This document summarizes research on using an affine combination of two time-varying least mean square (TVLMS) adaptive filters for applications such as echo cancellation and system identification. The affine combination aims to obtain faster convergence and lower steady-state error compared to individual TVLMS filters. Simulation results show the affine combination of TVLMS filters achieves mean square error of 0.0055 after 1000 iterations for noise cancellation, outperforming standard LMS, affine LMS, and RLS algorithms. The affine combination also performs well for system identification applications, identifying an unknown FIR filter model with low error. The approach provides dependent estimates of an unknown system response from each filter, and finds an optimal affine combining coefficient to minimize mean square error
Mixed Spectra for Stable Signals from Discrete Observationssipij
This paper concerns the continuous-time stable alpha symmetric processes which are inivitable in the modeling of certain signals with indefinitely increasing variance. Particularly the case where the spectral measurement is mixed: sum of a continuous measurement and a discrete measurement. Our goal is to estimate the spectral density of the continuous part by observing the signal in a discrete way. For that, we propose a method which consists in sampling the signal at periodic instants. We use Jackson's polynomial kernel to build a periodogram which we then smooth by two spectral windows taking into account the width of the interval where the spectral density is non-zero. Thus, we bypass the phenomenon of aliasing often encountered in the case of estimation from discrete observations of a continuous time process.
DESPECKLING OF SAR IMAGES BY OPTIMIZING AVERAGED POWER SPECTRAL VALUE IN CURV...ijistjournal
The document describes a novel algorithm for despeckling synthetic aperture radar (SAR) images using particle swarm optimization (PSO) in the curvelet domain. The algorithm first identifies homogeneous regions in the speckled image using variance calculations. It then uses PSO to optimize the thresholding of curvelet coefficients, with the objective of minimizing the average power spectral value. This provides an optimized threshold to apply curvelet-based despeckling. The proposed method is tested on standard images and shown to outperform conventional filters like median and Lee filters in reducing speckle noise.
The peer-reviewed International Journal of Engineering Inventions (IJEI) is started with a mission to encourage contribution to research in Science and Technology. Encourage and motivate researchers in challenging areas of Sciences and Technology.
Introduction to Common Spatial Pattern Filters for EEG Motor Imagery Classifi...Tatsuya Yokota
This document introduces common spatial pattern (CSP) filters for EEG motor imagery classification. CSP filters aim to find spatial patterns in EEG data that maximize the difference between two classes. The document outlines several CSP algorithms including standard CSP, common spatially standardized CSP, and spatially constrained CSP. CSP filters extract discriminative features from EEG data that can improve classification accuracy for brain-computer interface applications involving motor imagery tasks.
The document discusses the dynamic response of structures with uncertain properties. It begins with an introduction discussing how stochasticity impacts dynamic response and efficient quantification of uncertainty. It then covers stochastic single degree of freedom and multiple degree of freedom damped systems. Equivalent damping factors are derived for single degree systems with random natural frequencies. The spectral function approach is also introduced for representing multiple degree of freedom stochastic systems in the frequency domain.
IRJET- Wavelet based Galerkin Method for the Numerical Solution of One Dimens...IRJET Journal
This document presents a wavelet-based Galerkin method for numerically solving one-dimensional partial differential equations using Hermite wavelets. Hermite wavelets are used as the basis functions in the Galerkin method. The method is demonstrated on some test problems, and the numerical results obtained from the proposed method are compared to exact solutions and a finite difference method to evaluate the accuracy and efficiency of the proposed wavelet Galerkin approach.
Special Plenary Lecture at the International Conference on VIBRATION ENGINEERING AND TECHNOLOGY OF MACHINERY (VETOMAC), Lisbon, Portugal, September 10 - 13, 2018
http://www.conf.pt/index.php/v-speakers
Propagation of uncertainties in complex engineering dynamical systems is receiving increasing attention. When uncertainties are taken into account, the equations of motion of discretised dynamical systems can be expressed by coupled ordinary differential equations with stochastic coefficients. The computational cost for the solution of such a system mainly depends on the number of degrees of freedom and number of random variables. Among various numerical methods developed for such systems, the polynomial chaos based Galerkin projection approach shows significant promise because it is more accurate compared to the classical perturbation based methods and computationally more efficient compared to the Monte Carlo simulation based methods. However, the computational cost increases significantly with the number of random variables and the results tend to become less accurate for a longer length of time. In this talk novel approaches will be discussed to address these issues. Reduced-order Galerkin projection schemes in the frequency domain will be discussed to address the problem of a large number of random variables. Practical examples will be given to illustrate the application of the proposed Galerkin projection techniques.
IRJET- A Generalized Delta Shock Maintenance ModelIRJET Journal
1) The document presents a generalized delta shock maintenance model for repairable systems subject to random external shocks.
2) It defines key concepts such as repairable systems, perfect/minimal repair, and mean time to repair.
3) The model assumes the system is subject to two types of shocks arriving according to a Poisson process and derives the reliability function of the system using probability concepts like decomposition of Poisson processes.
Mixed Spectra for Stable Signals from Discrete Observationssipij
This paper concerns the continuous-time stable alpha symmetric processes which are inivitable in the modeling of certain signals with indefinitely increasing variance. Particularly the case where the spectral measurement is mixed: sum of a continuous measurement and a discrete measurement. Our goal is to estimate the spectral density of the continuous part by observing the signal in a discrete way. For that, we propose a method which consists in sampling the signal at periodic instants. We use Jackson's polynomial kernel to build a periodogram which we then smooth by two spectral windows taking into account the width of the interval where the spectral density is non-zero. Thus, we bypass the phenomenon of aliasing often encountered in the case of estimation from discrete observations of a continuous time process.
DESPECKLING OF SAR IMAGES BY OPTIMIZING AVERAGED POWER SPECTRAL VALUE IN CURV...ijistjournal
The document describes a novel algorithm for despeckling synthetic aperture radar (SAR) images using particle swarm optimization (PSO) in the curvelet domain. The algorithm first identifies homogeneous regions in the speckled image using variance calculations. It then uses PSO to optimize the thresholding of curvelet coefficients, with the objective of minimizing the average power spectral value. This provides an optimized threshold to apply curvelet-based despeckling. The proposed method is tested on standard images and shown to outperform conventional filters like median and Lee filters in reducing speckle noise.
The peer-reviewed International Journal of Engineering Inventions (IJEI) is started with a mission to encourage contribution to research in Science and Technology. Encourage and motivate researchers in challenging areas of Sciences and Technology.
Introduction to Common Spatial Pattern Filters for EEG Motor Imagery Classifi...Tatsuya Yokota
This document introduces common spatial pattern (CSP) filters for EEG motor imagery classification. CSP filters aim to find spatial patterns in EEG data that maximize the difference between two classes. The document outlines several CSP algorithms including standard CSP, common spatially standardized CSP, and spatially constrained CSP. CSP filters extract discriminative features from EEG data that can improve classification accuracy for brain-computer interface applications involving motor imagery tasks.
The document discusses the dynamic response of structures with uncertain properties. It begins with an introduction discussing how stochasticity impacts dynamic response and efficient quantification of uncertainty. It then covers stochastic single degree of freedom and multiple degree of freedom damped systems. Equivalent damping factors are derived for single degree systems with random natural frequencies. The spectral function approach is also introduced for representing multiple degree of freedom stochastic systems in the frequency domain.
IRJET- Wavelet based Galerkin Method for the Numerical Solution of One Dimens...IRJET Journal
This document presents a wavelet-based Galerkin method for numerically solving one-dimensional partial differential equations using Hermite wavelets. Hermite wavelets are used as the basis functions in the Galerkin method. The method is demonstrated on some test problems, and the numerical results obtained from the proposed method are compared to exact solutions and a finite difference method to evaluate the accuracy and efficiency of the proposed wavelet Galerkin approach.
Special Plenary Lecture at the International Conference on VIBRATION ENGINEERING AND TECHNOLOGY OF MACHINERY (VETOMAC), Lisbon, Portugal, September 10 - 13, 2018
http://www.conf.pt/index.php/v-speakers
Propagation of uncertainties in complex engineering dynamical systems is receiving increasing attention. When uncertainties are taken into account, the equations of motion of discretised dynamical systems can be expressed by coupled ordinary differential equations with stochastic coefficients. The computational cost for the solution of such a system mainly depends on the number of degrees of freedom and number of random variables. Among various numerical methods developed for such systems, the polynomial chaos based Galerkin projection approach shows significant promise because it is more accurate compared to the classical perturbation based methods and computationally more efficient compared to the Monte Carlo simulation based methods. However, the computational cost increases significantly with the number of random variables and the results tend to become less accurate for a longer length of time. In this talk novel approaches will be discussed to address these issues. Reduced-order Galerkin projection schemes in the frequency domain will be discussed to address the problem of a large number of random variables. Practical examples will be given to illustrate the application of the proposed Galerkin projection techniques.
IRJET- A Generalized Delta Shock Maintenance ModelIRJET Journal
1) The document presents a generalized delta shock maintenance model for repairable systems subject to random external shocks.
2) It defines key concepts such as repairable systems, perfect/minimal repair, and mean time to repair.
3) The model assumes the system is subject to two types of shocks arriving according to a Poisson process and derives the reliability function of the system using probability concepts like decomposition of Poisson processes.
The document discusses various image transforms. It begins by explaining why transforms are used, such as for fast computation and obtaining conceptual insights. It then introduces image transforms as unitary matrices that represent images using a discrete set of basis images. It proceeds to describe one-dimensional orthogonal and unitary transforms using matrices. It also discusses separable two-dimensional transforms and provides properties of unitary transforms such as energy conservation. Specific transforms discussed in more detail include the discrete Fourier transform, discrete cosine transform, discrete sine transform, and Hadamard transform.
This document contains the course calendar for a machine learning course covering topics like Bayesian estimation, Kalman filters, particle filters, hidden Markov models, Bayesian decision theory, principal component analysis, independent component analysis, and clustering algorithms. The calendar lists 15 classes over the semester, the topics to be covered in each class, and any dates where there will be no class. It also includes lecture plans and slides on principal component analysis, linear discriminant analysis, and comparing PCA and LDA.
ON OPTIMIZATION OF MANUFACTURING OF ELEMENTS OF AN BINARY-ROM CIRCUIT TO INCR...JaresJournal
This document discusses optimizing the manufacturing of elements in a binary-ROM circuit to increase integration rate. It proposes doping a heterostructure with specific areas doped via diffusion or ion implantation, then optimizing the annealing of dopants and/or radiation defects. It introduces solving equations to determine spatial and temporal distributions of dopant and defect concentrations during annealing. Optimizing these processes could allow decreasing element dimensions and increasing integration rate in the binary-ROM circuit.
OPTIMIZATION OF MANUFACTURING OF LOGICAL ELEMENTS "AND" MANUFACTURED BY USING...ijcsitcejournal
In this paper we introduce an approach to decrease dimensions of logical elements "AND" based on fieldeffect
heterotransistors. Framework the approach one shall consider a heterostructure with specific structure.
Several specific areas of the het
This document presents an approach to increase vertical integration of transistor-transistor logic elements by considering a heterostructure with a specific configuration. The heterostructure consists of a substrate and several epitaxial layers with defined sections. These sections would be doped through diffusion or ion implantation and then annealed. Mathematical models are developed to determine the spatial and temporal distributions of dopant and radiation defect concentrations during this process. Solving these models allows optimization of dopant and defect annealing to decrease the dimensions of the transistor elements and increase integration density on the heterostructure.
INVERSIONOF MAGNETIC ANOMALIES DUE TO 2-D CYLINDRICAL STRUCTURES –BY AN ARTIF...ijsc
Application of Artificial Neural Network Committee Machine (ANNCM) for the inversion of magnetic
anomalies caused by a long-2D horizontal circular cylinder is presented. Although, the subsurface targets
are of arbitrary shape, they are assumed to be regular geometrical shape for convenience of mathematical
analysis. ANNCM inversion extract the parameters of the causative subsurface targets include depth to the
centre of the cylinder (Z), the inclination of magnetic vector(Ɵ)and the constant term (A)comprising the
radius(R)and the intensity of the magnetic field(I). The method of inversion is demonstrated over a
theoretical model with and without random noise in order to study the effect of noise on the technique and
then extended to real field data. It is noted that the method under discussion ensures fairly accurate results
even in the presence of noise. ANNCM analysis of vertical magnetic anomaly near Karimnagar, Telangana,
India, has shown satisfactory results in comparison with other inversion techniques that are in vogue.The
statistics of the predicted parameters relative to the measured data, show lower sum error (<9.58%) and
higher correlation coefficient (R>91%) indicating that good matching and correlation is achieved between
the measured and predicted parameters.
NEW METHOD OF SIGNAL DENOISING BY THE PAIRED TRANSFORMmathsjournal
A parallel restoration procedure obtained through a splitting of the signal into multiple signals by the paired transform is described. The set of frequency-points is divided by disjoint subsets, and on each of these subsets, the linear filtration is performed separately. The method of optimal Wiener filtration of the noisy signal is considered. In such splitting, the optimal filter is defined as a set of sub filters applied on the splitting-signals. Two new models of filtration are described. In the first model, the traditional filtration is reduced to the processing separately the splitting-signals by the shifted discrete Fourier transforms (DFTs). In the second model, the not shifted DFTs are used over the splitting-signals and sub filters are applied. Such simplified model for splitting the filtration allows for saving 2 − 4( + 1) operations of complex multiplication, for the signals of length = 2^, > 2.
ON OPTIMIZATION OF MANUFACTURING OF FIELD EFFECT HETEROTRANSISTORS FRAMEWORK ...antjjournal
We consider an approach for increasing density of field-effect heterotransistors in a single-stage multi-path
operational amplifier. At the same time one can obtain decreasing of dimensions of the above transistors.
Dimensions of the elements could be decreased by manufacturing of these elements in a heterostructure
with specific structure. The manufacturing is doing by doping of required areas of the heterostructure by
diffusion or ion implantation with future optimization of annealing of dopant and/or radiation defects.
-type and -type four dimensional plane wave solutions of Einstein's field eq...inventy
In the present paper, we have studied - type and -type plane wave solutions of Einstein's field equations in general theory of relativity in the case where the zero mass scalar field coupled with gravitational field and zero mass scalar field coupled with gravitational & electromagnetic field and established the existence of these two types of plane wave solutions in . Furthermore we have considered the case of massive scalar field and shown that the non-existence of these two types of plane wave solutions in GR theory.
1) The document analyzes the boundedness and domain of attraction of a fractional-order wireless power transfer (WPT) system.
2) It establishes a fractional-order piecewise affine model of the WPT system and derives sufficient conditions for boundedness using Lyapunov functions and inequality techniques.
3) The results provide a way to estimate the domain of attraction of the fractional-order WPT system and systems with periodically intermittent control.
ON APPROACH TO INCREASE INTEGRATION RATE OF ELEMENTS OF AN COMPARATOR CIRCUITjedt_journal
In this paper we introduce an approach to increase integration rate of elements of an comparator circuit. Framework the approach we consider a heterostructure with special configuration. Several specific areas of the heterostructure should be doped by diffusion or ion implantation. Annealing of dopant and/or radiation defects should be optimized.
ON DECREASING OF DIMENSIONS OF FIELDEFFECT TRANSISTORS WITH SEVERAL SOURCESmsejjournal
This document analyzes mass and heat transport during manufacturing field-effect heterotransistors with several sources to decrease their dimensions. An analytical approach is introduced to model mass and heat transport during technological processes like doping and annealing. This approach accounts for nonlinearities in mass and heat transport and variations in physical parameters over space and time. The goal is to optimize doping distributions to increase compactness and homogeneity of transistor elements. Equations are developed to model concentration distributions of dopants and point defects over space and time during diffusion and ion implantation doping processes and subsequent annealing.
An Affine Combination Of Two Lms Adaptive Filtersbermudez_jcm
A recent paper studied the statistical behavior of an affine com- bination of two LMS adaptive filters that simultaneously adapt on the same inputs. The filter outputs are linearly combined to yield a performance that is better than that of either filter. Various de- cision rules can be used to determine the time-varying combining parameter λ(n). A scheme based on the ratio of error powers of the two filters was proposed. Monte Carlo simulations demon- strated nearly optimum performance for this scheme. The purpose of this paper is to analyze the statistitical behavior of such error power scheme. Expressions are derived for the mean behavior of λ(n) and for the weight mean-square deviation. Monte Carlo simulations show excellent agreement with the theoretical predictions.
Filtering Electrocardiographic Signals using filtered- X LMS algorithmIDES Editor
This document presents a study on using a filtered-X least mean square (FXLMS) algorithm to remove various types of noise from electrocardiogram (ECG) signals. The FXLMS algorithm is an adaptive noise cancellation technique that is shown to outperform a standard least mean square (LMS) algorithm in terms of signal-to-noise ratio when removing noise such as baseline wander, powerline interference, muscle artifacts, and motion artifacts from real ECG signals based on simulations using a publicly available ECG database. The key aspects of the FXLMS algorithm and its application to adaptive noise cancelation in ECG signals are discussed.
Image denoising using new adaptive based median filtersipij
Noise is a major issue while transferring images through all kinds of electronic communication. One of the
most common noise in electronic communication is an impulse noise which is caused by unstable voltage.
In this paper, the comparison of known image denoising techniques is discussed and a new technique using
the decision based approach has been used for the removal of impulse noise. All these methods can
primarily preserve image details while suppressing impulsive noise. The principle of these techniques is at
first introduced and then analysed with various simulation results using MATLAB. Most of the previously
known techniques are applicable for the denoising of images corrupted with less noise density. Here a new
decision based technique has been presented which shows better performances than those already being
used. The comparisons are made based on visual appreciation and further quantitatively by Mean Square
error (MSE) and Peak Signal to Noise Ratio (PSNR) of different filtered images..
Low power vlsi implementation adaptive noise cancellor based on least means s...shaik chand basha
We are trying to implement an adaptive filter with input weights. The adaptive parameters are obtained by simulating noise canceller on MATLAB. Simulink model of adaptive Noise canceller was developed and Processed by FPGA.
Performance analysis of adaptive noise canceller for an ecg signalRaj Kumar Thenua
In numerous applications of signal processing, communications and biomedical we are faced with the necessity to remove noise and distortion from the signals. Adaptive filtering is one of the most important areas in digital signal processing to remove background noise and distortion. In last few years various adaptive algorithms are developed for noise cancellation. In this paper we present an implementation of LMS (Least Mean Square), NLMS (Normalized Least Mean Square) and RLS (Recursive Least Square) algorithms on MATLAB platform with the intention to compare their performance in noise cancellation. We simulate the adaptive filter in MATLAB with a noisy ECG signal and analyze the performance of algorithms in terms of MSE (Mean Squared Error), SNR Improvement, computational complexity and stability. The obtained results shows that RLS has the best performance but at the cost of large computational complexity and memory requirement.
Noice canclellation using adaptive filters with adpative algorithms(LMS,NLMS,...Brati Sundar Nanda
This document discusses and compares various adaptive filtering algorithms for noise cancellation, including LMS, NLMS, RLS, and APA. It finds that RLS converges the fastest but has the highest complexity, while LMS converges the slowest but is simplest. NLMS and APA provide a balance between convergence speed and complexity. The document implements these algorithms on a noise cancellation problem and finds that RLS achieves the highest SNR improvement and best noise cancellation, followed by APA, NLMS, and LMS.
FITTED OPERATOR FINITE DIFFERENCE METHOD FOR SINGULARLY PERTURBED PARABOLIC C...ieijjournal
In this paper, we study the numerical solution of singularly perturbed parabolic convection-diffusion type
with boundary layers at the right side. To solve this problem, the backward-Euler with Richardson
extrapolation method is applied on the time direction and the fitted operator finite difference method on the
spatial direction is used, on the uniform grids. The stability and consistency of the method were established
very well to guarantee the convergence of the method. Numerical experimentation is carried out on model
examples, and the results are presented both in tables and graphs. Further, the present method gives a more
accurate solution than some existing methods reported in the literature.
FITTED OPERATOR FINITE DIFFERENCE METHOD FOR SINGULARLY PERTURBED PARABOLIC C...ieijjournal
In this paper, we study the numerical solution of singularly perturbed parabolic convection-diffusion type
with boundary layers at the right side. To solve this problem, the backward-Euler with Richardson
extrapolation method is applied on the time direction and the fitted operator finite difference method on the
spatial direction is used, on the uniform grids. The stability and consistency of the method were established
very well to guarantee the convergence of the method. Numerical experimentation is carried out on model
examples, and the results are presented both in tables and graphs. Further, the present method gives a more
accurate solution than some existing methods reported in the literature.
DESPECKLING OF SAR IMAGES BY OPTIMIZING AVERAGED POWER SPECTRAL VALUE IN CURV...ijistjournal
Synthetic Aperture Radar (SAR) images are inherently affected by multiplicative speckle noise, due to the coherent nature of scattering phenomena. In this paper, a novel algorithm capable of suppressing speckle noise using Particle Swarm Optimization (PSO) technique is presented. The algorithm initially identifies homogenous region from the corrupted image and uses PSO to optimize the Thresholding of curvelet coefficients to recover the original image. Average Power Spectrum Value (APSV) has been used as objective function of PSO. The Proposed algorithm removes Speckle noise effectively and the performance of the algorithm is tested and compared with Mean filter, Median filter, Lee filter, Statistic Lee filter, Kuan filter, frost filter and gamma filter., outperforming conventional filtering methods.
INVERSIONOF MAGNETIC ANOMALIES DUE TO 2-D CYLINDRICAL STRUCTURES –BY AN ARTIF...ijsc
Application of Artificial Neural Network Committee Machine (ANNCM) for the inversion of magnetic
anomalies caused by a long-2D horizontal circular cylinder is presented. Although, the subsurface targets
are of arbitrary shape, they are assumed to be regular geometrical shape for convenience of mathematical
analysis. ANNCM inversion extract the parameters of the causative subsurface targets include depth to the
centre of the cylinder (Z), the inclination of magnetic vector(Ɵ)and the constant term (A)comprising the
radius(R)and the intensity of the magnetic field(I). The method of inversion is demonstrated over a
theoretical model with and without random noise in order to study the effect of noise on the technique and
then extended to real field data. It is noted that the method under discussion ensures fairly accurate results
even in the presence of noise. ANNCM analysis of vertical magnetic anomaly near Karimnagar, Telangana,
India, has shown satisfactory results in comparison with other inversion techniques that are in vogue.The
statistics of the predicted parameters relative to the measured data, show lower sum error (<9.58%) and
higher correlation coefficient (R>91%) indicating that good matching and correlation is achieved between
the measured and predicted parameters.
The document discusses various image transforms. It begins by explaining why transforms are used, such as for fast computation and obtaining conceptual insights. It then introduces image transforms as unitary matrices that represent images using a discrete set of basis images. It proceeds to describe one-dimensional orthogonal and unitary transforms using matrices. It also discusses separable two-dimensional transforms and provides properties of unitary transforms such as energy conservation. Specific transforms discussed in more detail include the discrete Fourier transform, discrete cosine transform, discrete sine transform, and Hadamard transform.
This document contains the course calendar for a machine learning course covering topics like Bayesian estimation, Kalman filters, particle filters, hidden Markov models, Bayesian decision theory, principal component analysis, independent component analysis, and clustering algorithms. The calendar lists 15 classes over the semester, the topics to be covered in each class, and any dates where there will be no class. It also includes lecture plans and slides on principal component analysis, linear discriminant analysis, and comparing PCA and LDA.
ON OPTIMIZATION OF MANUFACTURING OF ELEMENTS OF AN BINARY-ROM CIRCUIT TO INCR...JaresJournal
This document discusses optimizing the manufacturing of elements in a binary-ROM circuit to increase integration rate. It proposes doping a heterostructure with specific areas doped via diffusion or ion implantation, then optimizing the annealing of dopants and/or radiation defects. It introduces solving equations to determine spatial and temporal distributions of dopant and defect concentrations during annealing. Optimizing these processes could allow decreasing element dimensions and increasing integration rate in the binary-ROM circuit.
OPTIMIZATION OF MANUFACTURING OF LOGICAL ELEMENTS "AND" MANUFACTURED BY USING...ijcsitcejournal
In this paper we introduce an approach to decrease dimensions of logical elements "AND" based on fieldeffect
heterotransistors. Framework the approach one shall consider a heterostructure with specific structure.
Several specific areas of the het
This document presents an approach to increase vertical integration of transistor-transistor logic elements by considering a heterostructure with a specific configuration. The heterostructure consists of a substrate and several epitaxial layers with defined sections. These sections would be doped through diffusion or ion implantation and then annealed. Mathematical models are developed to determine the spatial and temporal distributions of dopant and radiation defect concentrations during this process. Solving these models allows optimization of dopant and defect annealing to decrease the dimensions of the transistor elements and increase integration density on the heterostructure.
INVERSIONOF MAGNETIC ANOMALIES DUE TO 2-D CYLINDRICAL STRUCTURES –BY AN ARTIF...ijsc
Application of Artificial Neural Network Committee Machine (ANNCM) for the inversion of magnetic
anomalies caused by a long-2D horizontal circular cylinder is presented. Although, the subsurface targets
are of arbitrary shape, they are assumed to be regular geometrical shape for convenience of mathematical
analysis. ANNCM inversion extract the parameters of the causative subsurface targets include depth to the
centre of the cylinder (Z), the inclination of magnetic vector(Ɵ)and the constant term (A)comprising the
radius(R)and the intensity of the magnetic field(I). The method of inversion is demonstrated over a
theoretical model with and without random noise in order to study the effect of noise on the technique and
then extended to real field data. It is noted that the method under discussion ensures fairly accurate results
even in the presence of noise. ANNCM analysis of vertical magnetic anomaly near Karimnagar, Telangana,
India, has shown satisfactory results in comparison with other inversion techniques that are in vogue.The
statistics of the predicted parameters relative to the measured data, show lower sum error (<9.58%) and
higher correlation coefficient (R>91%) indicating that good matching and correlation is achieved between
the measured and predicted parameters.
NEW METHOD OF SIGNAL DENOISING BY THE PAIRED TRANSFORMmathsjournal
A parallel restoration procedure obtained through a splitting of the signal into multiple signals by the paired transform is described. The set of frequency-points is divided by disjoint subsets, and on each of these subsets, the linear filtration is performed separately. The method of optimal Wiener filtration of the noisy signal is considered. In such splitting, the optimal filter is defined as a set of sub filters applied on the splitting-signals. Two new models of filtration are described. In the first model, the traditional filtration is reduced to the processing separately the splitting-signals by the shifted discrete Fourier transforms (DFTs). In the second model, the not shifted DFTs are used over the splitting-signals and sub filters are applied. Such simplified model for splitting the filtration allows for saving 2 − 4( + 1) operations of complex multiplication, for the signals of length = 2^, > 2.
ON OPTIMIZATION OF MANUFACTURING OF FIELD EFFECT HETEROTRANSISTORS FRAMEWORK ...antjjournal
We consider an approach for increasing density of field-effect heterotransistors in a single-stage multi-path
operational amplifier. At the same time one can obtain decreasing of dimensions of the above transistors.
Dimensions of the elements could be decreased by manufacturing of these elements in a heterostructure
with specific structure. The manufacturing is doing by doping of required areas of the heterostructure by
diffusion or ion implantation with future optimization of annealing of dopant and/or radiation defects.
-type and -type four dimensional plane wave solutions of Einstein's field eq...inventy
In the present paper, we have studied - type and -type plane wave solutions of Einstein's field equations in general theory of relativity in the case where the zero mass scalar field coupled with gravitational field and zero mass scalar field coupled with gravitational & electromagnetic field and established the existence of these two types of plane wave solutions in . Furthermore we have considered the case of massive scalar field and shown that the non-existence of these two types of plane wave solutions in GR theory.
1) The document analyzes the boundedness and domain of attraction of a fractional-order wireless power transfer (WPT) system.
2) It establishes a fractional-order piecewise affine model of the WPT system and derives sufficient conditions for boundedness using Lyapunov functions and inequality techniques.
3) The results provide a way to estimate the domain of attraction of the fractional-order WPT system and systems with periodically intermittent control.
ON APPROACH TO INCREASE INTEGRATION RATE OF ELEMENTS OF AN COMPARATOR CIRCUITjedt_journal
In this paper we introduce an approach to increase integration rate of elements of an comparator circuit. Framework the approach we consider a heterostructure with special configuration. Several specific areas of the heterostructure should be doped by diffusion or ion implantation. Annealing of dopant and/or radiation defects should be optimized.
ON DECREASING OF DIMENSIONS OF FIELDEFFECT TRANSISTORS WITH SEVERAL SOURCESmsejjournal
This document analyzes mass and heat transport during manufacturing field-effect heterotransistors with several sources to decrease their dimensions. An analytical approach is introduced to model mass and heat transport during technological processes like doping and annealing. This approach accounts for nonlinearities in mass and heat transport and variations in physical parameters over space and time. The goal is to optimize doping distributions to increase compactness and homogeneity of transistor elements. Equations are developed to model concentration distributions of dopants and point defects over space and time during diffusion and ion implantation doping processes and subsequent annealing.
An Affine Combination Of Two Lms Adaptive Filtersbermudez_jcm
A recent paper studied the statistical behavior of an affine com- bination of two LMS adaptive filters that simultaneously adapt on the same inputs. The filter outputs are linearly combined to yield a performance that is better than that of either filter. Various de- cision rules can be used to determine the time-varying combining parameter λ(n). A scheme based on the ratio of error powers of the two filters was proposed. Monte Carlo simulations demon- strated nearly optimum performance for this scheme. The purpose of this paper is to analyze the statistitical behavior of such error power scheme. Expressions are derived for the mean behavior of λ(n) and for the weight mean-square deviation. Monte Carlo simulations show excellent agreement with the theoretical predictions.
Filtering Electrocardiographic Signals using filtered- X LMS algorithmIDES Editor
This document presents a study on using a filtered-X least mean square (FXLMS) algorithm to remove various types of noise from electrocardiogram (ECG) signals. The FXLMS algorithm is an adaptive noise cancellation technique that is shown to outperform a standard least mean square (LMS) algorithm in terms of signal-to-noise ratio when removing noise such as baseline wander, powerline interference, muscle artifacts, and motion artifacts from real ECG signals based on simulations using a publicly available ECG database. The key aspects of the FXLMS algorithm and its application to adaptive noise cancelation in ECG signals are discussed.
Image denoising using new adaptive based median filtersipij
Noise is a major issue while transferring images through all kinds of electronic communication. One of the
most common noise in electronic communication is an impulse noise which is caused by unstable voltage.
In this paper, the comparison of known image denoising techniques is discussed and a new technique using
the decision based approach has been used for the removal of impulse noise. All these methods can
primarily preserve image details while suppressing impulsive noise. The principle of these techniques is at
first introduced and then analysed with various simulation results using MATLAB. Most of the previously
known techniques are applicable for the denoising of images corrupted with less noise density. Here a new
decision based technique has been presented which shows better performances than those already being
used. The comparisons are made based on visual appreciation and further quantitatively by Mean Square
error (MSE) and Peak Signal to Noise Ratio (PSNR) of different filtered images..
Low power vlsi implementation adaptive noise cancellor based on least means s...shaik chand basha
We are trying to implement an adaptive filter with input weights. The adaptive parameters are obtained by simulating noise canceller on MATLAB. Simulink model of adaptive Noise canceller was developed and Processed by FPGA.
Performance analysis of adaptive noise canceller for an ecg signalRaj Kumar Thenua
In numerous applications of signal processing, communications and biomedical we are faced with the necessity to remove noise and distortion from the signals. Adaptive filtering is one of the most important areas in digital signal processing to remove background noise and distortion. In last few years various adaptive algorithms are developed for noise cancellation. In this paper we present an implementation of LMS (Least Mean Square), NLMS (Normalized Least Mean Square) and RLS (Recursive Least Square) algorithms on MATLAB platform with the intention to compare their performance in noise cancellation. We simulate the adaptive filter in MATLAB with a noisy ECG signal and analyze the performance of algorithms in terms of MSE (Mean Squared Error), SNR Improvement, computational complexity and stability. The obtained results shows that RLS has the best performance but at the cost of large computational complexity and memory requirement.
Noice canclellation using adaptive filters with adpative algorithms(LMS,NLMS,...Brati Sundar Nanda
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IJCER (www.ijceronline.com) International Journal of computational Engineering research
1. International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 7
An Affine Combination of TVLMS Adaptive Filters for Echo
Cancellation
1,
B. Srinivas, M.Tech, 2,Dr. K. Manjunathachari,
1,
Miste (Life Member)
2,
M.Tech, Ph.D
1,
Assistant Professor, ECE Department, Sagar Institute of Technology
2,
Professor & HOD, ECE Department, GITAM University, Hyderabad
Abstract
This paper deals with the statistical behaviour of an affine combination of the outputs of two TVLMS
adaptive filters that simultaneously adapting the same white Gaussian inputs and it’s cancelling the echoes by system
identification. The purpose of the combination is to obtain TVLMS adaptive filters with faster convergence and small
steady-state mean-square deviation (MSD). The linear combination is used in this paper, is a generalization of the
convex combination, in which the combination factor (n) is restricted to interval (0, 1). The viewpoint is taken that
each of the two Filters produces dependent estimates of the unknown channel. Thus, there exists a sequence of
optimal affine combining coefficients which minimizes the MSE and it find’s the unknown system response. These
results will be verified on the MAT LAB 7.8.0 version software by using signal processing tool box. The applications
of this paper are System Identification with low MSE and Echo Cancellation. Now a day in real time applications can
be implemented using an affine combination of TVLMS adaptive filters because, it is easy way to design,
implementation, robustness, with low MSE, and high level noise cancellation and it requires less number of
computational operations.
Keywords—Adaptive filters, affine combination, analysis, time varying least mean square (TVLMS), stochastic
algorithms.
1. INTRODUCTION
The design of many adaptive filters requires a trade-off between convergence speed and steady-state mean-
square error (MSE). A faster (slower) convergence speed yields a larger (smaller) steady-state mean-square deviation
(MSD) and MSE. This property is usually independent of the type of adaptive algorithm, i.e., least mean-square
(LMS), normalized least mean-square (NLMS), recursive least squares (RLS), or affine projection (AP). This design
trade-off is usually controlled by some design parameter of the weight update, such as the step size in LMS or AP,
the step size or the regularization parameter in NLMS or the forgetting factor in RLS. Variable step-size
modifications of the basic adaptive algorithms offer a possible solution to this design problem. Fig. 1 shows where
W1 (n) adaptive filter uses a larger step size than adaptive filter W2 (n) .
The key to this scheme is the selection of the scalar mixing parameter (n) combining the two filter outputs.
The mixing parameters adaptively optimized using a stochastic
Fig.1 Adaptive combining of two transversal adaptive fitters
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2. International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 7
gradient search which minimizes the quadratic error of the overall filter. The convex combination performed as well
as the best of its components in the MSE sense. These results indicate that a combination of adaptive filters can lead
to fast convergence rates and good steady-state performance, an attribute that is usually obtained only in variable
step-size algorithms. Thus, there is great interest in learning more about the properties of such adaptive structures.
This paper provides new results for the performance of the combined structure. The achievable performance is
studied for an affine combination of two TVLMS adaptive filters using the structure shown in Fig. 1 with stationary
signals. Here, the combination parameter (n) is not restricted to the range (0, 1). Thus, Fig. 1 is interpreted from the
viewpoint of a linear combiner. Each adaptive filter is estimating the unknown channel impulse response using the
same input data. Thus, W1 (n) and W2 (n) are statistically dependent estimates of the unknown channel. There exists
a single combining parameter sequence (n) which minimizes the MSD. The parameter (n) does not necessarily
lie within (0, 1) for all. Thus, the output in Fig. 1 is an affine combination of the individual outputs y1 (n) and y2 (n)
the convex combination is a particular case [1] - [2].
2. THE OPTIMAL AFFINE COMBINER
A. THE AFFINE COMBINER
The system under investigation is show in Fig. 1. Each filter uses the LMS adaption rule but with different step size
i , i 1,2;
Wi (n 1) Wi (n) i ei (n)Ui (n), i 1,2 (2.1)
Where e i (n) d(n) - WiT (n)U(n),i 1,2 (2.2)
d(n) e o (n) T
Wo (n)U(n),i 1,2 (2.3)
Where Wi (n),i 1,2 are the N-dimensional adaptive coefficient vectors, eo (n) is assumed zero-mean, and
statistically independent of any other signal in the system, and the input process u (n) is assumed wide-sense
stationary, U(n) u(n),....,u(n - N 1)T is the input vector. It will be assumed, without loss, that 1 2 , so that
W1 (n) will, in general, converge faster than W2 (n) . Also W2 (n) will converge to the lowest individual steady-state
weight maladjustments. The weight vectors W1 (n) and W2 (n) are coupled both deterministically and statistically
through U(n) and eo (n) [2] – [4].
The outputs of the two filters are combined as in Fig. 1:
y(n) (n)y1 (n) [1 - (n)]y2 (n) (2.4)
Where y i (n) WiT (n)U(n),i 1,2 can be any real number and the overall system error is given by
e(n) d(n) - y(n) (2.5)
The adaptive filter output combination is an affine combination, as y (n) can assume any value on the real line. This
setup generalizes the combination of adaptive filter outputs, and can be used to study the properties of the optimal
combination.
B. The Optimal mixing parameter
Equation (2.4) can be written as
y(n) (n)WT (n)U(n) [1 - (n)]W2 (n)U(n) (n)W1 (n) - W2 (n) W2 (n)T U(n)
1
T
(n)W (n) W2 (n)T U(n)
12 (2.6)
Where W12 (n) W1 (n) - W2 (n)
Equation (2.6) shows that y (n) can be interpreted as a combination of W2 (n) and weighted version of the difference
filter W12 (n) . It also shows that the combined adaptive filter has an equivalent weight vector given by
Weq (n) (n)W (n) W2 (n)
12 (2.7)
Subtracting Equation (2.1) for i=2 from Equation (2.1) for i=1 yields a recursion for W12 (n) :
W12 (n 1) I 1U(n)U T (n) W12 (n) 1 2 e 2 (n)U(n) (2.8)
Next, let us consider a rule for choosing (n) that minimizes the conditional MSE at time n E e 2 (n ) | W1 (n), W12 (n) .
Writing e(n) in equation (2.5) as
e(n) eo (n) Wo2 (n) - (n)W (n) U(n)
12
T
(2.9)
Where Wo2 (n) W0 (n) - W2 (n) yields
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3. International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 7
E e 2 (n ) | W1 (n), W12 (n)
2E e(n ) W12 (n)U(n) | W2 (n), W12 (n)
T
(n)
0 (2.10)
Using equation (2.9), taking the expectation over U (n) and defining the input conditional autocorrelation
matrix
R u E U(n)UT (n) | W12 (n), W12 (n) (2.11)
Solving Equation (2.11) for (n) o (n)
T
Wo2 (n)R u W12 (n)
o (n) T
(2.12)
W12 (n)R u W12 (n)
3. SYSTEM IDENTIFICATION
In the class of applications dealing with identification, an adaptive filter is used to provide a linear model that
represents the best fit to an unknown plant. Here, same input is given to both the adaptive filter and the plant. The
output of the plant will serve as the desired signal for the adaptation process. In this application the unknown system
is modelled by an FIR filter with adjustable coefficients. Both the unknown time –variant system and FIR filter
model are excited by an input sequence u(n). The adaptive FIR filter output y(n) is Compared with the unknown
system output d(n) to produce an estimation error e(n). The estimation error represents the difference between the
unknown system output and the model (estimated) output. The estimation error e(n) is then used as the input to an
adaptive control algorithm which corrects the individual tap weights of the filter. This process is repeated through
several iterations until the estimation error e(n) becomes sufficiently small in some statistical sense. The resultant FIR
filter response now represents that of the previously unknown system. It can show in Fig. 2 and Fig 3.
Fig.2 Block diagram of the System Identification
In Fig 3, instead of Adaptive algorithm, we can take the affine combination of two TVLMS algorithm, because it
provides new better results for the performance of the combined structure for system identification [5].
The following toolboxes are used during programming of above algorithms
1. Signal processing Toolbox.
2. Filter design toolbox.
3. General purpose commands.
Fig.3 Block diagram of the System Identification model
4. Applications
A. Advantages and Disadvantages of TVLMS algorithm
(i). The TVLMS algorithm changes (adapts) the filter tap weights so that e (n) is minimized in the mean-square
sense. When the processes are x (n) and d (n) are jointly stationary or Non- stationary, this algorithm converges
to a set of tap-weights which, on average, are equal to the wiener-Hopf solution [3] – [4].
(ii). Simplicity in implementation,
(iii) .inherently stable and robustness performance against different signal conditions, and
(iv). slow convergence (due to eigenvalue spread).
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4. International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 7
B. Applications
Because of their ability to perform well in unknown environments and track statistical time-variations,
adaptive filters have been employed in a wide range of fields. However, there are essentially four basic classes of
applications for adaptive filters. These are: Identification, inverse modeling, prediction, and interference cancellation,
with the main difference between them being the manner in which the desired response is extracted.The adjustable
parameters that are dependent upon the applications at hand are the number of filter taps, choice of FIR or IIR, choice
of training algorithm, and the learning rate. Beyond these, the underlying architecture required for realization is
independent of the application. Therefore, this thesis will focus on one particular application, namely noise
cancellation, as it is the most likely to require an embedded VLSI implementation. This is because it is sometimes
necessary to use adaptive noise cancellation in communication systems such as handheld radios and satellite systems
that are contained on a 16 single silicon chip, where real-time processing is required. Doing this efficiently is
important, because adaptive equalizers are a major component of receivers in modern communications systems and
can account for up to 90% of the total gate count [2].
1. System Identification
2. Inverse modelling
3. Prediction
4. Interference Cancellation: Adaptive Noise cancellation, Echo cancellation
5. RESULT ANALYSIS
Fig.4.shows the adaptive noise cancellation by using affine combination of TVLMS adaptive filters.
Fig.4: The adaptive noise cancellation
For simulations a sinusoidal signal of frequency 1500HZ is used as desired input. The input to the filter is a noisy
signal consisting of multiple sine frequencies and Gaussian random noise. The simulation results are also obtained for
the adaptive filter with standard LMS, TV-LMS, RLS, Affine combination of LMS and TVLMS algorithms using the
same configuration. The results are generated for different number of iterations ranging from 50 to 1000. Finally, the
performance of the TV LMS algorithms is compared with standard LMS, Affine LMS and RLS algorithms are good
and easiest one in the implementation part.
No. of iterations Affine combination of two
TVLMS
Adaptive filters MSE
50 0.0262
100 0.0147
500 0.0066
850 0.0057
900 0.0057
950 0.0056
1000 0.0055
Table 5.1: Iterations Vs MSE of TVLMS algorithm for de-noising noisy-sine wave
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5. International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 7
Fig.5: System identification of an FIR filter with 700 iterations (An affine combination of LMS adaptive filters using
stochastic gradient technique)
Fig.6: System identification of an FIR filter with 700 iterations (An affine combination of TVLMS adaptive filters
using stochastic gradient technique)
Fig.7: The magnitude response of FIR model
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6. International Journal Of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 7
Fig.7: The adaptive Echoes cancellation
6. Conclusions
This paper has studied the statistical behaviour of an affine combination of the outputs of two time varying LMS
adaptive filters that simultaneously adapt using the same Gaussian inputs. The purpose of the affine combination is to
obtain a TVLMS adaptive filter with faster convergence and small steady state Mean Square Deviation. First, the
optimal unrealizable affine combiner was studied and provided the best possible performance. Then, two new
schemes were proposed for practical applications. The first scheme performed nearly as well as the optimal
unrealizable combiner, providing the same convergence time and steady-state behaviour. A second new scheme was
investigated that depended upon the time-averaged instantaneous squared error of each adaptive filter. The viewpoint
is taken that each of the two Filters produces dependent estimates of the unknown channel. Thus, there exists a
sequence of optimal affine combining coefficients which minimizes the mean-square error (MSE) and it find’s the
unknown system response.Now a day in real time applications, video conferences can be implemented using an affine
combination of LMS adaptive filters because, it is easy way to design, implementation, robustness, with low MSE,
and high level noise cancellation and it requires less number of computational operations.
References
[1] LJUNG,L, and S.GUNNARSSON (1990). ―Adaption and Tracking in system identification-A survey.‖
Automatic am, vol. 26. pp. 7-21.
[2] ―Adaptive Filter Theory‖ – SIMON HAYKIN, Prentice Hall, Third Edition.
[3] Digital Signal Processing Primer – C.BITTON and RORABAUGH, TMH.
[4] ―Adaptive Noise Cancelling: principles and applications,‖ Proc, IEEE vol. 63, pp. 1692-1716.
[5] Neil J.Bershad and JC M. Bermudez, ―IEEE Trans.. On Signal Processing‖, vol
AUTHORS
Mr B.Srinivas, an M.Tech from JNT
University, Hyderabad, has an experience of
more than 6.5 years of Teaching. At present
Mr. B. Srinivas serving the Sagar Institute of
Technology (Sagar Group of Institutions),
Chevella, Ranga Reddy, and A.P, as an
Assistant Professor of the department of
Electronics and Communication Engineering.
He is the Life member of ISTE.
Dr. K. Manjunathachari, an M.Tech from
JNT University, Hyderabad, and Ph.D from
JNT University, Hyderabad, A.P, has an
experience of more than 16 years of Teaching
and 3 years of Industry. At present Dr. K.
Manjunathachari serving the GITAM
University, Hyderabad and A.P, as a
Professor and Head of the department of
Electronics and Communication Engineering.
Issn 2250-3005(online) November| 2012 Page 151