Daubechies Wavelet Filter Research Articles

A Wavelet-Based Steganographic Method for Text Hiding in an Audio Signal.

The developed method of steganographic hiding of text information in an audio signal based on the wavelet transform acquires a deep meaning in the conditions of the use by an attacker of deliberate unauthorized manipulations with a steganocoded audio signal to distort the text information embedded in it. Thus, increasing the robustness of the stego-system by compressing the steganocoded audio signal subject to the preservation of the integrity of text information, taking into account the features of the psychophysiological model of sound perception, is the main objective of this scientific research. The task of this scientific research is effectively solved using a multilevel discrete wavelet transform using adaptive block normalization of text information with subsequent recursive embedding in the low-frequency component of the audio signal and further scalar product of the obtained coefficients with the Daubechies wavelet filters. The results of the obtained experimental studies confirm the hypothesis, namely that it is proposed to use recursive embedding in the low-frequency component (approximating wavelet coefficients) followed by their scalar product with wavelet filters at each level of the wavelet decomposition, which will increase the average power of hidden data. It should be noted that upon analyzing the existing method, which is based on embedding text information in the high-frequency component (detailed wavelet coefficients), at the last level of the wavelet decomposition, we obtained the limit CR = 6, and in the developed, CR = 20, with full integrity of the text information in both cases. Therefore, the resistance of the stego-system is increased by 3.3 times to deliberate or passive compression of the audio signal in order to distort the embedded text information.

Novel DWT/IDWT Architecture for 3D with Nine Stage 2D Parallel Processing using Split Distributed Arithmetic

Novel high-speed memory optimized distributed arithmetic (DA)-based architecture is developed and modeled for 3D discrete wavelet transform (DWT). The memory requirement for the proposed architecture is designed to [Formula: see text] pixel dynamic memory space and [Formula: see text] ROM. The proposed 3D-DWT architecture implements 9/7 Daubechies wavelet filters, synthesizes 7127 bytes of memory for temporary storage and uses 758 adders, 36 multiplexers of 16:1 and 36 up counter to realize the 3D-DWT hardware. The 3D-DWT engine is implemented and tested in a Xilinx FPGA Vertex5 XC5VLX155T with high area and power efficiency. The maximum delay in the timing path is 2.676[Formula: see text]ns and the 3D-DWT works at maximum frequency of 381[Formula: see text]MHz clock.

Design and Performance Evaluation of Lattice Daubechies Wavelet Filter Banks for Less Complex Cognitive Transceivers

Wavelet Packet Based Multicarrier Modulation (WPMCM) system uses usually a prototype filter bank multicarrier. The Daubechies-D10 filter bank (D10FB) is the basic framework of multicarrier modulation (MCM) transceiver system. In this paper, Lattice structures of exact Daubechies-D10 wavelet filter are adopted to design a prototybe filter bank realized for a proposed structure of WPMCM transceiver system. Magnitude and phase responses of the exact Daubechies-D10 LPF and the proposed structure are compared. They are nearly the same in the passband with improved roll-off characteristics of the magnitude response of the proposed. Whilefrom the results, it appears that the proposed WPMCM structure possess low speed of operation, less complexity and less power consumption.It is also noticed from SNR comparison, that the performance evaluation of the proposed WPMCM transceiver system is a moderate between other two previously issued structures.

A lossless image compression algorithm using wavelets and fractional Fourier transform

The necessity of data transfer at a high speed, in fast-growing information technology, depends on compression algorithms. Maintaining quality of data reconstructed at high compression rate is a very difficult part of the data compression technique. In this paper, a new lossless image compression algorithm is proposed, which uses both wavelet and fractional transforms for image compression. Even though wavelets are the best choice for feature extraction from the source image at different frequency resolutions, the low-frequency sub-bands of wavelet decomposition are the untouched part in compression method in most of the existing methods. On the other hand, fractional Fourier transform is a convenient form of generalized Fourier transform that helps in the compact lossless coding of the source image with optimal fractional orders. Hence, we have used discrete fractional Fourier transform to compress those sensitive sub-bands of the wavelet transform, carefully. In this method, an image is split into low- and high-frequency sub-bands by using Daubechies wavelet filter and level 1 quantization is applied for both low-frequency and high-frequency sub-bands. The low-frequency sub-bands are compressed by using fractional Fourier transform with optimal fractional orders, and at the same time, high-frequency sub-bands are compressed by eliminating zeroes and storing only nonzero blocks and its position. The compressed wavelet coefficients are further compressed by the application of level 2 quantization and stored as a reduced array. This reduced array is encoded by using arithmetic encoder followed by run-length coding. The experimental results of the proposed algorithm with a different set of test images are compared with some of the existing image compression algorithms. The results show that the proposed method has significant improvement in image reconstruction quality.

Automated Recognition of Obstructive Sleep Apnea Using Ensemble Support Vector Machine Classifier

ECG is mainly used to diagnosis the obstructive sleep apnea (OSA) with a high degree of accuracy in clinical care applications. We have developed a real-time algorithm for the detection of sleep apnea disease based on electrocardiograph (ECG). In this study, features from ECG signals were extracted from 12 normal and 58 OSA patients from physionet apnea ECG database. The baseline noise, motion drift and muscle noise in raw ECG signals are removed using median filter and Daubechies wavelet filter. QRS detection algorithm extracts R-wave amplitude and R-wave time duration from de-noised signal. The proposed QRS detection algorithm contains four stages. The stages are calculation of QRS-complex slope, squaring function, moving-window integration and calculation of R-peak and QRS detection. Time domain features are calculated from the heart rate variability and ECG-derived respiration (EDR). Support vector machine (SVM) and ensemble support vector machine techniques are used for the detection of OSA.

Modified Wavelet Full-Approximation Scheme for the Numerical Solution of Nonlinear Volterra integral and integro-differential Equations

Abstract In this paper, modified wavelet full-approximation scheme is introduced for the numerical solution of nonlinear Volterra integral and integro-differential equations. Wavelet Prolongation and Restriction operators are developed using Daubechies wavelet filter coefficients. Results show that the proposed scheme offers an efficient and good accuracy with faster convergence in less computation cost, which is justified through the error analysis and CPU time.

Traditional Psychoacoustic Model and Daubechies Wavelets for Enhanced Speech Coder Performance

Speech compression techniques based on the traditional psychoacoustic model have been proposed by many researchers. We propose the Discrete Wavelet Transform (DWT) supported by the same psychoacoustic model for speech compression. This paper presents a traditional psychoacoustic model for processing equal partitions of the total bandwidth spectrum of audio signal frequencies in order to reduce redundancy by filtering out the tones and noise maskers in the speech signal. Here, uniform filter banks are used for efficient computation, for selection of appropriate threshold levels, and for better compression of Discrete Wavelet Transform coefficients. A Daubechies wavelet filter bank is nonlinear and asymmetric. It is equivalent to a cochlear filter in the human hearing system. The similarity between the Daubechies filter bank and our hearing system was the basis for developing a novel speech coder. Better performance in terms of the compression factor (CF) and the signal-to-noise ratio (SNR) resulted, as compared to the earlier methods.

An Enhanced Automated Diagnosis Method for Glaucoma Detection Using Wavelet

Glaucoma is a disease of the eye, characterized by increased Intraocular Pressure (IOP), excavation and atrophy of optic nerve. Without treatment, glaucoma can cause total permanent blindness. A technique for automated screening of glaucoma from retinal fundus images using wavelet features is presented in this paper. The discussed wavelet features such as mean and entropy values are obtained by using daubechies (db3) wavelet filter. Thus the features are validated by classifying the normal and glaucoma images using Artificial Neural Network (ANN). The performance is analysed with evaluation metrics in terms of accuracy and specificity. Thus the observed accuracy and specificity is 79% and 100% respectively.

Asymptotic Decorrelation of Wavelet Packet Transform for Certain Long-Memory Processes

The asymptotic decorrelation of the discrete wavelet transform for long-memory processes (such as the fractionally differenced (FD) process, the autoregressive fractionally integrated moving average (ARFIMA) process, and the Gegenbauer autoregressive moving average (GARMA) process) as well as the statistical inference techniques based on this property, have received much attention nowadays. In this paper, we investigate the asymptotic decorrelation property of the discrete wavelet packet transform (DWPT) for two classes of discrete-time long-memory processes containing the ARFIMA and the GARMA processes. Especially, we prove theoretically that the covariance across between-packet DWPT coefficients decays hyperbolically or exponentially fast as the width of the underlying Daubechies scaling and wavelet filters to generate the DWPT gets large. Meanwhile, we show that the covariance between within-packet DWPT coefficients converges hyperbolically fast to its corresponding counterpart when the underlying scaling and wavelet filters to generate the DWPT are the Shannon's ideal low- and high-pass filters.

Detection of precursory signals in front of impulsive P-waves

The precursory signals, which are preceding the impulsive P-waves, are analyzed in this study. These signals cause an error in automatic P-wave detectors which motivated us to propose an algorithm to identify these signals automatically. The first detail of multi-resolution analysis (MRA) of discrete wavelet transform (DWT) is used to detect these precursory signals. The proposed algorithm is built based on the parameters of the second order autoregressive (AR) model. 34 impulsive P-wave segments with precursory signals are used to test the ability of the proposed algorithm to detect these signals. Length of each precursory signal is calculated manually and using the proposed algorithm, where the difference between the two calculations is the error of the automatic detection. The proposed algorithm used both Haar and Daubechies wavelet filters of order 2 (abbreviated as Dau(2)) for comparison. It is found that the Dau(2) wavelet filter gave better results than Haar wavelet filter as will be discussed later. The mean and standard deviation of the error, when using Dau(2) wavelet, are -1 and 4 samples, respectively.

Performance Analysis of Neural Network Architecture Combined with DWT for Image Compression

Neural networks are significantly used in signal and image processing techniques for pattern recognition and template matching. In this work neural networks are used for image compression. In order to improve the performances image compression algorithm, DWT is combined with NN for achieving better MSE and increase in compression ration greater than 100%. NN architecture achieves maximum of 98% with use of four neurons in the hidden layer, with selection of LL sub band only the compression is improved by another 75%. The proposed architecture is analyzed for 20 images and MSE is found to be improved by a factor of 20%. Daubechies wavelet filter and Haar wavelet filters are used for DWT, input layer with one hidden layer and output layer consisting of tansig and purelin function us used for compression. The design proposed is suitable for high resolution image compression. General Terms

Wavelet based multiscale scheme for two-dimensional advection–dispersion equation

In this paper, wavelet based adaptive solver is developed for two dimensional advection dominating solute problem which generates sharp concentration front in the solution. In order to handle simultaneously smooth and shock-like behavior, the framework uses finite element discretization followed by wavelets for multiscale decomposition. Daubechies wavelet filter is incorporated to eliminate spurious oscillations at very high Peclet number. The developed solution is compared with the analytical solution to assess the accuracy and robustness. The advantages of the present method over the commonly used methods such as FDM and FEM for solving the problems which show non-physical oscillation in the numerical solution are demonstrated.

Pascal's triangle: An origin of Daubechies polynomials and an analytic expression for associated filter coefficients

After showing that Daubechies polynomial coefficients can be simply obtained from Pascal's triangle by some elementary additions, we propose a derivation of the spectral factorization by using the elementary symmetric functions. This derivation leads us to present an analytic expression, able to compute Daubechies wavelet filter coefficients from the roots of the associated Daubechies polynomial. Thus, these coefficients are directly obtained and without recurrence. At last, we measure the quality of the coefficient sets generated by this expression and we compare it with two well-known methods.

Classification of chaos-based digital modulation techniques using wavelet neural networks and performance comparison of wavelet families

This paper presents a comparative study of implementation of feature extraction and classification algorithms based on wavelet neural networks (WNN) for chaos-based digital modulation (CBDM) classification. Thirteen different feature extraction methods are generated by separately using Daubechies, Biorthogonal, Coiflets, and Symlets wavelet filters. WNN model is used, which consists of two layers: wavelet entropy and multi-layer perceptron (MLP) neural networks for expert CBDM classification. The chaos-based digital modulated signals used in this experimental study are Chaos Shift Keying (CSK), Chaotic On–Off Keying (COOK), Differential Chaos Shift Keying (DCSK), Correlation Delay Shift Keying (CDSK), Symmetric Chaos Shift Keying (SCSK) and Frequency-Modulated Differential Chaos Shift Keying (FM-DCSK). The performance of this comparison system is evaluated by using total 1806 CBDM signals for each of these feature extraction methods. Mean correct classification rate is about 98.76% for the sample CBDM signals.

Recognition of multi-scroll chaotic attractors using wavelet-based neural network and performance comparison of wavelet families

In this comparative study, the implementation of feature extraction and classification algorithm based on wavelet based neural network (WBNN) is presented for recognition of multi-scroll chaotic attractors using only one of the state variables of Chua’s circuit with a multi-segment resistor. Sixteen different feature extraction methods (Db1, Db2, Db6, Db10, Sym2, Sym3, Sym5, Bior1.1, Bior1.3, Bior2.2, Bior2.4, Bior2.6, Bior4.4, Coif1, Coif2, and Coif5) are generated by separately using Daubechies, Biorthogonal, Coiflets, and Symlets wavelet filters. WBNN model is used, which consists of two layers: adaptive wavelet entropy and multi layer perceptron (MLP) neural networks for expert multi-scroll chaotic attractor classification. The performance of this comparison system is evaluated by using total 600 different chaotic signals that have different initial values and resistors values for each of these feature extraction methods. The performance comparison of these features extraction methods and the advantages and disadvantages of the methods are examined.

Segmentation of optical coherence tomography images for differentiation of the cavernous nerves from the prostate gland

The cavernous nerves course along the surface of the prostate and are responsible for erectile function. Improvements in identification, imaging, and visualization of the cavernous nerves during prostate cancer surgery may improve nerve preservation and postoperative sexual potency. Two-dimensional (2-D) optical coherence tomography (OCT) images of the rat prostate were segmented to differentiate the cavernous nerves from the prostate gland. To detect these nerves, three image features were employed: Gabor filter, Daubechies wavelet, and Laws filter. The Gabor feature was applied with different standard deviations in the x and y directions. In the Daubechies wavelet feature, an 8-tap Daubechies orthonormal wavelet was implemented, and the low-pass sub-band was chosen as the filtered image. Last, Laws feature extraction was applied to the images. The features were segmented using a nearest-neighbor classifier. N-ary morphological postprocessing was used to remove small voids. The cavernous nerves were differentiated from the prostate gland with a segmentation error rate of only 0.058+/-0.019. This algorithm may be useful for implementation in clinical endoscopic OCT systems currently being studied for potential intraoperative diagnostic use in laparoscopic and robotic nerve-sparing prostate cancer surgery.

Heart sound reproduction based on neural network classification of cardiac valve disorders using wavelet transforms of PCG signals

Cardiac auscultatory proficiency of physicians is crucial for accurate diagnosis of many heart diseases. Plenty of diverse abnormal heart sounds with identical main specifications and different details representing the ambient noise are indispensably needed to train, assess and improve the skills of medical students in recognizing and distinguishing the primary symptoms of the cardiac diseases. This paper proposes a versatile multiresolution wavelet-based algorithm to first extract the main statistical characteristics of three well-known heart valve disorders, namely the aortic insufficiency, the aortic stenosis, and the pulmonary stenosis sounds as well as the normal ones. An artificial neural network (ANN) and statistical classifier are then applied alternatively to choose proper exclusive features. Both classification approaches suggest using Daubechies wavelet filter with four vanishing moments within five decomposition levels for the most prominent distinction of the diseases. The proffered ANN is a multilayer perceptron structure with one hidden layer trained by a back-propagation algorithm (MLP–BP) and it elevates the percentage classification accuracy to 94.42. Ultimately, the corresponding main features are manipulated in wavelet domain so as to sequentially regenerate the individual counterparts of the underlying signals.

Toward intelligent music information retrieval

Efficient and intelligent music information retrieval is a very important topic of the 21st century. With the ultimate goal of building personal music information retrieval systems, this paper studies the problem of intelligent music information retrieval. Huron points out that since the preeminent functions of music are social and psychological, the most useful characterization would be based on four types of information: genre, emotion, style,and similarity. This paper introduces Daubechies Wavelet Coefficient Histograms (DWCH)for music feature extraction for music information retrieval. The histograms are computed from the coefficients of the db/sub 8/ Daubechies wavelet filter applied to 3 s of music. A comparative study of sound features and classification algorithms on a dataset compiled by Tzanetakis shows that combining DWCH with timbral features (MFCC and FFT), with the use of multiclass extensions of support vector machine,achieves approximately 80% of accuracy, which is a significant improvement over the previously known result on this dataset. On another dataset the combination achieves 75% of accuracy. The paper also studies the issue of detecting emotion in music. Rating of two subjects in the three bipolar adjective pairs are used. The accuracy of around 70% was achieved in predicting emotional labeling in these adjective pairs. The paper also studies the problem of identifying groups of artists based on their lyrics and sound using a semi-supervised classification algorithm. Identification of artist groups based on the Similar Artist lists at All Music Guide is attempted. The semi-supervised learning algorithm resulted in nontrivial increases in the accuracy to more than 70%. Finally, the paper conducts a proof-of-concept experiment on similarity search using the feature set.

Wavelet-based parameter estimation for polynomial contaminated fractionally differenced processes

We consider the problem of estimating the parameters for a stochastic process using a time series containing a trend component. Trend, i.e., large scale variations in the series that are best modeled outside of a stochastic framework, is often confounded with low-frequency stochastic fluctuations. This problem is particularly evident in models such as fractionally differenced (FD) processes, which exhibit slowly decaying autocorrelations and can be extended to encompass nonstationary processes with substantial low frequency components. We use the discrete wavelet transform (DWT) to estimate parameters for stationary and nonstationary FD processes in a model of polynomial trend plus FD noise. Using Daubechies wavelet filters allows for automatic elimination of polynomial trends due to embedded differencing operations. Parameter estimation is based on an approximate maximum likelihood approach made possible by the fact that the DWT decorrelates FD processes approximately. We consider this decorrelation in detail, examining the between- and within-scale wavelet correlations separately. Better between-scale decorrelation can be achieved by increasing the length of the wavelet filter, whereas the within-scale correlations can be handled via explicit modeling by a low-order autoregressive process. We demonstrate our methodology by applying it to a popular climate dataset.

Asymptotic Decorrelation of Between-Scale Wavelet Coefficients

In recent years there has been much interest in the analysis of time series using a discrete wavelet transform (DWT) based upon a Daubechies wavelet filter. Part of this interest has been sparked by the fact that the DWT approximately decorrelates certain stochastic processes, including stationary fractionally differenced (FD) processes with long memory characteristics and certain nonstationary processes such as fractional Brownian motion. It is shown that, as the width of the wavelet filter used to form the DWT increases, the covariance between wavelet coefficients associated with different scales decreases to zero for a wide class of stochastic processes. These processes are Gaussian with a spectral density function (SDF) that is the product of the SDF for a (not necessarily stationary) FD process multiplied by any bounded function that can serve as an SDF on its own. We demonstrate that this asymptotic theory provides a reasonable approximation to the between-scale covariance properties of wavelet coefficients based upon filter widths in common use. Our main result is one important piece of an overall strategy for establishing asymptotic results for certain wavelet-based statistics.