scholarly journals Biomedical Photoacoustic Imaging Optimization with Deconvolution and EMD Reconstruction

2018 ◽  
Vol 8 (11) ◽  
pp. 2113 ◽  
Author(s):  
Chengwen Guo ◽  
Yingna Chen ◽  
Jie Yuan ◽  
Yunhao Zhu ◽  
Qian Cheng ◽  
...  
Keyword(s):  
Numerical Simulations ◽  
Empirical Mode Decomposition ◽  
Photoacoustic Imaging ◽  
Optimization Method ◽  
Positive Polarity ◽  
Image Optimization ◽  
Mode Decomposition ◽  
Phantom Studies ◽  
Micro Structures

A photoacoustic (PA) signal of an ideal optical absorbing particle is a single N-shape wave. PA signals are a combination of several individual N-shape waves. However, the N-shape wave basis leads to aliasing between adjacent micro-structures, which deteriorates the quality of final PA images. In this paper, we propose an image optimization method by processing raw PA signals with deconvolution and empirical mode decomposition (EMD). During the deconvolution procedure, the raw PA signals are de-convolved with a system dependent deconvolution kernel, which is measured in advance. EMD is subsequently adopted to further process the PA signals adaptively with two restrictive conditions: positive polarity and spectrum consistency. With this method, signal aliasing is alleviated, and the micro-structures and detail information, previously buried in the reconstructing images, can now be revealed. To validate our proposed method, numerical simulations and phantom studies are implemented, and reconstructed images are used for illustration.

Research on Feature Extraction for Ultrasonic Echo Signal Based on EEMD Approach

2013 ◽  
Vol 321-324 ◽  
pp. 1311-1316 ◽  
Author(s):  
Jian Ming Yu ◽  
Ze Zhang
Keyword(s):  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
Thin Plates ◽  
Echo Signal ◽  
Stationary Characteristic ◽  
Bonding Quality ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Ultrasonic Echo

The bonding quality of composite materials have a critical influence on the quality of the product in modern industry, while the current technology can only make judgments on bonding and de-bonding instead of quantitative evaluation of different de-bonding degrees. We present HHT method to extract features of echo signals used for quantitative recognition of bonding quality of thin plates. For the non-stationary characteristic of the ultrasonic echo signal, empirical mode decomposition(EMD) and ensemble empirical mode decomposition(EEMD) are put forward to decompose the signal and calculate its energy torque. The HHT method highlights the time-frequency performance of echo signals effectively. The simulated signals verify that EEMD has more excellent decomposition performance than EMD, that is, EEMD diminishes the mode mixing to some extent generated from EMD decomposition.

scholarly journals A NOVEL DENOISING ALGORITHM FOR AIRBORNE LIDAR POINT CLOUD BASED ON EMPIRICAL MODE DECOMPOSITION

2019 ◽  
Vol XLII-2/W13 ◽  
pp. 1021-1025
Author(s):  
Z. Hui ◽  
P. Cheng ◽  
L. Wang ◽  
Y. Xia ◽  
H. Hu ◽  
...  
Keyword(s):  
Empirical Mode Decomposition ◽  
Point Cloud ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Morphological Operations ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Processing Step ◽  
Cloud Applications

<p><strong>Abstract.</strong> Denoising is a key pre-processing step for many airborne LiDAR point cloud applications. However, the previous algorithms have a number of problems, which affect the quality of point cloud post-processing, such as DTM generation. In this paper, a novel automated denoising algorithm is proposed based on empirical mode decomposition to remove outliers from airborne LiDAR point cloud. Comparing with traditional point cloud denoising algorithms, the proposed method can detect outliers from a signal processing perspective. Firstly, airborne LiDAR point clouds are decomposed into a series of intrinsic mode functions with the help of morphological operations, which would significantly decrease the computational complexity. By applying OTSU algorithm to these intrinsic mode functions, noise-dominant components can be detected and filtered. Finally, outliers are detected automatically by comparing observed elevations and reconstructed elevations. Three datasets located at three different cities in China were used to verify the validity and robustness of the proposed method. The experimental results demonstrate that the proposed method removes both high and low outliers effectively with various terrain features while preserving useful ground details.</p>

Denoising signals for photoacoustic imaging in frequency domain based on empirical mode decomposition

Optik ◽  
2018 ◽  
Vol 160 ◽  
pp. 402-414 ◽  
Author(s):  
Qinglin Kong ◽  
Qian Song ◽  
Yan Hai ◽  
Rui Gong ◽  
Jietao Liu ◽  
...  
Keyword(s):  
Frequency Domain ◽  
Empirical Mode Decomposition ◽  
Photoacoustic Imaging ◽  
Mode Decomposition

scholarly journals Application research on the time–frequency analysis method in the quality detection of ultrasonic wire bonding

2021 ◽  
Vol 17 (5) ◽  
pp. 155014772110183
Author(s):  
Wuwei Feng ◽  
Xin Chen ◽  
Cuizhu Wang ◽  
Yuzhou Shi
Keyword(s):  
Empirical Mode Decomposition ◽  
Frequency Analysis ◽  
Recognition Accuracy ◽  
Wire Bonding ◽  
Analysis Method ◽  
Time Frequency Analysis ◽  
Time Frequency ◽  
Mode Decomposition ◽  
Ultrasonic Wire Bonding

Imperfection in a bonding point can affect the quality of an entire integrated circuit. Therefore, a time–frequency analysis method was proposed to detect and identify fault bonds. First, the bonding voltage and current signals were acquired from the ultrasonic generator. Second, with Wigner–Ville distribution and empirical mode decomposition methods, the features of bonding electrical signals were extracted. Then, the principal component analysis method was further used for feature selection. Finally, an artificial neural network was built to recognize and detect the quality of ultrasonic wire bonding. The results showed that the average recognition accuracy of Wigner–Ville distribution and empirical mode decomposition was 78% and 93%, respectively. The recognition accuracy of empirical mode decomposition is obviously higher than that of the Wigner–Ville distribution method. In general, using the time–frequency analysis method to classify and identify the fault bonds improved the quality of the wire-bonding products.

scholarly journals Application of Empirical Mode Decomposition and Extreme Learning Machine Algorithms on Prediction of the Surface Vibration Signal

Energies ◽  
2021 ◽  
Vol 14 (22) ◽  
pp. 7519
Author(s):  
Yan Shen ◽  
Ping Wang ◽  
Xuesong Wang ◽  
Ke Sun
Keyword(s):  
Extreme Learning Machine ◽  
Empirical Mode Decomposition ◽  
Diesel Engines ◽  
Ground Vibration ◽  
Vibration Signal ◽  
Signal Component ◽  
Surface Vibration ◽  
Mode Decomposition ◽  
Learning Machine

Accurately predicting surface vibration signals of diesel engines is the key to evaluating the operation quality of diesel engines. Based on an improved empirical mode decomposition and extreme learning machine algorithm, the characteristics of diesel engine surface vibration signal were detected, predicted, and analyzed. First, the surface vibration signal was decomposed into a series of signal components by an improved empirical mode decomposition algorithm. Then, the extreme learning machine algorithm was applied to each signal component to obtain the predicted value of the corresponding signal component and determine the characteristics of the ground vibration signal. Compared with the empirical mode decomposition–extremum learning machine algorithm and the extremum learning machine algorithm, the results show that the improved empirical mode decomposition–extremum learning machine algorithm is feasible and effective.

scholarly journals Diagnosis of Friction on an Unbalanced Rotor by Phase-Shift Empirical Mode Decomposition Integration and Recurrence Plot

2021 ◽  
Vol 11 (17) ◽  
pp. 7973
Author(s):  
Ignacio Torres-Contreras ◽  
Juan Carlos Jáuregui-Correa ◽  
Salvador Echeverría-Villagómez ◽  
Juan P. Benítez-Rangel ◽  
Stephanie Camacho-Martínez
Keyword(s):  
Phase Shift ◽  
Empirical Mode Decomposition ◽  
Phase Plane ◽  
Recurrence Plot ◽  
Phase Shifting ◽  
Unbalanced Rotor ◽  
Mode Decomposition ◽  
New Phase ◽  
Emd Method

The friction and imbalance of components in rotating machines are some of the most recurrent failures that significantly increase vibration levels, thus affecting the reliability of the devices, the shelf life of its elements, and the quality of the product. There are many publications related to the different techniques for the diagnosis of friction and imbalance. In this paper, an alternative and new phase-shift empirical mode decomposition integration (PSEMDI) method is proposed to transform the acceleration into its velocity and displacement in order to construct the phase plane and recurrence plot (RP) and analyze the friction. The focus of PSEMDI and RP is to analyze nonlinear failures in mechanical systems. In machinery fault diagnosis, the main reason for using RP is to solve the integration of acceleration, and this can be achieved by phase-shifting the intrinsic mode function (IMF) with the empirical mode decomposition (EMD). Although the highest IMFs contain some frequencies, most of them have very few; thus, by applying the phase shift identity, the integration can be carried out maintaining the nonlinearities. The proposed method is compared with Simpson’s integration and detrending with the EMD method (here referred to as SDEMDI). The experimental RP results show that the proposed method gives significantly more information about the velocity and displacement spectra and it is more stable and proportional than the SDEMDI method. The results of the proposed integration method are compared with vibration measurements obtained with an interferometer.

IMPROVEMENT OF ENSEMBLE EMPIRICAL MODE DECOMPOSITION BY OVER-SAMPLING

2013 ◽  
Vol 05 (03) ◽  
pp. 1350012 ◽  
Author(s):  
RAÏS EL'HADI BEKKA ◽  
YAAKOUB BERROUCHE
Keyword(s):  
Signal Processing ◽  
Numerical Simulations ◽  
Empirical Mode Decomposition ◽  
Computing Time ◽  
Ensemble Empirical Mode Decomposition ◽  
Computational Time ◽  
Nonstationary Signals ◽  
Mode Decomposition ◽  
Mode Mixing

The empirical mode decomposition (EMD) is a useful method for the analysis of nonlinear and nonstationary signals and found immediate applications in diverse areas of signal processing. However, the major inconvenience of EMD is the mode mixing. The ensemble EMD (EEMD) was proposed to solve the problem of mode-mixing with the assistance of added noises producing the residue noise in the signal reconstructed. The residue noise in the IMFs can be reduced with a large number of ensemble trials at the expense of the increase of computational time. Improving the computing time of the EEMD by reducing the number of ensemble trials was thus proposed in this paper by over-sampling the signal to be decomposed. Numerical simulations were conducted to demonstrate proposed approach.

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