Application in Water Flooded Layer Recognition Based on Wavelet Packet Transform and Support Vector Machine

Image segmentation is necessary but significant element in less intensity image investigation, pattern recognition, and in robotic systems. It is one of the most complex and demanding tasks in image processing. Image segmentation is the process of separating an image into various regions such that each region is identical. This paper proposes a new medical image segmentation method that integrates multi-resolution wavelet packet decomposition with the watershed transform for MRI image. The wavelet packet transform (WPT) is applied to the input image, creating detail and approximation coefficients. If watershed technique alone is used for segmentation, then over cluster is present. To overcome this, the proposed technique which combines wavelet packet and watershed algorithm is developed. First, the wavelet packet transform is applied to produce multi-resolution images, followed by applying watershed for segmentation to the approximation sub-bands. Finally, Inverse WPT is implemented to obtain the segmented image. Due to wavelet packet decomposition, the quantity of the disturbance can be decreased and leads to a tough segmentation. This proposed work concludes that wavelet packet and watershed transform facilitate to get the elevated precision even in strident images.

This paper deals with feature selection for electroencephalogram (EEG) signal classification. The wavelet packet transform (WPT) technique is used to decompose EEG signals. Then, features are computed and extracted from different wavelet packet coefficients for each frequency sub-band. Then, principal components analysis (PCA) is used for feature dimension reduction. The features are used as inputs for support vector machine (SVM) classifier. The SVM classify EEG signals into two categories: Epileptic or Healthy. To select the best feature basis, a feature ranking method based on SVM recursive feature elimination technique (SVM-RFE) is used. Best results are obtained for a combination of three features, namely: range, maximum and energy values of wavelet coefficients in the sub-bands. The classifier was tested against data obtained from three different datasets. Data length is approximately 120 hours. Obtained results were compared to those obtained in state of the art papers. The classification accuracy reached 99.91% for the first dataset (around 1 hour 20 minutes data) and reached 100% for the second dataset (around 20 hours data) and reached 99.78% for the third dataset (around 98 hours data). Those results are among the best classification results reported so far.

Highlights A low time complexity and high accuracy recognition method of tomato’s disease stages was proposed.Small number of disease image samples can support a stable and high accuracy method with less computation time and less memory cost.Provided a solution to establish a real-time and low energy consumption disease recognize system.Abstract. Disease stages recognition of tomato is important for the timely diagnosis and prevention of tomato diseases. In this article, the color and texture features were extracted, and the kernel mutual subspace method (KMSM) was introduced to establish a rapid Tomato’s disease stage recognition method. Firstly, the color and textural features were extracted from tomato leaf and mapped to a high-dimensional space using a Gaussian kernel function. Secondly, a nonlinear disease feature subspace was established by applying principal component analysis (PCA) based on the mapped high-dimensional space. Finally, disease stages are recognized by calculating the canonical angles between the testing subspace and reference sub-spaces. To validate recognition rate of the proposed method, we conducted 10-fold cross-validation for experiments using the tomato disease sets contained in PlantVillage and artificial intelligence (AI) Challenger 2018 datasets. Also compared with the support vector machine (SVM) and VGG 16 methods, the results show that, accuracy of those three evaluated methods on the PlantVillage datasets are 99.34%, 89.2%, and 97%, respectively; and accuracy on the AI Challenger 2018 datasets are 98.66%, 71.14%, and 73.93%, respectively. Moreover, average training and recognition time of the proposed method is 0.1496 and 0.008 s, respectively, which is faster than SVM and VGG16 methods. In conclusion, the proposed method can be carried out in real-time recognition intelligent equipment which requires less memory space, less computation time, and with higher recognition rate, and low energy consumption. Keywords: Color and textural feature, High dimensional feature space, Kernel mutual subspace method, Tomato’s disease stages recognition,.

In this paper, a Hybrid Wavelet Packet and Directional transform (HW&DT) method is proposed to compress an image effectively. The image is pixel de-correlated using Daubechies Wavelet Packet transform and set of wavelet packet coefficients are transformed using Directional transform. The Directional transform pairs DCT-II, DCT-V, DCT-VIII and DST-VII are useful in retrieving the texture information in different directions. Then the coefficients of the hybrid transform are uniformly quantized and entropy coded using Huffman coding, generating the Bit-Stream. The performance evaluation of the proposed work is done using the compression parameters like Structural Similarity Index (SSIM), Bit-Saving (%), Peak Signal to Noise ratio (PSNR) and Mean Square Error (MSE). The experimental results confirm the improvement in Bit-Saving for the image.KeywordsDiscrete cosine transform (DCT)Wavelet packet transform (WPT)Uniform quantizationHuffman coding

The massive photometric data collected from multiple large-scale sky surveys offer significant opportunities for measuring distances of celestial objects by photometric redshifts. However, catastrophic failure is an unsolved problem with a long history and it still exists in the current photometric redshift estimation approaches (such as the k-nearest neighbor (KNN) algorithm). In this paper, we propose a novel two-stage approach by integration of KNN and support vector machine (SVM) methods together. In the first stage, we apply the KNN algorithm to photometric data and estimate their corresponding zphot. Our analysis has found two dense regions with catastrophic failure, one in the range of zphot ϵ [0.3, 1.2] and the other in the range of zphot ϵ [1.2, 2.1]. In the second stage, we map the photometric input pattern of points falling into the two ranges from their original attribute space into a high dimensional feature space by using a Gaussian kernel function from an SVM. In the high dimensional feature space, many outliers resulting from catastrophic failure by simple Euclidean distance computation in KNN can be identified by a classification hyperplane of SVM and can be further corrected. Experimental results based on the Sloan Digital Sky Survey (SDSS) quasar data show that the two-stage fusion approach can significantly mitigate catastrophic failure and improve the estimation accuracy of photometric redshifts of quasars. The percents in different |δz| ranges and root mean square (rms) error by the integrated method are 83.47%, 89.83%, 90.90% and 0.192, respectively, compared to the results by KNN (71.96%, 83.78%, 89.73% and 0.204).

Here, we propose an effective classification strategy for THz pulsed signals of breast tissues based on wavelet packet energy (WPE) feature exaction and machine learning classifiers. The parafin-embedded breast tissue samples were adopted in this study and identified as tumor (226 samples), healthy fibrous tissue (233 samples) or adipose tissue (178 samples) based on the histological results. Firstly, the THz pulsed signals of tissue samples were acquired using a standard transmission THz time-domain spectrometer. Then, the signals were decomposed by the wavelet packet transform (WPT) and the features of the WPE were extracted. To reduce the dimensionality of extracted features, the principal components analysis (PCA) method was employed. Six different machine learning classifiers were then performed and compared for automatic classification of different tissue samples. The highest classification accuracy is up to 97% using the fine Gaussian support vector machine (SVM) approach. The results indicate that the WPE feature exaction combined with machine learning classifier can be used for automatic evaluation of biological tissue THz signals with good accuracy.

Beam string structure (BSS) is introduced as a new type of hybrid prestressed string structures. The composition and mechanics features of BSS are discussed. The main principles of wavelet packet transform (WPT), principal component analysis (PCA) and support vector machine (SVM) have been reviewed. WPT is applied to the structural response signals, and feature vectors are obtained by feature extraction and PCA. The feature vectors are used for training and classification as the inputs of the support vector machine. The method is used to a single one-way arched beam string structure for damage detection. The cable prestress loss and web members damage experiment for a beam string structure is carried through. Different prestressing forces are applied on the cable to simulate cable prestress loss, the prestressing forces are calculated by the frequencies which are solved by Fourier transform or wavelet transform under impulse excitation. Test results verify this method is accurate and convenient. The damage cases of web members on the beam are tested to validate the efficiency of the method presented in this study. Wavelet packet decomposition is applied to the structural response signals under ambient vibration, feature vectors are obtained by feature extraction method. The feature vectors are used for training and classification as the inputs of the support vector machine. The structural damage position and degree can be identified and classified, and the test result is highly accurate especially combined with principle component analysis.

This study presents a fault diagnosis system for vehicle heating, ventilation and air conditioning (HVAC) acoustic signal with various feature extractions in deep learning neural network. Traditionally, sound used for fault diagnosis or signal classification is observed the difference of energy in time or frequency domains. Unfortunately, the frequency smearing effect often arises in some critical conditions. In the present study, discrete wavelet transform (DWT) and wavelet packet transform (WPT) are proposed in fault diagnosis. Meanwhile, when using mechanical learning methods, the data are relatively large, in order to reduce the amount of data, DWT and WPT low-frequency decomposition could be used to improve the performance. Furthermore, the signal characteristics more comprehensive, this study attempts to use the feature extraction method of wavelet packet conversion to improve the signal characteristics. In the experiment process, the operation state of the blade blower in the vehicle air conditioner, four different faults were designed, test database was established through sound to classify, and identify the data using deep neural networks to achieve the purpose of blower fault diagnosis. In data analysis, the original signal is presented through wavelet packet decomposition and discrete packet conversion technology, compared with traditional time and frequency domain signals to explore the identification rate, identification speed and related issues. Experimental results show that using WPT combined with deep neural networks have good fault diagnosis and discrimination capabilities, training, and identification time is shorter than time-frequency domain signals.

Classification of hyperspectral remote sensing data with support vector machines (SVMs) is investigated. SVMs have been introduced recently in the field of remote sensing image processing. Using the kernel method, SVMs map the data into higher dimensional space to increase the separability and then fit an optimal hyperplane to separate the data. In this paper, two kernels have been considered. The generalization capability of SVMs as well as the ability of SVMs to deal with high dimensional feature spaces have been tested in the situation of very limited training set. SVMs have been tested on real hyperspectral data. The experimental results show that SVMs used with the two kernels are appropriate for remote sensing classification problems.

In structural health monitoring (SHM), data-driven is a model free method that mines the internal characteristics of the raw measured responses, does not need considering the structural model. This paper proposes a new data-driven damage detection method based on wavelet packet transform (WPT) combining with principal component analysis (PCA). In this method, the responses are firstly decomposed by WPT, and each layer of wavelet packet energy of each measurement is obtained. Since the wavelet packet components of small energy magnitudes are easily contaminated by noises and should be discarded. PCA is utilised to reduce and eliminate redundant information of wavelet packet energy, and a damage index named the rate of the wavelet packet energy score change (WPESCR) is proposed to locate the damage. A simple beam is employed to demonstrate the effectiveness of this method. The numerical and experimental results show that the method combined with WPT and PCA can successfully locate the damage. As a data-driven method, the new WPESCR indicator is a better candidate for continuous online SHM.

Support vector machine (SVM) is gaining much popularity as a powerful machine learning technique. SVM was originally developed for pattern classification and later extended to regression. One of main features of SVM is that it generalizes the maximal margin linear classifiers into high dimensional feature spaces through nonlinear mappings defined implicitly by kernels in the Hilbert space so that it may produce nonlinear classifiers in the original data space. On the oilier hand, the authors developed a family of various SVMs using multi-objective programming and goal programming (MOP/GF) techniques. This paper extends the family of SVM for classification to regression, and discusses their performance through numerical experiments.

Computer based recognition and detection of abnormalities in ECG signals is proposed. For this purpose, the Support Vector Machines (SVM) are combined with the advantages of Hermite transform representation. SVM represent a special type of classification techniques commonly used in medical applications. Automatic classification of ECG could make the work of cardiologic departments faster and more efficient. It would also reduce the number of false diagnosis and, as a result, save lives. The working principle of the SVM is based on translating the data into a high dimensional feature space and separating it using a linear classificator. In order to provide an optimal representation for SVM application, the Hermite transform domain is used. This domain is proved to be suitable because of the similarity of the QRS complex with Hermite basis functions. The maximal signal information is obtained using a small set of features that are used for detection of irregular QRS complexes. The aim of the paper is to show that these features can be employed for automatic ECG signal analysis.

Series arc fault (SAF) poses a great challenge to the safe and stable operation of civil low-voltage distribution systems. For the accurate and rapid detection of SAF, this article proposes an SAF detection method using wavelet packet transform (WPT), residual convolution neural network (RCNN), and support vector machine (SVM). First, the raw current signal is decomposed into four wavelet components based on WPT. Then, the 1-D wavelet components are converted into 2-D matrices. Afterward, the matrices are input into RCNN through different channels. Finally, the detection results can be yielded by SVM. The effectiveness of the proposed method is verified based on offline experiments. The average detection accuracy of the proposed method is 99.72%, which is higher than that of the eight comparison methods. Moreover, the results of online experiments indicate that the detection time of the proposed method is less than 100 ms and can satisfy the requirement of standard 1699.

This paper investigates the use of advance signal processing techniques for the classification of four common reciprocating compressor conditions. Non-stationary vibration signals from the compressor are decomposed based on WPT (Wavelet Packet Transform) and EMD (Empirical Mode Decomposition) and harmonic changes of the optimal components of each technique is used for condition monitoring and diagnostics of the machine. Performance comparison of WPT and EMD is done for the envelope analysis of the optimal wavelet packet and the IMF (Intrinsic Mode Function) component that can extract salient features of the four compressor conditions including one normal and three fault conditions (intercooler leakage, discharge valve leakage and the combination of intercooler and discharge valve leakage). Harmonic changes produced after envelope analysis of the best packet and IMF component are used for classification and results show that WPT technique is more robust and effective in fault classification compared to EMD.

An optimum algorithm in pathological voice quality assessment using wavelet-packet-based features, linear discriminant analysis and support vector machine