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.

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.

Dual-Tree Wavelet Packet Transform for Ultrasonic Data Transmission Through Metal Structures

2015 ◽  
Author(s):  
Jinjiang Wang ◽  
Robert X. Gao ◽  
Xinyao Tang ◽  
Zhaoyan Fan ◽  
Peng Wang
Keyword(s):  
Signal Processing ◽  
Data Transmission ◽  
Signal To Noise Ratio ◽  
Wavelet Packet ◽  
Experimental Studies ◽  
Wavelet Packet Transform ◽  
Signal To Noise ◽  
Metallic Structures ◽  
Time Frequency ◽  
Noise Ratio

Data communication through metallic structures is generally encountered in manufacturing equipment and process monitoring and control. This paper presents a signal processing technique for enhancing the signal-to-noise ratio and high-bit data transmission rate in ultrasound-based wireless data transmission through metallic structures. A multi-carrier coded-ultrasonic wave modulation scheme is firstly investigated to achieve high-bit data rate communication while reducing inter-symbol inference and data loss, due to the inherent signal attenuation, wave diffraction and reflection in metallic structures. To improve the signal-to-noise ratio, dual-tree wavelet packet transform (DT-WPT) has been investigated to separate multi-carrier signals under noise contamination, given its properties of shift-invariance and flexible time frequency partitioning. A new envelope extraction and threshold setting strategy for selected wavelet coefficients is then introduced to retrieve the coded digital information. Experimental studies are performed to evaluate the effectiveness of the developed signal processing method for manufacturing.

scholarly journals WAVELET-CONVERSION IN ELECTROCARDIO SIGNAL PROCESSING

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
V.F. Telezhkin ◽  
◽  
B.B. Saidov ◽  
P.А. Ugarov ◽  
A.N. Ragozin ◽  
...  
Keyword(s):  
Signal Processing ◽  
Wavelet Transform ◽  
Signal To Noise Ratio ◽  
Digital Signal ◽  
Automated Analysis ◽  
Discrete Wavelet ◽  
Ecg Signal ◽  
Signal To Noise ◽  
Time Frequency ◽  
Fourth Level

In the present work, processing of an electro cardio signal using a wavelet transform is consi-dered. In electrocardiography, various digital signal-processing techniques are used to detect, extract, and analyze the various components of an electrocardiogram. Among them, the wavelet transform technique gives promising results in the analysis of the time-frequency characteristics of the electrocardiogram components. The urgency of solving the problem of improving the quality of life of people with the help of early diagnosis and timely treatment of various cardiac diseases is obvious. The process of automated analysis of a huge database of electrocardiographic data is especially important. Wavelet analysis can be successfully used to smooth and remove noise in the ECG signal. Electrocardiogram signal, cleaned from noise components, looks clearer, while its volume is from 10 to 5% of the original signal, which largely solves the problem of storing cardiac records. Aim. Development of an algorithm for threshold processing of wavelet coefficients and filtering of an electrocardiography signal. Materials and methods. Cardiograms were taken for analysis. Then they were digitized and entered into a computer for processing. A program was written in the MATLAB environment that implements continuous and discrete wavelet transform. Results. The work shows the result of filtering the ECG signal with the addition of noise with a signal-to-noise ratio of 35 and 45 dB using the decomposition levels N = 2, N = 3, N = 4. Conclusion. Based on the analysis of the data obtained, it can be concluded that the second level of decomposition is the most optimal for filtering the ECG signal. With an increase in the level of decomposition, the output ratio decreases, at the level N = 4 the output signal-to-noise almost does not exceed the input one, therefore, the filtering becomes ineffective. The correlation coefficient to the fourth level is significantly reduced, which means a significant increase in the distortion introduced by the filtering algorithm.

scholarly journals Application of the empirical mode decomposition and Hilbert-Huang transform to seismic reflection data

Geophysics ◽  
2007 ◽  
Vol 72 (2) ◽  
pp. H29-H37 ◽  
Author(s):  
Bradley Matthew Battista ◽  
Camelia Knapp ◽  
Tom McGee ◽  
Vaughn Goebel
Keyword(s):  
Signal Processing ◽  
Seismic Reflection ◽  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Seismic Reflection Data ◽  
Hilbert Huang Transform ◽  
Time Frequency ◽  
Reflection Data ◽  
Mode Decomposition ◽  
The Hilbert Transform

Advancements in signal processing may allow for improved imaging and analysis of complex geologic targets found in seismic reflection data. A recent contribution to signal processing is the empirical mode decomposition (EMD) which combines with the Hilbert transform as the Hilbert-Huang transform (HHT). The EMD empirically reduces a time series to several subsignals, each of which is input to the same time-frequency environment via the Hilbert transform. The HHT allows for signals describing stochastic or astochastic processes to be analyzed using instantaneous attributes in the time-frequency domain. The HHT is applied herein to seismic reflection data to: (1) assess the ability of the EMD and HHT to quantify meaningful geologic information in the time and time-frequency domains, and (2) use instantaneous attributes to develop superior filters for improving the signal-to-noise ratio. The objective of this work is to determine whether the HHT allows for empirically-derived characteristics to be used in filter design and application, resulting in better filter performance and enhanced signal-to-noise ratio. Two data sets are used to show successful application of the EMD and HHT to seismic reflection data processing. Nonlinear cable strum is removed from one data set while the other is used to show how the HHT compares to and outperforms Fourier-based processing under certain conditions.

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 CMUT-Based Sensor for Acoustic Emission Application: Experimental and Theoretical Contributions to Sensitivity Optimization

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2042
Author(s):  
Redha Boubenia ◽  
Patrice Le Moal ◽  
Gilles Bourbon ◽  
Emmanuel Ramasso ◽  
Eric Joseph
Keyword(s):  
Signal Processing ◽  
Signal To Noise Ratio ◽  
Ultrasonic Transducer ◽  
Operating Conditions ◽  
Geometrical Parameters ◽  
Signal To Noise ◽  
Coupling Conditions ◽  
Sensor Structure ◽  
Noise Ratio ◽  
Electrical Connections

The paper deals with a capacitive micromachined ultrasonic transducer (CMUT)-based sensor dedicated to the detection of acoustic emissions from damaged structures. This work aims to explore different ways to improve the signal-to-noise ratio and the sensitivity of such sensors focusing on the design and packaging of the sensor, electrical connections, signal processing, coupling conditions, design of the elementary cells and operating conditions. In the first part, the CMUT-R100 sensor prototype is presented and electromechanically characterized. It is mainly composed of a CMUT-chip manufactured using the MUMPS process, including 40 circular 100 µm radius cells and covering a frequency band from 310 kHz to 420 kHz, and work on the packaging, electrical connections and signal processing allowed the signal-to-noise ratio to be increased from 17 dB to 37 dB. In the second part, the sensitivity of the sensor is studied by considering two contributions: the acoustic-mechanical one is dependent on the coupling conditions of the layered sensor structure and the mechanical-electrical one is dependent on the conversion of the mechanical vibration to electrical charges. The acoustic-mechanical sensitivity is experimentally and numerically addressed highlighting the care to be taken in implementation of the silicon chip in the brass housing. Insertion losses of about 50% are experimentally observed on an acoustic test between unpackaged and packaged silicon chip configurations. The mechanical-electrical sensitivity is analytically described leading to a closed-form amplitude of the detected signal under dynamic excitation. Thus, the influence of geometrical parameters, material properties and operating conditions on sensitivity enhancement is clearly established: such as smaller electrostatic air gap, and larger thickness, Young’s modulus and DC bias voltage.

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.

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