Search results for: normalized subband adaptive filter
1442 New Subband Adaptive IIR Filter Based On Polyphase Decomposition
Authors: Young-Seok Choi
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We present a subband adaptive infinite-impulse response (IIR) filtering method, which is based on a polyphase decomposition of IIR filter. Motivated by the fact that the polyphase structure has benefits in terms of convergence rate and stability, we introduce the polyphase decomposition to subband IIR filtering, i.e., in each subband high order IIR filter is decomposed into polyphase IIR filters with lower order. Computer simulations demonstrate that the proposed method has improved convergence rate over conventional IIR filters.
Keywords: Subband adaptive filter, IIR filtering. Polyphase decomposition.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 24871441 Adaptive Filtering in Subbands for Supervised Source Separation
Authors: Bruna Luisa Ramos Prado Vasques, Mariane Rembold Petraglia, Antonio Petraglia
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This paper investigates MIMO (Multiple-Input Multiple-Output) adaptive filtering techniques for the application of supervised source separation in the context of convolutive mixtures. From the observation that there is correlation among the signals of the different mixtures, an improvement in the NSAF (Normalized Subband Adaptive Filter) algorithm is proposed in order to accelerate its convergence rate. Simulation results with mixtures of speech signals in reverberant environments show the superior performance of the proposed algorithm with respect to the performances of the NLMS (Normalized Least-Mean-Square) and conventional NSAF, considering both the convergence speed and SIR (Signal-to-Interference Ratio) after convergence.Keywords: Adaptive filtering, multirate processing, normalized subband adaptive filter, source separation.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 9511440 Subband Adaptive Filter Exploiting Sparsity of System
Authors: Young-Seok Choi
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This paper presents a normalized subband adaptive filtering (NSAF) algorithm to cope with the sparsity condition of an underlying system in the context of compressive sensing. By regularizing a weighted l1-norm of the filter taps estimate onto the cost function of the NSAF and utilizing a subgradient analysis, the update recursion of the l1-norm constraint NSAF is derived. Considering two distinct weighted l1-norm regularization cases, two versions of the l1-norm constraint NSAF are presented. Simulation results clearly indicate the superior performance of the proposed l1-norm constraint NSAFs comparing with the classical NSAF.Keywords: Subband adaptive filtering, sparsity constraint, weighted l1-norm.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15241439 Impulsive Noise-Resilient Subband Adaptive Filter
Authors: Young-Seok Choi
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We present a new subband adaptive filter (R-SAF) which is robust against impulsive noise in system identification. To address the vulnerability of adaptive filters based on the L2-norm optimization criterion against impulsive noise, the R-SAF comes from the L1-norm optimization criterion with a constraint on the energy of the weight update. Minimizing L1-norm of the a posteriori error in each subband with a constraint on minimum disturbance gives rise to the robustness against the impulsive noise and the capable convergence performance. Experimental results clearly demonstrate that the proposed R-SAF outperforms the classical adaptive filtering algorithms when impulsive noise as well as background noise exist.Keywords: Subband adaptive filter, L1-norm, system identification, robustness, impulsive interference.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 14621438 Mean-Square Performance of Adaptive Filter Algorithms in Nonstationary Environments
Authors: Mohammad Shams Esfand Abadi, John Hakon Husøy
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Employing a recently introduced unified adaptive filter theory, we show how the performance of a large number of important adaptive filter algorithms can be predicted within a general framework in nonstationary environment. This approach is based on energy conservation arguments and does not need to assume a Gaussian or white distribution for the regressors. This general performance analysis can be used to evaluate the mean square performance of the Least Mean Square (LMS) algorithm, its normalized version (NLMS), the family of Affine Projection Algorithms (APA), the Recursive Least Squares (RLS), the Data-Reusing LMS (DR-LMS), its normalized version (NDR-LMS), the Block Least Mean Squares (BLMS), the Block Normalized LMS (BNLMS), the Transform Domain Adaptive Filters (TDAF) and the Subband Adaptive Filters (SAF) in nonstationary environment. Also, we establish the general expressions for the steady-state excess mean square in this environment for all these adaptive algorithms. Finally, we demonstrate through simulations that these results are useful in predicting the adaptive filter performance.Keywords: Adaptive filter, general framework, energy conservation, mean-square performance, nonstationary environment.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 21771437 Sparsity-Aware and Noise-Robust Subband Adaptive Filter
Authors: Young-Seok Choi
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This paper presents a subband adaptive filter (SAF) for a system identification where an impulse response is sparse and disturbed with an impulsive noise. Benefiting from the uses of l1-norm optimization and l0-norm penalty of the weight vector in the cost function, the proposed l0-norm sign SAF (l0-SSAF) achieves both robustness against impulsive noise and much improved convergence behavior than the classical adaptive filters. Simulation results in the system identification scenario confirm that the proposed l0-norm SSAF is not only more robust but also faster and more accurate than its counterparts in the sparse system identification in the presence of impulsive noise.Keywords: Subband adaptive filter, l0-norm, sparse system, robustness, impulsive interference.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17781436 An Efficient Adaptive Thresholding Technique for Wavelet Based Image Denoising
Authors: D.Gnanadurai, V.Sadasivam
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This frame work describes a computationally more efficient and adaptive threshold estimation method for image denoising in the wavelet domain based on Generalized Gaussian Distribution (GGD) modeling of subband coefficients. In this proposed method, the choice of the threshold estimation is carried out by analysing the statistical parameters of the wavelet subband coefficients like standard deviation, arithmetic mean and geometrical mean. The noisy image is first decomposed into many levels to obtain different frequency bands. Then soft thresholding method is used to remove the noisy coefficients, by fixing the optimum thresholding value by the proposed method. Experimental results on several test images by using this method show that this method yields significantly superior image quality and better Peak Signal to Noise Ratio (PSNR). Here, to prove the efficiency of this method in image denoising, we have compared this with various denoising methods like wiener filter, Average filter, VisuShrink and BayesShrink.Keywords: Wavelet Transform, Gaussian Noise, ImageDenoising, Filter Banks and Thresholding.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 28941435 SystemC Modeling of Adaptive Least Mean Square Filter
Authors: Kyu Han Kim, Soon Kyu Kwon, Heung Sun Yoon, Jong Tae Kim
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In this paper, we demonstrate the adaptive least-mean-square (LMS) filter modeling using SystemC. SystemC is a modeling language that allows designer to model both hardware and software component and makes it possible to design from high level system of abstraction to low level system of abstraction. We produced five adaptive least-mean-square filter models that are classed as five abstraction levels using SystemC proceeding from the abstract model to the more concrete model.Keywords: Adaptive Filter, Least-Mean-Square Algorithm, SystemC, Transversal Fir Filter.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 25251434 Performance Analysis of Adaptive LMS Filter through Regression Analysis using SystemC
Authors: Hyeong-Geon Lee, Jae-Young Park, Suk-ki Lee, Jong-Tae Kim
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The LMS adaptive filter has several parameters which can affect their performance. From among these parameters, most papers handle the step size parameter for controlling the performance. In this paper, we approach three parameters: step-size, filter tap-size and filter form. The regression analysis is used for defining the relation between parameters and performance of LMS adaptive filter with using the system level simulation results. The results present that all parameters have performance trends in each own particular form, which can be estimated from equations drawn by regression analysis.
Keywords: System level model, adaptive LMS FIR filter, regression analysis, systemC.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 27871433 Variable Regularization Parameter Normalized Least Mean Square Adaptive Filter
Authors: Young-Seok Choi
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We present a normalized LMS (NLMS) algorithm with robust regularization. Unlike conventional NLMS with the fixed regularization parameter, the proposed approach dynamically updates the regularization parameter. By exploiting a gradient descent direction, we derive a computationally efficient and robust update scheme for the regularization parameter. In simulation, we demonstrate the proposed algorithm outperforms conventional NLMS algorithms in terms of convergence rate and misadjustment error.Keywords: Regularization, normalized LMS, system identification, robustness.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18701432 Adaptive Notch Filter for Harmonic Current Mitigation
Authors: T. Messikh, S. Mekhilef, N. A. Rahim
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This paper presents an effective technique for harmonic current mitigation using an adaptive notch filter (ANF) to estimate current harmonics. The proposed filter consists of multiple units of ANF connected in parallel structure; each unit is governed by two ordinary differential equations. The frequency estimation is carried out based on the output of these units. The simulation and experimental results show the ability of the proposed tracking scheme to accurately estimate harmonics. The proposed filter was implemented digitally in TMS320F2808 and used in the control of hybrid active power filter (HAPF). The theoretical expectations are verified and demonstrated experimentally.
Keywords: Adaptive notch filter, Active power filter, harmonic filtering, Time varying frequency.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 30461431 Texture Characterization Based on a Chandrasekhar Fast Adaptive Filter
Authors: Mounir Sayadi, Farhat Fnaiech
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In the framework of adaptive parametric modelling of images, we propose in this paper a new technique based on the Chandrasekhar fast adaptive filter for texture characterization. An Auto-Regressive (AR) linear model of texture is obtained by scanning the image row by row and modelling this data with an adaptive Chandrasekhar linear filter. The characterization efficiency of the obtained model is compared with the model adapted with the Least Mean Square (LMS) 2-D adaptive algorithm and with the cooccurrence method features. The comparison criteria is based on the computation of a characterization degree using the ratio of "betweenclass" variances with respect to "within-class" variances of the estimated coefficients. Extensive experiments show that the coefficients estimated by the use of Chandrasekhar adaptive filter give better results in texture discrimination than those estimated by other algorithms, even in a noisy context.
Keywords: Texture analysis, statistical features, adaptive filters, Chandrasekhar algorithm.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16071430 New Data Reuse Adaptive Filters with Noise Constraint
Authors: Young-Seok Choi
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We present a new framework of the data-reusing (DR) adaptive algorithms by incorporating a constraint on noise, referred to as a noise constraint. The motivation behind this work is that the use of the statistical knowledge of the channel noise can contribute toward improving the convergence performance of an adaptive filter in identifying a noisy linear finite impulse response (FIR) channel. By incorporating the noise constraint into the cost function of the DR adaptive algorithms, the noise constrained DR (NC-DR) adaptive algorithms are derived. Experimental results clearly indicate their superior performance over the conventional DR ones.Keywords: Adaptive filter, data-reusing, least-mean square (LMS), affine projection (AP), noise constraint.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16191429 Elimination Noise by Adaptive Wavelet Threshold
Authors: Iman Elyasi, Sadegh Zarmehi
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Due to some reasons, observed images are degraded which are mainly caused by noise. Recently image denoising using the wavelet transform has been attracting much attention. Waveletbased approach provides a particularly useful method for image denoising when the preservation of edges in the scene is of importance because the local adaptivity is based explicitly on the values of the wavelet detail coefficients. In this paper, we propose several methods of noise removal from degraded images with Gaussian noise by using adaptive wavelet threshold (Bayes Shrink, Modified Bayes Shrink and Normal Shrink). The proposed thresholds are simple and adaptive to each subband because the parameters required for estimating the threshold depend on subband data. Experimental results show that the proposed thresholds remove noise significantly and preserve the edges in the scene.
Keywords: Image denoising, Bayes Shrink, Modified Bayes Shrink, Normal Shrink.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 24581428 Near Perfect Reconstruction Quadrature Mirror Filter
Authors: A. Kumar, G. K. Singh, R. S. Anand
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In this paper, various algorithms for designing quadrature mirror filter are reviewed and a new algorithm is presented for the design of near perfect reconstruction quadrature mirror filter bank. In the proposed algorithm, objective function is formulated using the perfect reconstruction condition or magnitude response condition of prototype filter at frequency (ω = 0.5π) in ideal condition. The cutoff frequency is iteratively changed to adjust the filters coefficients using optimization algorithm. The performances of the proposed algorithm are evaluated in term of computation time, reconstruction error and number of iterations. The design examples illustrate that the proposed algorithm is superior in term of peak reconstruction error, computation time, and number of iterations. The proposed algorithm is simple, easy to implement, and linear in nature.
Keywords: Aliasing cancellations filter bank, Filter banks, quadrature mirror filter (QMF), subband coding.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 25141427 Motor Imaginary Signal Classification Using Adaptive Recursive Bandpass Filter and Adaptive Autoregressive Models for Brain Machine Interface Designs
Authors: Vickneswaran Jeyabalan, Andrews Samraj, Loo Chu Kiong
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The noteworthy point in the advancement of Brain Machine Interface (BMI) research is the ability to accurately extract features of the brain signals and to classify them into targeted control action with the easiest procedures since the expected beneficiaries are of disabled. In this paper, a new feature extraction method using the combination of adaptive band pass filters and adaptive autoregressive (AAR) modelling is proposed and applied to the classification of right and left motor imagery signals extracted from the brain. The introduction of the adaptive bandpass filter improves the characterization process of the autocorrelation functions of the AAR models, as it enhances and strengthens the EEG signal, which is noisy and stochastic in nature. The experimental results on the Graz BCI data set have shown that by implementing the proposed feature extraction method, a LDA and SVM classifier outperforms other AAR approaches of the BCI 2003 competition in terms of the mutual information, the competition criterion, or misclassification rate.
Keywords: Adaptive autoregressive, adaptive bandpass filter, brain machine Interface, EEG, motor imaginary.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 28881426 A Family of Affine Projection Adaptive Filtering Algorithms With Selective Regressors
Authors: Mohammad Shams Esfand Abadi, Nader Hadizadeh Kashani, Vahid Mehrdad
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In this paper we present a general formalism for the establishment of the family of selective regressor affine projection algorithms (SR-APA). The SR-APA, the SR regularized APA (SR-RAPA), the SR partial rank algorithm (SR-PRA), the SR binormalized data reusing least mean squares (SR-BNDR-LMS), and the SR normalized LMS with orthogonal correction factors (SR-NLMS-OCF) algorithms are established by this general formalism. We demonstrate the performance of the presented algorithms through simulations in acoustic echo cancellation scenario.Keywords: Adaptive filter, affine projection, selective regressor.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 15621425 Performance Analysis of a Series of Adaptive Filters in Non-Stationary Environment for Noise Cancelling Setup
Authors: Anam Rafique, Syed Sohail Ahmed
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One of the essential components of much of DSP application is noise cancellation. Changes in real time signals are quite rapid and swift. In noise cancellation, a reference signal which is an approximation of noise signal (that corrupts the original information signal) is obtained and then subtracted from the noise bearing signal to obtain a noise free signal. This approximation of noise signal is obtained through adaptive filters which are self adjusting. As the changes in real time signals are abrupt, this needs adaptive algorithm that converges fast and is stable. Least mean square (LMS) and normalized LMS (NLMS) are two widely used algorithms because of their plainness in calculations and implementation. But their convergence rates are small. Adaptive averaging filters (AFA) are also used because they have high convergence, but they are less stable. This paper provides the comparative study of LMS and Normalized NLMS, AFA and new enhanced average adaptive (Average NLMS-ANLMS) filters for noise cancelling application using speech signals.Keywords: AFA, ANLMS, LMS, NLMS.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 19261424 Adaptive Line Enhancement of Narrowband Signal
Authors: Young-Seok Choi
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The Adaptive Line Enhancer (ALE) is widely used for enhancing narrowband signals corrupted by broadband noise. In this paper, we propose novel ALE methods to improve the enhancing capability. The proposed methods are motivated by the fact that the output of the ALE is a fine estimate of the desired narrowband signal with the broadband noise component suppressed. The proposed methods preprocess the input signal using ALE filter to regenerate a finer input signal. Thus the proposed ALE is driven by the input signal with higher signal-to-noise ratio (SNR). The analysis and simulation results are presented to demonstrate that the proposed ALE has better performance than conventional ALE’s.Keywords: Adaptive filter, adaptive line enhancer, noise, feedback.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 20811423 Fast Algorithm of Infrared Point Target Detection in Fluctuant Background
Authors: Yang Weiping, Zhang Zhilong, Li Jicheng, Chen Zengping, He Jun
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The background estimation approach using a small window median filter is presented on the bases of analyzing IR point target, noise and clutter model. After simplifying the two-dimensional filter, a simple method of adopting one-dimensional median filter is illustrated to make estimations of background according to the characteristics of IR scanning system. The adaptive threshold is used to segment canceled image in the background. Experimental results show that the algorithm achieved good performance and satisfy the requirement of big size image-s real-time processing.Keywords: Point target, background estimation, median filter, adaptive threshold, target detection.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18331422 Adaptive Extended Kalman Filter for Ballistic Missile Tracking
Authors: Gaurav Kumar, Dharmbir Prasad, Rudra Pratap Singh
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In the current work, adaptive extended Kalman filter (AEKF) is presented for solution of ground radar based ballistic missile (BM) tracking problem in re-entry phase with unknown ballistic coefficient. The estimation of trajectory of any BM in re-entry phase is extremely difficult, because of highly non-linear motion of BM. The estimation accuracy of AEKF has been tested for a typical test target tracking problem adopted from literature. Further, the approach of AEKF is compared with extended Kalman filter (EKF). The simulation result indicates the superiority of the AEKF in solving joint parameter and state estimation problems.Keywords: Adaptive, AEKF, ballistic missile, EKF, re-entry phase, target tracking.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 16411421 Adaptive Weighted Averaging Filter Using the Appropriate Number of Consecutive Frames
Authors: Mahmoud Saeidi, Ali Nazemipour
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In this paper, we propose a novel adaptive spatiotemporal filter that utilizes image sequences in order to remove noise. The consecutive frames include: current, previous and next noisy frames. The filter proposed in this paper is based upon the weighted averaging pixels intensity and noise variance in image sequences. It utilizes the Appropriate Number of Consecutive Frames (ANCF) based on the noisy pixels intensity among the frames. The number of consecutive frames is adaptively calculated for each region in image and its value may change from one region to another region depending on the pixels intensity within the region. The weights are determined by a well-defined mathematical criterion, which is adaptive to the feature of spatiotemporal pixels of the consecutive frames. It is experimentally shown that the proposed filter can preserve image structures and edges under motion while suppressing noise, and thus can be effectively used in image sequences filtering. In addition, the AWA filter using ANCF is particularly well suited for filtering sequences that contain segments with abruptly changing scene content due to, for example, rapid zooming and changes in the view of the camera.Keywords: Appropriate Number of Consecutive Frames, Adaptive Weighted Averaging, Motion Estimation, Noise Variance, Motion Compensation
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18121420 Adaptive Kalman Filter for Noise Estimation and Identification with Bayesian Approach
Authors: Farhad Asadi, S. Hossein Sadati
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Bayesian approach can be used for parameter identification and extraction in state space models and its ability for analyzing sequence of data in dynamical system is proved in different literatures. In this paper, adaptive Kalman filter with Bayesian approach for identification of variances in measurement parameter noise is developed. Next, it is applied for estimation of the dynamical state and measurement data in discrete linear dynamical system. This algorithm at each step time estimates noise variance in measurement noise and state of system with Kalman filter. Next, approximation is designed at each step separately and consequently sufficient statistics of the state and noise variances are computed with a fixed-point iteration of an adaptive Kalman filter. Different simulations are applied for showing the influence of noise variance in measurement data on algorithm. Firstly, the effect of noise variance and its distribution on detection and identification performance is simulated in Kalman filter without Bayesian formulation. Then, simulation is applied to adaptive Kalman filter with the ability of noise variance tracking in measurement data. In these simulations, the influence of noise distribution of measurement data in each step is estimated, and true variance of data is obtained by algorithm and is compared in different scenarios. Afterwards, one typical modeling of nonlinear state space model with inducing noise measurement is simulated by this approach. Finally, the performance and the important limitations of this algorithm in these simulations are explained.
Keywords: adaptive filtering, Bayesian approach Kalman filtering approach, variance tracking
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 6021419 Time-Derivative Estimation of Noisy Movie Data using Adaptive Control Theory
Authors: Soon-Hyun Park, Takami Matsuo
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This paper presents an adaptive differentiator of sequential data based on the adaptive control theory. The algorithm is applied to detect moving objects by estimating a temporal gradient of sequential data at a specified pixel. We adopt two nonlinear intensity functions to reduce the influence of noises. The derivatives of the nonlinear intensity functions are estimated by an adaptive observer with σ-modification update law.Keywords: Adaptive estimation, parameter adjustmentlaw, motion detection, temporal gradient, differential filter.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 18641418 Evaluation of Context Information for Intermittent Networks
Authors: S. Balaji, E. Golden Julie, Y. Harold Robinson
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The context aware adaptive routing protocol is presented for unicast communication in intermittently connected mobile ad hoc networks (MANETs). The selection of the node is done by the Kalman filter prediction theory and it also makes use of utility functions. The context aware adaptive routing is defined by spray and wait technique, but the time consumption in delivering the message is too high and also the resource wastage is more. In this paper, we describe the spray and focus routing scheme for avoiding the existing problems.
Keywords: Context aware adaptive routing, Kalman filter prediction, spray and wait, spray and focus, intermittent networks.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 8981417 Contrast Enhancement of Color Images with Color Morphing Approach
Authors: Javed Khan, Aamir Saeed Malik, Nidal Kamel, Sarat Chandra Dass, Azura Mohd Affandi
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Low contrast images can result from the wrong setting of image acquisition or poor illumination conditions. Such images may not be visually appealing and can be difficult for feature extraction. Contrast enhancement of color images can be useful in medical area for visual inspection. In this paper, a new technique is proposed to improve the contrast of color images. The RGB (red, green, blue) color image is transformed into normalized RGB color space. Adaptive histogram equalization technique is applied to each of the three channels of normalized RGB color space. The corresponding channels in the original image (low contrast) and that of contrast enhanced image with adaptive histogram equalization (AHE) are morphed together in proper proportions. The proposed technique is tested on seventy color images of acne patients. The results of the proposed technique are analyzed using cumulative variance and contrast improvement factor measures. The results are also compared with decorrelation stretch. Both subjective and quantitative analysis demonstrates that the proposed techniques outperform the other techniques.
Keywords: Contrast enhancement, normalized RGB, adaptive histogram equalization, cumulative variance.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 10981416 Watermarking Scheme for Color Images using Wavelet Transform based Texture Properties and Secret Sharing
Authors: Nagaraj V. Dharwadkar, B.B.Amberker
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In this paper, a new secure watermarking scheme for color image is proposed. It splits the watermark into two shares using (2, 2)- threshold Visual Cryptography Scheme (V CS) with Adaptive Order Dithering technique and embeds one share into high textured subband of Luminance channel of the color image. The other share is used as the key and is available only with the super-user or the author of the image. In this scheme only the super-user can reveal the original watermark. The proposed scheme is dynamic in the sense that to maintain the perceptual similarity between the original and the watermarked image the selected subband coefficients are modified by varying the watermark scaling factor. The experimental results demonstrate the effectiveness of the proposed scheme. Further, the proposed scheme is able to resist all common attacks even with strong amplitude.Keywords: VCS, Dithering, HVS, DWT.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 20391415 The Performance Analysis of Error Saturation Nonlinearity LMS in Impulsive Noise based on Weighted-Energy Conservation
Authors: T Panigrahi, G Panda, Mulgrew
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This paper introduces a new approach for the performance analysis of adaptive filter with error saturation nonlinearity in the presence of impulsive noise. The performance analysis of adaptive filters includes both transient analysis which shows that how fast a filter learns and the steady-state analysis gives how well a filter learns. The recursive expressions for mean-square deviation(MSD) and excess mean-square error(EMSE) are derived based on weighted energy conservation arguments which provide the transient behavior of the adaptive algorithm. The steady-state analysis for co-related input regressor data is analyzed, so this approach leads to a new performance results without restricting the input regression data to be white.Keywords: Error saturation nonlinearity, transient analysis, impulsive noise.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17711414 An Efficient Separation for Convolutive Mixtures
Authors: Salah Al-Din I. Badran, Samad Ahmadi, Dylan Menzies, Ismail Shahin
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This paper describes a new efficient blind source separation method; in this method we uses a non-uniform filter bank and a new structure with different sub-bands. This method provides a reduced permutation and increased convergence speed comparing to the full-band algorithm. Recently, some structures have been suggested to deal with two problems: reducing permutation and increasing the speed of convergence of the adaptive algorithm for correlated input signals. The permutation problem is avoided with the use of adaptive filters of orders less than the full-band adaptive filter, which operate at a sampling rate lower than the sampling rate of the input signal. The decomposed signals by analysis bank filter are less correlated in each sub-band than the input signal at full-band, and can promote better rates of convergence.
Keywords: Blind source separation (BSS), estimates, full-band, mixtures, Sub-band.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 17711413 A Modified Speech Enhancement Using Adaptive Gain Equalizer with Non linear Spectral Subtraction for Robust Speech Recognition
Authors: C. Ganesh Babu, P. T. Vanathi
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In this paper we present an enhanced noise reduction method for robust speech recognition using Adaptive Gain Equalizer with Non linear Spectral Subtraction. In Adaptive Gain Equalizer method (AGE), the input signal is divided into a number of subbands that are individually weighed in time domain, in accordance to the short time Signal-to-Noise Ratio (SNR) in each subband estimation at every time instant. Instead of focusing on suppression the noise on speech enhancement is focused. When analysis was done under various noise conditions for speech recognition, it was found that Adaptive Gain Equalizer method algorithm has an obvious failing point for a SNR of -5 dB, with inadequate levels of noise suppression for SNR less than this point. This work proposes the implementation of AGE when coupled with Non linear Spectral Subtraction (AGE-NSS) for robust speech recognition. The experimental result shows that out AGE-NSS performs the AGE when SNR drops below -5db level.
Keywords: Adaptive Gain Equalizer, Non Linear Spectral Subtraction, Speech Enhancement, and Speech Recognition.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1693