Impulsive noise

Noise reduction or noise removal is an important task in image processing. In general, Results of the noise removal have a strong influence on the quality of the following image processing techniques. On the other side, the integrated system of neurofuzzy networks are more interesting and applied for different applications. In this contribution, two neurofuzzy network schemes have been presented for impulsive noise removal. The computation is reduced by using an artificial image in training. High performances are obtained. Results of neurofuzzy schemes show that the performance is increased as the ratio of the noise is increased. The presented schemes are used for grayscale and also for true color images.

A new Takagi neuro-fuzzy filter is presented for the noise reduction of images corrupted with impulsive and Poisson noises. In the current paper, the number of neuro-fuzzy connections is reduced to be equal to the number of membership functions. Also, the time of computation is reduced by using artificial image for training the presented neuro-fuzzy filter. The filter can be applied effectively to reduce heavy noise. Experimental results are obtained to show the feasibility of the proposed approach. These results are good when compared to other filters by numerical measures and visual inspection. The presented scheme is applied to grayscale and truecolor images. The presented scheme is efficient, fast, and can be extended by adding other filter as input, without smoothing an image.

The results of the noise removal have a strong influence on the quality of the other image processing techniques. And since the main goal for any noise removal method is the preservation of the edges and image information, this influence is the principal drawback of the smoothing filters. In this paper, a modified Mamdani neurofuzzy network scheme has been proposed and presented to improve effects of smoothing filters in removing impulsive noise. The number of connections is reduced to be equal to the number of membership functions plus one. A training strategy for the presented neurofuzzy is based on artificial image which reduces the time of computation. The Analysis shows that the proposed neurofuzzy works well in suppressing impulsive at different noise ratios for grayscale and truecolor images.

For improved localization around the indoor/outdoor transit area of buildings, this paper proposes an efficient algorithm combining the measurements from the WLAN (Wireless Local Area Network) and the GPS (Global Positioning System) for. The proposed hybrid localization algorithm considers both multipath errors and NLOS (Non-Line-of-Sight) errors, which occur in most wireless localization systems. To detect and isolate multipath errors occurring in GPS measurements, the propose algorithm utilizes conventional multipath test statistics. To convert WLAN signal strength measurements to range estimates in the presence of NLOS errors, a simple and effective calibration algorithm is designed to compute conversion parameters. By selecting and combining the reliable GPS and WLAN measurements, the proposed hybrid localization algorithm provides more accurate location estimates. An experiment result demonstrates the performance of the proposed algorithm.

In the last decade Internet of Things (IoT) grew up in an exponential behavior with applications requiring long range and low power wireless transmissions. Factory of the Future (FoF), also called Industry 4.0, aims to use IoT technologies to enhance productivity, therefore adding high reliability constraints. Several IoT standards were proposed and LoRa has emerged as a high potential candidate for a variety of applications. LoRa modulation is based on a chirp spread-spectrum technique and offers efficient transmission up to 50 kbps over several kilometers. The performance of LoRa in terms of symbol or bit error probability has been theoretically analyzed in few recent papers for a Gaussian channel. However, the industrial environment is often corrupted with impulsive non Gaussian noise generated by high-power equipment. In this paper, the impact of impulsive noise, modeled by the Middleton Class-A noise, is introduced and the robustness of a LoRa communication is studied. Compared to the Gaussian case, simulations show that impulsive noise may severely degrade system performance. This Signal-to-Noise Ratio loss can reach up to 10 dB, but increasing the spreading factor can reduce the noise impact.

To effectively process impulse noise for narrowband powerline communications (NB-PLCs) transceivers, capturing comprehensive statistics of nonperiodic asynchronous impulsive noise (APIN) is a critical task. However, existing mathematical noise generative models only capture part of the characteristics of noise. In this study, we propose a novel generative adversarial network (GAN) called noise generation GAN (NGGAN) that learns the complicated characteristics of practically measured noise samples for data synthesis. To closely match the statistics of complicated noise over the NB-PLC systems, we measured the NB-PLC noise via the analog coupling and bandpass filtering circuits of a commercial NB-PLC modem to build a realistic dataset. To train NGGAN, we adhere to the following principles: 1) we design the length of input signals that the NGGAN model can fit to facilitate cyclostationary noise generation; 2) the Wasserstein distance is used as a loss function to enhance the similarity between the generated noise and training data; and 3) to measure the similarity performances of GAN-based models based on the mathematical and practically measured datasets, we conduct both quantitative and qualitative analyses. The training datasets include: 1) a piecewise spectral cyclostationary Gaussian model (PSCGM); 2) a frequency-shift (FRESH) filter; and 3) practical measurements from NB-PLC systems. Simulation results demonstrate that the generated noise samples from the proposed NGGAN are highly close to the real noise samples. The principal component analysis (PCA) scatter plots and Fréchet inception distance (FID) analysis have shown that NGGAN outperforms other GAN-based models by generating noise samples with superior fidelity and higher diversity.

Communication over the available power line commonly known as Power Line Communication (PLC) can be an alternate or a component of hybrid network. The reason for this is it's readily availability that can solve the last mile and last inch challenges. However, the channel is characterized by non-white noise uncommon to other channels used. In this paper, the noise in indoor power line in several sites is studied. This is modeled using suitable fits for narrowband and broadband application. The channel capacity is estimated both for Additive White Gaussian and Water Filling approach. Results show that a channel capacity of 0.089 Mbps to 1.48 Mbps can be obtain for narrowband channel with a transmit power of few watt. For broadband communication in the frequency range 1-30 MHz a channel capacity of 84 Mbps to 206 Mbps can be obtain for a transmit power as low as 1mW. This result can be used for simulation of power line channels for testing and designing systems and development of s...

In this paper, we study the existence and uniqueness of solutions for the impulsive semilinear fuzzy integrodifferential equations with nonlocal conditions and forcing term with memory in n-dimensional fuzzy vector space (E n N , d ε ) by using Banach fixed point theorem. That is an extension of the result of Kwun et al. [9] to impulsive system.

We study the existence and uniqueness of solutions and controllability for the semilinear fuzzy integrodifferential equations in n-dimensional fuzzy vector space E N n by using Banach fixed point theorem, that is, an extension of the result of J. H. Park et al. to n-dimensional fuzzy vector space.

We study the existence and uniqueness of solutions and nonlocal controllability for the impulsive semilinear nonlocal fuzzy integrodifferential equations in n-dimensional fuzzy vector space E N n by using short-term perturbations techniques and Banach fixed point theorem. This is an extension of the result of Kwun et al. Kwun et al., 2009 to impulsive system.

Los métodos de medida de la exposición a altos niveles sonoros están orientados fundamentalmente al ámbito laboral, sin embargo la escucha de música en reproductores personales es una fuente de exposición cuyo análisis necesitaría de otros instrumentos. Presentamos el diseño de un sistema capaz de analizar y evaluar esta exposición usando varios métodos: los auriculares personales del reproductor sobre un HATS diseñado al efecto o bien unos auriculares calibrados y dotados de micrófonos para medir los niveles sonoros, más un sistema de análisis de las señales. El resultado es un sistema que en base a la información facilitada por el individuo a prueba y el análisis normativo de la señal es capaz de estimar sus pérdidas auditivas. Se aporta un nuevo instrumento de medida útil en todos los ámbitos, desde el caso de los reproductores personales a cualquier otra situación sin la presencia de la persona afectada.

Asynchronous and cyclostationary impulsive noise can severely impact the bit-error-rate (BER) of OFDM-based powerline communication systems. In this paper, we analyze an adaptive nonlinear analog front end filter that mitigates various types of impulsive noise without detrimental effects such as self-interference and out-of-band power leakage caused by other nonlinear approaches like clipping and blanking. Our proposed Adaptive Nonlinear Differential Limiter (ANDL) is constructed from a linear analog filter by applying a feedbackbased nonlinearity, controlled by a single resolution parameter. We present a simple practical method to find the value of this resolution parameter that ensures the mitigation of impulsive without impacting the desired OFDM signal. Unlike many prior approaches for impulsive noise mitigation that assume a statistical noise model, ANDL is blind to the exact nature of the noise distribution, and is designed to be fully compatible with existing linear front end filters. We demonstrate the potency of ANDL by simulating the OFDM-based narrowband PLC compliant with the IEEE standards. We show that the proposed ANDL outperforms other approaches in reducing the BER in impulsive noise environments.

Impulsive noise is a major impediment to orthogonal frequency-division multiplexing (OFDM) based underwater acoustic (UWA) communications. In this work, we evaluate the performance of a memoryless analog nonlinear preprocessor (MANP) that is used to mitigate outliers. The proposed MANP exhibits intermittent nonlinearity only in the presence of the impulsive noise and suppresses the power of outliers based on their amplitudes. Since the outliers are distinguishable in the analog domain prior to anti-aliasing filtering, the MANP outperforms its digital counterparts in all scenarios. Experimental results using data collected in an under-ice environment, demonstrate the superior BER performance of our approach relative to classical nonlinear approaches such as blanking and clipping. Index Terms-Impulsive noise, memoryless analog nonlinear preprocessor (MANP), orthogonal frequency-division multiplexing (OFDM), underwater acoustic (UWA) communications.

Gran parte de las Infraestructuras Avanzadas de Medicion (AMI, Advanced Measurement Infrastructures), desplegadas en los sistemas de distribucion de energia electrica para el registro de los parametros facturables y de calidad de servicio utilizan las mismas redes de energia como medio de comunicacion (PLC, Power Line Communications). Para ello, utilizan la banda de frecuencias NB-PLC (Narrowband PLC, o PLC de banda estrecha, entre 90 y 500 KHz), con esquemas de modulacion tales como la Multiplexacion por Division de Frecuencias Ortogonales (OFDM, Orthogonal Frequency Division Multiplexing). Dado que las redes de distribucion de energia no se disenan como canales de comunicacion, presentan serios desafios para la obtencion de comunicaciones confiables. En este trabajo se propone modelar en Matlab/Simulink® el canal PLC con fines de docencia e investigacion, para una configuracion tipica de distribucion en Baja Tension.

چهره انسان يك شيء ثابت نيست و فاكتورهاي متنوعي که منجر به نمايش‌هاي مختلف چهره مي‌شــوند وجود دارند. در الگوریتم‌های تشخیص چهره، فاكتورهاي ذاتي و تصـادفي که باعث ایجاد اختلاف در ظاهر چهره می‌شوند، وجود داده‌های ناقص در پایگاه داده‌ها، حجم پايگاه داده‌ها، اختلاف در ابعاد تصاوير ذخيره شده و تغيير حالات چهره می‌توانند فرايند تشخیص چهره را با مشكلات مختلفی روبرو سازند. با ارائه روشي كه بتواند اين مشكلات را تا حد قابل توجهي رفع کند، می‌توان گستره‌ي كاربرد تكنيك‌های تشخيص چهره را افزايش داد. در این مطالعه برای بهبود تشخیص چهره با داده‌های ناقص از یک شبکه عصبی کانولوشنی عمیق استفاده شده است. روش پیشنهادی از چند گام مختلف تشکیل شده است. در مرحله اول پس از انتخاب و استخراج داده اولیه از پایگاه داده، پیش پردازش اطلاعات با اعمال فیلتر، تبدیل هیستوگرام و تشخیص لبه انجام می‌شود. در مرحله دوم استخراج نقاط مهم برای هر تصویر انجام می‎‌شود. خروجی این مرحله به عنوان ورودی به الگوریتم بهینه‌سازی زنبور عسل داده شده تا برای انتخاب ویژگی‌های موثر و شناخت آن‌ها در راستای بهینه‌سازی استفاده گردد. در نهایت از یک شبکه عصبی کانولوشنی عمیق در دو گام آموزش و آزمون برای تشخیص چهره استفاده شده است. روش پیشنهادی در محیط متلب شبیه‌سازی شده و توسط معیارهای دقت، صحت و پوشش مورد ارزیابی قرار گرفته است. نتایج بدست آمده با دقت 96.11 % بیانگر بهبود تشخیص چهره نسبت به سایر کارهای انجام شده در سال‌های اخیر و همچنین کاهش هزینه در فرآیند تشخیصِ انجام شده می‌باشد.

The Fifth Generation (5G) wireless communications systems are expected to satisfy higher data rates, network scalability, increased number of connections and higher traffic densities in a cost-effective manner. The key essence of 5G technology resides in exploring the frequency bands at millimeter-Wave (mmWave) frequencies. As is well known, the presence of Impulsive Noise (IN) corrupts signals and leads to increased bit error rate and decreased spectral efficiency. In this paper, the performance of mmWave systems in multi-path fading channel and IN is studied and a new thresholding mechanism for the clipping and blanking filters to suppress the impulsive components of noise is suggested. The paper also presents the mathematical expressions to determine the optimum threshold selection for the filters. Simulation results show that use of clipping and blanking filters with the optimal threshold values reduces the adverse effect of IN and improves system performance significantly.

This paper proposes the robust proportionate adaptive filtering algorithms and their respective efficient very large-scale integration (VLSI) architectures for sparse system identification under impulsive noise, several types of algorithms are combined to obtain optimum results. Here, we rendered a relative analysis on these algorithms and the algorithms are mapped on to the hardware to show that the improvement is obtained with respect to convergence rate and hardware complexity of VLSI architectures and has negligible hardware overhead with improved robustness. Good performance and convergence rate is obtained by combining the delayed μlaw proportionate (DMP) and least mean logarithmic square (LMLS) algorithms i.e. delayed µ-law proportionate least mean logarithmic square (DMP LMLS). Robust proportionate adaptive filter is coded in system verilog and synthesized using cadence genus compiler with 90 nm technology library.

The Fifth Generation (5G) wireless communications systems are expected to satisfy higher data rates, network scalability, increased number of connections and higher traffic densities in a cost-effective manner. The key essence of 5G technology resides in exploring the frequency bands at millimeter-Wave (mmWave) frequencies. As is well known, the presence of Impulsive Noise (IN) corrupts signals and leads to increased bit error rate and decreased spectral efficiency. In this paper, the performance of mmWave systems in multi-path fading channel and IN is studied and a new thresholding mechanism for the clipping and blanking filters to suppress the impulsive components of noise is suggested. The paper also presents the mathematical expressions to determine the optimum threshold selection for the filters. Simulation results show that use of clipping and blanking filters with the optimal threshold values reduces the adverse effect of IN and improves system performance significantly.

We introduce a novel family of adaptive robust channel estimators for highly challenging underwater acoustic (UWA) channels. Since the underwater environment is highly non-stationary and subjected to impulsive noise, we use adaptive filtering techniques based on minimization of a logarithmic cost function, which results in a better trade-off between the convergence rate and the steady state performance of the algorithm. To improve the convergence performance of the conventional first and second order linear estimation methods while mitigating the stability issues related to impulsive noise, we intrinsically combine different norms of the error in the cost function using a logarithmic term. Hence, we achieve a comparable convergence rate to the faster algorithms, while significantly enhancing the stability against impulsive noise in such an adverse communication medium. Furthermore, we provide a thorough analysis for the tracking and steady-state performances of our proposed methods in the presence of impulsive noise. In our analysis, we not only consider the impulsive noise, but also take into account the frequency and phase offsets commonly experienced in real life experiments. We demonstrate the performance of our algorithms through highly realistic experiments performed on accurately simulated underwater acoustic channels.

A direction-of-arrival (DOA) estimation method using an array antenna has been developed based on a database retrieval technique. This method uses a database consisting of a set of correlation matrices of the array output vectors for various combinations of the quantized angles and signal powers. When a correlation matrix is estimated from an observed output vector, several correlation matrices close to the estimated one are searched out from the database, and the DOA is estimated based on the retrieved data. This method gives an accurate estimation, but the use of uniform quantization step size requires a large amount of storage space. In this paper, the relation between the quantization step size and the estimation accuracy is analyzed, and a nonuniform quantization scheme is developed to reduce the database size without sacrificing the estimation accuracy. A clustering technique is also introduced to alleviate the performance degradation caused by the retrieval of data which have similar correlation matrices but have much different angles. We show by simulations that the nonuniform quantization reduces the database size and the clustering improves the estimation accuracy, and that the proposed method is applicable to the array of three elements at the present.

The performance of Power Line Communication (PLC) degrades due to the presence of different types of noise interferences generated by electrical appliances. This paper investigates the bit-error rate (BER) performance of a higherorder 64-QAM constellation with orthogonal frequency multiplexing modulation (OFDM) in presence of impulsive noise modelled as Middleton Class A over a multipath PLC. It is observed that BER for the impulsive noise is higher than the background noise. The BER further deteriorates on increasing the level of the impulsive noise, even while being injected into the PLC channel at a lower rate. Investigations would assist applying methods to mitigate and reduce the effect of impulsive noise over PLC systems for higher constellations with a view to increase the data rates.

Adaptive networks with mobile nodes possess the distributed adaptation abilities in addition to the collective patterns of motion. Thus, mobile adaptive networks have been used in new applications such as modeling the biological networks and source localization. The original mobile adaptive networks needs full cooperation among the neighbor nodes meaning that each node gathers the information from all of its neighbour nodes. This strategy requires large amount of communications per iteration per node. To address this problem, in this paper we propose a mobile diffusion adaptive network with selective cooperation. In the proposed algorithm each node selects a subset of its neighbors so that its steady-state performance be  as close as possible to the traditional mobile diffusion network. Since the selective cooperation reduces the learning rate we also use affine projection algorithm (APA) as the learning rules at the nodes. Our simulation results reveal that the proposed algorithm i...

Robust adaptive estimation of unknown parameter has been an important issue in recent years for reliable operation in the distributed networks. The conventional adaptive estimation algorithms that rely on mean square error (MSE) criterion exhibit good performance in the presence of Gaussian noise, but their performance drastically decreases under impulsive noise. In this paper, we propose a robust adaptive estimation algorithm for networks with cyclic cooperation. We model the impulsive noise as the realization of alpha-stable distribution. Here, we move beyond MSE criterion and define the estimation problem in terms of a modified cost function which exploits higher order moments of the error. To derive a distributed and adaptive solution, we first recast the problem as an equivalent form amenable to distributed implementation. Then, we resort to the steepest-descent and statistical approximation to obtain the proposed algorithm. We present some simulations results which reveal the ...

Suppression of noise and artifacts is a necessary step in biomedical data processing. Adaptive filtering is known as useful method to overcome this problem. Among various contaminants, there are some situations such as electrical activities of muscles contribute to impulsive noise. This paper deals with modeling real-life muscle noise with α-stable probability distribution and adaptive filtering noise cancellation assessment with maximum correntropy criterion (MCC) as adaptive technique. Based on our test on some data of MIT-BIH arrhythmia and EMBC databases, we achieve an improved SNR in any electrocardiogram (ECG) signal corrupted by impulsive noise. The worst achieved improvement based on setting the best parameter values using trial and error for both filter and utilized algorithm is 9.5 dB with correlation coefficient value of 0.93. The SNR improvement on the whole utilized database records is 11.03 dB on average. The proposed algorithm is applied to the records from MIT-BIH ar...

A direction-of-arrival (DOA) estimation method using an array antenna has been developed based on a database retrieval technique. This method uses a database consisting of a set of correlation matrices of the array output vectors for various combinations of the quantized angles and signal powers. When a correlation matrix is estimated from an observed output vector, several correlation matrices close to the estimated one are searched out from the database, and the DOA is estimated based on the retrieved data. This method gives an accurate estimation, but the use of uniform quantization step size requires a large amount of storage space. In this paper, the relation between the quantization step size and the estimation accuracy is analyzed, and a nonuniform quantization scheme is developed to reduce the database size without sacrificing the estimation accuracy. A clustering technique is also introduced to alleviate the performance degradation caused by the retrieval of data which have similar correlation matrices but have much different angles. We show by simulations that the nonuniform quantization reduces the database size and the clustering improves the estimation accuracy, and that the proposed method is applicable to the array of three elements at the present.

Introduccion: El desempeno de los dispositivos de proteccion auditiva puede verse afectado por factores como las caracteristicas antropometricas del trabajador, asi mismo el uso simultaneo con otros elementos de proteccion personal que interfieren con el ajuste apropiado puede restarle efectividad a la atenuacion que ofrecen estos dispositivos. Objetivo: Determinar los valores reales de atenuacion de dispositivos de proteccion auditiva tipo copa cuando son usados simultaneamente con elementos de proteccion personal respiratoria, visual, rostro y cabeza, estableciendo si existen diferencias entre los niveles teoricos, ajustados y reales. Materiales y metodos: Se llevo a cabo un estudio de corte transversal en el cual se realizaron 52 pruebas de ajuste cuantitativas por inmision sonora en oido real (F-MIRE por sus siglas en ingles) basados en los requisitos establecidos por la norma internacional ISO 11904-1:2002 realizadas con un microfono tipo sonda en distintos sujetos que represen...

Robust adaptive estimation of unknown parameter has been an important issue in recent years for reliable operation in the distributed networks. The conventional adaptive estimation algorithms that rely on mean square error (MSE) criterion exhibit good performance in the presence of Gaussian noise, but their performance drastically decreases under impulsive noise. In this paper, we propose a robust adaptive estimation algorithm for networks with cyclic cooperation. We model the impulsive noise as the realization of alpha-stable distribution. Here, we move beyond MSE criterion and define the estimation problem in terms of a modified cost function which exploits higher order moments of the error. To derive a distributed and adaptive solution, we first recast the problem as an equivalent form amenable to distributed implementation. Then, we resort to the steepest-descent and statistical approximation to obtain the proposed algorithm. We present some simulations results which reveal the ...

Conventional subspace decomposition-based direction of arrival (DOA) estimation methods require eigenvalue decomposition of spatial covariance matrix, therefore these methods are computationally intensive, and their implementation is difficult in real-time applications. However, a DOA estimation method based on fixed step size least mean square (LMS) algorithm has overcome this deficiency but the suitable selection of step size is very difficult. A DOA estimation method based on the variable step size LMS algorithm is proposed in this article. In the proposed method, the step size is updated by using the estimated error signal. The spatial spectrum is obtained by the reciprocal of array pattern, where the peak values indicate the estimated DOAs of signals. The proposed method can provide better performance with low computational cost. The performance of proposed method is verified by the simulations and compared with some existing methods.

This article presents new exact expressions, written in terms of elementary transcendental functions, for calculating the bit error probability of M-ary Quadrature Amplitude Modulation (M-QAM) scheme considering the wireless communication channel modeled by a Markov chain with N states. For the numerical evaluation of the expressions obtained, a particular case of a Markov chain with two states is considered, with each state representing distinct scenarios. In the first scenario it is considered the presence of Gated Additive White Gaussian Noise (GAWGN) and fading η-µ or κ-µ, while the second scenario considers the presence of the Double Gated Additive White Gaussian Noise (G 2 AWGN) and fading η-µ or κ-µ. Bit error probability curves as a function of the signal-to-permanent-noise ratio for different values of the signal-to-impulsive-noise ratio, fading parameters and modulation order M are also presented.

For clean signal, noise cancellation techniques are explored day-by-day. At the user end the clean signal is highly essential for different purposes. In this authors have considered the bio-medical signal that is corrupted with impulsive noise. It is very important to separate from the signal, as its occurrence is sudden and often similar to the signal. The popular adaptive algorithms have been tested for cancellation of impulsive noise. Further most used Wilcoxon LMS is also verified for impulsive noise case. Finally it has been modified for the same purpose. The result found excellent in terms of less MSE, SNR improvement and faster convergence.