Selected Wavelet Coefficients Research Articles

Fault diagnosis and analysis of hydraulic brake based on friction vibration signal

The friction vibration signal corresponding to different fault types of brakes is significantly different, thus a hydraulic disc brake fault diagnosis and judgment scheme based on vibration signal detection was proposed and validated in the paper. According to the composition and electric control principle of hydraulic brake, the vibration signal detection and brake pressure control strategies were established. The key hardware and isolation circuit of the detection system were designed, which can effectively improve the stability and anti-interference ability in the process of vibration data acquisition and transmission with SPI and CAN bus communication scheme. In order to eliminate noise interference in friction vibration signal and improve the accuracy of fault diagnosis with advantage, wavelet threshold denoising method was applied to brake disc vibration signal processing. The denoised signal can be obtained by inverting the selected wavelet coefficients to separate the useful components of the signal from the invalid noise. For the research on the correlation between fault types and vibration signal characteristics, a friction vibration test bench for hydraulic braking system was built to obtain and process the output signal from vibration sensor. The frequency spectrum response laws of vibration signals were compared and analyzed when the brake disc was normal and had surface defects, including microcracks, excessive wear and fatigue peeling. The external parameters such as brake pressure and spindle speed were keeping constant, thus eliminating the influence of working factors on vibration characteristics. In order to study the influence of brake pressure failure on friction vibration, the variation law of vibration signal is compared and analyzed under the condition of hydraulic valve leakage and pressure maintaining failure. The results show that the friction vibration signal can be used as an effective basis for judging the surface defects and brake pressure faults of brake discs. Under the condition of wavelet threshold denoising, accurate and effective vibration parameters can be obtained, which is of great significance to the safety and reliability of the braking system.

Electroencephalogram-Based Motor Imagery Classification Using Deep Residual Convolutional Networks.

The classification of electroencephalogram (EEG) signals is of significant importance in brain-computer interface (BCI) systems. Aiming to achieve intelligent classification of motor imagery EEG types with high accuracy, a classification methodology using the wavelet packet decomposition (WPD) and the proposed deep residual convolutional networks (DRes-CNN) is proposed. Firstly, EEG waveforms are segmented into sub-signals. Then the EEG signal features are obtained through the WPD algorithm, and some selected wavelet coefficients are retained and reconstructed into EEG signals in their respective frequency bands. Subsequently, the reconstructed EEG signals were utilized as input of the proposed deep residual convolutional networks to classify EEG signals. Finally, EEG types of motor imagination are classified by the DRes-CNN classifier intelligently. The datasets from BCI Competition were used to test the performance of the proposed deep learning classifier. Classification experiments show that the average recognition accuracy of this method reaches 98.76%. The proposed method can be further applied to the BCI system of motor imagination control.

Condition monitoring of deep-hole drilling process based on improved empirical wavelet de-noising and high multiple frequency components of rotation frequency

The machining condition monitoring is an important task to improve product quality and avoid economic losses in deep-hole drilling. Out-of-roundness-tolerance is one of major defects in deep-hole drilling process. In this work, the spindle vibration signals generated in deep-hole drilling process are studied to seek the characteristics which can reveal the roundness error change. Considering the influence of background noise on monitoring effect and the sensitivity of high multiple frequency components of spindle vibration signal towards the roundness error change, an improved empirical wavelet de-noising method is proposed to remove the noise of the spindle vibration signal. The high multiple frequency components of the rotation frequency are extracted from the de-noised spindle vibration signal. Firstly, the spindle vibration signal is decomposed by empirical wavelet transform into a set of wavelet coefficients, and then an improved thresholding function is proposed to process the Fourier spectrum of wavelet coefficients for removing the noise from the wavelet coefficients. Secondly, the wavelet coefficients are reconstructed based on the de-noised wavelet coefficients, and the wavelet coefficients which contain high multiple components of rotation frequency are selected. Finally, the energy entropy of the selected wavelet coefficients is calculated as a roundness error monitoring indicator to detect the roundness error of deep hole. The proposed method is verified with well-designed deep-hole drilling tests. The test results show that the monitoring indicator can effectively reflect the roundness error change in deep-hole drilling process.

Embedding Distortion Analysis in Wavelet-domain Watermarking

Imperceptibility and robustness are two complementary fundamental requirements of any watermarking algorithm. Low-strength watermarking yields high imperceptibility, but exhibits poor robustness. High-strength watermarking schemes achieve good robustness but often infuse distortions resulting in poor visual quality in host images. This article analyses the embedding distortion for wavelet-based watermarking schemes. We derive the relationship between distortion, measured in mean square error (MSE), and the watermark embedding modification and propose the linear proportionality between MSE and the sum of energy of the selected wavelet coefficients for watermark embedding modification. The initial proposition assumes the orthonormality of discrete wavelet transform. It is further extended for non-orthonormal wavelet kernels using a weighting parameter that follows the energy conservation theorems in wavelet frames. The proposed analysis is verified by experimental results for both non-blind and blind watermarking schemes. Such a model is useful to find the optimum input parameters, including the wavelet kernel, coefficient selection, and subband choices for wavelet domain image watermarking.

Structural health monitoring for delamination detection and location in wind turbine blades employing guided waves

AbstractWind power is becoming one of the most important renewable energies in the world. The reduction in operating and maintenance costs of the wind turbines has been identified as one of the biggest challenges to establish this energy as an alternative to fossil fuels. Predictive maintenance can detect a potential failure at an early stage reducing operating costs. Structural health monitoring together with non‐destructive techniques are an effective method to detect incipient delamination in wind turbine blades. Ultrasonic guided waves offer possibilities to inspect delamination and disunion between layers in composite structures. Delamination results in a concentration of tensions in certain areas near the fault, which can propagate and create the total break of the blade. This paper presents a new approach for disunity detection between layers comparing two real blades, also new in the literature, one of them built with three disbonds introduced in its manufacturing process. The signals are denoised by Daubechies wavelet transform. The threshold for the denoising is obtained by a wavelet coefficients selection rule using the Birgé‐Massart penalization method. The signals were normalized and their envelopes were obtained by Hilbert transform. Finally, a pattern recognition based on correlations was applied.

Alcohol use disorder detection using EEG Signal features and flexible analytical wavelet transform

The frequent excessive drinking of alcohol severely affects the neuronal composition and working of the brain and consequently developed Alcohol Use Disorder (AUD). Subjects suffering from AUD are prone to various diseases, psychological and cognitive issues if not identified and treated timely. Electroencephalogram (EEG) signals are used to record the internal structure and activity of the brain. The manual screening of EEG signals for AUD detection is complicated for practitioners because EEG signals are recorded in microvolts (μv) and consists of the inherent internal complexity of the brain. Therefore, an automated computer-aided diagnosis (CAD) is used for assisting the medical practitioner in AUD screening process. The recorded EEG signals of a subject are nonlinear and oscillatory, and CAD methods examine these signals in their frequency sub-bands. In this paper, flexible analytical wavelets transform (FAWT) based machine learning models are proposed for automated alcoholism detection. In the proposed methodology, EEG signals are decomposed into approximate and detailed wavelet coefficients using FAWT. The statistical features such as mean, standard deviation, kurtosis, skewness, and Shannon entropy are extracted from the selected wavelet coefficients. The features are fed to the various machine learning models including Least Square -Support Vector Machine (LS-SVM), Support Vector Machine (SVM) and Naïve Bayes learners for training. The training and testing are performed using 10-fold cross-validation. The performance of models is evaluated using all essential measures such as accuracy, sensitivity, specificity, F-measure, precision, Matthews correlation coefficient (MCC) and ROC. The results suggest that LS-SVM using polynomial kernel performed best with accuracy 99.17%, Sensitivity 99.17%, and Specificity as 99.44% using 10-fold cross-validation technique.

Detection of total viable count in spiced beef using hyperspectral imaging combined with wavelet transform and multiway partial least squares algorithm

AbstractThe feasibility of using hyperspectral imaging technology combined with wavelet transform and multiway partial least squares (N‐PLS) algorithm to predict the total viable count (TVC) of spiced beef during storage was investigated. The mean spectral data were extracted from the hyperspectral images and further decomposed in nine levels by daubechies8 (db8) wavelet function to obtain an approximation coefficient (A9) and nine detail coefficients (D1–D9). Selecting wavelet coefficients to compose different three‐dimension matrixes, further integrating N‐PLS algorithm to establish the predictive models for detecting TVC in spiced beef. The experimental results show that the N‐PLS model with D4, D5, D6, D7 coefficient (also named D4,5,6,7‐N‐PLS) exhibit an excellent prediction capability for TVC of spiced beef sample with a higher determination coefficients in prediction ( ) of 0.934 and lower root mean squared errors estimated by prediction of 0.755 than other N‐PLS models, raw spectra PLS model, and Unfold‐PLS models. Therefore, the established model using three‐dimension data array and N‐PLS algorithm has great potential in the TVC value detection of spiced beef and other meat productions.Practical applicationsSpiced beef is one of the most popular meat products in China owing to its good taste, low fat, and high protein. Unfortunately, the spiced beef during storage is just a suitable habitat for many pathogens and spoilage microbes to colonize. Thus, it is necessary to monitor the microbiological contamination to guarantee the sanitary quality of meat productions. The total viable count (TVC) of bacteria was considered as a key indicator for the freshness evaluation of meat productions, Traditional techniques for determination of TVC are laborious, time consuming, and unsuitable for modern meat industrial processing and production technologies. Therefore, this study attempted to use hyperspectral imaging technology combined with wavelet transform and multiway partial least squares (N‐PLS) algorithm to predict the TVC of spiced beef during storage. The proposed method is helpful for most consumers to judge easily the freshness state of meat productions and further purchase them.

Protection of medical images and patient related information in healthcare: Using an intelligent and reversible watermarking technique

This work presents an intelligent technique based on reversible watermarking for protecting patient and medical related information. In the proposed technique ‘IRW-Med’, the concept of companding function is exploited for reducing embedding distortion, while Integer Wavelet Transform (IWT) is used as an embedding domain for achieving reversibility. Histogram processing is employed to avoid underflow/overflow. In addition, the learning capabilities of Genetic Programming (GP) are exploited for intelligent wavelet coefficient selection. In this context, GP is used to evolve models that not only make an optimal tradeoff between imperceptibility and capacity of the watermark, but also exploit the wavelet coefficient hidden dependencies and information related to the type of sub band. The novelty of the proposed IRW-Med technique lies in its ability to generate a model that can find optimal wavelet coefficients for embedding, and also acts as a companding factor for watermark embedding. The proposed IRW-Med is thus able to embed watermark with low distortion, take out the hidden information, and also recovers the original image. The proposed IRW-Med technique is effective with respect to capacity and imperceptibility and effectiveness is demonstrated through experimental comparisons with existing techniques using standard images as well as a publically available medical image dataset.

On ultrasonic communication through metal structure for machine embedded sensing

With the development of machine embedded sensing, wireless communication through metal structures is becoming a critical issue in manufacturing equipment, process monitoring and control. To address it, this paper presents a multi-carrier ultrasonic communication scheme to achieve high-bit data rate for machine embedded sensing through metal structures. To extract the coded information from ultrasonic signal and denoise the signal, a new signal processing method, named multi-scale enveloping symbolic analysis (MuSEnSA), is also formulated by integrating dual-tree complex wavelet packet transform and symbolic analysis. Dual-tree complex wavelet packet transform (DT-CWPT) is firstly investigated by separating 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 by taking advantage of symbolic analysis. Numerical and experimental studies are performed to evaluate the effectiveness of the developed signal processing method for injection molding monitoring.

A comparative study of feature ranking techniques for epileptic seizure detection using wavelet transform

In this paper, an attempt is made to obtain optimal wavelet function and wavelet based Electroencephalograph (EEG) features for detection of epilepsy using appropriate feature ranking techniques. The EEG data includes normal, pre-ictal and ictal EEG signals. Initially, signals are decomposed using 16 discrete wavelets and the best basis wavelet is selected using Maximum Energy to Permutation Entropy ratio criterion. A range of statistical, fractal and entropy based features are calculated from selected wavelet coefficients. The performance of three different feature ranking techniques i.e. Fisher Score, ReliefF and Information Gain is investigated on computed features. Classification of the ranked features is performed by machine learning technique Least Square-Support Vector Machine. Features ranked through Fisher Score ranking technique show high discrimination ability and classified with high classification accuracy. Classification results ensure the suitability of proposed best basis wavelet based feature extraction methodology and Fisher Score ranking technique in epilepsy detection.

Telltale Watermarks for Counting JPEG Compressions

Telltale Watermarks for Counting JPEG Compressions

Orthogonal projection approach and continuous wavelet transform-feed forward neural networks for simultaneous spectrophotometric determination of some heavy metals in diet samples

Simultaneous spectrophotometric determination of a mixture of overlapped complexes of Fe3+, Mn2+, Cu2+, and Zn2+ ions with 2-(3-hydroxy-1-phenyl-but-2-enylideneamino) pyridine-3-ol(HPEP) by orthogonal projection approach-feed forward neural network (OPA-FFNN) and continuous wavelet transform-feed forward neural network (CWT-FFNN) is discussed. Ionic complexes HPEP were formulated with varying reagent concentration, pH and time of color formation for completion of complexation reactions. It was found that, at 5.0×10−4molL−1 of HPEP, pH 9.5 and 10min after mixing the complexation reactions were completed. The spectral data were analyzed using partial response plots, and identified non-linearity modeled using FFNN. Reducing the number of OPA-FFNN and CWT-FFNN inputs were simplified using dissimilarity pure spectra of OPA and selected wavelet coefficients. Once the pure dissimilarity plots ad optimal wavelet coefficients are selected, different ANN models were employed for the calculation of the final calibration models. The performance of these two approaches were tested with regard to root mean square errors of prediction (RMSE %) values, using synthetic solutions. Under the working conditions, the proposed methods were successfully applied to the simultaneous determination of metal ions in different vegetable and foodstuff samples. The results show that, OPA-FFNN and CWT-FFNN were effective in simultaneously determining Fe3+, Mn2+, Cu2+, and Zn2+ concentration. Also, concentrations of metal ions in the samples were determined by flame atomic absorption spectrometry (FAAS). The amounts of metal ions obtained by the proposed methods were in good agreement with those obtained by FAAS.

ECG PVC Classification Algorithm based on Fusion SVM and Wavelet Transform

In the process of ventricular premature beat (PVC) and normal sinus rhythm (NSR) identification base on electrocardiogram (ECG), there exists problems like negative effect from ECG rhythm and low recognition rate. This paper proposes the electrocardiogram PVC classification algorithm based on support vector machine (SVM) and wavelet algorithm. The algorithm uses the wavelet transform to analyze ECG beating model, which is not influenced by the change of ECG waveform. The two feature sets respectively compose of statistical parameters of the wavelet coefficients and the selected wavelet coefficients. PVC and NSR are analyzed by using SVM. The experimental results show that this method improves the recognition rate of ECG.

On Combining Wavelets Expansion and Sparse Linear Models for Regression on Metabolomic Data and Biomarker Selection

Wavelet thresholding of spectra has to be handled with care when the spectra are the predictors of a regression problem. Indeed, a blind thresholding of the signal followed by a regression method often leads to deteriorated predictions. The scope of this article is to show that sparse regression methods, applied in the wavelet domain, perform an automatic thresholding: the most relevant wavelet coefficients are selected to optimize the prediction of a given target of interest. This approach can be seen as a joint thresholding designed for a predictive purpose. The method is illustrated on a real world problem where metabolomic data are linked to poison ingestion. This example proves the usefulness of wavelet expansion and the good behavior of sparse and regularized methods. A comparison study is performed between the two-steps approach (wavelet thresholding and regression) and the one-step approach (selection of wavelet coefficients with a sparse regression). The comparison includes two types of wavelet bases, various thresholding methods, and various regression methods and is evaluated by calculating prediction performances. Information about the location of the most important features on the spectra was also obtained and used to identify the most relevant metabolites involved in the mice poisoning.

Image Compression Based on Wavelet Transform

The wavelet analysis has some important applications in image processing, including image compression, image de-noising and so on.Wavelet analysis for two-dimensional image compression is a key aspect in the field of its applications.This paper studied the application of wavelet analysis in BMP image coding, the characteristics of wavelet coefficients and wavelet subimage, these lay the foundation for further selection of wavelet coefficients and optimize, and analyzed the theory of EZW algorithm, illustrates the better results of the applications on using wavelet theory in image processing.

Classification of Textures using Patch Based Energy Features of Selected Wavelet Coefficients

Classification of Textures using Patch Based Energy Features of Selected Wavelet Coefficients

Toward a continuous wavelet transform-based search method for feature selection for classification of spectroscopic data

In this study, taking advantage of multi-resolution property of wavelet transform along with a simple statistics, a careful search method for feature selection is proposed. This method enables identifying a set of wavelet coefficients as features that maximizes the discrimination of chemical spectra in different classes. In spite of variety of application of discrete wavelet transform (DWT) and wavelet packet transform (WPT) in manifesting the information content of signal, continuous wavelet transform (CWT) is used in this approach. Continuous wavelet transform due to its redundancy property is able to provide a finer multi-resolution space that visualizes all information content of spectra. The proposed method is tested on three spectral data sets consisting of 1H NMR analysis of table wines, near infrared absorbance spectra of meat samples and NIR diffuse transmission spectra of pharmaceutical tablets. Using the suggested method a small number of features are selected and fed as an input to the different classifiers, Partial Least Squares Discriminant Analysis (PLS-DA), K-Nearest Neighbor (K-NN) and Soft Independent Modeling of Class Analogy (SIMCA). It is confirmed that the classification models developed on the selected wavelet coefficients yield higher correct classification rate (CCR) than the models constructed with original features which confirm the adequacy of the generated feature space for classification.

SVM correction based geometrically invariant digital watermarking algorithm

Geometric distortion is known as one of the most difficult attacks to resist, for it can desynchronize the location of the watermark and hence causes incorrect watermark detection. It is a challenging work to design a robust image watermarking scheme against geometric distortions. Based on the Support Vector Machine (SVM) geometric distortions correction, we propose a new image watermarking algorithm with good visual quality and reasonable resistance toward geometric distortions in this paper. Firstly, the significant bitplane image is extracted from original host, and DWT is performed on the significant bitplane image. Then, the corresponding low-pass subband is divided into small blocks. Finally, the digital watermark is embedded into host image by adaptively modulating the selected wavelet coefficients in small blocks. The main steps of digital watermark detecting procedure include: (1) the significant bitplane image is extracted from test image, and some low-order pseudo-Zernike moments of the significant bitplane image are computed, which are regarded as the effective feature vectors; (2) the appropriate kernel function is selected for training, and a SVM training model can be obtained; (3) the test image is corrected with the well trained SVM model; (4) the digital watermark is extracted from the corrected test image. Experimental results show that the proposed image watermarking is not only invisible and robust against common image processing operations such as filtering, noise adding, and JPEG compression etc., but also robust against the geometrical distortions.

A blind scene-based watermarking for video copyright protection

Due to the replicable nature of video many illegal copies of the original video can be made. So it demands to deliver methods for preventing illegal copying. This paper identifies a new digital watermarking approach for copyright protection of video based on wavelet transformation. First, the motion part of color video is detected by scene change analysis, and then by applying 3D wavelet transformation over detected motion part, 10 sub-bands of wavelet coefficients are obtained. In order to insert the watermark, 3D coefficients of HL, LH and HH with their third level are selected. After all, by using a spread spectrum technique, the watermark is embedded into the selected wavelet coefficients. In extraction step, the original video is not needed, namely, blind detection. So, the resultant watermarking scheme can be used for public watermarking applications, where the original video is not available for watermark extraction. The experimental results show a good performance of the proposed method for transparency and robustness. Furthermore, the robustness of the proposed method against various kinds of attacks such as median filtering, Gaussian noise, frame dropping, frame averaging, frame swapping and lots of lossy compression including MPEG-4, MPEG-2, and H.264 shows the fidelity of our claim.

A robust digital watermarking algorithm in undecimated discrete wavelet transform domain

In this paper, a robust digital image watermarking algorithm is proposed based on fuzzy support vector machine (FSVM) correction. The significant bitplane image is extracted from original host, and undecimated discrete wavelet transform (UDWT) is performed on the significant bitplane image. The lowpass subband is divided into small blocks, and digital watermark is embedded into original host by adaptively modulating the selected wavelet coefficients in small blocks. In order to resist geometric distortion attacks, FSVM is utilized to learn geometric distortion parameters by using low order Zernike moments. The watermark extraction is carried out after watermarked image has been synchronized without original host. Simulation also shows outstanding robustness of the proposed algorithm against most common image processing operations and geometric distortions.