Noise detection

The presence of noise in PLC can eventually lead to information corruption. In this design, we present the usage of several classification learners in detection of noise that might found in received PLC signals at the receiving end of the OFDM channel. A database of 5,000 PLC signals with their corresponding categories was used for training and evaluation. Four neural networks were studied through experiments: radial basis function (RBF) neural network, supervised Kohonen network, counter propagation neural network, and X-Y fused neural network. The results of the experiments indicate that the RBF model achieves the best performance among the proposed methods, overall classification accuracy of 98.2%. Furthermore, the remaining proposed algorithms: CPNN and XYF networks are considerably robust classification learners, resulting in true classification percentages of 87.9%, 95.3% and 92.1% respectively.

Noise, emitted by wind turbines, is one of the main health risk factors which has been recently considered in many researches. Noise annoyance is among the most important human responses to noise. The aim of this work was to modeling of annoyance due to noise at workplace coming from wind turbines in workers. All workers of a wind power plant consisted the study sample. The equivalent noise level was measured using a task-based method. Moreover, data related to noise annoyance and noise sensitivity were acquired by standardized methods. Based on the results, noise exposure, noise sensitivity, visibility, age, and experience affected noise annoyance. According to path analysis, the most indirect and direct effect on noise annoyance were attributed to noise exposure. Age, sensitivity, and noise exposure were positively associated to annoyance. It can be concluded that there is a significant relationship between age, experience, sensitivity to noise, and exposure to the wind turbine no...

In most reliability studies, failure rates· are treated as constant, however they are changing with age of the components. The goal of this study is to examine the impact of components with time varying failure rates on distribution reliability. The paper describes a development procedure for modeling restoration after a fault and calculating associated reliability indices and customers' outage costs.

An efficient VLSI implementation of Adaptive Rank Order Filter (AROF) and Adaptive Median Filter (AMF) is proposed in this paper. Impulse noise is introduced in digital images during image acquisition and transmission. The noise reduction algorithms remove noise without degrading image information. Linear filters tend to blur an image; hence they are not commonly used. Non-linear filters provide more satisfactory results in comparison to linear filters. The proposed paper adapts the filter based on the level of noise intensity in the image. AMF provides better filtering properties than standard median filters for images corrupted with 60% noise density. AROF provides better filtering properties than it is possible with AMF for images corrupted with higher noise densities (>60%). The VLSI architecture for AROF and AMF implements pipelining with parallel processing in order to speed up the filtering process. The performance of the proposed algorithm is compared with Peak Signal to ...

Standardni medijan filter poznat je kao učinkovito riješenje problema uklanjanja šumova iz slika, međutim kada se radi o slikama koje su teže oštećene šumma, standardni medijan filter gubi na učinkovitosti. U ovom je radu napravljen program za uklanjanje impulsnog sol i papar šuma primjenom medijan filtra, adaptivog medijan filtra i adaptivog medijan filtra s BDND algoritmom. Analizirana je kvaliteta slika nakon uklanjanja impulsnog šuma navedenim algoritmima te je utvrđeno da adaptivni medijan filtar učinkovito otklanja šum u slikama s manje detalja i s izraženim rubovima. Za slike s mnoštvom detalja najbolje rezultate pokazuje adaptivni medijan filtar s BDND algoritmom, ali ovaj način otklanjanja šuma zahtjeva značajno više procesorskog vremena i nije pogodan kod visokih postotaka šumom oštećenih elemenata slike.Standard median filter is known as an effective solution in noise removing from corrupted images, however when it comes to badly corrupted images standard median filter lo...

Abstract: Considering the wide growth of the wind turbine market over the last decade as well as their increasing power size, more and more potential conflicts have arisen in society due to the noise radiated by these plants. Our goal was to determine whether the annoyance caused by wind farms is related to aspects other than noise. To accomplish this, an auditory experiment on the recognition of wind turbine noise was conducted to people with long experience of wind turbine noise exposure and to people with no previous experience to this type of noise source. Our findings demonstrated that the trend of the auditory recognition is the same for the two examined groups, as far as the increase of the distance and the decrease of the values of sound equivalent levels and loudness are concerned. Significant differences between the two groups were observed as the distance increases. People with wind turbine noise

This paper proposes an fuzzy based adaptive mean filtering (FBAMF) scheme to remove high density impulse noise from images. The FBAMF is a two-stage filter where, in the first stage, a fuzzy detection technique is used to differentiate between corrupted and uncorrupted pixel by calculating the membership value of each and every pixel. Then, the corrupted pixel subjected to the second stage where they are replaced by mean value of uncorrupted neighbouring pixels selected from a window adaptively. If the numbers of uncorrupted pixels in the selected window are not sufficient, a window of next higher size is chosen. Thus, window size is automatically adapted based on the density of noise in the image. As a result window size may vary pixel to pixel while filtering. Comparison shows the proposed filter effectively removes the impulse noise with significant image quality compared with conventional method such as the Standard Median Filter(SMF), Adaptive Median Filter(AMF), Progressive Switching Median Filter(PSMF) and recently proposed methods such as Efficient Decision Based Algorithm (EDBA), Improved Efficient Decision-Based Algorithm (IDBA) and fuzzy-based decision algorithm (FBDA). The visual and quantitative results show that the performance of the proposed filter in the preservation of edges and details is better even at noise level as high as 95%. The efficiency of the proposed algorithm is evaluated using different standard images.

Salt and pepper noise is a type of impulse noise, where certain amount of black and white dots appear in the image. The intensity is accumulated in 8 bit integer, giving 256 possible gray levels in the range (0 -255).In this range salt and pepper noise takes either minimum or maximum intensity. Positive impulse appears as white (salt) points with intensity ‗255' and negative impulse appears as black (pepper) points with intensity ‗0' respectively. Salt and pepper noise removal is not an easy task mostly when noise density in the contaminated image is high and restoration of image quality is essential. Different filters like MF, SMF, AMF, PSMF, DBA, DBUTMF, and MDBUTMF and so on are noticed useful for taking away low, moderate and high density salt and pepper noise. The purpose of this paper is to present these filters first and then revise their art to enhance their performances and usefulness. The comparison shows that some of these filters are very fruitful in some particular noise density levels and hence classified applications on these situations are recommended based on the output of investigations.

In this paper, we query the de-facto final conclusion in the field of image denoising that the family of median filters is the first choice when facing with the task of impulse noise removal. We analyze the factors which cause the performance degradation of the mean filter. By introducing the priority queue and defining each pixel's priority, we demonstrate the fact that when properly used, even the simple mean filter can perform better than median filters. The proposed algorithm (PQMF) is highly efficient and effective in removing impulse noises, especially in highly corrupted images. Extensive simulation results show that when equipped with our proposed module, the performance of many impulse noise restoration algorithms can be promoted to state-of-the-art level.