Showing papers on "Impulse noise published in 2011"
TL;DR: Mixed evidence was obtained for the traditional arousal and masking explanations for noise effects, and the overall pattern of findings was most consistent with the maximal adaptability theory, a mental-resource-based explanation of stress and performance variation.
Abstract: Noise is a pervasive and influential source of stress. Whether through the acute effects of impulse noise or the chronic influence of prolonged exposure, the challenge of noise confronts many who must accomplish vital performance duties in its presence. Although noise has diffuse effects, which are shared in common with many other chronic forms of stress, it also exerts its own specific influences on various forms of cognitive and motor response. We present a quantitative evaluation of these influences so that their harmful effects can be mitigated, their beneficial effects exploited, and any residual effects incorporated and synthesized into selection, training, and design strategies to facilitate human performance capacities. Predictions of single and joint moderator effects were made on the basis of major theories of noise and performance, specifically those explanations based on arousal, masking, or cognitive-resource mechanisms. These predictions were tested through moderator analyses of effects as a function of task type, performance measure, noise type and schedule, and the intensity and duration of exposure. Observed outcome effects (797 effect sizes derived from 242 studies) varied as a function of each of these moderators. Collective findings identified continuous versus intermittent noise, noise type, and type of task as the major distinguishing characteristics that moderated response. Mixed evidence was obtained for the traditional arousal and masking explanations for noise effects. The overall pattern of findings was most consistent with the maximal adaptability theory, a mental-resource-based explanation of stress and performance variation.
328 citations
TL;DR: Zhang et al. as discussed by the authors proposed a l"1-l"0 minimization approach, where the l" 1 term is used for impulse denoising and the l' 0 term was used for sparse representation over a dictionary of images patches.
182 citations
TL;DR: An iterative framelet-based approximation/sparsity deblurring algorithm (IFASDA) for the proposed functional, which has a content-dependent fidelity term which assimilates the strength of fidelity terms measured by the l1 and l2 norms.
Abstract: This paper studies a problem of image restoration that observed images are contaminated by Gaussian and impulse noise. Existing methods for this problem in the literature are based on minimizing an objective functional having the l1 fidelity term and the Mumford-Shah regularizer. We present an algorithm on this problem by minimizing a new objective functional. The proposed functional has a content-dependent fidelity term which assimilates the strength of fidelity terms measured by the l1 and l2 norms. The regularizer in the functional is formed by the l1 norm of tight framelet coefficients of the underlying image. The selected tight framelet filters are able to extract geometric features of images. We then propose an iterative framelet-based approximation/sparsity deblurring algorithm (IFASDA) for the proposed functional. Parameters in IFASDA are adaptively varying at each iteration and are determined automatically. In this sense, IFASDA is a parameter-free algorithm. This advantage makes the algorithm more attractive and practical. The effectiveness of IFASDA is experimentally illustrated on problems of image deblurring with Gaussian and impulse noise. Improvements in both PSNR and visual quality of IFASDA over a typical existing method are demonstrated. In addition, Fast_IFASDA, an accelerated algorithm of IFASDA, is also developed.
178 citations
03 Apr 2011
TL;DR: In this paper, a simple model, in the frequency band up to 100 MHz, was derived by considering the noise generated at the source and taking into account the effect of the channel.
Abstract: This paper reviews existing noise models including both background and impulsive noise for the in-home PLC scenario, highlighting similarities and differences. With reference to the impulsive noise, it is shown that a simple model, in the frequency band up to 100 MHz, can be derived by considering the noise generated at the source and taking into account the effect of the channel. Capacity considerations are then made, comparing erasure decoding strategies or full decoding strategies.
109 citations
TL;DR: The proposed method showed promising results and high noise robustness to a wide range of heart sounds, however, more tests are needed to address any bias that may have been introduced by different sources of heartSounds in the current training set, and to concretely validate the method.
Abstract: A new framework for heart sound analysis is proposed. One of the most difficult processes in heart sound analysis is segmentation, due to interference form murmurs. Equal number of cardiac cycles were extracted from heart sounds with different heart rates using information from envelopes of autocorrelation functions without the need to label individual fundamental heart sounds (FHS). The complete method consists of envelope detection, calculation of cardiac cycle lengths using auto-correlation of envelope signals, features extraction using discrete wavelet transform, principal component analysis, and classification using neural network bagging predictors. The proposed method was tested on a set of heart sounds obtained from several on-line databases and recorded with an electronic stethoscope. Geometric mean was used as performance index. Average classification performance using ten-fold cross-validation was 0.92 for noise free case, 0.90 under white noise with 10 dB signal-to-noise ratio (SNR), and 0.90 under impulse noise up to 0.3 s duration. The proposed method showed promising results and high noise robustness to a wide range of heart sounds. However, more tests are needed to address any bias that may have been introduced by different sources of heart sounds in the current training set, and to concretely validate the method. Further work include building a new training set recorded from actual patients, then further evaluate the method based on this new training set.
98 citations
TL;DR: In this paper, an approach to impulse noise removal is presented, which is a switching filter which identifies the noisy pixels and then corrects them by using median filter, in order to identify pixels corrupted by noise, local intensity extrema is applied.
Abstract: In this study an approach to impulse noise removal is presented. The introduced algorithm is a switching filter which identifies the noisy pixels and then corrects them by using median filter. In order to identify pixels corrupted by noise an analysis of local intensity extrema is applied. Comprehensive analysis of the algorithm performance [in terms of peak signal-to-noise ratio (PSNR) and Structural SIMilarity (SSIM) index] is presented. Results obtained on wide range of noise corruption (up to 98%) are shown and discussed. Moreover, comparison with well-established methods for impulse noise removal is provided. Presented results reveal that the proposed algorithm outperforms other approaches to impulse noise removal and its performance is close to ideal switching median filter. For high noise densities, the method correctly detects up to 100% of noisy pixels.
86 citations
TL;DR: An algorithm based on minimizing the squared logarithmic transformation of the error signal is proposed in this correspondence and is more robust for impulsive noise control and does not need the parameter selection and thresholds estimation according to the noise characteristics.
Abstract: To overcome the limitations of the existing algorithms for active impulsive noise control, an algorithm based on minimizing the squared logarithmic transformation of the error signal is proposed in this correspondence. The proposed algorithm is more robust for impulsive noise control and does not need the parameter selection and thresholds estimation according to the noise characteristics. These are verified by theoretical analysis and numerical simulations.
86 citations
TL;DR: The theoretical analyses reveal the advantages of input normalization and the M-estimation in combating impulsive noise and a convergence performance analysis for the S-LMS/S-LMM family for Gaussian inputs and additive Gaussian or contaminated Gaussian noises is presented.
Abstract: The sequential partial-update least mean square (S-LMS)-based algorithms are efficient methods for reducing the arithmetic complexity in adaptive system identification and other industrial informatics applications. They are also attractive in acoustic applications where long impulse responses are encountered. A limitation of these algorithms is their degraded performances in an impulsive noise environment. This paper proposes new robust counterparts for the S-LMS family based on M-estimation. The proposed sequential least mean M-estimate (S-LMM) family of algorithms employ nonlinearity to improve their robustness to impulsive noise. Another contribution of this paper is the presentation of a convergence performance analysis for the S-LMS/S-LMM family for Gaussian inputs and additive Gaussian or contaminated Gaussian noises. The analysis is important for engineers to understand the behaviors of these algorithms and to select appropriate parameters for practical realizations. The theoretical analyses reveal the advantages of input normalization and the M-estimation in combating impulsive noise. Computer simulations on system identification and joint active noise and acoustic echo cancellations in automobiles with double-talk are conducted to verify the theoretical results and the effectiveness of the proposed algorithms.
80 citations
01 Dec 2011
TL;DR: A canonical statistical-physical model of the instantaneous statistics of asynchronous noise based on the physical properties of the PLC network is derived and the distribution is validated using simulated and measured PLC noise data.
Abstract: Powerline distribution networks are increasingly being employed to support smart grid communication infrastructure and in-home LAN connectivity. However, their primary function of power distribution results in a hostile environment for communication systems. In particular, asynchronous impulsive noise, with levels as high as 50dB above thermal noise, causes significant degradation in communication performance. Much of the prior work uses limited empirical measurements to propose a statistical model for instantaneous statistics of asynchronous noise. In this paper, we (i) derive a canonical statistical-physical model of the instantaneous statistics of asynchronous noise based on the physical properties of the PLC network, and (ii) validate the distribution using simulated and measured PLC noise data. The results of this paper can be used to analyze, simulate, and mitigate the effect of the asynchronous noise on PLC systems.
79 citations
03 Apr 2011
TL;DR: A new IN mitigation technique is introduced that is based on the application of block-based compressed sensing (CS) and makes use of null-subcarriers in PLC orthogonal frequency division multiplexing (OFDM) transmission systems and the burst structure of IN to detect the location and the values of IN samples in the received signal.
Abstract: Impulse noise (IN) is one of the major impairments for data transmission over power lines. For power line communications (PLC) systems with bandwidths in the high kHz to MHz range, IN occurs in bursts. As long as those bursts are sufficiently short compared to a signal-processing (e.g. coding) frame, there is hope to successfully mitigate IN. In this paper, we introduce a new IN mitigation technique that is based on the application of block-based compressed sensing (CS). It makes use of null-subcarriers in PLC orthogonal frequency division multiplexing (OFDM) transmission systems and the burst structure of IN to detect the location and the values of IN samples in the received signal. We also devise a semi-analytical error-rate performance evaluation for coded OFDM over IN channels, which allows insights into how CS-based IN detection can be used for improved reliability of transmission. Numerical results for typical PLC transmission settings demonstrate the efficacy of the proposed application of CS for IN detection.
79 citations
TL;DR: The present work enhances the basic DUDE scheme by incorporating statistical modeling tools that have proven successful in addressing similar issues in lossless image compression, and significantly surpass the state of the art in the case of salt and pepper (S&P) and -ary symmetric noise, and perform well for Gaussian noise.
Abstract: We present an extension of the discrete universal denoiser DUDE, specialized for the denoising of grayscale images. The original DUDE is a low-complexity algorithm aimed at recovering discrete sequences corrupted by discrete memoryless noise of known statistical characteristics. It is universal, in the sense of asymptotically achieving, without access to any information on the statistics of the clean sequence, the same performance as the best denoiser that does have access to such information. The DUDE, however, is not effective on grayscale images of practical size. The difficulty lies in the fact that one of the DUDE's key components is the determination of conditional empirical probability distributions of image samples, given the sample values in their neighborhood. When the alphabet is relatively large (as is the case with grayscale images), even for a small-sized neighborhood, the required distributions would be estimated from a large collection of sparse statistics, resulting in poor estimates that would not enable effective denoising. The present work enhances the basic DUDE scheme by incorporating statistical modeling tools that have proven successful in addressing similar issues in lossless image compression. Instantiations of the enhanced framework, which is referred to as iDUDE, are described for examples of additive and nonadditive noise. The resulting denoisers significantly surpass the state of the art in the case of salt and pepper (S&P) and -ary symmetric noise, and perform well for Gaussian noise.
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TL;DR: This work combines M-ary FSK with diversity and coding to make the transmission robust against permanent frequency disturbances and impulse noise and can be considered as a form of coded Frequency Hopping and is thus extendable to any frequency range.
Abstract: We discuss the application of coded modulation for power-line communications. We combine M-ary FSK with diversity and coding to make the transmission robust against permanent frequency disturbances and impulse noise. We give a particular example of the coding/modulation scheme that is in agreement with the existing CENELEC norms. The scheme can be considered as a form of coded Frequency Hopping and is thus extendable to any frequency range.
TL;DR: The experiments show that the proposed method outperforms other state-of-the-art filters both visually and in terms of objective quality measures such as the mean absolute error (MAE), the peak-signal-to-noise ratio (PSNR) and the normalized color difference (NCD).
Abstract: In this paper, a new fuzzy filter for the removal of random impulse noise in color video is presented. By working with different successive filtering steps, a very good tradeoff between detail preservation and noise removal is obtained. One strong filtering step that should remove all noise at once would inevitably also remove a considerable amount of detail. Therefore, the noise is filtered step by step. In each step, noisy pixels are detected by the help of fuzzy rules, which are very useful for the processing of human knowledge where linguistic variables are used. Pixels that are detected as noisy are filtered, the others remain unchanged. Filtering of detected pixels is done by blockmatching based on a noise adaptive mean absolute difference. The experiments show that the proposed method outperforms other state-of-the-art filters both visually and in terms of objective quality measures such as the mean absolute error (MAE), the peak-signal-to-noise ratio (PSNR) and the normalized color difference (NCD).
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TL;DR: New theory and algorithms to recover signals that are approximately sparse in some general dictionary but corrupted by a combination of interference having a sparse representation in a second general dictionary and measurement noise are developed.
Abstract: This paper develops new theory and algorithms to recover signals that are approximately sparse in some general dictionary (i.e., a basis, frame, or over-/incomplete matrix) but corrupted by a combination of interference having a sparse representation in a second general dictionary and measurement noise. The algorithms and analytical recovery conditions consider varying degrees of signal and interference support-set knowledge. Particular applications covered by the proposed framework include the restoration of signals impaired by impulse noise, narrowband interference, or saturation/clipping, as well as image in-painting, super-resolution, and signal separation. Two application examples for audio and image restoration demonstrate the efficacy of the approach.
TL;DR: Analytical and simulation results show that the proposed Lp-norm detector yields significant performance gains compared to conventional energy detection in non-Gaussian noise and approaches the performance of the locally optimal detector which requires knowledge of the noise distribution.
Abstract: In cognitive radio (CR) systems, reliable spectrum sensing techniques are required in order to avoid interference to the primary users of the spectrum. Whereas most of the existing literature on spectrum sensing considers impairment by additive white Gaussian noise (AWGN) only, in practice, CRs also have to cope with various types of non-Gaussian noise such as man-made impulsive noise, co-channel interference, and ultra-wideband interference. In this paper, we propose an adaptive Lp-norm detector which does not require any a priori knowledge about the primary user signal and performs well for a wide range of circularly symmetric non-Gaussian noises with finite moments. We analyze the probabilities of false alarm and missed detection of the proposed detector in Rayleigh fading in the low signal-to-noise ratio regime and investigate its asymptotic performance if the number of samples available for spectrum sensing is large. Furthermore, we consider the deflection coefficient for optimization of the Lp-norm parameters and discuss its connection to the probabilities of false alarm and missed detection. Based on the deflection coefficient an adaptive algorithm for online optimization of the Lp-norm parameters is developed. Analytical and simulation results show that the proposed Lp-norm detector yields significant performance gains compared to conventional energy detection in non-Gaussian noise and approaches the performance of the locally optimal detector which requires knowledge of the noise distribution.
TL;DR: This paper proposes the FPGA implementation of an adaptive algorithm that is robust to impulsive noise using these two approaches and final comparison results are provided in order to test accuracy, performance, and logic occupation.
Abstract: Adaptive filters are used in a wide range of applications such as echo cancellation, noise cancellation, system identification, and prediction. Its hardware implementation becomes essential in many cases where real-time execution is needed. However, impulsive noise affects the proper operation of the filter and the adaptation process. This noise is one of the most damaging types of signal distortion, not always considered when implementing algorithms, particularly in specific hardware platforms. Field-programmable gate arrays (FPGAs) are used widely for real-time applications where timing requirements are strict. Nowadays, two main design processes can be followed for embedded system design, namely, a hardware description language (e.g., VHDL) and a high-level synthesis design tool. This paper proposes the FPGA implementation of an adaptive algorithm that is robust to impulsive noise using these two approaches. Final comparison results are provided in order to test accuracy, performance, and logic occupation.
TL;DR: This letter proposes a new technique of restoring images distorted by random-valued impulse noise, based on finding the optimum direction, by calculating the standard deviation in different directions in the filtering window.
Abstract: This letter proposes a new technique of restoring images distorted by random-valued impulse noise. The detection process is based on finding the optimum direction, by calculating the standard deviation in different directions in the filtering window. The tested pixel is deemed original if it is similar to the pixels in the optimum direction. Extensive simulations prove that the proposed technique has superior performance, when compared to other existing methods, especially at high noise rates.
TL;DR: In first proposed algorithm, the reference and the error signals are thresholded before being used in the update equation of FxLMP algorithm, and a modified normalized step size is proposed to improve the robustness of the Fx LMP algorithm.
TL;DR: A class of second-order improved and edge-preserving PDE denoising models is proposed based on the two new controlling functions in order to deal with random-valued impulse noise reliably.
Abstract: This paper is concerned with partial differential equation (PDE)-based image denoising for random-valued impulse noise. We introduce the notion of ENI (the abbreviation for “edge pixels, noisy pixels, and interior pixels”) that denotes the number of homogeneous pixels in a local neighborhood and is significantly different for edge pixels, noisy pixels, and interior pixels. We redefine the controlling speed function and the controlling fidelity function to depend on ENI. According to our two controlling functions, the diffusion and fidelity process at edge pixels, noisy pixels, and interior pixels can be selectively carried out. Furthermore, a class of second-order improved and edge-preserving PDE denoising models is proposed based on the two new controlling functions in order to deal with random-valued impulse noise reliably. We demonstrate the performance of the proposed PDEs via application to five standard test images, corrupted by random-valued impulse noise with various noise levels and comparison with the related second-order PDE models and the other special filtering methods for random-valued impulse noise. Our two controlling functions are extended to automatically other PDE models.
TL;DR: An effective algorithm for removing impulse noise from corrupted images is presented under the framework of switching median filtering and provides better performance in terms of PSNR and MAE than many other median filters for impulse noise removal.
Abstract: An effective algorithm for removing impulse noise from corrupted images is presented under the framework of switching median filtering. Firstly, noisy pixels are distinguished by Local Outlier Factor incorporating with Boundary Discriminative Noise Detection (LOFBDND). Then, the directional weighted median filter is adopted to remove the detected impulses by replacing each noisy pixel with the weighted mean of its neighbors in the filtering window. Our noise detection algorithm makes the decision so accurate that the miss detection rate and false detection rate are very low. Extensive simulation results show that our method provides better performance in terms of PSNR and MAE than many other median filters for impulse noise removal.
TL;DR: A new robust recursive least-squares adaptive filtering algorithm that uses a priori error-dependent weights that offers improved robustness as well as better tracking compared to the conventional RLS andursive least-M estimate adaptation algorithms.
Abstract: A new robust recursive least-squares (RLS) adaptive filtering algorithm that uses a priori error-dependent weights is proposed. Robustness against impulsive noise is achieved by choosing the weights on the basis of the L1 norms of the crosscorrelation vector and the input-signal autocorrelation matrix. The proposed algorithm also uses a variable forgetting factor that leads to fast tracking. Simulation results show that the proposed algorithm offers improved robustness as well as better tracking compared to the conventional RLS and recursive least-M estimate adaptation algorithms.
03 Oct 2011
TL;DR: This work proposes an algorithm that utilizes the guard band null carriers for the impulsive noise estimation and cancellation and exploits the structure present in the problem and the available a priori information jointly for sparse signal recovery.
Abstract: Impulsive noise is the bottleneck that limits the distance at which DSL communications can take place. By considering impulsive noise a sparse vector, recently developed sparse reconstruction algorithms can be utilized to combat it. We propose an algorithm that utilizes the guard band null carriers for the impulsive noise estimation and cancellation. Instead of relying on l 1 minimization as done in some popular general-purpose compressive sensing (CS) schemes, the proposed method exploits the structure present in the problem and the available a priori information jointly for sparse signal recovery. The computational complexity of the proposed algorithm is very low as compared to the sparse reconstruction algorithms based on l 1 minimization. A performance comparison of the proposed method with other techniques, including l 1 minimization and another recently developed scheme for sparse signal recovery, is provided in terms of achievable rates for a DSL line with impulse noise estimation and cancellation.
TL;DR: The channel capacity for the alpha-stable channels is developed both for the symmetric and the skewed cases and numerical bounds for its lossy coding performance are provided.
Abstract: Alpha-stable distributions have found various applications in the literature especially in modelling impulsive noise in communications channels. Despite various schemes for receiver design under alpha-stable noise, the channel coding problem has not been addressed yet. In this letter, we develop the channel capacity for the alpha-stable channels both for the symmetric and the skewed cases and provide numerical bounds for its lossy coding performance. Blahut-Arimoto algorithm is used to calculate the capacity of alpha-stable channel and its efficiency is demonstrated.
TL;DR: In this article, measurements of impulsive noise in a 400/275/132-kV electricity substation were made with three antennas having overlapping bands covering the range 100 MHz-6 GHz.
Abstract: Measurements of impulsive noise in a 400/275/132-kV electricity substation are presented. The measurements are made with three antennas having overlapping bands covering the range 100 MHz-6 GHz. This range includes those bands relevant to modern wireless local area network and wireless personal area network technologies (e.g., IEEE 802.11a/b/g and IEEE 802.15.1/4), which, if proved to be sufficiently robust in the presence of impulsive noise, could play a useful role in substation monitoring and control. Impulsive events are extracted from the measured data using the wavelet packet transform, and the statistical distributions of pulse rate, pulse amplitude, pulse duration, and pulse rise time are presented. An unexpected quasi-periodic component of the noise is observed.
TL;DR: Experimental results show that the images filtered with the proposed method contain less noisy pixels than those obtained through the vector median filter.
Abstract: This paper describes a new filter for impulse noise reduction in colour images which is aimed at improving the noise reduction capability of the classical vector median filter. The filter is inspired by the application of a vector marginal median filtering process over a selected group of pixels in each filtering window. This selection, which is based on the vector median, along with the application of the marginal median operation constitutes an adaptive process that leads to a more robust filter design. Also, the proposed method is able to process colour images without introducing colour artifacts. Experimental results show that the images filtered with the proposed method contain less noisy pixels than those obtained through the vector median filter.
TL;DR: A novel two-stage noise removal technique for image enhancement and noise removal and a Neural Network (NN) is proposed in order to improve the sensitive regions with higher visual quality.
Abstract: enhancement is plays a vital role in various applications. There are many techniques to remove the noise from the image and produce the clear visual of the image. Moreover, there are several filters and image smoothing techniques available in the literature. All these available techniques have certain limitations. Recently, neural networks are found to be a very efficient tool for image enhancement. A novel two-stage noise removal technique for image enhancement and noise removal is proposed in this paper. In noise removal stage, an adaptive two-level feed forward Neural Network (NN) with a Modified Levenberg-Marquardt training algorithm was used to eliminate the impulse noise. In the image enhancement stage, the fuzzy decision rules inspired by the Human Visual System (HVS) are used to categorize the image pixels into human perception sensitive class and nonsensitive class, and to enhance the quality of the image. The Hyper trapezoidal fuzzy membership function is used in the proposed technique. In order to improve the sensitive regions with higher visual quality, a Neural Network (NN) is proposed. The experiment is conducted with standard image. It is observed from the experimental result that the proposed two stage technique shows significant performance when compared to existing methods.
TL;DR: Simulation results show that the proposed Clipping/Blanking technique slightly improves the BER performance of the narrowband PLC system for smart grid applications and two-way communication between smart meters and utilities1.
Abstract: Performance of the narrowband power line communication (PLC) is significantly degraded by the impulsive noise with very large amplitudes and short durations. In practical applications, the simple memoryless nonlinearity techniques (Clipping, Blanking, and Clipping/Blanking) are often used in order to mitigate the effect of the impulsive noise. In this paper, we propose an optimal Clipping/Blanking technique for impulsive noise reduction in narrowband (9-490 kHz) PLC system. This optimal technique is based on the minimum bit error rate (BER) search. To this end, we have derived the transfer function of a typical low voltage (LV) PLC network using the common bottom-up approach and scattering matrix method. Our simulation results, in terms of BER versus signal to noise ratio (SNR), show that the proposed technique slightly improves the BER performance of the narrowband PLC system for smart grid applications and two-way communication between smart meters and utilities1.
TL;DR: A new Decision Based median filtering algorithm is presented for the removal of impulse noise from digital images by replacing the impulse noise corrupted pixel by the median of the pixel scanned in four directions.
Abstract: In this paper, a new Decision Based median filtering algorithm is presented for the removal of impulse noise from digital images. Here, we replace the impulse noise corrupted pixel by the median of the pixel scanned in four directions.The signal restoration scheme of this filter adapts to the varied impulse noise ratios while determining an appropriate signal restorer from a reliable neighbourhood. The experimental results of this filter applied on various images corrupted with almost all ratios of impulse noise favour the filter in terms of objectivity and subjectivity than many of the other prominent impulse noise filters.
TL;DR: The aim of this paper is to study the problem of load-time variation in power line communication (PLC) systems and to analyze asynchronous impulsive noise and related channel variations due to switch commutations by using a numerical model of the time-varying communication channel developed by using scattering parameters in the wavelet domain.
Abstract: The aim of this paper is to study the problem of load-time variation in power line communication (PLC) systems and to analyze asynchronous impulsive noise and related channel variations due to switch commutations. A numerical model of the time-varying communication channel is developed by using scattering parameters in the wavelet domain. The proposed method uses the N-port description of the elements that constitute a time-varying PLC system in terms of real matrices with constant elements. This represents a valid alternative to the time domain description usually adopted for analyzing time-varying networks. The comparison with results obtained from other numerical models and with experimental data has confirmed the accuracy and the efficiency of the proposed method.
03 Nov 2011
TL;DR: In this article, a methodology based on median filters for the removal of salt and pepper noise by its detection followed by filtering in both binary and gray level images has been proposed in order to preserve the edges and fine details during filtering.
Abstract: A methodology based on median filters for the removal of Salt and Pepper noise by its detection followed by filtering in both binary and gray level images has been proposed in this paper. Linear and nonlinear filters have been proposed earlier for the removal of impulse noise; however the removal of impulse noise often brings about blurring which results in edges being distorted and poor quality. Therefore the necessity to preserve the edges and fine details during filtering is the challenge faced by researchers today. The proposed method consists of noise detection followed by the removal of detected noise by median filter using selective pixels that are not noise themselves. The noise detection is based on simple thresholding of pixels. Computer simulations were carried out to analyse the performance of the proposed method and the results obtained were compared to that of conventional median filter and center weighted median (CWM) filter.