Impulsive noise (IN) is the most dominant factor degrading the performance of communication systems over powerlines. In order to improve performance of high-speed power line communication (PLC), this work focuses on mitigating burst IN effects based on compressive sensing (CS), and an adaptive burst IN mitigation method, namely combination of adaptive interleaver and permutation of null carriers is designed. First, the long burst IN is dispersed by an interleaver at the receiver and the characteristic of noise is estimated by the method of moment estimation, finally, the generated sparse noise is reconstructed by changing the number of null carriers(NNC) adaptively according to noise environment. In our simulations, the results show that the proposed IN mitigation technique is simple and effective for mitigating burst IN in PLC system, it shows the advantages to reduce the burst IN and to improve the overall system throughput. In addition, the performance of the proposed technique outpeformences other known nonlinear noise mitigation methods and CS methods.

Speech enhancement has been proposed to reduce the impulsive noise whose frequency characteristic is wideband. On the other hand, it is challenging to reduce the ringing sound, which is narrowband in impulsive noise. Therefore, we propose the modeling of the ringing sound and its estimation by a linear predictor (LP). However, it is difficult to estimate the ringing sound only in noisy speech due to the auto-correlation property of speech. The proposed system adopts the 4th order moment-based adaptive algorithm by noticing the difference between the 4th order statistics of speech and impulsive noise. The brief analysis and simulation results show that the proposed system has the potential to reduce ringing sound while keeping the quality of enhanced speech.

In this paper, we propose an enhanced selected mapping (e-SLM) technique to improve the performance of OFDM-PLC systems under impulsive noise. At the transmitter, the best transmit sequence is selected from among possible candidates so as to minimize the weighted sum of transmit signal peak power and the estimated receive one, where the received signal peak power is estimated at the transmitter using channel state information (CSI). At the receiver, a nonlinear blanking is applied to hold the impulsive noise under a given threshold, where impulsive noise detection accuracy is improved by the proposed e-SLM. We evaluate the probability of false alarms raised by impulsive noise detection and bit error rate (BER) of OFDM-PLC system using the proposed e-SLM. The results show the effectiveness of the proposed method in OFDM-PLC system compared with the conventional blanking technique.

Spectrum sensing is a fundamental requirement for cognitive radio, and it is a challenging problem in impulsive noise modeled by symmetric alpha-stable (SαS) distributions. The Gaussian kernelized energy detector (GKED) performs better than the conventional detectors in SαS distributed noise. However, it fails to detect the DC signal and has high computational complexity. To solve these problems, this paper proposes a more efficient and robust detector based on a Gaussian function (GF). The analytical expressions of the detection and false alarm probabilities are derived and the best parameter for the statistic is calculated. Theoretical analysis and simulation results show that the proposed GF detector has much lower computational complexity than the GKED method, and it can successfully detect the DC signal. In addition, the GF detector performs better than the conventional counterparts including the GKED detector in SαS distributed noise with different characteristic exponents. Finally, we discuss the reason why the GF detector outperforms the conventional counterparts.

Affine projection sign algorithm (APSA) is an important adaptive filtering method to combat the impulsive noisy environment. However, the performance of APSA is poor, if its regularization parameter is not well chosen. We propose a variable regularization APSA (VR-APSA) approach, which adopts a gradient-based method to recursively reduce the norm of the a priori error vector. The resulting VR-APSA leverages the time correlation of both the input signal matrix and error vector to adjust the value of the regularization parameter. Simulation results confirm that our algorithm exhibits both fast convergence and small misadjustment properties.

This paper mainly discusses the time-difference-of-arrival (TDOA) estimation problem of digital modulation signal under impulsive noise and cochannel interference environment. Since the conventional TDOA estimation algorithms based on the second-order cyclic statistics degenerate severely in impulsive noise and the TDOA estimation algorithms based on correntropy are out of work in cochannel interference, a novel signal-selective algorithm based on the generalized cyclic correntropy is proposed, which can suppress both impulsive noise and cochannel interference. Theoretical derivation and simulation results demonstrate the effectiveness and robustness of the proposed algorithm.

In this letter, robust sparse signal recovery is considered in the presence of heavy-tailed impulsive noise. Two Bayesian approaches are developed where a Bayesian framework is constructed by utilizing the Laplace distribution to model the noise. By rewriting the noise-fitting term as a reweighted quadratic function which is optimized in the sparse signal space, the Type I Maximum A Posteriori (MAP) approach is proposed. Next, by exploiting the hierarchical structure of the sparse prior and the likelihood function, we develop the Type II Evidence Maximization approach optimized in the hyperparameter space. The numerical results verify the effectiveness of the proposed methods in the presence of impulsive noise.

This paper proposes a robust bilateral filter which can handle mixed Gaussian and impulsive noise by hybridizing the conventional bilateral filter and the switching median filter. The effectiveness of the proposed method is verified in comparison with other conventional methods by some experiments using the natural digital images.

Impulsive noise interference is a significant problem for the Integrated Services Digital Broadcasting for Terrestrial (ISDB-T) receivers due to its effect on the orthogonal frequency division multiplexing (OFDM) signal. In this paper, an adaptive scheme to suppress the effect of impulsive noise is proposed. The impact of impulsive noise can be detected by using the guard band in the frequency domain; furthermore the position information of the impulsive noise, including burst duration, instantaneous power and arrived time, can be estimated as well. Then a time-domain window function with adaptive parameters, which are decided in terms of the estimated information of the impulsive noise and the carrier-to-noise ratio (CNR), is employed to suppress the impulsive interference. Simulation results confirm the validity of the proposed scheme, which improved the bit error rate (BER) performance for the ISDB-T receivers in both AWGN channel and Rayleigh fading channel.

Noise removal in engineering drawing is an important operation performed before other image analysis tasks. Many algorithms have been developed to remove salt-and-pepper noise from document images. Cleaning algorithms should remove noise while keeping the real part of the image unchanged. Some algorithms have disadvantages in cleaning operation that leads to removing of weak features such as short thin lines. Others leave the image with hairy noise attached to image objects. In this article a noise removal procedure called TrackAndMayDel (TAMD) is developed to enhance the noise removal of salt-and-pepper noise in binary images of engineering drawings. The procedure could be integrated with third party algorithms' logic to enhance their ability to remove noise by investigating the structure of pixels that are part of weak features. It can be integrated with other algorithms as a post-processing step to remove noise remaining in the image such as hairy noise attached with graphical elements. An algorithm is proposed by incorporating TAMD in a third party algorithm. Real scanned images from GREC'03 contest are used in the experiment. The images are corrupted by salt-and-pepper noise at 10%, 15%, and 20% levels. An objective performance measure that correlates with human vision as well as MSE and PSNR are used in this experiment. Performance evaluation of the introduced algorithm shows better-quality images compared to other algorithms.

A statistical model based on a partitioned Markov-chains model has previously been developed to represent time domain behavior of the asynchronous impulsive noise over a broadband power line communication (PLC) network. However, the estimation of its model parameters using the Simplex method can easily trap the final solution at a local optimum. This study proposes an estimation scheme based on the genetic algorithm (GA) to overcome this difficulty. Experimental results show that the proposed scheme yields estimates that more closely match the experimental data statistics.

We propose a robust iterative multiuser receiver for decoding convolutional coded code-division multiple access (CDMA) signals in both Gaussian and non-Gaussian channel noise. The receiver is derived from a modified maximum a-posteriori (MAP) algorithm called the max-log-MAP algorithm for robustness against erroneous channel variance estimation. Furthermore, the effect of destructive outliers arising from impulsive noise is mitigated in the proposed receiver by incorporating the robust Huber penalty function into the multiuser detector. The proposed receiver is shown to perform satisfactorily over Gaussian and non-Gaussian impulsive channels. In every iteration, cumulative improvement in the quality of the a-posteriori probabilities is also demonstrated.

Fingerprints are useful for biometric purposes because of their well known properties of distinctiveness and persistence over time. However, owing to skin conditions or incorrect finger pressure, original fingerprint images always contain noise. Especially, some of them contain useless components, which are often mistaken for the terminations that are an essential minutia of a fingerprint. Mathematical Morphology (MM) is a powerful tool in image processing. In this paper, we propose a linear time algorithm to eliminate impulsive noise and useless components, which employs generalized and ordinary morphological operators based on Euclidean distance transform. There are two contributions. The first is the simple and efficient MM method to eliminate impulsive noise, which can be restricted to a minimum number of pixels. We know the performance of MM is heavily dependent on structuring elements (SEs), but finding an optimal SE is a difficult and nontrivial task. So the second contribution is providing an automatic approach without any experiential parameter for choosing appropriate SEs to eliminate useless components. We have developed a novel algorithm for the binarization of fingerprint images [1]. The information of distance transform values can be obtained directly from the binarization phase. The results show that using this method on fingerprint images with impulsive noise and useless components is faster than existing denoising methods and achieves better quality than earlier methods.

This paper introduces new methods of robust speech recognition using discrete-mixture HMMs (DMHMMs). The aim of this work is to develop robust speech recognition for adverse conditions that contain both stationary and non-stationary noise. In particular, we focus on the issue of impulsive noise, which is a major problem in practical speech recognition system. In this paper, two strategies were utilized to solve the problem. In the first strategy, adverse conditions are represented by an acoustic model. In this case, a large amount of training data and accurate acoustic models are required to present a variety of acoustic environments. This strategy is suitable for recognition in stationary or slow-varying noise conditions. The second is based on the idea that the corrupted frames are treated to reduce the adverse effect by compensation method. Since impulsive noise has a wide variety of features and its modeling is difficult, the second strategy is employed. In order to achieve those strategies, we propose two methods. Those methods are based on DMHMM framework which is one type of discrete HMM (DHMM). First, an estimation method of DMHMM parameters based on MAP is proposed aiming to improve trainability. The second is a method of compensating the observation probabilities of DMHMMs by threshold to reduce adverse effect of outlier values. Observation probabilities of impulsive noise tend to be much smaller than those of normal speech. The motivation in this approach is that flooring the observation probability reduces the adverse effect caused by impulsive noise. Experimental evaluations on Japanese LVCSR for read newspaper speech showed that the proposed method achieved the average error rate reduction of 48.5% in impulsive noise conditions. Also the experimental results in adverse conditions that contain both stationary and impulsive noises showed that the proposed method achieved the average error rate reduction of 28.1%.

A novel effective method for detection and removal impulse noise in highly corrupted color images is proposed. This detection-estimation method consists of two steps. Outliers are first detected using spatial relations between the color components. Then the detected noise pixels are replaced with the output of the vector median filter over a local spatially connected area excluding the outliers. Simulation results in a test color image show a superior performance of the proposed filtering algorithm comparing to the conventional vector median filter. The comparisons are made using a mean square error and a mean absolute error criteria.

In our previous research, we proposed a nonlinear digital filter to Estimate the Smoothed and Differential values of the sensor inputs by using Sliding mode system (ESDS). This estimator is able to eliminate impulsive noise efficiently from time series data. We applied this filter to processing outputs of robot sensors, and it became possible to perform robust environment recognition. ESDS is designed using a theory of variable structure system (VSS) with sliding mode. In short, ESDS is a nonlinear filter. Therefore, it is very difficult to clarify the behavior of the system analytically. Consequentially, we deal with the step function with impulsive noise as an example, and we attempt to eliminate this impulsive noise by keeping the sudden shift of signals. In this case, there is a trade-off between the noise elimination ability and the tracking performance for an input signal. Although ESDS is a nonlinear filter, it has the same trade-off as linear filters such as a low-pass filter. In order to satisfy these two conditions simultaneously, we use two filters whose parameters are independent of each other. Furthermore, in order to repress the adverse affect of impulsive noise in the steady-state, we introduced the boundary layer. Generally, a boundary layer is used so as to inhibit the harmful effect of chattering. Chattering is caused in the sliding mode system when the state of the system vibrates on the switching line of a sliding mode system. By introducing the boundary layer to ESDS, we can repress the adverse effect of impulsive noise in the steady-state. According to these considerations, we clarify the relationship between these characteristics of ESDS and the arbitrary parameters.

The objective of this paper is to evaluate the impacts of impulsive class-A noise, co-channel interference due to other piconet, Rician fading on the packet error rate (PER), and throughput performance in the Bluetooth scatternet. Simulation results illustrate the significant difference in performance between synchronous and asynchronous Bluetooth systems. The paper also provides the insights on how to design Bluetooth scatternet for minimal PER and maximum throughput performance.

In this letter, blind adaptive H multiuser detection is developed by employing a generalized sidelobe canceler (GSC) with and without subweight partition scheme. It is shown that the adaptive H algorithm with subweight approach has the advantages of fast convergence speed, insensitivity of dynamic estimate error, and suitability for arbitrary ambient noise over the conventional H and the RLS-based adaptive algorithms.

Performance of turbo codes over an impulsive noise channel is analyzed by extending an evaluation method over AWGN channels. Burst noise generation is considered with respect to the noise model by applying a hidden Markov model (HMM). A bound calculation method is derived by using a combined trellis which consists of code trellis and HMM trellis. In the simulation, an iterative decoder using the combined trellis into each component decoder is proposed. By using this method, the simulation results show the expectation of the coincidence with the calculated bounds at larger Eb/N0 for various conditions. Search results of optimum component code using proposed bound are shown.

In this letter, we propose a robust adaptive filter with a Variable Forgetting Factor (VFF) for impulsive noise suppression. The proposed method can restrict the perturbation of the parameters using M-estimator and adaptively reduce the error propagation caused by the impulsive noise using VFF. Simulations show that the performance of the proposed algorithm is less vulnerable to the impulsive noise than those of the conventional Kalman filter based algorithms.