=Paper=
{{Paper
|id=Vol-3149/short4
|storemode=property
|title=Intelligent Classification Enhancement using Siny-Hard Wavelet Thresholding (short paper)
|pdfUrl=https://ceur-ws.org/Vol-3149/short4.pdf
|volume=Vol-3149
|authors=Ibraheem H. M. Al-Dosari,Viktor Sykhomlyn,Alexander Selyukov
|dblpUrl=https://dblp.org/rec/conf/ttsiit/Al-DosariSS22
}}
==Intelligent Classification Enhancement using Siny-Hard Wavelet Thresholding (short paper)==
https://ceur-ws.org/Vol-3149/short4.pdf
Intelligent Classification Enhancement
using Siny-Hard Wavelet Thresholding
Ibraheem H. M. Al-Dosaria, Viktor Sykhomlynb, and Alexander Selykovc
1
Al-Rafidain University College, computer communications engineering department, Baghdad, Iraq
2
Vice-Rector forUkrainian State Employment Service Training Institute (USESTI), Ukraine
3
Kyiv National University of Construction and Architecture, Povitriflotskyi ave., 31, 03037, Kyiv,
Ukraine
Abstract
Many signal transmission over communications system face an inherent noise attack
the transmitted signal and cause the degradation in the signal quality at the receiver
end. One of the popular techniques to overcome this noise attack is to make a
prepossessing for the noisy signal before transmission over the channel. The aim of
the work is to use Wavelet based signal denoising method for noise removal and
enhance the intelligent classification results. In this work a new proposed wavelet
thresholding method is formulated and implemented for signal enhancement. The
proposed method is compared with classical method using different performance
indices such as NMSE (normalized mean square error) and ESNR (enhancement in
signal to noise ratio). The results for new proposed method shows outperforming 10%
in ESNR and 5 % in NMSE when using symlet8 wavelet mother function with5
decomposing levels. The conducted results have confirmed the success for the new
proposed wavelet thresholding method in signal denoising, this enhancement in
processed signal will improve the signal quality at the receiving end and increasing
signal to noise ratio enhancement for the overall communication system.
Keywords 1
Hardy-sine thresholding, wavelet, denoising, classification.
1. Introduction
In any existing measurement system in real life there is always an inherent noise emerge
which is non-stationary in nature. It is necessary to suppress this unrequired noise using some
of the effective denoising methods. Wavelet analysis can be recognized as a multi-resolution
tool that is used for removing noise from the desired signal and hence denoising the non-
stationary signal successfully.
Sometimes statistical computations are required to choose the suitable wavelet analysis for
the denoising process, because there are many types of noise attacking the signal. So it is better
to correlate the signal with the utilized wavelet in order to isolate most of the signal energy in
the passband and filtering only the band where the expecting noise exists [1].
Wavelet denoising process can be summarized by some steps in the following sequence,
initially transforming the noisy signal from time domain to the wavelet domain, where this
mapping step will redistribute the energy of the noisy signal in a suitable manner that is
distinguish easily between the wavelet coefficient that almost represent the signal part which is
call approximation coefficient.
On the other hand, the wavelet coefficients that almost represent the noise part which is
called the details coefficients. The second step for denoising procedure involved by
Emerging Technology Trends on the Smart Industry and the Internet of Things, January 19, 2022, Kyiv, Ukraine
EMAIL: ibraheemdoser77@gmail.com (I. H. M. Al-Dosari); suhomlin63@ukr.net (V. Sykhomlyn); selukov@3g.ua (A. Selyukov)
ORCID: 0000-0002-7362-7870 (I. H. M. Al-Dosari); 0000-0001-9558-1968 (V. Sykhomlyn); 0000-0001-7979-3434 (A.
Selyukov)
Β©οΈ 2022 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
141
thresholding process using suitable threshold to remove some part from the noise part amplitude
fairly, finally, reconstruction are made for the thresholded details coefficients with the
approximations coefficients to get a denoised signal with minimum error between the denoised
and original signals [2].
Wavelets analysis has many applications in signal and image processing fields, denoising
and compression processes play an important role in the wavelet analysis applications. Some
of the recent researches try to propose new thresholding techniques for wavelet denoising in
order to enhance the signal to noise ratio in the output of the wavelet de-noisier [3].
Some other researches try to optimize the threshold selection to reduce the noise effect; the
suitable value of the threshold should be chosen in trade way between high and low values. The
high selected threshold will permit some noise to be passed with the required signal and hence
keeping the noise unresolved, while low value threshold will suppress some of the original
signal and considers it as a noise part. Therefore choosing the threshold based on the noise
power estimation is the successful way to balance between the two scenarios [4].
The object of study is the utilization for the artificial neural network in classification
problem to diagnose the size of the leak in fluid pipeline system, the process involved an
application for wavelet based denoising method to enhance the signal under study which is used
as an input to the intelligent classifier.
The subject of study is the wavelet thresholding methods [5β11] and a comparison between
traditional method and the new proposed method using some popular performance indices.
The purpose of the work is to improve the intelligent classification results for the overall
system by enhancing the transmitted signal through the communications system using wavelet
based denoising method with new proposed thresholding technique known as Siny-hard
thresholding.
1. Problem Statement
For any communication system, a transmitted signal travels though medium and attacked
by some inherent noise. So it is required to remove this noise and improve the signal quality in
order to get good results when using this denoised signal. The denoised signal will be used for
intelligent leak classification for a fluid pipeline system.
2. Review of the Literature
Some of the recent year's related work in developing the wavelet thresholding methods can
be summarized as follows, a new adaptive thresholding method was proposed by researchers
in order to enhance the denoising method as compared to the global thresholding method
depending on the truth for noise degradation in the details coefficients for decomposition of
wavelet transform [5]. In 2018 other researchers proposed a new thresholding function by
modifying the classical soft and hard thresholding functions, in this proposal the researcher add
two parameters for controlling the thresholding processes and make it more adaptive with the
input signal [6]. Another researcher utilize the concept of logarithm in order to enhance the
image by removing the noise corrupted it [7]. The process of image denoising based on
logarithmic thresholding outperformed the classical thresholding by about 35% when
augmented with proper threshold selection rule for many types of noise such as speckle, pepper,
and Gaussian [8]. A mixed thresholding was proposed for denoising signal and image denoising
using Matlab simulation tool with many performance indices such as signal to noise ratio
enhancement and minimization mean square error [9].
The denoising process is augmented with hybrid threshold selection rule , such that better
results was achieved for hard thresholding than traditional methods [10].
A modified thresholding called improved thresholding was proposed based on classification
for low and high scale wavelet coefficients as two state mixed model for Gaussian distribution,
also the new thresholding method utilized the combination for soft and hard thresholding and
get more advantage against the traditional methods [11].
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3. Material and Methods
In any comparison process it is nessacary to use some suitable performance measure to
examine the new proposed method against the existing methods. In this work , the paper adopt
some of the popular performance indices In order to evaluate the performance for the new
proposed thresholding method, difference between signal to noise ratios at the input and output
of the denoising process is one of the performance measure for the quality of the new proposed
thresholding [12].
This performance index always calculated based on dB value, another performance measure
is the mean square error with its different form such as average error or root mean square error.
Table 1 shows some of the used performance measure in this work.
In order to get a good analysis results in the wavelet tool, it is better to choose the right
wavelet mother function for decomposing the signal under denoising process [13].
Choosing for the suitable wavelet depends on many aspects such as entropy, cumulative
energy concentrated in the wavelet approximations coefficients, or the maximum correlation
between the signals under test with the wavelet mother function [14]. Table (2) demonstrated
the correlation between the noisy signal and some proposed wavelet functions.
4. Experiments
In any communication system it is required to evaluate the signal transmission quality in
order to ensure the reception for the data in the receiver end. So if the signal is attacked by some
noise or disturbance in the transmission path, it is required to evaluate the signal degradation
using some performance measure and try to enhance the signal quality based on signal to noise
ration index, such that the an acceptable signal quality reached to the receiver with a good
overall signal to noise ratio. [15-16]
In this work a new wavelet thresholding method is proposed as shown in figure (1) in order
to enhance the performance for signal transmission in the communication system. The proposed
method is considered as a modification for the traditional soft and hard thresholding methods
after augmenting the later methods by a sinusoidal function in the passband region yielding a
new hardy-sine and softy-sine thresholding functions.in addition to that, a two controlling
parameters are inserted in the proposed function to control the amplitude and frequency for the
ripples in the passband.
The proposed thresholding functions for the wavelet based denoising method are clarified
by mathematical models known as siny-soft and siny-hard thresholding as follows:
a) Hard thresholding
ππ |ππ | β₯ π
ππ = { (1)
0 |ππ | < π
b) Soft thresholding
[sign (Wj )(|Wj | β Ξ»)] |Wj | β₯ Ξ»
Q j ={ (2)
0 |Wj | < Ξ»
c) Siny-Hardy thresholding
Wj + asin(bΟWj ) |Wj | > Ξ»
Qj= { (3)
0 |Wj | β€ Ξ»
Where; Q j Is an output signal from wavelet thresholding at level j
Wj Is an input signal to wavelet thresholding at level j, Ξ» Is a threshold
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5. Results
A pressure signal is generated from a sensor mounted on the water pipeline of 2'' diameter and it is
required to classify the artificial leak type among different values (0.25'', 0.5'', 0.75'', or 1'') which was
made by artificial valve fixed on the pipeline.
So in order to improve the classification process a preprocessing is essential to enhance the signal
quality and remove the inherent noise by sensor and other nearby devices using new proposed wavelet
thresholding method as shown in figure (1).
The classification is implemented using neural network trained using Matlab simulation tool as
shown in figure (2) , and the result for leak detection of size 0.25'' is demonstrated at figure(3)
6. Discussion
Referring to Table 2 a cross correlation coefficient between some wavelet mother function and the
noisy signal was computed in order to choose the suitable wavelet function for further analysis in
decomposition and reconstruction processes. The results show that sym8 is the wavelet function with
highest correlation coefficient as compared with other used functions.
So the denoising process used sym8 wavelet function with different decomposition level and the
performance is evaluated using 4 performance indices as shown in Table 3.
As the NMSE, RMSE,and PDR decreased or in other statement as ESNR increased , this indicates
the better performance for the denoising process, so table 3 shows the performance evaluation for
denoising process for both hard and hardy-sine (or siny β hard) thresholding. From the appeared results
it was clear that hardy-sine was almost better than hard thresholding and level 5 is the best
decomposition level among the used levels and there is about 10% enhancement in the ESNR value for
the hardy-sine over the traditional hard thresholding.
7. Conclusions
The scientific novelty of the obtained results is that the new proposed wavelet thresholding method
is succeed in denosing the signal under study with performance better than the traditional method. The
practical significance of obtained results is that the wavelet based new thresholding method is proposed
and simulated using Matlab software. This trail can be extended for real time application using digital
signal processer boards and filtering tasks.
Prospects for further research are to study the possibility for application of the new proposed wavelet
thresholding method for other signals and image processing field
The results for the new proposed thresholding method emphases the possibility for further utilization
of this new thresholding method for signal or image enhancement. Preprocessing the signal before
transmission will improve the signal quality and remove most of the noise prior to transmit it via noisy
medium. So even the intelligent classifier efficiency will be improved in conjugate with the signal
improvement and hence minimizing the error rate caused by signal contamination through transmission
phase. Cross correlation, pre-calculation for the best wavelet mother function and proper level leads to
an ideal denoising strategy with almost accepted results.
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Figure 1: Proposed Siny-hard and softy-sine
Figure 2: Training performance for the neural network explaining the MSE for each Epoch
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Figure 3: original, noisy, and denoised signals with classification result for the ANN output
Table 1
Different performance indices for evaluation of signal compression algorithm
Performance index Formula
Signal to noise ratio
πβ1
10 log10 [ βπ=0 π 2 (π) ]
[π (π) β π Μ (π)]2
Normalized Mean square πβ1
1
error β [π (π) β π Μ (π)]2
π
π=0
πβ1
Root mean square error 1
β β [π (π) β π Μ (π)]2
2π
π=0
Percentage root mean
βπβ1
π=0 [π (π) β π Μ (π)]
2
square difference β β 100%
βπβ1
π=0 [π (π)]
2
Table 2
Cross correlation results between signal and wavelets functions
wavelet scaling function cross correlation
biorthogonal 6.8 7.0701
symlet 8 53.5657
Coiflet 2 25.4343
discrete Meyer 13.9638
reverse biorthogonal 4.4 18.9874
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Table 3
Results for signal denoising using hard and hardy-sine thresholdings
decomposition level 1 2 3 4 5 6 7
NMSE 0.1341 0.0719 0.0386 0.0264 0.0221 0.0239 0.2175
Hard RMSE 0.2589 0.1896 0.139 0.1149 0.105 0.1093 0.3298
Thresholding ESNR 2.6964 5.4046 8.1015 9.7555 10.5345 10.1856 0.5966
PDR 0.1187 0.0869 0.0637 0.0527 0.0481 0.0501 0.1512
decomposition level 1 2 3 4 5 6 7
NMSE 0.1331 0.0709 0.0376 0.0254 0.0211 0.0229 0.2165
Hardy-sine RMSE 0.2579 0.1886 0.138 0.1139 0.104 0.1083 0.3288
Thresholding ESNR 2.7964 5.5046 8.5015 10.1555 11.5845 11.2156 1.6066
PDR 0.1177 0.0859 0.0627 0.0517 0.0471 0.0491 0.1502
8. Acknowledgements
The work is supported by the computer communication engineering department at Al-Rafidain
University College represented by its dean Prof. Dr. Mahmood J. Abu-Alshaeer. So I would like to
express my sincere appreciation to Prof. Dr. Mahmood J. Abu-Alshaeer for his help, support, and
encouragement during all the periods of my employment.
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