Wavelet Packet Transform Research Articles

Quantification of scar collagen texture and prediction of scar development via second harmonic generation images and a generative adversarial network.

Widely used for medical analysis, the texture of the human scar tissue is characterized by irregular and extensive types. The quantitative detection and analysis of the scar texture as enabled by image analysis technology is of great significance to clinical practice. However, the existing methods remain disadvantaged by various shortcomings, such as the inability to fully extract the features of texture. Hence, the integration of second harmonic generation (SHG) imaging and deep learning algorithm is proposed in this study. Through combination with Tamura texture features, a regression model of the scar texture can be constructed to develop a novel method of computer-aided diagnosis, which can assist clinical diagnosis. Based on wavelet packet transform (WPT) and generative adversarial network (GAN), the model is trained with scar texture images of different ages. Generalized Boosted Regression Trees (GBRT) is also adopted to perform regression analysis. Then, the extracted features are further used to predict the age of scar. The experimental results obtained by our proposed model are better compared to the previously published methods. It thus contributes to the better understanding of the mechanism behind scar development and possibly the further development of SHG for skin analysis and clinic practice.

Numerical method to predict vibration characteristics induced by cavitation in centrifugal pumps

Vibration is a common side effect of cavitation in centrifugal pumps, which are also affected by other physical processes. Therefore, the study of cavitation based on raw vibration signals may introduce potential discrepancies under practical operating conditions. With the objective of accurately identifying the cavitation stages in a centrifugal pump, this study employs the wavelet packet transform (WPT) to process the original signal. Subsequently, the different vibration acceleration signals obtained by processing the raw signals using the proposed method under different working conditions are compared. The unequal interval weight grey (UIWG) model is proposed based on the measurements performed to reconstruct the functional relationship between cavitation and vibration, including in cases where the physical parameters of the research object are unavailable. The results reveal that the post-signal energy exhibits monotonic characteristics in certain frequency bands, despite the presence of interference under practical operating conditions. In addition, the UIWG model is verified to be robust as it is capable of self-correcting its monotonicity based on limited discrete data. In conclusion, the UIWG model equipped with WPT is an effective means to predict cavitation-induced vibrations.

Defect Identification of Pipeline Ultrasonic Inspection Based on Multi-Feature Fusion and Multi-Criteria Feature Evaluation

This paper presents a novel model for ultrasonic defect identification relying on multi-feature fusion and multi-criteria feature evaluation (MFF-MCFE). Based on feature extraction, feature selection, pattern recognition and data fusion algorithm, this model analyzes ultrasonic echo signal data from single-probe ultrasonic inspection, and based on wavelet packet transform (WPT), empirical mode decomposition (EMD) and discrete wavelet transform (DWT), the main features from the collected ultrasonic echo signals are also extracted. These features are also evaluated by means of Representation Entropy (RE), Fisher’s ratio (FR) and Mahalanobis distance (MD), and the results are fused with Dempster–Shafer (D-S) evidence theory and the corresponding feature subsets are formed according to the fusion result. The support vector machine (SVM) is used as the classifier to recognize the defect signal, and the subsequent classification results are integrated by D-S evidence theory, which leads to the final recognition results. On this basis, a series of experiments were carried out to compare the performance of the developed model with that of the models using single feature sets and single feature evaluation criterion. Meanwhile, the principal component analysis (PCA) was also involved in the corresponding comparative analysis. The experimental results showed that this model is suitable for the identification and diagnosis of pipeline defects, and its classification accuracy could be reached up to 96.29% with stronger robustness and stability.

Computer Aided System for Malignant Melanoma Detection

Melanoma is one of the skin cancers that attacks the cells of melanocytes that produce skin-forming pigments. In this project a new intelligent method of classifying melanoma lesions is implemented. The system consists of four stages; image pre-processing, image segmentation, feature extraction, and image classification. As the first step of the image analysis, pre-processing techniques are implemented to remove noise and undesired structures from the images using techniques such as median filtering and contrast enhancement. In the second step, a simple thresholding method is used to segment and localize the lesion, a boundary tracing algorithm is also implemented to validate the segmentation. Then, a wavelet approach is used to extract the features, more specifically Wavelet Packet Transform (WPT).Finally, the dimensionality of the selected features is reduced with Principal Component Analysis(PCA) and later supplied to an Artificial Neural Network and Support Vector Machine classifiers for classification. Incident rates of melanoma skin cancer have been rising since last two decades. So, early, fast and effective detection of skin cancer is paramount importance. If detected at an early stage. Skin has one of the highest cure rates, and the most cases, the treatment is quite simple and involves excision of the lesion. Moreover, at an early stage, skin cancer is very economical to treat, while at a late stage, cancerous lesions usually result in near fatal consequences and extremely high cost associated with the necessary treatments.

Classification and Inspection of Debonding Defects in Solid Rocket Motor Shells Using Machine Learning Algorithms

Debonding problems along the propellant/liner/insulation interface are a critical factor affecting the integrity of solid rocket motors and one of the major causes of their structural failure. Due to the complexity of interface debonding detection and its low accuracy, a method of wavelet packet transform (WPT) combined with machine learning is proposed. In this research, multi-layer structure specimens were prepared to simulate the structure of a solid rocket motor. First, ultrasonic non-destructive testing technology was used to obtain defect data. Then, WPT algorithm was employed to extract characteristic signals of the defect data. Moreover, k-nearest neighbor model, Random Forest model and support vector machine model were applied to the classification. The results showed that the accuracies of the three models were 84.67%, 90.66% and 95.33%, respectively. Positive results indicate that WPT with machine learning model exhibited excellent classification performance. Therefore, WPT combined with machine learning can achieve a precise classification of debonding defects and has the potential to assist or even automate the debonding inspection process of solid rocket motors.

Weighted entropy and modified MDL for compression and denoising data in smart grid

Denoising and compression of power system data from the measurement and monitoring instruments in smart grid is an important topic. Compression is essential for the transmission and storage of a big amount of smart grid data through the communication channels and denoising is essential as the noise produces erroneous results in further analysis of the power system data. This paper presents a technique for denoising and lossy compression of data in smart grid communication using wavelet packet transform (WPT). The paper proposes weighted entropy to calculate the best basis of a signal from the complete WPT. Then the paper presents the application of the proposed best basis algorithm for the denoising and compression of the signal. A modified minimum description length (MDL) algorithm is proposed that allows to adjust the threshold for the denoising that does not require the use of a noise estimation formula. A set of real power system data recorded by various measurement and monitoring instruments in smart grid is utilized to assess the effectiveness of the proposed technique. Both the denoising and compression performance on the simulation show promising results such that the proposed algorithm could be considered as a potential technique for the real time noise removal and compression application. Results from the comparison are presented with Shannon entropy - MDL based WPT, wavelet transform (WT) based method, fuzzy transform and Matlab’s state of art technique.

Exploring photoacoustic spectroscopy-based machine learning together with metabolomics to assess breast tumor progression in a xenograft model ex vivo

In the current study, a breast tumor xenograft was established in athymic nude mice by subcutaneous injection of the MCF-7 cell line and assessed the tumor progression by photoacoustic spectroscopy combined with machine learning tools. The advancement of breast tumors in nude mice was validated by tumor volume kinetics and histopathology and corresponding image analysis by TissueQuant software compared to controls. The ex vivo tumors in progressive conditions belonging to time points, day 5th, 10th, 15th & 20th, were excited with 281 nm pulsed laser light and recorded the corresponding photoacoustic spectra in time domain. The spectra were then pre-processed, augmented for a 10-fold increase in the data strength, and subjected to wavelet packet transformation for feature extraction and selection using MATLAB software. In the present study, the top 10 features from all the time point groups under study were selected based on their prediction ranking values using the mRMR algorithm. The chosen features of all the time-point groups were then subjected to multi-class Support Vector Machine (SVM) algorithms for learning and classifying into respective time point groups under study. The analysis demonstrated accuracy values of 95.2%, 99.5%, and 80.3% with SVM- Radial Basis Function (SVM-RBF), SVM-Polynomial & SVM-Linear, respectively. The serum metabolomic levels during tumor progression complemented photoacoustic patterns of tumor progression, depicting breast cancer pathophysiology.The current study reports the assessment of tumor progression in athymic nude mice by Photoacoustic spectroscopy-based machine learning tools. The progressive tumors were classified using multi-class Support Vector Machine (SVM) algorithms with 99.5% accuracy. The serum metabolomic levels during tumor progression complemented photoacoustic spectral features, depicting breast cancer pathophysiology.

Novel Architecture for Lifting Discrete Wavelet Packet Transform With Arbitrary Tree Structure

This brief presents a novel pipelined VLSI architecture for computing discrete wavelet packet transform (DWPT) with an arbitrary wavelet tree. Coefficients for different levels are computed in a series of stages. Each stage consists of a bypassed wavelet filter and circuit for reordering intermediate coefficients. The proposed lifting-based wavelet filter computes high- and low-pass coefficients in series. In order to accommodate the arbitrary tree structure, the filter either computes the coefficients or bypass the samples. The reordering of intermediate coefficients forms a subband required for next-level computation. The coefficients are computed in a serial manner and reordering of intermediate coefficients reduce not only the memory elements but also the circuit complexity. The proposed pipelined architecture reduces the requirement of memory elements by 50%. Furthermore, the hardware implementation results show that the area and power requirement are reduced by 33% and 20%, respectively.

Forecasting Model of Silicon Content in Molten Iron Using Wavelet Decomposition and Artificial Neural Networks

Silicon content forecasting models have been requested by the operational team to anticipate necessary actions during the blast furnace operation when producing molten iron, to control the quality of the product and reduce costs. This paper proposed a new algorithm to perform the silicon content time series up to 8 h ahead, immediately after the molten iron chemical analysis is delivered by the laboratory. Due to the delay of the laboratory when delivering the silicon content measurement, the proposed algorithm considers a minimum useful forecasting horizon of 3 h ahead. In a first step, it decomposes the silicon content time series into different subseries using the Maximal Overlap Discrete Wavelet Packet Transform (MODWPT). Next, all subseries forecasts were determined through Nonlinear Autoregressive (NAR) networks, and finally, these forecasts were summed to furnish the long-term forecast of silicon content. Using data from a real industry, we showed that the prediction error was within an acceptable range according to the blast furnace technical team.

A Rigorous Wavelet-Packet Transform to Retrieve Snow Depth from SSMIS Data and Evaluation of its Reliability by Uncertainty Parameters

A Rigorous Wavelet-Packet Transform to Retrieve Snow Depth from SSMIS Data and Evaluation of its Reliability by Uncertainty Parameters

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

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.

Comparative Study on MATLAB based Joint Photographic Experts Group Image Size Reduction Using Shearlet and Wavelet Packet Transform for X-ray Images with Potential Hospital Data Storage Applications

Aim: Aim of this study is to compress X-ray images using wavelet packet transform and shearlet transform. These two transforms were compared for the better compression ratio to store large hospital data files in small memory space. Materials and methods: Sample X-ray images were, Wavelet packet (30) and Shearlet (30) for compression. Compression ratio was calculated. The significance of the data were calculated using SPSS software. Results: There was statistical significance between the wavelet packet and shearlet transform based compression ratio data (p<0.05, independent sample t- test). Conclusion: wavelet packet transform has a better compression ratio than shearlet transform. Wavelet packet transform appears to produce the most consistent results with higher standard deviation (3.3) when compared with shearlet transform which appears to produce lower results with lower standard deviation (0.8).

An Efficient Deep Learning Paradigm for Deceit Identification Test on EEG Signals

Brain-Computer Interface is the collaboration of the human brain and a device that controls the actions of a human using brain signals. Applications of brain-computer interface vary from the field of entertainment to medical. In this article, a novel Deceit Identification Test is proposed based on the Electroencephalogram signals to identify and analyze the human behavior. Deceit identification test is based on P300 signals, which have a positive peak from 300 ms to 1,000 ms of the stimulus onset. The aim of the experiment is to identify and classify P300 signals with good classification accuracy. For preprocessing, a band-pass filter is used to eliminate the artifacts. The feature extraction is carried out using “symlet” Wavelet Packet Transform (WPT). Deep Neural Network (DNN) with two autoencoders having 10 hidden layers each is applied as the classifier. A novel experiment is conducted for the collection of EEG data from the subjects. EEG signals of 30 subjects (15 guilty and 15 innocent) are recorded and analyzed during the experiment. BrainVision recorder and analyzer are used for recording and analyzing EEG signals. The model is trained for 90% of the dataset and tested for 10% of the dataset and accuracy of 95% is obtained.

Studies of filtering effect on fault diagnosis of spindle device in hoist

Spindle devices, which are among the core components of mine hoists, are typical rotor-bearing systems. Vibration-based fault diagnosis techniques are often used to help prevent mechanical failures of such systems. The fault vibration signals generally include pulse information reflecting fault type, independent vibration components caused by other non-faulty mechanical components, noise in the surrounding environment and so on. The reduction of noise in the vibration signal collected by the sensor is of practical significance for the correct diagnosis of subsequent rotating machinery faults. To solve this problem, a fault diagnosis method based on a smooth (SM) filtering algorithm combined with variational mode decomposition (VMD) and a support vector machine (SVM) is proposed. Wavelet transform (WT) and wavelet packet transform (WPT) methods are used to compare the noise reduction. The reliability and effectiveness of the method are verified by experiments on a hoist mechanical fault simulator. Experimental results show that the proposed method has high prediction accuracy and can provide a good practical reference for fault diagnosis of rotating machinery.

A Fall Posture Classification and Recognition Method Based on Wavelet Packet Transform and Support Vector Machine

An accidental fall seriously threatens the health and safety of the elderly. The injuries caused by a fall have a lot to do with the different postures during the fall. Therefore, recognizing the posture of falling is essential for the rescue and care of the elderly. In this paper, a novel method was proposed to improve the classification and recognition accuracy of fall postures. Firstly, the wavelet packet transform was used to extract multiple features from sample data. Secondly, random forest was used to evaluate the importance of the extracted features and obtain effective features through screening. Finally, the support vector machine classifier based on the linear kernel function was used to realize the falling posture recognition. The experiment results on “Simulated Falls and Daily Living Activities Data Set” show that the proposed method can distinguish different types of fall postures and achieve 99% classification accuracy.

Fault diagnosis of a rolling bearing based on the wavelet packet transform and a deep residual network with lightweight multi-branch structure

Deep residual networks (DRNs) are a state-of-the-art deep learning model used in the data-driven fault diagnosis field. Their especially deep architectures give them sufficient capacity to deal with very complex diagnosis issues. However, a neural network with excellent performance usually requires hundreds of thousands of parameters, which is unaffordable for use in current industrial machines due to their limited computational resources. To enable practical applications for fault diagnosis, developing deep learning methods that can perform powerfully and have an economical computational burden is necessary. This study proposes a novel bearing fault diagnosis method based on the wavelet packet transform (WPT) and a lightweight variant of DRN called a multi-branch deep residual network (MB-DRN) in order to resolve the above issues. WPT is utilized to map raw signals into the time-frequency domain, from which the MB-DRN can extract a set of robust features more easily. Additionally, MB-DRN builds several small-sized convolutional layer branches in each building block to increase the network non-linearity, the construction of layer branches can be achieved freely and this design strategy largely saves the parameter usage while approaching a stronger model’s capacity. Two rolling bearing datasets with variable operating conditions were conducted on the proposed method to validate performance. The results verify the necessity of the WPT-based data processing method and show that MB-DRN can outperform the accuracies of standard DRN with only one quarter of the parameter amount, revealing the significant potential of the proposed method for realistic industrial fault diagnosis applications.

A hybrid attention improved ResNet based fault diagnosis method of wind turbines gearbox

It is significant to boost the performance of fault diagnosis of wind turbine gearboxes. In this paper, a hybrid attention improved residual network (HA-ResNet) based method is proposed to diagnose the fault of wind turbines gearbox by highlighting the essential frequency bands of wavelet coefficients and the fault features of convolution channels. First, the paper performed wavelet packet transformation (WPT) on the raw signal and improved the ResNet by the band attention to highlight features of wavelet coefficients. Second, a fault diagnosis framework based on channel attention is designed to effectively improve the nonlinear feature extraction ability of deep convolutional networks. The proposed method is verified by a simulation dataset of the drivetrain diagnostic simulator (DDS) and the measured data from a wind farm. The results illustrate the superior performance of the HA-ResNet based fault diagnosis method for time–frequency feature extraction of vibration signals, frequency band information enhancement, and recognition accuracy improvement.

Handling Data Heterogeneity in Electricity Load Disaggregation via Optimized Complete Ensemble Empirical Mode Decomposition and Wavelet Packet Transform

Global warming is a leading world issue driving the common social objective of reducing carbon emissions. People have witnessed the melting of ice and abrupt changes in climate. Reducing electricity usage is one possible method of slowing these changes. In recent decades, there have been massive worldwide rollouts of smart meters that automatically capture the total electricity usage of houses and buildings. Electricity load disaggregation (ELD) helps to break down total electricity usage into that of individual appliances. Studies have implemented ELD models based on various artificial intelligence techniques using a single ELD dataset. In this paper, a powerline noise transformation approach based on optimized complete ensemble empirical model decomposition and wavelet packet transform (OCEEMD–WPT) is proposed to merge the ELD datasets. The practical implications are that the method increases the size of training datasets and provides mutual benefits when utilizing datasets collected from other sources (especially from different countries). To reveal the effectiveness of the proposed method, it was compared with CEEMD–WPT (fixed controlled coefficients), standalone CEEMD, standalone WPT, and other existing works. The results show that the proposed approach improves the signal-to-noise ratio (SNR) significantly.

Denoising of LCR Wave Signal of Residual Stress for Rail Surface Based on Lifting Scheme Wavelet Packet Transform

According to the acousto elastic effect, the residual stress on the surface of the rail can be evaluated by measuring the change in the propagation velocity of ultrasonic waves, such as longitudinal critically refracted (LCR) waves on the surface of the rail. The LCR wave signal is often polluted by a variety of noise sources, coupled with the influence of the poor surface condition of the inspected component, which greatly reduces the detectability and online measurement ability of the LCR wave signal. This paper proposes the application of the lifting scheme wavelet packet transform (LSWPT) denoising method to solve the noise suppression problem of LCR wave signal. The traditional wavelet transform (WT), wavelet packet transform (WPT), as well as the lifting scheme wavelet transform (LSWT) and lifting scheme wavelet packet transform are compared and analyzed in the soft thresholding and hard thresholding processing of denoising ability and efficiency of the noisy LCR wave signal. The experimental results show that the LSWPT method has the characteristics of fast calculation speed and a good denoising effect, and it is an efficient method of denoising signals for on-line ultrasonic measurement of residual stress on the rail surface.

The Investigation into the Tribological Impact of Alternative Fuels on Engines Based on Acoustic Emission

The wide use of different alternative fuels (AL) has led to challenges to the internal combustion (IC) engine tribology. To avoid any unpredicted damages to lubrication joints by using AL fuels, this study aims to accurately evaluate the influences of alternative fuels on the tribological behavior of IC engines. Recent achievements of the acoustic emission (AE) mechanism in sliding friction provide an opportunity to explain the tribological AE responses on engines. The asperity–asperity–collision (AAC) and fluid–asperity–shearing (FAS) mechanisms were applied to explain the AE responses from the piston ring and cylinder liner system. A new adaptive threshold–wavelet packets transform (WPT) method was developed to extract tribological AE features. Experimental tests were conducted by fueling three fuels: pure diesel (PD), biodiesel (BD), and Fischer–Tropsch (F–T) diesel. The FAS–AE indicators of biodiesel and F–T diesel show a tiny difference compared to the baseline diesel using two types of lubricants. Biodiesel produces more AAC impacts with higher AAC–AE responses than F–T diesel, which occurs at high speeds due to high temperatures and more particles after combustion than diesel. This new algorithm demonstrated the high performance of using AE signals in monitoring the tribological impacts of alternative fuels on engines.