Impulse noise

About: Impulse noise is a research topic. Over the lifetime, 4816 publications have been published within this topic receiving 63970 citations.

Statistics of biological noise and performance of generalized energy detectors for passive detection

Abstract: Underwater noise due to snapping shrimp is highly impulsive, and often dominates the ambient noise environment of warm, shallow waters at frequencies above 1 kHz. We report here on the statistics of bandpass snapping shrimp noise data, and on the modeling of the joint distribution of the in-phase and quadrature components using bivariate versions of the generalized Gaussian (GG), generalized Cauchy, and Gaussian-Gaussian mixture models. We evaluate the performance of several generalized energy detectors for passive bandpass detection, by inserting stochastic signals into the noise data. Detection thresholds were measured for an integration time of 0.5 s and false alarm probabilities down to 1%. The locally optimum detector based on the mixture model gave the best weak signal detection performance, with an 8 dB reduction in detection threshold over conventional energy detection. A significance test detector based on the GG model performed 1-2 dB worse, but exhibited better strong signal performance.

Statistical channel and noise modeling of vehicular DC-lines for data communication

Abstract: This paper presents an investigation of the properties of vehicular DC-lines for the purpose of data communication. In doing so, the aim is to model the transfer channel that represents these lines. The basis for this investigation is established by measurements of battery lines in vehicles. First various line segments are regarded without disturbance. In a second step the existing interference is investigated. With particular interest the impulsive noise is regarded. Important is the possibility to calculate its random occurrence by a statistical process called Poisson process. The present results are the basis for further investigations of data communication over DC-lines in cars.

A Noise Fading Technique for Images Highly Corrupted with Impulse Noise

Abstract: A novel noise fading technique based on noise detection and median filtering is proposed in this paper. This technique can be used for denoising the images extremely corrupted with impulse noise. This paper introduces a spike detection technique (SDT) and pixel restoring median filter (PRMF) for denoising the corrupted images. The SDT is used for discriminating between corrupted and uncorrupted image pixels. The corrupted pixels are restored using PRMF technique. Our iterative denoising technique is repeated until the corrupted pixels in the recovered image reduce to zero. The performance of our denoising scheme is evaluated with salt and pepper noise and also with random impulse noise for different standard images. It is observed that the proposed denoising scheme outperforms all existing impulse-denoising schemes. This technique can also be used for color image impulse noise removal. This technique can remove very high noise up to 98% and the images denoised with our method shows improvement in terms of visual quality, PSNR value and mutual information. This scheme prevents image blurring and is computationally simple. Hence it is suitable for real-time applications

Ambient noise imaging in warm shallow waters; robust statistical algorithms and range estimation.

Abstract: The high frequency ambient noise in warm shallow waters is dominated by snapping shrimp. The loud snapping noises they produce are impulsive and broadband. As the noise propagates through the water, it interacts with the seabed, sea surface, and submerged objects. An array of acoustic pressure sensors can produce images of the submerged objects using this noise as the source of acoustic "illumination." This concept is called ambient noise imaging (ANI) and was demonstrated using ADONIS, an ANI camera developed at the Scripps Institution of Oceanography. To overcome some of the limitations of ADONIS, a second generation ANI camera (ROMANIS) was developed at the National University of Singapore. The acoustic time series recordings made by ROMANIS during field experiments in Singapore show that the ambient noise is well modeled by a symmetric α-stable (SαS) distribution. As high-order moments of SαS distributions generally do not converge, ANI algorithms based on low-order moments and fractiles are developed and demonstrated. By localizing nearby snaps and identifying the echoes from an object, the range to the object can be passively estimated. This technique is also demonstrated using the data collected with ROMANIS.