Abstract: The prediction of transmembrane helical segments
(TMHs) in membrane proteins is an important field in the
bioinformatics research. In this paper, a new method based on discrete
wavelet transform (DWT) has been developed to predict the number
and location of TMHs in membrane proteins. PDB coded as 1KQG
was chosen as an example to describe the prediction of the number and
location of TMHs in membrane proteins by using this method. To
access the effect of the method, 80 proteins with known 3D-structure
from Mptopo database are chosen at random as the test objects
(including 325 TMHs), 308 of which can be predicted accurately, the
average predicted accuracy is 96.3%. In addition, the above 80
membrane proteins are divided into 13 groups according to their
function and type. In particular, the results of the prediction of TMHs
of the 13 groups are satisfying.
Abstract: Medical image modalities such as computed
tomography (CT), magnetic resonance imaging (MRI), ultrasound
(US), X-ray are adapted to diagnose disease. These modalities
provide flexible means of reviewing anatomical cross-sections and
physiological state in different parts of the human body. The raw
medical images have a huge file size and need large storage
requirements. So it should be such a way to reduce the size of those
image files to be valid for telemedicine applications. Thus the image
compression is a key factor to reduce the bit rate for transmission or
storage while maintaining an acceptable reproduction quality, but it is
natural to rise the question of how much an image can be compressed
and still preserve sufficient information for a given clinical
application. Many techniques for achieving data compression have
been introduced. In this study, three different MRI modalities which
are Brain, Spine and Knee have been compressed and reconstructed
using wavelet transform. Subjective and objective evaluation has
been done to investigate the clinical information quality of the
compressed images. For the objective evaluation, the results show
that the PSNR which indicates the quality of the reconstructed image
is ranging from (21.95 dB to 30.80 dB, 27.25 dB to 35.75 dB, and
26.93 dB to 34.93 dB) for Brain, Spine, and Knee respectively. For
the subjective evaluation test, the results show that the compression
ratio of 40:1 was acceptable for brain image, whereas for spine and
knee images 50:1 was acceptable.
Abstract: This paper studies the effect of different compression
constraints and schemes presented in a new and flexible paradigm to
achieve high compression ratios and acceptable signal to noise ratios
of Arabic speech signals. Compression parameters are computed for
variable frame sizes of a level 5 to 7 Discrete Wavelet Transform
(DWT) representation of the signals for different analyzing mother
wavelet functions. Results are obtained and compared for Global
threshold and level dependent threshold techniques. The results
obtained also include comparisons with Signal to Noise Ratios, Peak
Signal to Noise Ratios and Normalized Root Mean Square Error.
Abstract: The wavelet transform is one of the most important
method used in signal processing. In this study, we have introduced
frequency-energy characteristics of local earthquakes using discrete
wavelet transform. Frequency-energy characteristic was analyzed
depend on difference between P and S wave arrival time and noise
within records. We have found that local earthquakes have similar
characteristics. If frequency-energy characteristics can be found
accurately, this gives us a hint to calculate P and S wave arrival time.
It can be seen that wavelet transform provides successful
approximation for this. In this study, 100 earthquakes with 500
records were analyzed approximately.
Abstract: In this paper a novel method for finding the fault zone
on a Thyristor Controlled Series Capacitor (TCSC) incorporated
transmission line is presented. The method makes use of the Support
Vector Machine (SVM), used in the classification mode to
distinguish between the zones, before or after the TCSC. The use of
Discrete Wavelet Transform is made to prepare the features which
would be given as the input to the SVM. This method was tested on a
400 kV, 50 Hz, 300 Km transmission line and the results were highly
accurate.
Abstract: Water pipe network is installed underground and once equipped, it is difficult to recognize the state of pipes when the leak or burst happens. Accordingly, post management is often delayed
after the fault occurs. Therefore, the systematic fault management system of water pipe network is required to prevent the accident and
minimize the loss. In this work, we develop online fault detection system of water pipe network using data of pipes such as flow rate
or pressure. The transient model describing water flow in pipelines
is presented and simulated using MATLAB. The fault situations such
as the leak or burst can be also simulated and flow rate or pressure data when the fault happens are collected. Faults are detected using
statistical methods of fast Fourier transform and discrete wavelet transform, and they are compared to find which method shows the
better fault detection performance.
Abstract: In this work, the primary compressive strength
components of human femur trabecular bone are qualitatively
assessed using image processing and wavelet analysis. The Primary
Compressive (PC) component in planar radiographic femur trabecular
images (N=50) is delineated by semi-automatic image processing
procedure. Auto threshold binarization algorithm is employed to
recognize the presence of mineralization in the digitized images. The
qualitative parameters such as apparent mineralization and total area
associated with the PC region are derived for normal and abnormal
images.The two-dimensional discrete wavelet transforms are utilized
to obtain appropriate features that quantify texture changes in medical
images .The normal and abnormal samples of the human femur are
comprehensively analyzed using Harr wavelet.The six statistical
parameters such as mean, median, mode, standard deviation, mean
absolute deviation and median absolute deviation are derived at level
4 decomposition for both approximation and horizontal wavelet
coefficients. The correlation coefficient of various wavelet derived
parameters with normal and abnormal for both approximated and
horizontal coefficients are estimated. It is seen that in almost all cases
the abnormal show higher degree of correlation than normals. Further
the parameters derived from approximation coefficient show more
correlation than those derived from the horizontal coefficients. The
parameters mean and median computed at the output of level 4 Harr
wavelet channel was found to be a useful predictor to delineate the
normal and the abnormal groups.
Abstract: The clinical usefulness of heart rate variability is
limited to the range of Holter monitoring software available. These
software algorithms require a normal sinus rhythm to accurately
acquire heart rate variability (HRV) measures in the frequency
domain. Premature ventricular contractions (PVC) or more
commonly referred to as ectopic beats, frequent in heart failure,
hinder this analysis and introduce ambiguity. This investigation
demonstrates an algorithm to automatically detect ectopic beats by
analyzing discrete wavelet transform coefficients. Two techniques
for filtering and replacing the ectopic beats from the RR signal are
compared. One technique applies wavelet hard thresholding
techniques and another applies linear interpolation to replace ectopic
cycles. The results demonstrate through simulation, and signals
acquired from a 24hr ambulatory recorder, that these techniques can
accurately detect PVC-s and remove the noise and leakage effects
produced by ectopic cycles retaining smooth spectra with the
minimum of error.
Abstract: We describe a novel method for removing noise (in wavelet domain) of unknown variance from microarrays. The method is based on the following procedure: We apply 1) Bidimentional Discrete Wavelet Transform (DWT-2D) to the Noisy Microarray, 2) scaling and rounding to the coefficients of the highest subbands (to obtain integer and positive coefficients), 3) bit-slicing to the new highest subbands (to obtain bit-planes), 4) then we apply the Systholic Boolean Orthonormalizer Network (SBON) to the input bit-plane set and we obtain two orthonormal otput bit-plane sets (in a Boolean sense), we project a set on the other one, by means of an AND operation, and then, 5) we apply re-assembling, and, 6) rescaling. Finally, 7) we apply Inverse DWT-2D and reconstruct a microarray from the modified wavelet coefficients. Denoising results compare favorably to the most of methods in use at the moment.
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: 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: 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: 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: In this paper, we propose a fully-utilized, block-based 2D DWT (discrete wavelet transform) architecture, which consists of four 1D DWT filters with two-channel QMF lattice structure. The proposed architecture requires about 2MN-3N registers to save the intermediate results for higher level decomposition, where M and N stand for the filter length and the row width of the image respectively. Furthermore, the proposed 2D DWT processes in horizontal and vertical directions simultaneously without an idle period, so that it computes the DWT for an N×N image in a period of N2(1-2-2J)/3. Compared to the existing approaches, the proposed architecture shows 100% of hardware utilization and high throughput rates. To mitigate the long critical path delay due to the cascaded lattices, we can apply the pipeline technique with four stages, while retaining 100% of hardware utilization. The proposed architecture can be applied in real-time video signal processing.
Abstract: In this paper, a watermarking algorithm that uses the wavelet transform with Multiple Description Coding (MDC) and Quantization Index Modulation (QIM) concepts is introduced. Also, the paper investigates the role of Contourlet Transform (CT) versus Wavelet Transform (WT) in providing robust image watermarking. Two measures are utilized in the comparison between the waveletbased and the contourlet-based methods; Peak Signal to Noise Ratio (PSNR) and Normalized Cross-Correlation (NCC). Experimental results reveal that the introduced algorithm is robust against different attacks and has good results compared to the contourlet-based algorithm.
Abstract: In 1990 [1] the subband-DFT (SB-DFT) technique was proposed. This technique used the Hadamard filters in the decomposition step to split the input sequence into low- and highpass sequences. In the next step, either two DFTs are needed on both bands to compute the full-band DFT or one DFT on one of the two bands to compute an approximate DFT. A combination network with correction factors was to be applied after the DFTs. Another approach was proposed in 1997 [2] for using a special discrete wavelet transform (DWT) to compute the discrete Fourier transform (DFT). In the first step of the algorithm, the input sequence is decomposed in a similar manner to the SB-DFT into two sequences using wavelet decomposition with Haar filters. The second step is to perform DFTs on both bands to obtain the full-band DFT or to obtain a fast approximate DFT by implementing pruning at both input and output sides. In this paper, the wavelet-based DFT (W-DFT) with Haar filters is interpreted as SB-DFT with Hadamard filters. The only difference is in a constant factor in the combination network. This result is very important to complete the analysis of the W-DFT, since all the results concerning the accuracy and approximation errors in the SB-DFT are applicable. An application example in spectral analysis is given for both SB-DFT and W-DFT (with different filters). The adaptive capability of the SB-DFT is included in the W-DFT algorithm to select the band of most energy as the band to be computed. Finally, the W-DFT is extended to the two-dimensional case. An application in image transformation is given using two different types of wavelet filters.
Abstract: In this paper, the implementation of low power,
high throughput convolutional filters for the one dimensional
Discrete Wavelet Transform and its inverse are presented. The
analysis filters have already been used for the implementation of a
high performance DWT encoder [15] with minimum memory
requirements for the JPEG 2000 standard. This paper presents the
design techniques and the implementation of the convolutional filters
included in the JPEG2000 standard for the forward and inverse DWT
for achieving low-power operation, high performance and reduced
memory accesses. Moreover, they have the ability of performing
progressive computations so as to minimize the buffering between
the decomposition and reconstruction phases. The experimental
results illustrate the filters- low power high throughput characteristics
as well as their memory efficient operation.
Abstract: This paper presents a VLSI design approach of a highspeed
and real-time 2-D Discrete Wavelet Transform computing. The
proposed architecture, based on new and fast convolution approach,
reduces the hardware complexity in addition to reduce the critical
path to the multiplier delay. Furthermore, an advanced twodimensional
(2-D) discrete wavelet transform (DWT)
implementation, with an efficient memory area, is designed to
produce one output in every clock cycle. As a result, a very highspeed
is attained. The system is verified, using JPEG2000
coefficients filters, on Xilinx Virtex-II Field Programmable Gate
Array (FPGA) device without accessing any external memory. The
resulting computing rate is up to 270 M samples/s and the (9,7) 2-D
wavelet filter uses only 18 kb of memory (16 kb of first-in-first-out
memory) with 256×256 image size. In this way, the developed design
requests reduced memory and provide very high-speed processing as
well as high PSNR quality.
Abstract: This paper proposes a novel feature extraction method,
based on Discrete Wavelet Transform (DWT) and K-L Seperability
(KLS), for the classification of Functional Data (FD). This method
combines the decorrelation and reduction property of DWT and the
additive independence property of KLS, which is helpful to extraction
classification features of FD. It is an advanced approach of the
popular wavelet based shrinkage method for functional data reduction
and classification. A theory analysis is given in the paper to prove the
consistent convergence property, and a simulation study is also done
to compare the proposed method with the former shrinkage ones. The
experiment results show that this method has advantages in improving
classification efficiency, precision and robustness.
Abstract: The scientific community has invested a great deal of effort in the fields of discrete wavelet transform in the last few decades. Discrete wavelet transform (DWT) associated with the vector quantization has been proved to be a very useful tool for the compression of image. However, the DWT is very computationally intensive process requiring innovative and computationally efficient method to obtain the image compression. The concurrent transformation of the image can be an important solution to this problem. This paper proposes a model of concurrent DWT for image compression. Additionally, the formal verification of the model has also been performed. Here the Symbolic Model Verifier (SMV) has been used as the formal verification tool. The system has been modeled in SMV and some properties have been verified formally.