Low Snr ◽  
Radar Signals ◽  
Core Idea ◽  
Code Type ◽  
Electronic Intelligence

A novel effective detection method is proposed for electronic intelligence (ELINT) systems detecting polyphase codes radar signal in the low signal-to-noise ratio (SNR) scenario. The core idea of the proposed method is first to calculate the time-frequency distribution of polyphase codes radar signals via Wigner-Ville distribution (WVD); then the modified Hough transform (HT) is employed to cumulate all the energy of WVD’s ridges effectively to achieve signal detection. Compared with the generalised Wigner Hough transform (GWHT) method, the proposed method has a superior performance in low SNR and is not sensitive to the code type. Simulation results verify the validity of the proposed method.

The Research on Bispectrum Detection of Underwater Target in Frequency Domain

2012 ◽  
Vol 182-183 ◽  
pp. 1761-1765
Author(s):  
Guang Jin He ◽  
Jin Fang Cheng ◽  
Wei Zhang
Keyword(s):  
Power Spectrum ◽  
Frequency Domain ◽  
Time Domain ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Radiated Noise ◽  
Low Snr ◽  
Noise Ratio ◽  
Underwater Target

As the non-Gaussianity of ship-radiated noise reduces fast when the Signal-to-Noise Ratio (SNR) becomes low, a bispectrum detector in the frequency domain is proposed to ease the problem. First, FFT method is applied on the received data to calculate the power spectrum. Second, the non-Gaussianity of the power spectrum series is tested by Hinich-Wilson Gaussian Test rule. Last, the bispectrum detector based on non-Gaussianity is used to determine whether there are ship-radiated signals. The bispectrum detector in frequency domain is applied to detect simulated noise and real ship-radiated noise. The results are compared with the detector which is in the signal’s time domain. The comparison illustrates that the bispectrum detector based on the power spectrum series(in frequency domain) is much better in detecting low SNR signals, which is very valuable in far distance detection.

scholarly journals Convolution Network with Custom Loss Function for the Denoising of Low SNR Raman Spectra

Sensors ◽  
2021 ◽  
Vol 21 (14) ◽  
pp. 4623
Author(s):  
Sinead Barton ◽  
Salaheddin Alakkari ◽  
Kevin O’Dwyer ◽  
Tomas Ward ◽  
Bryan Hennelly
Keyword(s):  
Raman Spectra ◽  
Loss Function ◽  
Signal To Noise Ratio ◽  
Biomedical Science ◽  
Clinical Tool ◽  
Signal To Noise ◽  
Low Snr ◽  
Low Intensity ◽  
Spectral Peaks ◽  
Noise Ratio

Raman spectroscopy is a powerful diagnostic tool in biomedical science, whereby different disease groups can be classified based on subtle differences in the cell or tissue spectra. A key component in the classification of Raman spectra is the application of multi-variate statistical models. However, Raman scattering is a weak process, resulting in a trade-off between acquisition times and signal-to-noise ratios, which has limited its more widespread adoption as a clinical tool. Typically denoising is applied to the Raman spectrum from a biological sample to improve the signal-to-noise ratio before application of statistical modeling. A popular method for performing this is Savitsky–Golay filtering. Such an algorithm is difficult to tailor so that it can strike a balance between denoising and excessive smoothing of spectral peaks, the characteristics of which are critically important for classification purposes. In this paper, we demonstrate how Convolutional Neural Networks may be enhanced with a non-standard loss function in order to improve the overall signal-to-noise ratio of spectra while limiting corruption of the spectral peaks. Simulated Raman spectra and experimental data are used to train and evaluate the performance of the algorithm in terms of the signal to noise ratio and peak fidelity. The proposed method is demonstrated to effectively smooth noise while preserving spectral features in low intensity spectra which is advantageous when compared with Savitzky–Golay filtering. For low intensity spectra the proposed algorithm was shown to improve the signal to noise ratios by up to 100% in terms of both local and overall signal to noise ratios, indicating that this method would be most suitable for low light or high throughput applications.

The Time-Frequency Analysis for Energy Dissipation Shed-Tunnel’s Rockfall Impact Signal Based on Wavelet Theory

2011 ◽  
Vol 243-249 ◽  
pp. 5085-5088
Author(s):  
Lin Feng Wang ◽  
Hong Mei Tang ◽  
Hong Kai Chen
Keyword(s):  
Energy Dissipation ◽  
Frequency Analysis ◽  
Calculation Result ◽  
Signal To Noise Ratio ◽  
Prevention Measures ◽  
Autocorrelation Analysis ◽  
Wavelet Theory ◽  
Signal To Noise ◽  
Time Frequency Analysis ◽  
Time Frequency

Shed-tunnel is one of common prevention measures along the highway. Through the wavelet theory we denoised the rockfall impact signal when the rock impact the ordinary shed-tunnel and the energy dissipation shed-tunnel. And then we evaluated the wavelet theory’s denoise effect by the signal-to-noise ratio. The calculation result indicated that the denoise effect is very good. At last, through the autocorrelation analysis and time-frequency analysis for the rockfall impact signal, it was found that the ordinary shed-tunnel’s impact signals didn’t have obvious frequency and the frequency contained many component,but the energy dissipation shed-tunnel’s impact signals had obvious frequency. So the energy dissipation shed-tunnel’s impact signals had a relatively fixed cycle and frequency. The received frequency of rockfall impact by the time-frequency analysis could provide the basis for the design of energy dissipation shed-tunnel’s natural frequency.

scholarly journals Energetic and Informational Components of Speech-on-Speech Masking in Binaural Speech Intelligibility and Perceived Listening Effort

2019 ◽  
Vol 23 ◽  
pp. 233121651985459 ◽  
Author(s):  
Jan Rennies ◽  
Virginia Best ◽  
Elin Roverud ◽  
Gerald Kidd
Keyword(s):  
Speech Intelligibility ◽  
Signal To Noise Ratio ◽  
Spatial Separation ◽  
Signal To Noise ◽  
Listening Effort ◽  
Complex Sound ◽  
Time Frequency ◽  
Sound Fields ◽  
Energetic Masking

Speech perception in complex sound fields can greatly benefit from different unmasking cues to segregate the target from interfering voices. This study investigated the role of three unmasking cues (spatial separation, gender differences, and masker time reversal) on speech intelligibility and perceived listening effort in normal-hearing listeners. Speech intelligibility and categorically scaled listening effort were measured for a female target talker masked by two competing talkers with no unmasking cues or one to three unmasking cues. In addition to natural stimuli, all measurements were also conducted with glimpsed speech—which was created by removing the time–frequency tiles of the speech mixture in which the maskers dominated the mixture—to estimate the relative amounts of informational and energetic masking as well as the effort associated with source segregation. The results showed that all unmasking cues as well as glimpsing improved intelligibility and reduced listening effort and that providing more than one cue was beneficial in overcoming informational masking. The reduction in listening effort due to glimpsing corresponded to increases in signal-to-noise ratio of 8 to 18 dB, indicating that a significant amount of listening effort was devoted to segregating the target from the maskers. Furthermore, the benefit in listening effort for all unmasking cues extended well into the range of positive signal-to-noise ratios at which speech intelligibility was at ceiling, suggesting that listening effort is a useful tool for evaluating speech-on-speech masking conditions at typical conversational levels.

scholarly journals Feature Extraction of Gear Fault Signal Based on Sobel Operator and WHT

2013 ◽  
Vol 20 (3) ◽  
pp. 551-559 ◽  
Author(s):  
Jian-Hua Cai ◽  
Wei-Wen Hu
Keyword(s):  
Feature Extraction ◽  
Edge Detection ◽  
Hough Transform ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Cross Term ◽  
Time Performance ◽  
Sobel Operator ◽  
Gear Fault ◽  
Influence Of Noise

Taking Wigner-Ville distribution of gear fault signal as a picture,Sobeloperator was applied for edge detection of picture and then Hough transform was used to extract signal feature. Some simulated and measured signals have been processed to demonstrate the effectiveness of new method, which was compared with traditional Wigner-Hough transform and SPWD-Hough transform. The results show that the proposed method can suppress cross term which is produced from using Wigner-Ville distribution to analyze multi-component signal, especially under the condition of low signal to noise ratio. The improved Wigner-Hough transform can effectively suppress the influence of noise and has a good real-time performance because its algorithm is fast. The proposed method provides an effective method to determine the state of gear accurately.

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