IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Noisy Speech Enhancement Using Soft Thresholding on Selected Intrinsic Mode F...CSCJournals
In this paper, a new speech enhancement method is introduced. It is essentially based on the Empirical Mode Decomposition technique (EMD) and a soft thresholding approach applied on selected modes. The proposed method is a fully data driven approach. First the noisy speech signal is decomposed adaptively into intrinsic oscillatory components called Intrinsic Mode Functions (IMFs) by using a time decomposition called sifting process. Second, selected IMFs are soft thresholded and added to the remaining IMFs with the residue to reconstitute the enhanced speech signal. The proposed approach is evaluated using speech signals from NOISEUS database corrupted with additive white Gaussian noise. Our algorithm is compared to other state of the art algorithms.
Speech Processing in Stressing Co-Channel Interference Using the Wigner Distr...CSCJournals
The Fractional Fourier Transform (FrFT) can provide significant interference suppression (IS) over other techniques in real-life non-stationary environments because it can operate with very few samples. However, the optimum rotational parameter ‘a’ must first be estimated. Recently, a new method to estimate ‘a’ based on the value that minimizes the projection of the product of the Wigner Distributions (WDs) of the signal-of-interest (SOI) and interference was proposed. This is more easily calculated by recognizing its equivalency to choosing ‘a’ for which the product of the energies of the SOI and interference in the FrFT domain is minimized, termed the WD-FrFT algorithm. The algorithm was shown to estimate ‘a’ more accurately than minimum mean square error FrFT (MMSE-FrFT) methods and perform far better than MMSE Fast Fourier Transform (MMSE-FFT) methods, which only operate in the frequency domain. The WD-FrFT algorithm significantly improves interference suppression (IS) capability, even at low signal-to-noise ratio (SNR). In this paper, we apply the proposed WD-FrFT technique to recovering a speech signal in non-stationary co-channel interference. Using mean-square error (MSE) between the SOI and its estimate as the performance metric, we show that the technique greatly outperforms the conventional methods, MMSE-FrFT and MMSE-FFT, which fail with just one non-stationary interferer, and continues to perform well in the presence of severe co-channel interference (CCI) consisting of multiple, equal power, non-stationary interferers. This method therefore has great potential for separating co-channel signals in harsh, noisy, non-stationary environments.
Ensemble Empirical Mode Decomposition: An adaptive method for noise reduction IOSR Journals
Empirical mode decomposition (EMD), a data analysis technique, is used to denoise non-stationary
and non-linear processes. The method does not require any pre & post processing of signal and use of any
specified basis functions. But EMD suffers from a problem called mode mixing. So to overcome this problem a
new method known as Ensemble Empirical mode decomposition (EEMD) has been introduced. The presented
paper gives the detail of EEMD and its application in various fields. EEMD is a time–space analysis method, in
which the added white noise is averaged out with sufficient number of trials; and the averaging process results
in only the component of the signal (original data). EEMD is a truly noise-assisted data analysis (NADA)
method and represents a substantial improvement over the original EMD.
An Optimized Transform for ECG Signal CompressionIDES Editor
A significant feature of the coming digital era is the
exponential increase in digital data, obtained from various
signals specially the biomedical signals such as
electrocardiogram (ECG), electroencephalogram (EEG),
electromyogram (EMG) etc. How to transmit or store these
signals efficiently becomes the most important issue. A digital
compression technique is often used to solve this problem.
This paper proposed a comparative study of transform based
approach for ECG signal compression. Adaptive threshold is
used on the transformed coefficients. The algorithm is tested
for 10 different records from MIT-BIH arrhythmia database
and obtained percentage root mean difference as around
0.528 to 0.584% for compression ratio of 18.963:1 to 23.011:1
for DWT. Among DFT, DCT and DWT techniques, DWT has
been proven to be very efficient for ECG signal coding.
Further improvement in the CR is possible by efficient
entropy coding.
International Journal of Engineering Research and Applications (IJERA) aims to cover the latest outstanding developments in the field of all Engineering Technologies & science.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
Images may contain different types of noises. Removing noise from image is often the first step in image processing, and remains a challenging problem in spite of sophistication of recent research. This ppt presents an efficient image denoising scheme and their reconstruction based on Discrete Wavelet Transform (DWT) and Inverse Discrete Wavelet Transform (IDWT).
Noisy Speech Enhancement Using Soft Thresholding on Selected Intrinsic Mode F...CSCJournals
In this paper, a new speech enhancement method is introduced. It is essentially based on the Empirical Mode Decomposition technique (EMD) and a soft thresholding approach applied on selected modes. The proposed method is a fully data driven approach. First the noisy speech signal is decomposed adaptively into intrinsic oscillatory components called Intrinsic Mode Functions (IMFs) by using a time decomposition called sifting process. Second, selected IMFs are soft thresholded and added to the remaining IMFs with the residue to reconstitute the enhanced speech signal. The proposed approach is evaluated using speech signals from NOISEUS database corrupted with additive white Gaussian noise. Our algorithm is compared to other state of the art algorithms.
Speech Processing in Stressing Co-Channel Interference Using the Wigner Distr...CSCJournals
The Fractional Fourier Transform (FrFT) can provide significant interference suppression (IS) over other techniques in real-life non-stationary environments because it can operate with very few samples. However, the optimum rotational parameter ‘a’ must first be estimated. Recently, a new method to estimate ‘a’ based on the value that minimizes the projection of the product of the Wigner Distributions (WDs) of the signal-of-interest (SOI) and interference was proposed. This is more easily calculated by recognizing its equivalency to choosing ‘a’ for which the product of the energies of the SOI and interference in the FrFT domain is minimized, termed the WD-FrFT algorithm. The algorithm was shown to estimate ‘a’ more accurately than minimum mean square error FrFT (MMSE-FrFT) methods and perform far better than MMSE Fast Fourier Transform (MMSE-FFT) methods, which only operate in the frequency domain. The WD-FrFT algorithm significantly improves interference suppression (IS) capability, even at low signal-to-noise ratio (SNR). In this paper, we apply the proposed WD-FrFT technique to recovering a speech signal in non-stationary co-channel interference. Using mean-square error (MSE) between the SOI and its estimate as the performance metric, we show that the technique greatly outperforms the conventional methods, MMSE-FrFT and MMSE-FFT, which fail with just one non-stationary interferer, and continues to perform well in the presence of severe co-channel interference (CCI) consisting of multiple, equal power, non-stationary interferers. This method therefore has great potential for separating co-channel signals in harsh, noisy, non-stationary environments.
Ensemble Empirical Mode Decomposition: An adaptive method for noise reduction IOSR Journals
Empirical mode decomposition (EMD), a data analysis technique, is used to denoise non-stationary
and non-linear processes. The method does not require any pre & post processing of signal and use of any
specified basis functions. But EMD suffers from a problem called mode mixing. So to overcome this problem a
new method known as Ensemble Empirical mode decomposition (EEMD) has been introduced. The presented
paper gives the detail of EEMD and its application in various fields. EEMD is a time–space analysis method, in
which the added white noise is averaged out with sufficient number of trials; and the averaging process results
in only the component of the signal (original data). EEMD is a truly noise-assisted data analysis (NADA)
method and represents a substantial improvement over the original EMD.
An Optimized Transform for ECG Signal CompressionIDES Editor
A significant feature of the coming digital era is the
exponential increase in digital data, obtained from various
signals specially the biomedical signals such as
electrocardiogram (ECG), electroencephalogram (EEG),
electromyogram (EMG) etc. How to transmit or store these
signals efficiently becomes the most important issue. A digital
compression technique is often used to solve this problem.
This paper proposed a comparative study of transform based
approach for ECG signal compression. Adaptive threshold is
used on the transformed coefficients. The algorithm is tested
for 10 different records from MIT-BIH arrhythmia database
and obtained percentage root mean difference as around
0.528 to 0.584% for compression ratio of 18.963:1 to 23.011:1
for DWT. Among DFT, DCT and DWT techniques, DWT has
been proven to be very efficient for ECG signal coding.
Further improvement in the CR is possible by efficient
entropy coding.
International Journal of Engineering Research and Applications (IJERA) aims to cover the latest outstanding developments in the field of all Engineering Technologies & science.
International Journal of Engineering Research and Applications (IJERA) is a team of researchers not publication services or private publications running the journals for monetary benefits, we are association of scientists and academia who focus only on supporting authors who want to publish their work. The articles published in our journal can be accessed online, all the articles will be archived for real time access.
Our journal system primarily aims to bring out the research talent and the works done by sciaentists, academia, engineers, practitioners, scholars, post graduate students of engineering and science. This journal aims to cover the scientific research in a broader sense and not publishing a niche area of research facilitating researchers from various verticals to publish their papers. It is also aimed to provide a platform for the researchers to publish in a shorter of time, enabling them to continue further All articles published are freely available to scientific researchers in the Government agencies,educators and the general public. We are taking serious efforts to promote our journal across the globe in various ways, we are sure that our journal will act as a scientific platform for all researchers to publish their works online.
Images may contain different types of noises. Removing noise from image is often the first step in image processing, and remains a challenging problem in spite of sophistication of recent research. This ppt presents an efficient image denoising scheme and their reconstruction based on Discrete Wavelet Transform (DWT) and Inverse Discrete Wavelet Transform (IDWT).
An Improved Empirical Mode Decomposition Based On Particle Swarm OptimizationIJRES Journal
End effect is the main factor affecting the application of Empirical Mode Decomposition (EMD).
This paper presents an improve EMD for decomposing short signal. First, analyzing the frequency components
of signal to be decomposed, and construct the parameter equation with the amplitude and initial phase of signal
as unknowns. Second, employing particle swarm optimization (PSO) to estimate the unknown parameters, and
extending the inspected signal according to the obtained parameters. Thirdly, using EMD to decompose the
extended signal into a series of intrinsic mode functions (IMFs) and a residual. The IMFs of original signal are
extracted from these obtained IMFs. The correlation coefficients between the IMFs and the signal are calculated
to judge the pseudo-IMFs. The simulation result shows that the presented method is effective and extends the
application of EMD.
Real-time DSP Implementation of Audio Crosstalk Cancellation using Mixed Unif...CSCJournals
For high fidelity sound reproduction, it is necessary to use long filter coefficients in audio crosstalk cancellation. To implement these long filters on real-time DSP processors, conventional overlap save technique suffers from more computational power as well as processing delay. To overcome these technical problems, mixed uniform partitioned convolution technique is proposed. This method is derived by combining uniform partitioned convolution with mixed filtering technique. With the proposed method, it is possible to perform audio crosstalk cancellation even at the order of ten thousand filter taps with less computations and short processing delay. The proposed technique was implemented on 32-bit floating point DSP processor and design was provided with efficient memory management to achieve optimization in computational complexity. The computational comparison of this method with conventional methods shows that the proposed technique is very efficient for long filters
Conditional Averaging a New Algorithm for Digital FilterIDES Editor
This paper aims at designing a new algorithm for
digital filters. The traditional methods like FIR, IIR have been
improved in recent times with new approaches. However, the
developments have used complex arithmetic calculation and
dedicated DSP processors. In this research project, effort has
been made to reduce such complexities using a procedure
based on the technique of Conditional Averaging. The entire
algorithm is developed using more of conditional statements
and less of arithmetic calculations.
Digital signals are filtered at different stages of
signal processing. However high speed processor is used for
different calculations associated with filtration process. An
averaging is one such scheme used in simple FIR filter, which
performs low pass filtering operation. Conditional Averaging
is a new technique, which is one of the improvements in
continuous time averaging. Conditional Averaging algorithm
is explained in this practice with different examples for the
design of low pass filter. This algorithm has been successfully
tested using digital starter kit with TMS3206416v DSP
processor. Using code composer studio, the entire algorithm is
written in C/C++ language and compiled into an assembly
language. Conditional averaging can be implemented with any
general purpose processor to arrive at other types of filters
with certain necessary modifications.
Interference Cancellation by Repeated Filtering in the Fractional Fourier Tra...IJERA Editor
The Fractional Fourier Transform (FrFT) is a useful tool that separates signals-of-interest (SOIs) from
interference and noise in non-stationary environments. This requires estimation of the rotational parameter „a‟ to
rotate the signal to a new domain along an axis „ta‟, in which the interference can best be filtered out. The value
of „a‟ is typically chosen as that which minimizes the mean-square error (MSE) between the desired SOI and its
estimate or that minimizes the overlap between the signal and noise, projected onto the axis „ta‟. In this paper, we
extend this concept to perform repeated filtering, in multiple FrFT domains to reduce the MSE further than can
be done with a single FrFT. We perform this solely using MSE as the metric by which to compute „a‟ at each
stage, thereby simplifying the approach and improving performance over conventional single stage FrFT
methods or methods based solely on the frequency domain filtering, such as the Fast Fourier transform (FFT).
We show that the proposed method improves the MSE two or three orders of magnitude over the conventional
methods using L ≤ 3 stages of FrFT filtering
Dwpt Based FFT and Its Application to SNR Estimation in OFDM SystemsCSCJournals
In this paper, wavelet packet (WP) based FFT and its application to SNR estimation is proposed. OFDM systems demodulate data using FFT. The proposed solution computes the exact FFT using WP and its computational complexity is of the same order as FFT, i.e. O (Nlog2 N). SNR estimation is done inside wavelet packet based FFT block unlike previous SNR estimations techniques which perform SNR estimation after FFT. Wavelet packet analyzed data is used to perform SNR estimation in colored noise. The proposed estimator takes into consideration the different noise power levels of the colored noise over the OFDM sub-carriers. The OFDM band is divided into several sub-bands using wavelet packet and noise in each sub-band is considered white. The second-order statistics of the transmitted OFDM preamble are calculated in each sub-band and the power noise is estimated. The proposed estimator is compared with Reddy’s estimator for colored noise in terms of mean squared error (MSE).
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Signal and image processing on satellite communication using MATLABEmbedded Plus Trichy
Basic Explanations about satellite imaging and signal processing with the help of MATLAB.
Contact us: 23,Nandhi koil Street, Near Nakoda Showroom,Theppakulam,Trichy
Mb.No:9360212155.
Mail:embeddedplusproject@gmail.com,
FB:www.facebook.com/embeddedplusproject
Comparative Analysis of Distortive and Non-Distortive Techniques for PAPR Red...IDES Editor
OFDM is a popular and widely accepted modulation
and multiplexing technique in the area of wireless
communication. IEEE 802.15, a wireless specification defined
for WPAN is an emerging wireless technology for short range
multimedia applications. Two general categories of 802.15
are the low rate 802.15.4 (ZigBee) and high rate 802.15.3
(UWB). In their physical (PHY) layer design, OFDM is a
competing technique due to the various advantages it renders
in the practical wireless media. OFDM has been a popular
technique for many years and adopted as the core technique
in a number of wireless standards. It makes the system more
immune to interference like InterSymbol Interference (ISI)
and InterCarrier Interference (ICI) and dispersive effects of
the channel. It is also a spectrally efficient scheme since the
spectra of the signal are overlapping in nature. Despite these
advantages OFDM suffers from a serious problem of high
Peak to Average Power. This limits the system’s capabilities
and increases the complexity. This paper compares the signal
distortion technique of Amplitude Clipping and the
distortionless technique of SLM for Peak to Average Power
reduction
An Improved Empirical Mode Decomposition Based On Particle Swarm OptimizationIJRES Journal
End effect is the main factor affecting the application of Empirical Mode Decomposition (EMD).
This paper presents an improve EMD for decomposing short signal. First, analyzing the frequency components
of signal to be decomposed, and construct the parameter equation with the amplitude and initial phase of signal
as unknowns. Second, employing particle swarm optimization (PSO) to estimate the unknown parameters, and
extending the inspected signal according to the obtained parameters. Thirdly, using EMD to decompose the
extended signal into a series of intrinsic mode functions (IMFs) and a residual. The IMFs of original signal are
extracted from these obtained IMFs. The correlation coefficients between the IMFs and the signal are calculated
to judge the pseudo-IMFs. The simulation result shows that the presented method is effective and extends the
application of EMD.
Real-time DSP Implementation of Audio Crosstalk Cancellation using Mixed Unif...CSCJournals
For high fidelity sound reproduction, it is necessary to use long filter coefficients in audio crosstalk cancellation. To implement these long filters on real-time DSP processors, conventional overlap save technique suffers from more computational power as well as processing delay. To overcome these technical problems, mixed uniform partitioned convolution technique is proposed. This method is derived by combining uniform partitioned convolution with mixed filtering technique. With the proposed method, it is possible to perform audio crosstalk cancellation even at the order of ten thousand filter taps with less computations and short processing delay. The proposed technique was implemented on 32-bit floating point DSP processor and design was provided with efficient memory management to achieve optimization in computational complexity. The computational comparison of this method with conventional methods shows that the proposed technique is very efficient for long filters
Conditional Averaging a New Algorithm for Digital FilterIDES Editor
This paper aims at designing a new algorithm for
digital filters. The traditional methods like FIR, IIR have been
improved in recent times with new approaches. However, the
developments have used complex arithmetic calculation and
dedicated DSP processors. In this research project, effort has
been made to reduce such complexities using a procedure
based on the technique of Conditional Averaging. The entire
algorithm is developed using more of conditional statements
and less of arithmetic calculations.
Digital signals are filtered at different stages of
signal processing. However high speed processor is used for
different calculations associated with filtration process. An
averaging is one such scheme used in simple FIR filter, which
performs low pass filtering operation. Conditional Averaging
is a new technique, which is one of the improvements in
continuous time averaging. Conditional Averaging algorithm
is explained in this practice with different examples for the
design of low pass filter. This algorithm has been successfully
tested using digital starter kit with TMS3206416v DSP
processor. Using code composer studio, the entire algorithm is
written in C/C++ language and compiled into an assembly
language. Conditional averaging can be implemented with any
general purpose processor to arrive at other types of filters
with certain necessary modifications.
Interference Cancellation by Repeated Filtering in the Fractional Fourier Tra...IJERA Editor
The Fractional Fourier Transform (FrFT) is a useful tool that separates signals-of-interest (SOIs) from
interference and noise in non-stationary environments. This requires estimation of the rotational parameter „a‟ to
rotate the signal to a new domain along an axis „ta‟, in which the interference can best be filtered out. The value
of „a‟ is typically chosen as that which minimizes the mean-square error (MSE) between the desired SOI and its
estimate or that minimizes the overlap between the signal and noise, projected onto the axis „ta‟. In this paper, we
extend this concept to perform repeated filtering, in multiple FrFT domains to reduce the MSE further than can
be done with a single FrFT. We perform this solely using MSE as the metric by which to compute „a‟ at each
stage, thereby simplifying the approach and improving performance over conventional single stage FrFT
methods or methods based solely on the frequency domain filtering, such as the Fast Fourier transform (FFT).
We show that the proposed method improves the MSE two or three orders of magnitude over the conventional
methods using L ≤ 3 stages of FrFT filtering
Dwpt Based FFT and Its Application to SNR Estimation in OFDM SystemsCSCJournals
In this paper, wavelet packet (WP) based FFT and its application to SNR estimation is proposed. OFDM systems demodulate data using FFT. The proposed solution computes the exact FFT using WP and its computational complexity is of the same order as FFT, i.e. O (Nlog2 N). SNR estimation is done inside wavelet packet based FFT block unlike previous SNR estimations techniques which perform SNR estimation after FFT. Wavelet packet analyzed data is used to perform SNR estimation in colored noise. The proposed estimator takes into consideration the different noise power levels of the colored noise over the OFDM sub-carriers. The OFDM band is divided into several sub-bands using wavelet packet and noise in each sub-band is considered white. The second-order statistics of the transmitted OFDM preamble are calculated in each sub-band and the power noise is estimated. The proposed estimator is compared with Reddy’s estimator for colored noise in terms of mean squared error (MSE).
International Journal of Engineering Research and Development (IJERD)IJERD Editor
journal publishing, how to publish research paper, Call For research paper, international journal, publishing a paper, IJERD, journal of science and technology, how to get a research paper published, publishing a paper, publishing of journal, publishing of research paper, reserach and review articles, IJERD Journal, How to publish your research paper, publish research paper, open access engineering journal, Engineering journal, Mathemetics journal, Physics journal, Chemistry journal, Computer Engineering, Computer Science journal, how to submit your paper, peer reviw journal, indexed journal, reserach and review articles, engineering journal, www.ijerd.com, research journals,
yahoo journals, bing journals, International Journal of Engineering Research and Development, google journals, hard copy of journal
Signal and image processing on satellite communication using MATLABEmbedded Plus Trichy
Basic Explanations about satellite imaging and signal processing with the help of MATLAB.
Contact us: 23,Nandhi koil Street, Near Nakoda Showroom,Theppakulam,Trichy
Mb.No:9360212155.
Mail:embeddedplusproject@gmail.com,
FB:www.facebook.com/embeddedplusproject
Comparative Analysis of Distortive and Non-Distortive Techniques for PAPR Red...IDES Editor
OFDM is a popular and widely accepted modulation
and multiplexing technique in the area of wireless
communication. IEEE 802.15, a wireless specification defined
for WPAN is an emerging wireless technology for short range
multimedia applications. Two general categories of 802.15
are the low rate 802.15.4 (ZigBee) and high rate 802.15.3
(UWB). In their physical (PHY) layer design, OFDM is a
competing technique due to the various advantages it renders
in the practical wireless media. OFDM has been a popular
technique for many years and adopted as the core technique
in a number of wireless standards. It makes the system more
immune to interference like InterSymbol Interference (ISI)
and InterCarrier Interference (ICI) and dispersive effects of
the channel. It is also a spectrally efficient scheme since the
spectra of the signal are overlapping in nature. Despite these
advantages OFDM suffers from a serious problem of high
Peak to Average Power. This limits the system’s capabilities
and increases the complexity. This paper compares the signal
distortion technique of Amplitude Clipping and the
distortionless technique of SLM for Peak to Average Power
reduction
This is an updated presentation that details the evolution, purpose, and process of OSPI's curriculum project entitled, "Since Time Immemorial: Tribal Sovereignty in Washington State." This project, currently funded by federal, state, and tribal entities, is the first of its kind in the nation and is the response to the passage of 2005 House Bill 1495 that strongly encourages school districts to adopt curriculum on local tribal history and tribal sovereignty. For more information contact Joan Banker at the Indian Education Office in Olympia, WA. joan.banker@k12.wa.us.
Ensemble Empirical Mode Decomposition: An adaptive method for noise reductionIOSR Journals
Abstract:Empirical mode decomposition (EMD), a data analysis technique, is used to denoise non-stationary and non-linear processes. The method does not require any pre & post processing of signal and use of any specified basis functions. But EMD suffers from a problem called mode mixing. So to overcome this problem a new method known as Ensemble Empirical mode decomposition (EEMD) has been introduced. The presented paper gives the detail of EEMD and its application in various fields. EEMD is a time–space analysis method, in which the added white noise is averaged out with sufficient number of trials; and the averaging process results in only the component of the signal (original data). EEMD is a truly noise-assisted data analysis (NADA) method and represents a substantial improvement over the original EMD. Keywords –Data analysis, Empirical mode decomposition, intrinsic mode function, mode mixing, NADA,
Recovery of low frequency Signals from noisy data using Ensembled Empirical M...inventionjournals
International Journal of Engineering and Science Invention (IJESI) is an international journal intended for professionals and researchers in all fields of computer science and electronics. IJESI publishes research articles and reviews within the whole field Engineering Science and Technology, new teaching methods, assessment, validation and the impact of new technologies and it will continue to provide information on the latest trends and developments in this ever-expanding subject. The publications of papers are selected through double peer reviewed to ensure originality, relevance, and readability. The articles published in our journal can be accessed online.
Empirical mode decomposition and normal shrink tresholding for speech denoisingijitjournal
In this paper a signal denoising scheme based on Empirical mode decomposition (EMD) is presented. the
noisy signal is decomposed in an adaptive manner by the EMD algorithm which allows to obtain intrinsic
oscillatory called Intrinsic mode functions (IMFs)component by a process called sifting process. The basic
principle of the method is to decompose a speech signal corrupted by additive white Gaussian random
noise into segments each frame is categorised as either signal-dominant or noise-dominant then
reconstruct the signal with IMFs signal dominant frame previously filtered or thresholded.It is shown, on
the basis of intensivesimulations that EMD improves the signal to noise ratio and address the problem of
signal degradation. The denoising method is applied to real signal with different noise levels and the
results compared to Winner and universal threshold of DONOHO and JOHNSTONE [11] with soft and
hard tresholding.Theeffect of level noise value on the performances of the proposed denoising is analysed.
Signal Processing of Radar Echoes Using Wavelets and Hilbert Huang Transformsipij
Atmospheric Radar Signal Processing is one field of Signal Processing where there is a lot of scope for development of new and efficient tools for spectrum cleaning, detection and estimation of desired parameters. The wavelet transform and HHT (Hilbert-Huang transform) are both signal processing methods. This paper is based on comparing HHT and Wavelet transform applied to Radar signals. Wavelet analysis is one of the most important methods for removing noise and extracting signal from any data. The de-noising application of the wavelets has been used in spectrum cleaning of the atmospheric radar signals. HHT can be used for processing non-stationary and nonlinear signals. HHT is one of the timefrequency analysis techniques which consists of two parts: Empirical Mode Decomposition (EMD) and instantaneous frequency solution. EMD is a numerical sifting process to decompose a signal into its fundamental intrinsic oscillatory modes, namely intrinsic mode functions (IMFs). A series of IMFs can be obtained after the application of EMD. In this paper wavelets and EMD has been applied to the time series data obtained from the mesosphere-stratosphere-troposphere (MST) region near Gadanki, Tirupati for 6 beam directions. The Algorithm is developed and tested using Matlab. Moments were estimated and analysis has brought out improvement in some of the characteristic features like SNR, Doppler width, Noise power of the atmospheric signals. The results showed that the proposed algorithm is efficient for dealing non-linear and non- stationary signals contaminated with noise. The results were compared using ADP (Atmospheric Data Processor) and plotted for validation of the proposed algorithm.
A Novel Method for Speaker Independent Recognition Based on Hidden Markov ModelIDES Editor
In this paper, we address the speaker independent
recognition of Chinese number speeches 0~9 based on HMM.
Our former results of inside and outside testing achieved
92.5% and 76.79% respectively. To improve further the
performance, two important features of speech; MFCC and
cluster number of vector quantification, are unified together
and evaluated on various values. The best performance
achieve 96.2% and 83.1% on MFCC Number = 20 and VQ
clustering number = 64.
A Review on Image Denoising using Wavelet Transformijsrd.com
this paper proposes different approaches of wavelet based image denoising methods. The search for efficient image denoising methods is still a valid challenge at the crossing of functional analysis and statistics. Wavelet algorithms are very useful tool for signal processing such as image denoising. The main of modify the coefficient is remove the noise from data or signal. In this paper, the technique was extended up to almost remove noise Gaussian.
Chaotic signals denoising using empirical mode decomposition inspired by mult...IJECEIAES
Empirical mode decomposition (EMD) is an effective noise reduction method to enhance the noisy chaotic signal over additive noise. In this paper, the intrinsic mode functions (IMFs) generated by EMD are thresholded using multivariate denoising. Multivariate denoising is multivariable denosing algorithm that is combined wavelet transform and principal component analysis to denoise multivariate signals in adaptive way. The proposed method is compared at a various signal to noise ratios (SNRs) with different techniques and different types of noise. Also, scale dependent Lyapunov exponent (SDLE) is used to test the behavior of the denoised chaotic signal comparing with clean signal. The results show that EMD-MD method has the best root mean square error (RMSE) and signal to noise ratio gain (SNRG) comparing with the conventional methods.
Estimation of Ensembles in an Adventitious Wave by EMD and EEMD TechniquesIJERDJOURNAL
Abstract:- The Empirical Mode Decomposition (EMD) is a basic building block of Hilbert-Huang Transformation. The main principle behind EMD is to decompose a sound wave into its intrinsic mode function (IMF). The time domain analysis is fairly described with EMD breaking down. The basis is derived from the same sound wave only. The analysis is useful for affected respiratory sound waves, which are non-linear and non-stationary in nature. Huang and Wu established another milestone in the area of EMD called Ensemble Empirical Mode Decomposition (EEMD). The EEMD is now can be used to interpolate a sound wave from the affected waveform. The EEMD specify a certain TRUE IMF components as an ensemble mean, every component consists of a sound wave in addition to Gaussian Noise of certain amplitude. In this paper, the mean and standard deviation at a different instance of the affected wheezing respiratory wave is discussed. The comparison between crackle wave and the wheezing wave is listed with respect to mean and standard deviation. The results specified in the table shows that the EEMD is an efficient technique for minimizing noise affected with abnormal lung waves.
We present a causal speech enhancement model working on the
raw waveform that runs in real-time on a laptop CPU. The proposed model is based on an encoder-decoder architecture with
skip-connections. It is optimized on both time and frequency
domains, using multiple loss functions. Empirical evidence
shows that it is capable of removing various kinds of background noise including stationary and non-stationary noises,
as well as room reverb. Additionally, we suggest a set of
data augmentation techniques applied directly on the raw waveform which further improve model performance and its generalization abilities. We perform evaluations on several standard
benchmarks, both using objective metrics and human judgements. The proposed model matches state-of-the-art performance of both causal and non causal methods while working
directly on the raw waveform.
Index Terms: Speech enhancement, speech denoising, neural
networks, raw waveform
In the recent years, large scale information transfer by remote computing and the development
of massive storage and retrieval systems have witnessed a tremendous growth. To cope up with the
growth in the size of databases, additional storage devices need to be installed and the modems and
multiplexers have to be continuously upgraded in order to permit large amounts of data transfer between
computers and remote terminals. This leads to an increase in the cost as well as equipment. One solution
to these problems is “COMPRESSION” where the database and the transmission sequence can be
encoded efficiently. In this we investigated for optimum wavelet, optimum level, and optimum scaling
factor.
Use of Discrete Sine Transform for A Novel Image Denoising TechniqueCSCJournals
In this paper, we propose a new multiresolution image denoising technique using Discrete Sine Transform. Wavelet techniques have been in use for multiresolution image processing. Discrete Cosine Transform is also extensively used for image compression. Similar to the Discrete Wavelet and Discrete Cosine Transform it is now found that Discrete Sine Transform also possess some good qualities for image processing; specifically for image denoising. Algorithm for image denoising using Discrete Sine Transform is proposed with simulation works for experimental verification. The method is computationally efficient and simple in theory and application.
An Effective Approach for Chinese Speech Recognition on Small Size of Vocabularysipij
In this paper, an effective approach for Chinese speech recognition on small vocabulary size is proposed - the independent speech recognition of Chinese words based on Hidden Markov Model (HMM). The features of speech words are generated by sub-syllable of Chinese characters. Total 640 speech samples are recorded by 4 native males and 4 females with frequently speaking ability. The preliminary results of inside and outside testing achieve 89.6% and 77.5%, respectively. To improve the performance, keyword spotting criterion is applied into our system. The final precision rates for inside and outside testing in average achieve 92.7% and 83.8%. The results prove that the approach for Chinese speech recognition on small vocabulary is effective.
Data Compression using Multiple Transformation Techniques for Audio Applicati...iosrjce
IOSR Journal of Computer Engineering (IOSR-JCE) is a double blind peer reviewed International Journal that provides rapid publication (within a month) of articles in all areas of computer engineering and its applications. The journal welcomes publications of high quality papers on theoretical developments and practical applications in computer technology. Original research papers, state-of-the-art reviews, and high quality technical notes are invited for publications.
A New Approach for Speech Enhancement Based On Eigenvalue Spectral SubtractionCSCJournals
In this paper, a phase space reconstruction-based method is proposed for speech enhancement. The method embeds the noisy signal into a high dimensional reconstructed phase space and uses Spectral Subtraction idea. The advantages of the proposed method are fast performance, high SNR and good MOS. In order to evaluate the proposed method, ten signals of TIMIT database mixed with the white additive Gaussian noise and then the method was implemented. The efficiency of the proposed method was evaluated by using qualitative and quantitative criteria.
Deep Learning Based Voice Activity Detection and Speech EnhancementNAVER Engineering
발표자: 김준태 (KAIST 박사과정)
발표일: 2018.10
Voice activity detection (VAD) and speech enhancement (SE) are important front-end technologies for noise robust speech recognition system.
From incoming noisy signal, VAD detects the speech signal only and SE removes the noise signal while conserving the speech signal.
For VAD and SE, this presentation will cover the traditional methods, deep learning based methods, and our papers as follows:
1. J. Kim and M. Hahn, "Voice Activity Detection Using an Adaptive Context Attention Model," in IEEE Signal Processing Letters, vol. 25, no. 8, pp. 1181-1185, Aug. 2018.
2. J. Kim and M. Hahn, "Speech Enhancement Using a Two Step Network," submitted to IEEE Signal Processing Letters, 2018.
Also, this presentation will briefly introduce some experimental results in real-world environment (far-field, noisy environment), conducted on the embedded board.
For VAD,
Traditional VAD methods.
Deep learning based VAD methods.
Paper presentation: J. Kim and M. Hahn, "Voice Activity Detection Using an Adaptive Context Attention Model," in IEEE Signal Processing Letters, vol. 25, no. 8, pp. 1181-1185, Aug. 2018.
End point detection based on VAD.
Experimental results of DNN-EPD on embedded board in real-world environment.
For SE,
Traditional SE methods.
Deep learning based SE methods.
Paper presentation: J. Kim and M. Hahn, "Speech Enhancement Using a Two Step Network," submitted to IEEE Signal Processing Letters, 2018.
Experimental results in real-world environment.
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
State of ICS and IoT Cyber Threat Landscape Report 2024 previewPrayukth K V
The IoT and OT threat landscape report has been prepared by the Threat Research Team at Sectrio using data from Sectrio, cyber threat intelligence farming facilities spread across over 85 cities around the world. In addition, Sectrio also runs AI-based advanced threat and payload engagement facilities that serve as sinks to attract and engage sophisticated threat actors, and newer malware including new variants and latent threats that are at an earlier stage of development.
The latest edition of the OT/ICS and IoT security Threat Landscape Report 2024 also covers:
State of global ICS asset and network exposure
Sectoral targets and attacks as well as the cost of ransom
Global APT activity, AI usage, actor and tactic profiles, and implications
Rise in volumes of AI-powered cyberattacks
Major cyber events in 2024
Malware and malicious payload trends
Cyberattack types and targets
Vulnerability exploit attempts on CVEs
Attacks on counties – USA
Expansion of bot farms – how, where, and why
In-depth analysis of the cyber threat landscape across North America, South America, Europe, APAC, and the Middle East
Why are attacks on smart factories rising?
Cyber risk predictions
Axis of attacks – Europe
Systemic attacks in the Middle East
Download the full report from here:
https://sectrio.com/resources/ot-threat-landscape-reports/sectrio-releases-ot-ics-and-iot-security-threat-landscape-report-2024/
zkStudyClub - Reef: Fast Succinct Non-Interactive Zero-Knowledge Regex ProofsAlex Pruden
This paper presents Reef, a system for generating publicly verifiable succinct non-interactive zero-knowledge proofs that a committed document matches or does not match a regular expression. We describe applications such as proving the strength of passwords, the provenance of email despite redactions, the validity of oblivious DNS queries, and the existence of mutations in DNA. Reef supports the Perl Compatible Regular Expression syntax, including wildcards, alternation, ranges, capture groups, Kleene star, negations, and lookarounds. Reef introduces a new type of automata, Skipping Alternating Finite Automata (SAFA), that skips irrelevant parts of a document when producing proofs without undermining soundness, and instantiates SAFA with a lookup argument. Our experimental evaluation confirms that Reef can generate proofs for documents with 32M characters; the proofs are small and cheap to verify (under a second).
Paper: https://eprint.iacr.org/2023/1886
Enhancing Performance with Globus and the Science DMZGlobus
ESnet has led the way in helping national facilities—and many other institutions in the research community—configure Science DMZs and troubleshoot network issues to maximize data transfer performance. In this talk we will present a summary of approaches and tips for getting the most out of your network infrastructure using Globus Connect Server.
Welcome to the first live UiPath Community Day Dubai! Join us for this unique occasion to meet our local and global UiPath Community and leaders. You will get a full view of the MEA region's automation landscape and the AI Powered automation technology capabilities of UiPath. Also, hosted by our local partners Marc Ellis, you will enjoy a half-day packed with industry insights and automation peers networking.
📕 Curious on our agenda? Wait no more!
10:00 Welcome note - UiPath Community in Dubai
Lovely Sinha, UiPath Community Chapter Leader, UiPath MVPx3, Hyper-automation Consultant, First Abu Dhabi Bank
10:20 A UiPath cross-region MEA overview
Ashraf El Zarka, VP and Managing Director MEA, UiPath
10:35: Customer Success Journey
Deepthi Deepak, Head of Intelligent Automation CoE, First Abu Dhabi Bank
11:15 The UiPath approach to GenAI with our three principles: improve accuracy, supercharge productivity, and automate more
Boris Krumrey, Global VP, Automation Innovation, UiPath
12:15 To discover how Marc Ellis leverages tech-driven solutions in recruitment and managed services.
Brendan Lingam, Director of Sales and Business Development, Marc Ellis
Observability Concepts EVERY Developer Should Know -- DeveloperWeek Europe.pdfPaige Cruz
Monitoring and observability aren’t traditionally found in software curriculums and many of us cobble this knowledge together from whatever vendor or ecosystem we were first introduced to and whatever is a part of your current company’s observability stack.
While the dev and ops silo continues to crumble….many organizations still relegate monitoring & observability as the purview of ops, infra and SRE teams. This is a mistake - achieving a highly observable system requires collaboration up and down the stack.
I, a former op, would like to extend an invitation to all application developers to join the observability party will share these foundational concepts to build on:
Accelerate your Kubernetes clusters with Varnish CachingThijs Feryn
A presentation about the usage and availability of Varnish on Kubernetes. This talk explores the capabilities of Varnish caching and shows how to use the Varnish Helm chart to deploy it to Kubernetes.
This presentation was delivered at K8SUG Singapore. See https://feryn.eu/presentations/accelerate-your-kubernetes-clusters-with-varnish-caching-k8sug-singapore-28-2024 for more details.
Alt. GDG Cloud Southlake #33: Boule & Rebala: Effective AppSec in SDLC using ...James Anderson
Effective Application Security in Software Delivery lifecycle using Deployment Firewall and DBOM
The modern software delivery process (or the CI/CD process) includes many tools, distributed teams, open-source code, and cloud platforms. Constant focus on speed to release software to market, along with the traditional slow and manual security checks has caused gaps in continuous security as an important piece in the software supply chain. Today organizations feel more susceptible to external and internal cyber threats due to the vast attack surface in their applications supply chain and the lack of end-to-end governance and risk management.
The software team must secure its software delivery process to avoid vulnerability and security breaches. This needs to be achieved with existing tool chains and without extensive rework of the delivery processes. This talk will present strategies and techniques for providing visibility into the true risk of the existing vulnerabilities, preventing the introduction of security issues in the software, resolving vulnerabilities in production environments quickly, and capturing the deployment bill of materials (DBOM).
Speakers:
Bob Boule
Robert Boule is a technology enthusiast with PASSION for technology and making things work along with a knack for helping others understand how things work. He comes with around 20 years of solution engineering experience in application security, software continuous delivery, and SaaS platforms. He is known for his dynamic presentations in CI/CD and application security integrated in software delivery lifecycle.
Gopinath Rebala
Gopinath Rebala is the CTO of OpsMx, where he has overall responsibility for the machine learning and data processing architectures for Secure Software Delivery. Gopi also has a strong connection with our customers, leading design and architecture for strategic implementations. Gopi is a frequent speaker and well-known leader in continuous delivery and integrating security into software delivery.
A tale of scale & speed: How the US Navy is enabling software delivery from l...sonjaschweigert1
Rapid and secure feature delivery is a goal across every application team and every branch of the DoD. The Navy’s DevSecOps platform, Party Barge, has achieved:
- Reduction in onboarding time from 5 weeks to 1 day
- Improved developer experience and productivity through actionable findings and reduction of false positives
- Maintenance of superior security standards and inherent policy enforcement with Authorization to Operate (ATO)
Development teams can ship efficiently and ensure applications are cyber ready for Navy Authorizing Officials (AOs). In this webinar, Sigma Defense and Anchore will give attendees a look behind the scenes and demo secure pipeline automation and security artifacts that speed up application ATO and time to production.
We will cover:
- How to remove silos in DevSecOps
- How to build efficient development pipeline roles and component templates
- How to deliver security artifacts that matter for ATO’s (SBOMs, vulnerability reports, and policy evidence)
- How to streamline operations with automated policy checks on container images
DevOps and Testing slides at DASA ConnectKari Kakkonen
My and Rik Marselis slides at 30.5.2024 DASA Connect conference. We discuss about what is testing, then what is agile testing and finally what is Testing in DevOps. Finally we had lovely workshop with the participants trying to find out different ways to think about quality and testing in different parts of the DevOps infinity loop.
Securing your Kubernetes cluster_ a step-by-step guide to success !KatiaHIMEUR1
Today, after several years of existence, an extremely active community and an ultra-dynamic ecosystem, Kubernetes has established itself as the de facto standard in container orchestration. Thanks to a wide range of managed services, it has never been so easy to set up a ready-to-use Kubernetes cluster.
However, this ease of use means that the subject of security in Kubernetes is often left for later, or even neglected. This exposes companies to significant risks.
In this talk, I'll show you step-by-step how to secure your Kubernetes cluster for greater peace of mind and reliability.
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
Elevating Tactical DDD Patterns Through Object Calisthenics
Iy2617051711
1. Sreedevi Gandham, T. Sreenivasulu Reddy / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 6, November- December 2012, pp.1705-1711
Enhanced Signal Denoising Performance by EMD-based
Techniques
Sreedevi Gandham*, T. Sreenivasulu Reddy
Department of Electronics and Communication Engineering
Sri Venkateswara University, Tirupati, Andhra Pradesh-517502. India.
Abstract
Empirical mode decomposition (EMD) is improve the understanding of the way EMD
one of the most efficient methods used for non- operates or to enhance its performance, EMD still
parametric signal denoising. In this study wavelet lacks a sound mathematical theory and is essentially
thresholding principle is used in the described by an algorithm. However, partly due to
decomposition modes resulting from applying the fact that it is easily and directly applicable and
EMD to a signal. The principles of hard and soft partly because it often results in interesting and
wavelet thresholding including translation useful decomposition outcomes, it has found a vast
invariant denoising were appropriately modified number of diverse applications such as biomedical,
to develop denoising methods suited for watermarking and audio processing to name a few.
thresholding EMD modes. We demonstrated In this study, inspired by standard wavelet
that, although a direct application of this thresholding and translation invariant thresholding,
principle is not feasible in the EMD case, it can a few EMD-based denoising techniques are
be appropriately adapted by exploiting the developed and tested in different signal scenarios
special characteristics of the EMD decomposition and white Gaussian noise. It is shown that although
modes. In the same manner, inspired by the the main principles between wavelet and EMD
translation invariant wavelet thresholding, a thresholding are the same, in the case of EMD, the
similar technique adapted to EMD is developed, thresholding operation has to be properly adapted in
leading to enhanced denoising performance. order to be consistent with the special characteristics
Keywords: Denoising, EMD, Wavelet of the signal modes resulting from EMD. The
thresholding possibility of adapting the wavelet thresholding
principles in thresholding the decomposition modes
I Introduction of EMD directly, is explored. Consequently, three
Denoising aims to remove the noise and to novel EMD-based hard and soft thresholding
recover the original signal regardless of the signal’s strategies are presented.
frequency content. Traditional denoising schemes
are based on linear methods, where the most II EMD: A Brief description and
common choice is the Wiener filtering and they notation
have their own limitations(Kopsinis and McLauglin, EMD[3] adaptively decomposes a
2008).Recently, a new data-driven technique, multicomponent signal [4] x(t) into a number of the
so-called IMFs, h (t ),1 i L
referred to as empirical mode decomposition (EMD) (i )
has been introduced by Huang et al. (1998) for L
analyzing data from nonstationary and nonlinear x(t ) h(i ) (t ) d (t ) (1)
processes. The EMD has received more attention in i 1
terms of applications, interpretation, and where d(t) is a remainder which is a non-zero-mean
improvement. Empirical mode decomposition slowly varying function with only few extrema.
(EMD) method is an algorithm for the analysis of Each one of the IMFs, say, the (i) th one , is
multicomponent signals that breaks them down into estimated with the aid of an iterative process, called
a number of amplitude and frequency modulated sifting, applied to the residual multicomponent
(AM/FM) zero-mean signals, termed intrinsic mode signal.
functions (IMFs). In contrast to conventional
decomposition methods such as wavelets, which
x(t ), i 1
x (i ) (t ) (2)
x(t ) j 1 h (t ), i 2.
perform the analysis by projecting the signal under i 1 ( i )
consideration onto a number of predefined basis
vectors, EMD expresses the signal as an expansion The sifting process used in this paper is the
of basis functions that are signal-dependent and are standard one [3]. According to this, during the (n+1)
estimated via an iterative procedure called sifting. th sifting iteration, the temporary IMF estimate
Although many attempts have been made to
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2. Sreedevi Gandham, T. Sreenivasulu Reddy / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 6, November- December 2012, pp.1705-1711
hni ) (t ) is improving according to the following
( lower frequencies locally in the time-frequency
domain than its preceeding ones.
steps.2
1. Find the local maxima and minima of hni ) (t ) .
(
2. Interpolate, using natural cubic splines, along the
points of hni ) (t ) estimated in the first step in order
(
to form an upper and a lower envelope.
3. Compute the mean of two envelopes.
(i )
4. Obtain the refined estimate hn 1 (t ) of the IMF by
subtracting the mean found in the previous step
(i )
from the current IMF estimate hn (t ) .
5. Proceed from step 1) again unless a stopping
criterion has been fulfilled.
Figure 1.Empirical mode decomposition of the noisy
The sifting process is effectively an empirical signal shown in (a).
but powerful technique for the estimation of the
(i )
mean m (t ) of the residual multicomponent signal
III Signal Denoising
Thresholding is a technique used for signal
x(i ) (t ) localy, a quantity that we term total local and image denoising. The discrete wavelet
mean. Although the notion of the total local mean is transform uses two types of filters: (1) averaging
somewhat vague, especially for multicomponent filters, and (2) detail filters. When we decompose a
signals, in the EMD context it means that its signal using the wavelet transform, we are left with
(i ) a set of wavelet coefficients that correlates to the
subtraction from x (t ) will lead to a signal, which high frequency subbands. These high frequency
is actually the corresponding IMF, i.e., subbands consist of the details in the data set.
h(i ) (t ) x(i ) (t ) m(i ) (t ), that is going to have If these details are small enough, they
the following properties. might be omitted without substantially affecting the
1) Zero mean. main features of the data set. Additionally, these
2) All the maxima and all the minima of small details are often those associated with noise;
therefore, by setting these coefficients to zero, we
h(i ) (t ) will be positive and negative, are essentially killing the noise. This becomes the
respectively. Often, but not always, the basic concept behind thresholding-set all frequency
IMFs resemble sinusoids that are both subband coefficients that are less than a particular
amplitude and (frequency modulated (AM threshold to zero and use these coefficients in an
/FM). inverse wavelet transformation to reconstruct the
data set.
By construction, the number of say N i 3.1. IMF Thresholding based Denoising
An alternative denoising procedure inspired
(i )
extrema of h (t ) positioned in time instances by wavelet thresholding is proposed. EMD
thresholding can exceed the performance achieved
r(i )
j [r , r ,....., rNi)i ] and the corresponding
1
(i )
2
(i )
(
by wavelet thresholding only by adapting the
IMF points h(i ) (rj(i ) ), j 1,....., N (i) , will thresholding function to the special nature of IMFs.
EMD can be interpreted as a subband-like filtering
alternate between maxima and minima, i.e,. positive procedure resulting in essentially uncorrelated
and negative values. As a result, in any pair of IMFs. Although the equivalent filter-bank structure
extrema rj(i ) [h(i ) (rj(i ) ), h(i ) (rj()1 )] , corresponds
i is by no means predetermined and fixed as in
wavelet decomposition, one can in principle perform
to a single zero-crossing z (ji ) . Depending on the thresholding in each IMF in order to locally exclude
IMF shape, the number of zero-crossings can be low-energy IMF parts that are expected to be
N i or N i -1. Moreover, each IMF
4
significantly corrupted by noise.
either A direct application of wavelet
lets say the one of the order, I, have fewer extrema thresholding in the EMD case translates to
than all over the lower order of IMFs, j=1, i-1,
leading to fewer and fewer oscillations as the IMF
order increases. In other words, each IMF occupies
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3. Sreedevi Gandham, T. Sreenivasulu Reddy / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 6, November- December 2012, pp.1705-1711
h(i ) (t ), h(i ) (t ) Ti
h (i ) (t )
0, h(i ) (t ) Ti
for hard thresholding and to
sgn(h(i ) (t ))( h(i ) (t ) Ti ),
h(i ) (t ) Ti
h (i ) (t )
0, h(i ) (t ) Ti
Figure 2. EMD Direct Thresholding.The top-right
for soft thresholding, where, in both thresholding numbers are the SNR values after denoising.
cases, indicates the thresholded IMF. The reason
for adopting different thresholds per mode will The reasoning underlying the significance
become clearer in the sequel. test procedure above is fairly simple but strong. If
A generalized reconstruction of the denoised signal the energy of the IMFs resulting from the
is given by decomposition of a noise-only signal with certain
characteristics is known, then in actual cases of
M2 L signals comprising both information and noise
x(t )
ˆ h (i ) (t )
k M1
k M 2 1
h(i ) (t ) following the specific characteristics, a significant
discrepancy between the energy of a noise-only IMF
Where the introduction of parameters and the corresponding noisy-signal IMF indicates
gives us flexibility on the exclusion of the noisy the presence of useful information. In a denoising
low-order IMFs and on the optional thresholding of scenario, this translates to partially reconstructing
the high-order ones, which in white Gaussian noise the signal using only the IMFs that contain useful
conditions contain little noise energy. There are two information and discarding those that carry
major differences, which are interconnected, primarily noise, i.e., the IMFs that share similar
between wavelet and direct EMD thresholding amounts of energy with the noise-only case.
(EMD-DT) shown above. In practice, the noise-only signal is never
First, in contrast to wavelet denoising available in order to apply EMD and estimate the
where thresholding is applied to the wavelet IMF energies, so the usefulness of the above
components, in the EMD case, thresholding is technique relies on whether or not the energies of
applied to the samples of each IMF, which are the noise-only IMFs can be estimated directly based
basically the signal portion contained in each on the actual noisy signal. The latter is usually the
adaptive subband. An equivalent procedure in the case due to a striking feature of EMD. Apart from
wavelet method would be to perform thresholding the first noise-only IMF, the power spectra of the
on the reconstructed signals after performing the other IMFs exhibit self-similar characteristics akin
synthesis function on each scale separately. to those that appear in any dyadic filter structure. As
Secondly, as a consequence of the first a result, the IMF energies should linearly decrease
difference,the IMF samples are not Gaussian in a semilog diagram of, e.g., with respect to for. It
distributed with variance equal to the noise variance, also turns out that the first IMF carries the highest
as the wavelet components are irrespective of scale. amounts of energy
In our study of thresholds, multiples of the
IMF-dependent universal threshold, i.e, where is a
constant, are used. Moreover, the IMF energies can
be computed directly based on the variance estimate
of the first IMF.
3.2 Conventional EMD Denoising
The first attempt at using EMD as a
denoising tool emerged from the need to know
whether a specific IMF contains useful information Figure 3. EMD Conventional Denoising. The top-
or primarily noise. Thus, significance IMF test right numbers are the SNR values after denoising.
procedures were simultaneously developed based on
the statistical analysis of modes resulting from the IV Wavelet based Denoising
decomposition of signals solely consisting of Employing a chosen orthonormal wavelet
fractional Gaussian noise and white Gaussian noise, basis, an orthogonal N× N matrix W is appropriately
respectively. built. Discrete wavelet transform (DWT) c =Wx
where, x=[x(1), x(2), . . . , x(N)] is the vector of the
signal samples and c = [c1, c2, . . . , cN] contains the
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4. Sreedevi Gandham, T. Sreenivasulu Reddy / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 6, November- December 2012, pp.1705-1711
resultant wavelet coefficients. Under white Gaussian also different loss functions. One of the main
noise conditions and due to the orthogonality of advantages of our approach is to naturally provide a
matrix W, any wavelet coefficient 𝑐 𝑖 follows normal thresholding rule, and consequently, a threshold
distribution with variance the noise variance σ and value adapted to the signal/noise under study.
mean the corresponding coefficient value 𝑐 𝑖 of the Moreover, we will also show that the MAP
DWT of the noiseless signal x(t). estimation is also closely related to wavelet
Provided that the signal under regularization of ill-posed inverse stochastic
consideration is sparse in the wavelet domain the problems with appropriate penalties and loss
DWT is expected to distribute the total energy of functions, that parallel Bridge estimation techniques
x(t) in only a few wavelet components lending for nonparametric regression as introduced by the
themselves to high amplitudes. As a result, the sake of simplicity (but see our discussion later), we
amplitude of most of the wavelet components is assume in the sequel that the wavelet coefficients
attributed to noise only. The fundamental reasoning of the signal and the noise are two independent
of wavelet soft thresholding is to set to zero all the sequences of i.i.d. random variables.
components which are lower than a threshold related Hard thresholding zeroes out all values in
to noise level and appropriately shrink the rest of the the frequency band that is found to be Gaussian. The
components hard thresholding coefficient is
There has recently been a great deal of
research interest in wavelet thresholding techniques
for signal and image denoising developed wavelet 0, if the band is Gaussian
ch
shrinkage and thresholding methods for 1, if the band is not Gaussian.
reconstructing signals from noisy data, where the
noise is assumed to be white and Gaussian. They
have also shown that the resulting estimates of the
unknown function are nearly minimax over a large
class of function spaces and for a wide range of loss
functions.
The model generally adopted for the
observed process is
y(i) f (i) (i), i 1,....., K 2 J ( J N )
Where ξ(i)} is usually assumed to be a Figure 4. Wavelet Translation invariant Hard
random noise vector with independent and thresholding.
identically distributed (i.i.d.) Gaussian components
with zero mean and variance 𝜎 2 . Note however that Soft Thresholding is obtained by
the assumption of Gaussianity is alleviated in the multiplying values in the specific frequency band
sequel. Estimation of the underlying unknown signal that is found to be Gaussian, by a coefficient
f is of interest. We subsequently consider a between 0 and 1. Using a coefficient of 0 is the
(periodic) discrete wavelet expansion of the same as hard thresholding, whereas using a
observation signal, leading to the following additive coefficient of 1 is the same as leaving the frequency
model: band undisturbed. The soft thresholding coefficient
is calculated using
Wyj ,k W f j ,k W j ,k , k {1.......2 j K } 34
ˆ
Under the Gaussian noise assumption,
c ,
1.5
thresholding techniques successfully utilize the
unitary transform property of the wavelet Where µ4 g is the bootstrapped kurtosis of the
decomposition to distinguish statistically the signal particular frequency band, and denotes the absolute
components from those of the noise. In order to fix value. The bootstrapped kurtosis value µ4 g is
terminology, we recall that a thresholding rule sets limited not to exceed 4.5, which will be explained
to zero all coefficients with an absolute value below later. The bootstrapped kurtosis value µ4 g is
a certain threshold j 0 . obtained by using the Bootstrap principle, which
We exhibit close connections between calls for resamplings from the data set many times
wavelet thresholding and Maximum A Posteriori with replacement, to obtain N resampled sets of the
(MAP) estimation (or, equivalently, wavelet same length as the original data set. Next, the
regularization) using exponential power prior kurtosis value for each resample is found. Finally,
distributions. Our approach differs from those the Bootstrapped kurtosis µ4 g is defined as the
previously mentioned by using a different prior and estimated mean obtained from N kurtosis values. It
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5. Sreedevi Gandham, T. Sreenivasulu Reddy / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 6, November- December 2012, pp.1705-1711
should be noted that the thresholding coefficient c is 7. Iterate K-1 time between steps 3)-6),where K is
a function of the frequency band’s degree of the number of averaging iteration in order to
Gaussianity. Equation above shows that the obtain k denoised versions of
coefficient c gets closer to 0 as the bootstrapped
x, ie., x1 , x2 ,....., xk .
kurtosis value µ4 g for a specific frequency band
gets closer to the theoretical value 3, and vice versa. 8. Average the resulted denoised signals
k
Therefore, the closer a frequency band gets to being
x(t ) (1/ k ) xk (t ).
Gaussian, the smaller contribution it has after soft k 1
thresholding. 4.2. Clear iterative interval thresholding
In order to simplify the presentation of our When the noise is relatively low, enhanced
result further, we will drop the dependence on the performance compared to EMD-IIT denoising can
resolution level j and the time index k of the be achieved with a variant called clear iterative
quantities involved subsequently. interval-thresholding (EMD-CIIT). The need for
4.1. Iterative EMD Interval-Thresholding such a modification comes from the fact that the
Direct application of translation invariant first IMF, especially when the signal SNR is high, is
denoising to the EMD case will not work. This likely to contain some signal portions as well. If this
arises from the fact that the wavelet components of is the case, then by randomly altering its sample
the circularly shifted versions of the signal positions, the information signal carried on the first
correspond to atoms centered on different signal IMF will spread out contaminating the rest of the
instances. In the case of the data-driven EMD signal along its length. In such an unfortunate
decomposition, the major processing components, situation, the denoising performance will decline. In
which are the extrema, are signal dependent, leading order to overcome this disadvantage of EMD-IIT it
to fixed relative extrema positions with respect to is not the first IMF that is altered directly but the
the signal when the latter is shifted. As a result, the first IMF after having all the parts of the useful
EMD of shifted versions of the noisy signal information signal that it contains removed. The
corresponds to identical IMFs sifted by the same “extraction” of the information signal from the first
amount. Consequently, noise averaging cannot be IMF can be realized with any thresholding method,
achieved in this way. The different denoised either EMD-based or wavelet-based. It is important
versions of the noisy signal in the case of EMD can to note that any useful signal resulting from the
only be constructed from different IMF versions thresholding operation of the first IMF has to be
after being thresholded. Inevitably, this is possible summed with the partial reconstruction of the last 1
only by decomposing different noisy versions of the IMFs.
signal under consideration itself. Algorithm
We know that in white Gaussian noise 1. Perform an EMD expansion of the original noisy
conditions, the first IMF is mainly noise, and more signal x
specifically comprises the larger amount of noise
2. Perform a thresholding operation to the first IMF
compared to the rest of the IMFs. By altering in a
random way the positions of the samples of the first of x(t ) to obtain a denoised version
IMF and then adding the resulting noise signal to the
h (1) (t ) of h(1) (t ) .
sum of the rest of the IMFs, we can obtain a
3. Compute the actual noise signal that existed in
different noisy version of the original signal.
Algorithm
(1)
h(1) (t ), hn (t ) h(1) (t ) h (1) (t ) .
1. Perform an EMD expansion of the original 4.Perform a partial reconstruction using the last L-1
noisy signal x. IMFs plus the information signal contained in the
2. Perform a partial reconstruction using the last
x p (t ) i 2 h(i ) (t ) h (1) (t ).
L
L-1 IMFs only,𝑥 𝑝 (t) = 𝑖 𝐿 = 2ℎ 𝑖𝑡 first IMF,
3. Randomly alter the sample position of the first 5. Randomly alter the sample positions of the noise-
IMF ha (t ) ALTER(h (t )) . only part of the first IMF,
(1) (1)
4. Construct a different noisy version of the ha (t ) ALTER(hn (t )).
(1) (1)
original signal xa (t ) x p (t ) ha (t ).
(1)
5. Perform EMD on the new altered noisy signal
xa (t ).
6. Perform EMD-IT denoising (12)or(13) on the
IMFs of xa (t ) to obtain a denoised version
x1 (t ) of x.
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6. Sreedevi Gandham, T. Sreenivasulu Reddy / International Journal of Engineering Research and
Applications (IJERA) ISSN: 2248-9622 www.ijera.com
Vol. 2, Issue 6, November- December 2012, pp.1705-1711
Figure 5. Result of the EMD-based Denoising with SNR db
Clear Iterative Interval Thresholding method using Meth - -5 -2 0 2 5 10
twenty Iterations. Piece ods 10
5. Simulation results and discussion Regul
ar EMD 7. 10 14. 15 17. 18 22.
signal - 63 .9 27 .9 11 .8 22
4096 CIIT 6 5 8
sampl H(P)
es EMD 8. 10 13. 14 17. 19 23.
- 26 .6 53 .4 44 .2 06
CIIT 1 2 1
H(c)
Figure 6. SNR after denoising with respect to the
number of shifting iterations EMD 8. 10 12. 14 17. 18 22.
-IIT 28 .1 97 .1 18 .8 81
H(c) 3 9 5
EMD 6. 10 13. 15 16. 18 21.
-IIT 78 .6 54 .2 51 .5 93
H(P) 9 2 1
V Conclusions
Figure 7. Performance evaluation of the piece- In the present paper, the principles of hard
regular signal using EMD-based denoising methods and soft wavelet thresholding, including translation
invariant denoising, were appropriately modified to
develop denoising methods suited for thresholding
EMD modes. The novel techniques presented
exhibit an enhanced performance compared to
wavelet denoising in the cases where the signal SNR
is low and/ or the sampling frequency is high. These
preliminary results suggest further efforts for
Figure 8. Performance evaluation of EMD-based improvement of EMD-based denoising when
hard thresholding techniques. denoising of signals with moderate to high SNR
SNR performance and variance of EMD-Based would be appropriate.
Denoising methods applied on Doppler and
Piece-Regular signal. Table 1 & 2 Refernces
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