Abstract: The X-ray technology has been used in non-destructive evaluation in the Power System, in which a visual non-destructive inspection method for the electrical equipment is provided. However, lots of noise is existed in the images that are got from the X-ray digital images equipment. Therefore, the auto defect detection which based on these images will be very difficult to proceed. A theory on X-ray image de-noising algorithm based on wavelet transform is proposed in this paper. Then the edge detection algorithm is used so that the defect can be pushed out. The result of experiment shows that the method which utilized by this paper is very useful for de-noising on the X-ray images.
Abstract: Effectiveness of Artificial Neural Networks (ANN)
and Support Vector Machines (SVM) classifiers for fault diagnosis of
rolling element bearings are presented in this paper. The
characteristic features of vibration signals of rotating driveline that
was run in its normal condition and with faults introduced were used
as input to ANN and SVM classifiers. Simple statistical features such
as standard deviation, skewness, kurtosis etc. of the time-domain
vibration signal segments along with peaks of the signal and peak of
power spectral density (PSD) are used as features to input the ANN
and SVM classifier. The effect of preprocessing of the vibration
signal by Discreet Wavelet Transform (DWT) prior to feature
extraction is also studied. It is shown from the experimental results
that the performance of SVM classifier in identification of bearing
condition is better then ANN and pre-processing of vibration signal
by DWT enhances the effectiveness of both ANN and SVM classifier
Abstract: –In this paper the damage in clamped-free, clampedclamped and free-free beam are analyzed considering samples
without and with structural modifications. The damage location is
investigated by the use of the bispectrum and wavelet analysis. The
mathematical models are obtained using 2D elasticity theory and the
Finite Element Method (FEM). The numerical and experimental data
are approximated using the Particle Swarm Optimizer (PSO) method
and this way is possible to adjust the localization and the severity of
the damage. The experimental data are obtained through
accelerometers placed along the sample. The system is excited using
impact hammer.
Abstract: An emotional speech recognition system for the
applications on smart phones was proposed in this study to combine
with 3G mobile communications and social networks to provide users
and their groups with more interaction and care. This study developed
a mechanism using the support vector machines (SVM) to recognize
the emotions of speech such as happiness, anger, sadness and normal.
The mechanism uses a hierarchical classifier to adjust the weights of
acoustic features and divides various parameters into the categories of
energy and frequency for training. In this study, 28 commonly used
acoustic features including pitch and volume were proposed for
training. In addition, a time-frequency parameter obtained by
continuous wavelet transforms was also used to identify the accent and
intonation in a sentence during the recognition process. The Berlin
Database of Emotional Speech was used by dividing the speech into
male and female data sets for training. According to the experimental
results, the accuracies of male and female test sets were increased by
4.6% and 5.2% respectively after using the time-frequency parameter
for classifying happy and angry emotions. For the classification of all
emotions, the average accuracy, including male and female data, was
63.5% for the test set and 90.9% for the whole data set.
Abstract: We describe a novel method for removing noise (in wavelet domain) of unknown variance from microarrays. The method is based on a smoothing of the coefficients of the highest subbands. Specifically, we decompose the noisy microarray into wavelet subbands, apply smoothing within each highest subband, and reconstruct a microarray from the modified wavelet coefficients. This process is applied a single time, and exclusively to the first level of decomposition, i.e., in most of the cases, it is not necessary a multirresoltuion analysis. Denoising results compare favorably to the most of methods in use at the moment.
Abstract: Sleep stage scoring is the process of classifying the
stage of the sleep in which the subject is in. Sleep is classified into
two states based on the constellation of physiological parameters.
The two states are the non-rapid eye movement (NREM) and the
rapid eye movement (REM). The NREM sleep is also classified into
four stages (1-4). These states and the state wakefulness are
distinguished from each other based on the brain activity. In this
work, a classification method for automated sleep stage scoring
based on a single EEG recording using wavelet packet decomposition
was implemented. Thirty two ploysomnographic recording from the
MIT-BIH database were used for training and validation of the
proposed method. A single EEG recording was extracted and
smoothed using Savitzky-Golay filter. Wavelet packets
decomposition up to the fourth level based on 20th order Daubechies
filter was used to extract features from the EEG signal. A features
vector of 54 features was formed. It was reduced to a size of 25 using
the gain ratio method and fed into a classifier of regression trees. The
regression trees were trained using 67% of the records available. The
records for training were selected based on cross validation of the
records. The remaining of the records was used for testing the
classifier. The overall correct rate of the proposed method was found
to be around 75%, which is acceptable compared to the techniques in
the literature.
Abstract: Wavelet neural networks (WNNs) have emerged as a vital alternative to the vastly studied multilayer perceptrons (MLPs) since its first implementation. In this paper, we applied various clustering algorithms, namely, K-means (KM), Fuzzy C-means (FCM), symmetry-based K-means (SBKM), symmetry-based Fuzzy C-means (SBFCM) and modified point symmetry-based K-means (MPKM) clustering algorithms in choosing the translation parameter of a WNN. These modified WNNs are further applied to the heterogeneous cancer classification using benchmark microarray data and were compared against the conventional WNN with random initialization method. Experimental results showed that a WNN classifier with the MPKM algorithm is more precise than the conventional WNN as well as the WNNs with other clustering algorithms.
Abstract: Symbolic Circuit Analysis (SCA) is a technique used
to generate the symbolic expression of a network. It has become a
well-established technique in circuit analysis and design. The
symbolic expression of networks offers excellent way to perform
frequency response analysis, sensitivity computation, stability
measurements, performance optimization, and fault diagnosis. Many
approaches have been proposed in the area of SCA offering different
features and capabilities. Numerical Interpolation methods are very
common in this context, especially by using the Fast Fourier
Transform (FFT). The aim of this paper is to present a method for
SCA that depends on the use of Wavelet Transform (WT) as a
mathematical tool to generate the symbolic expression for large
circuits with minimizing the analysis time by reducing the number of
computations.
Abstract: The recent growth of using multimedia transmission
over wireless communication systems, have challenges to protect the
data from lost due to wireless channel effect. Images are corrupted
due to the noise and fading when transmitted over wireless channel,
in wireless channel the image is transmitted block by block, Due to
severe fading, entire image blocks can be damaged. The aim of this
paper comes out from need to enhance the digital images at the
wireless receiver side. Proposed Boundary Interpolation (BI)
Algorithm using wavelet, have been adapted here used to
reconstruction the lost block in the image at the receiver depend on
the correlation between the lost block and its neighbors. New
Proposed technique by using Boundary Interpolation (BI) Algorithm
using wavelet with Pixel interleaver has been implemented. Pixel
interleaver work on distribute the pixel to new pixel position of
original image before transmitting the image. The block lost through
wireless channel is only effects individual pixel. The lost pixels at the
receiver side can be recovered by using Boundary Interpolation (BI)
Algorithm using wavelet. The results showed that the New proposed
algorithm boundary interpolation (BI) using wavelet with pixel
interleaver is better in term of MSE and PSNR.
Abstract: In this paper we present a novel approach for wavelet compression of electrocardiogram (ECG) signals based on the set partitioning in hierarchical trees (SPIHT) coding algorithm. SPIHT algorithm has achieved prominent success in image compression. Here we use a modified version of SPIHT for one dimensional signals. We applied wavelet transform with SPIHT coding algorithm on different records of MIT-BIH database. The results show the high efficiency of this method in ECG compression.
Abstract: Due to some reasons, observed images are degraded which are mainly caused by noise. Recently image denoising using the wavelet transform has been attracting much attention. Waveletbased approach provides a particularly useful method for image denoising when the preservation of edges in the scene is of importance because the local adaptivity is based explicitly on the values of the wavelet detail coefficients. In this paper, we propose several methods of noise removal from degraded images with Gaussian noise by using adaptive wavelet threshold (Bayes Shrink, Modified Bayes Shrink and Normal Shrink). The proposed thresholds are simple and adaptive to each subband because the parameters required for estimating the threshold depend on subband data. Experimental results show that the proposed thresholds remove noise significantly and preserve the edges in the scene.
Abstract: In this paper we propose a Multiple Description Image Coding(MDIC) scheme to generate two compressed and balanced rates descriptions in the wavelet domain (Daubechies biorthogonal (9, 7) wavelet) using pairwise correlating transform optimal and application method for Generalized Multiple Description Coding (GMDC) to image coding in the wavelet domain. The GMDC produces statistically correlated streams such that lost streams can be estimated from the received data. Our performance test shown that the proposed method gives more improvement and good quality of the reconstructed image when the wavelet coefficients are normalized by Gaussian Scale Mixture (GSM) model then the Gaussian one ,.
Abstract: In this paper, an innovative watermarking scheme for audio signal based on genetic algorithms (GA) in the discrete wavelet transforms is proposed. It is robust against watermarking attacks, which are commonly employed in literature. In addition, the watermarked image quality is also considered. We employ GA for the optimal localization and intensity of watermark. The watermark detection process can be performed without using the original audio signal. The experimental results demonstrate that watermark is inaudible and robust to many digital signal processing, such as cropping, low pass filter, additive noise.
Abstract: Design of Converter transformer insulation is a major
challenge. The insulation of these transformers is stressed by both
AC and DC voltages. Particle contamination is one of the major
problems in insulation structures, as they generate partial discharges
leading it to major failure of insulation. Similarly corona discharges
occur in transformer insulation. This partial discharge due to particle
movement / corona formation in insulation structure under different
voltage wave shapes, are different. In the present study, UHF
technique is adopted to understand the discharge activity and could
be realized that the characteristics of UHF signal generated under
low and high fields are different. In the case of corona generated
signal, the frequency content of the UHF sensor output lies in the
range 0.3-1.2 GHz and is not much varied except for its increase in
magnitude of discharge with the increase in applied voltage. It is
realized that the current signal injected due to partial
discharges/corona is about 4ns duration measured for first one half
cycle. Wavelet technique is adopted in the present study. It allows
one to identify the frequency content present in the signal at different
instant of time. The STD-MRA analysis helps one to identify the
frequency band in which the energy content of the UHF signal is
maximum.
Abstract: Multimedia security is an incredibly significant area
of concern. A number of papers on robust digital watermarking have
been presented, but there are no standards that have been defined so
far. Thus multimedia security is still a posing problem. The aim of
this paper is to design a robust image-watermarking scheme, which
can withstand a different set of attacks. The proposed scheme
provides a robust solution integrating image moment normalization,
content dependent watermark and discrete wavelet transformation.
Moment normalization is useful to recover the watermark even in
case of geometrical attacks. Content dependent watermarks are a
powerful means of authentication as the data is watermarked with its
own features. Discrete wavelet transforms have been used as they
describe image features in a better manner. The proposed scheme
finds its place in validating identification cards and financial
instruments.
Abstract: In this paper, a new secure watermarking scheme for
color image is proposed. It splits the watermark into two shares using
(2, 2)- threshold Visual Cryptography Scheme (V CS) with Adaptive
Order Dithering technique and embeds one share into high textured
subband of Luminance channel of the color image. The other share
is used as the key and is available only with the super-user or the
author of the image. In this scheme only the super-user can reveal
the original watermark. The proposed scheme is dynamic in the sense
that to maintain the perceptual similarity between the original and the
watermarked image the selected subband coefficients are modified
by varying the watermark scaling factor. The experimental results
demonstrate the effectiveness of the proposed scheme. Further, the
proposed scheme is able to resist all common attacks even with strong
amplitude.
Abstract: The processing of the electrocardiogram (ECG) signal consists essentially in the detection of the characteristic points of
signal which are an important tool in the diagnosis of heart diseases. The most suitable are the detection of R waves. In this paper, we
present various mathematical tools used for filtering ECG using digital filtering and Discreet Wavelet Transform (DWT) filtering. In
addition, this paper will include two main R peak detection methods
by applying a windowing process: The first method is based on calculations derived, the second is a time-frequency method based on
Dyadic Wavelet Transform DyWT.
Abstract: A multi-agent system is developed here to predict
monthly details of the upcoming peak of the 24th solar magnetic
cycle. While studies typically predict the timing and magnitude of
cycle peaks using annual data, this one utilizes the unsmoothed
monthly sunspot number instead. Monthly numbers display more
pronounced fluctuations during periods of strong solar magnetic
activity than the annual sunspot numbers. Because strong magnetic
activities may cause significant economic damages, predicting
monthly variations should provide different and perhaps helpful
information for decision-making purposes. The multi-agent system
developed here operates in two stages. In the first, it produces twelve
predictions of the monthly numbers. In the second, it uses those
predictions to deliver a final forecast. Acting as expert agents, genetic
programming and neural networks produce the twelve fits and
forecasts as well as the final forecast. According to the results
obtained, the next peak is predicted to be 156 and is expected to
occur in October 2011- with an average of 136 for that year.
Abstract: Monitoring lightning electromagnetic pulses (sferics)
and other terrestrial as well as extraterrestrial transient radiation signals
is of considerable interest for practical and theoretical purposes
in astro- and geophysics as well as meteorology. Managing a continuous
flow of data, automisation of the detection and classification
process is important. Features based on a combination of wavelet and
statistical methods proved efficient for analysis and characterisation
of transients and as input into a radial basis function network that is
trained to discriminate transients from pulse like to wave like.
Abstract: The information revealed by derivatives can help to
better characterize digital near-end crosstalk signatures with the
ultimate goal of identifying the specific aggressor signal.
Unfortunately, derivatives tend to be very sensitive to even low
levels of noise. In this work we approximated the derivatives of both
quiet and noisy digital signals using a wavelet-based technique. The
results are presented for Gaussian digital edges, IBIS Model digital
edges, and digital edges in oscilloscope data captured from an actual
printed circuit board. Tradeoffs between accuracy and noise
immunity are presented. The results show that the wavelet technique
can produce first derivative approximations that are accurate to
within 5% or better, even under noisy conditions. The wavelet
technique can be used to calculate the derivative of a digital signal
edge when conventional methods fail.