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 A Combined Denoising Method for Microseismic Signals from Coal Seam Hydraulic Fracturing: Multithreshold Wavelet Packet Transform and Improved Hilbert-Huang Transform

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Zhengxing Yu ◽  
Jinglin Wen ◽  
Quanjie Zhu ◽  
Haitao Ma ◽  
Yu Feng
Keyword(s):  
Coal Seam ◽  
Hydraulic Fracturing ◽  
Signal To Noise Ratio ◽  
Wavelet Packet ◽  
Wavelet Packet Transform ◽  
Complex Nature ◽  
Signal To Noise ◽  
Denoising Method ◽  
Hilbert Huang Transform ◽  
Noise Ratio

Coal seam hydraulic fracturing (CSHF) has recently been applied to mitigate frequent regional rockburst risk in deep mines before mining practice, as an effective substitute for conventional labor-intensive and time-consuming rockburst prevention measures. Due to the complex nature of CSHF microseismic signals—e.g., nonstationary, transient, and low signal-to-noise ratio—conventional denoising methods tend to yield undesirable results that may preclude reliable evaluation of hydraulic fracturing performance using microseismic data. We propose an advanced denoising method MWPT-IHHT to achieve twice denoising in a fine and adaptive manner. This method combines a multithreshold wavelet packet transform (MWPT) and an improved Hilbert-Huang transform (IHHT), with each being improved compared to their conventional counterparts. A quantitative comparison using synthetic signals suggests the outperformance of the proposed method over the commonly used denoising methods in suppressing noises in terms of signal-to-noise ratio, signal similarity, and energy percentage. The desirable denoising results of two typical real CSHF signals in a CSHF test at Huafeng Coal Mine further demonstrate the applicability and effectiveness of the proposed MWPT-IHHT method.

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 Evaluation of an Adaptive Dynamic Compensation System in Cochlear Implant Listeners

2020 ◽  
Vol 24 ◽  
pp. 233121652097034
Author(s):  
Florian Langner ◽  
Andreas Büchner ◽  
Waldo Nogueira
Keyword(s):  
Signal Processing ◽  
Cochlear Implant ◽  
Speech Intelligibility ◽  
Discrimination Task ◽  
Signal To Noise Ratio ◽  
Compensation System ◽  
Signal To Noise ◽  
Dynamic Compensation ◽  
Front End ◽  
Noise Ratio

Cochlear implant (CI) sound processing typically uses a front-end automatic gain control (AGC), reducing the acoustic dynamic range (DR) to control the output level and protect the signal processing against large amplitude changes. It can also introduce distortions into the signal and does not allow a direct mapping between acoustic input and electric output. For speech in noise, a reduction in DR can result in lower speech intelligibility due to compressed modulations of speech. This study proposes to implement a CI signal processing scheme consisting of a full acoustic DR with adaptive properties to improve the signal-to-noise ratio and overall speech intelligibility. Measurements based on the Short-Time Objective Intelligibility measure and an electrodogram analysis, as well as behavioral tests in up to 10 CI users, were used to compare performance with a single-channel, dual-loop, front-end AGC and with an adaptive back-end multiband dynamic compensation system (Voice Guard [VG]). Speech intelligibility in quiet and at a +10 dB signal-to-noise ratio was assessed with the Hochmair–Schulz–Moser sentence test. A logatome discrimination task with different consonants was performed in quiet. Speech intelligibility was significantly higher in quiet for VG than for AGC, but intelligibility was similar in noise. Participants obtained significantly better scores with VG than AGC in the logatome discrimination task. The objective measurements predicted significantly better performance estimates for VG. Overall, a dynamic compensation system can outperform a single-stage compression (AGC + linear compression) for speech perception in quiet.

Importance of time-frequency units for sentence recognition as a function of signal-to-noise ratio

2018 ◽  
Vol 143 (3) ◽  
pp. 1751-1751 ◽  
Author(s):  
Frederic Apoux ◽  
Brittney Carter ◽  
Karl P. Velik ◽  
Eric Healy
Keyword(s):  
Signal To Noise Ratio ◽  
Signal To Noise ◽  
Time Frequency ◽  
Sentence Recognition ◽  
Noise Ratio

Random denoising and signal nonlinearity approach by time-frequency peak filtering using weighted frequency reassignment

Geophysics ◽  
2013 ◽  
Vol 78 (6) ◽  
pp. V229-V237 ◽  
Author(s):  
Hongbo Lin ◽  
Yue Li ◽  
Baojun Yang ◽  
Haitao Ma
Keyword(s):  
Weight Function ◽  
Instantaneous Frequency ◽  
Seismic Data ◽  
Signal To Noise Ratio ◽  
Random Noise ◽  
Signal To Noise ◽  
Transform Method ◽  
Time Frequency ◽  
Frequency Peak ◽  
Noise Ratio

Time-frequency peak filtering (TFPF) may efficiently suppress random noise and hence improve the signal-to-noise ratio. However, the errors are not always satisfactory when applying the TFPF to fast-varying seismic signals. We begin with an error analysis for the TFPF by using the spread factor of the phase and cumulants of noise. This analysis shows that the nonlinear signal component and non-Gaussian random noise lead to the deviation of the pseudo-Wigner-Ville distribution (PWVD) peaks from the instantaneous frequency. The deviation introduces the signal distortion and random oscillations in the result of the TFPF. We propose a weighted reassigned smoothed PWVD with less deviation than PWVD. The proposed method adopts a frequency window to smooth away the residual oscillations in the PWVD, and incorporates a weight function in the reassignment which sharpens the time-frequency distribution for reducing the deviation. Because the weight function is determined by the lateral coherence of seismic data, the smoothed PWVD is assigned to the accurate instantaneous frequency for desired signal components by weighted frequency reassignment. As a result, the TFPF based on the weighted reassigned PWVD (TFPF_WR) can be more effective in suppressing random noise and preserving signal as compared with the TFPF using the PWVD. We test the proposed method on synthetic and field seismic data, and compare it with a wavelet-transform method and [Formula: see text] prediction filter. The results show that the proposed method provides better performance over the other methods in signal preserving under low signal-to-noise ratio.

Threshold Noise Reduction Research of Improved EEMD Method

2012 ◽  
Vol 226-228 ◽  
pp. 237-240 ◽  
Author(s):  
Mei Jun Zhang ◽  
Hao Chen ◽  
Chuang Wang ◽  
Qing Cao
Keyword(s):  
Noise Reduction ◽  
Signal To Noise Ratio ◽  
Frequency Noise ◽  
Signal To Noise ◽  
Time Frequency ◽  
Noise Ratio ◽  
Reduction Effect ◽  
Eemd Method ◽  
Simulation Signal ◽  
Improved Eemd

In order to extract effectively detection signals in the noise background for non-stationary signal.On the basis of EEMD, improved EEMD is put forward, the improve EEMD threshold noise reduction is researched in this paper.The simulation signal compared the noise reduction effect of the wavelet,EMD,EEMD,and the improved EEMD. The improved EEMD threshold noise reduction have the best noise reduction result , the highest signal-to-noise ratio, the smallest standard deviation error.After the improved EEMD threshold noise reduction , the measurement signal time domain waveform smooth. More high frequency noise was obviously reduced in Hilbert time- frequency spectrum. Signal-to-noise ratio significantly improve, and signal characteristics are very clear.

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