Reverse Biorthogonal Wavelet Research Articles

Wavelet-based energy features for diagnosis of melanoma from dermoscopic images

Melanoma causes majority of deaths related to skin cancer if not detected and treated at an early stage. It is considered as one of the dangerous types of cancer, since it quickly spreads to other parts. A novel approach has been proposed in this paper to detect Melanoma from dermoscopic images. Pre-processing is done to remove hair and noise in the image. Initial segmentation is carried out with watershed transform. This is followed by Maximal Similarity Region Merging process. After pre-processing and segmentation, wavelet-based energy features are extracted using daubechies (DB3), and reverse biorthogonal (RBIO3.3, RBIO3.5, and RBIO3.7) wavelet filters. 12 features are extracted using four wavelet filters. Using the Gain Ratio feature selection method, the most discriminative six features are selected for the classification purpose. Then classification has been done by using K-nearest neighbour, support vector machine, random forest, and Naive Bayes classifier. The highest sensitivity of 97.5% is achieved in the case of support vector machine.

Performance based Analysis of Wavelets Family for Image Compression-A Practical Approach

Image compression has become vital in today‟s scenario because lots of digital images has become spread over the servers which uses lots of space. The size of image files increased in the past 15 years due to the rapid technological advancement and increasing quality of images. In such conditions, it has become significant to use an efficient algorithm to compress images in such a manner so that it gives better compression with minimum loss in quality with respect to different quality measures. The proposed image compression method is implemented in frequency domain using wavelet transform. In this paper, Haar, Symlet, Coiflets, reverse biorthogonal, biorthogonal, and Daubechies wavelets are analyzed for the compression. There are mother wavelets, each of them is analyzed with respect to image compression scheme and resulting wavelets are given as the output which wavelets are resulting in goodcompression.

Reverse bi-orthogonal wavelets & fuzzy classifiers for the automatic detection of spike waves in the EEG of the hypoxic ischemic pre-term fetal sheep.

There exists a 6-8 hour window of opportunity for the treatment of perinatal Hypoxic-Ischemic Encephalopathy (HIE) following the original insult after which significant irreversible brain injury manifests leading to debilitating neurological conditions such as epilepsy and cerebral palsy. At present, there are no identified biomarkers in the electroencephalogram (EEG) that are currently being used to help classify if a HIE insult has occurred or not. However, high frequency micro-scale transients in the form of spikes, sharp waves and slow waves appear in the EEG, post insult, that could provide precursory information whether a HIE insult has occurred or not. This paper describes the superiority of using reverse bi-orthogonal wavelets (RBIO-WT), in the form of the rbio2.8 mother wavelet, in conjunction with a Type-1 Fuzzy Logic System (Type-I FLS) classifier for accurate micro-scale spike wave transient detection in the EEG of Pre-term Fetal Sheep. The algorithm performance for spike detection was assessed over the most critical time period of 25 minutes within the first 8 hours, post occlusion using an in utero fetal sheep model. Obtained results demonstrate that the suggested algorithm detected spikes with a considerably high overall performance of 99.25% using the developed RBIO-WT Type-I FLS.

Wavelet Decomposition-Based Analysis of Mismatch Negativity Elicited by a Multi-Feature Paradigm

In this study, event-related potentials (ERPs) collected from normally hearing subjects and elicited by a multi-feature paradigm were investigated, and mismatch negativity (MMN) was detected. Standard stimuli and five types of deviant stimuli were presented in a specified sequence, while EEG data were recorded digitally at a 1024 sec–1 sampling rate. Two wavelet analyses were compared with a traditional difference-wave (DW) method. The Reverse biorthogonal wavelet ot the order of 6.8 and the quadratic B-Spline wavelet were applied for seven-level decomposition. The sixth-level approximation coefficients were appropriate for extracting the MMN from the averaged trace. The results obtained showed that wavelet decomposition (WLD) methods extract MMN as well as a band-pass digital filter (DF). The differences of the MMN peak latency between deviant types elicited by B-Spline WLD were more significant than those extracted by the DW, DF, or Reverse biorthogonal WLD. Also, wavelet coefficients of the delta-theta range indicated good discrimination between some combinations of the deviant types.

Nonlinear dynamic analysis of high speed bearings due to combined localized defects

In this paper, the dynamic behavior of a high-speed rotating shaft under the combined localized defects on outer race, inner race and rolling element has been analyzed. An adaptive algorithm based on wavelet packet decomposition and Hilbert transform is used to extract the bearing fault characteristic component from the vibration signals. The reverse Biorthogonal wavelet 5.5 is considered as the most appropriate wavelet for decomposing the bearing vibration signal, on the basis of the maximum energy-to-Shannon entropy ratio criterion. The imperial trend suggested in diagnosing the combined localized defects of rolling bearing is based on experimental analysis at various rotor speeds. The results are presented in the form of envelope analysis, bifurcation diagrams and Poincaré maps that show the appearance nonlinearity in the dynamic response due to the rotor speed and the presence of combined localized defect. Significant peaks are reported at BPFO, BSF, FTF and modulation of BPFI with rotational frequency in envelope spectrum. Fundamental train frequency also shows noticeable excitation. A modified mathematical model shows good agreement with experimental results.

A reliable iris recognition algorithm based on reverse biorthogonal wavelet transform

This article describes an iris recognition algorithm designed to analyze noisy iris biometric data. The methods forming part of the authentication process were developed and optimized by the authors using visible wavelength images of an eye taken under unconstrained conditions (at a different perspectives, illuminations, occlusion grades, etc.), mainly contained in the UBIRIS.v2 database. The whole iris authentication system was submitted by the authors to the International Iris Recognition Contest NICE.II, where it took eighth place, while the iris segmentation stage itself took second place in the previous contest — NICE.I.This paper is focused on the iris feature extraction stage — the method developed by the authors to analyze noisy iris biometric data. Several techniques used for more efficient and robust analysis of such images and issues concerning the best wavelet selection are also presented in this paper.

Wavelet-Based Reactive Power and Energy Measurement in the Presence of Power Quality Disturbances

This paper investigates the performance of electronic reactive power/energy meters by evaluating differences that arise as a result of (1) the distinct operating principles of the meter and (2) the presence of stationary and nonstationary power quality disturbances. The paper also includes a comparative study to determine the effectiveness of using orthogonal, bi-orthogonal, or reverse bi-orthogonal wavelets for such application. Moreover, the paper identifies the reactive power quantities required for accurate assessment and monitoring of reactive energy in the presence of power quality disturbances. Finally, the paper provides recommendations on reactive power and energy measurement for future generations of smart meters.

Leg Motion Classification with Artificial Neural Networks Using Wavelet-Based Features of Gyroscope Signals

We extract the informative features of gyroscope signals using the discrete wavelet transform (DWT) decomposition and provide them as input to multi-layer feed-forward artificial neural networks (ANNs) for leg motion classification. Since the DWT is based on correlating the analyzed signal with a prototype wavelet function, selection of the wavelet type can influence the performance of wavelet-based applications significantly. We also investigate the effect of selecting different wavelet families on classification accuracy and ANN complexity and provide a comparison between them. The maximum classification accuracy of 97.7% is achieved with the Daubechies wavelet of order 16 and the reverse bi-orthogonal (RBO) wavelet of order 3.1, both with similar ANN complexity. However, the RBO 3.1 wavelet is preferable because of its lower computational complexity in the DWT decomposition and reconstruction.

An empirical analysis to study the cyclical trends on stock exchange using wavelet methods

During the past few decades, there have been many evidences to believe that the stock markets around the world follow cyclical trends. In this paper, we study the cyclical trends using wavelet function based on various time windows on some major stock market indices. We use two methods of Daubechies and reverse bi-orthogonal wavelet methods and determine the optimal values of both methods. The results are used for Tehran stock exchange using the most recent ten years daily information as an empirical study. The details of our analysis on TEDPIX index for the last decade indicate that there are, at least, four trends of weekly, monthly, quarterly and yearly and the cycles would be expected to be repeated in future.

An investigation of speech enhancement using wavelet filtering method

This paper investigates the utilization of wavelet filters via multistage convolution by Reverse Biorthogonal Wavelets (RBW) in high and low pass band frequency parts of speech signal. Speech signal is decomposed into two pass bands of frequency; high and low, and then the noise is removed in each band individually in different stages via wavelet filters. This approach provides better outcomes because it does not cut the speech information, which occurs when utilizing conventional thresholding. We tested the proposed method via several noise probability distribution functions. Subjective evaluation is engaged in conjunction with objective evaluation to accomplish optimal investigation method. The method is simple but has surprise high quality results. The method shows superiority over Donoho and Johnstone thresholding method and Birge-Massart thresholding strategy method.

Continuous wavelet and derivative transform applied to the overlapping spectra for the quantitative spectrophotometric multi-resolution of triamterene and hydrochlorothiazide in triamterene-H tablets

In present paper, continuous wavelet transform as well as first and second derivative spectrophotometry methods are proposed for simultaneous quantitative analysis of triamterene and hydrochlorothiazide without the time-consuming extraction step. In the wavelet transform method, different wavelet families were tested and subsequently coiflets wavelet family (coif1) and reverse biorthogonal wavelet family (rbio 2.8) were selected and applied under the optimal conditions for simultaneous analysis. The two above mentioned methods are based upon use of the continuous wavelet and derivative transforms of the spectra and measurement at zero-crossing wavelengths. Validation of the developed methods was confirmed by analyzing various synthetic mixtures of the investigated drugs. The experimental results obtained from the continuous wavelet transform approach were statistically compared with those yielded by derivative spectrophotometry methods and therefore led to successful results.

New image-quality measure based on wavelets

We present an innovative approach to the objective quality evaluation that could be computed using the mean difference between the original and tested images in different wavelet subbands. Discrete wavelet transform (DWT) subband decomposition properties are similar to human visual system characteristics facilitating integration of DWT into image-quality evaluation. DWT decomposition is done with multiresolution analysis of a signal that allows us to decompose a signal into approximation and detail subbands. DWT coefficients were computed using reverse biorthogonal spline wavelet filter banks. Wavelet coefficients are used to compute new image-quality measure (IQM). IQM is defined as perceptual weighted difference between coefficients of original and degraded image.

Application of continuous wavelet transform in vibration based damage detection method for beams and plates

In this paper a method for estimating the damage location in beam and plate structures is presented. A Plexiglas cantilever beam and a steel plate with four fixed boundary conditions are tested experimentally. The estimated mode shapes of the beam are analysed by the one-dimensional continuous wavelet transform. The formulation of the two-dimensional continuous wavelet transform for plate damage detection is presented. The location of the damage is indicated by a peak in the spatial variation of the transformed response. Applications of Gaussian wavelet for one-dimensional problems and reverse biorthogonal wavelet for two-dimensional structures are presented. The proposed wavelet analysis can effectively identify the defect position without knowledge of neither the structure characteristics nor its mathematical model.