Impulse noise

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

Synergistic interactions of noise and other ototraumatic agents

Abstract: The following is a review of the literature on interaction of noise and other agents, both ototraumatic and nonototraumatic. A short description of the anatomical effects of exposure to intense sound previews the interaction literature. The effects of exposure to combinations of continuous and impulse noise are discussed. This is followed by a review of data on interactions of noise and ototoxic drugs and noise and whole-body vibration, including discussion of putative mechanisms of synergism. In addition, preliminary results of noise-cisplatinum interaction are presented which suggest that if cisplatinum is presented during noise exposure in dose schedules simulating human chemotherapy schedules, hearing threshold shifts and histological damage is much greater than that caused by either agent in isolation. The clinical relevance of the interactions is discussed, along with potential synergistic interactions not yet investigated.

Hyperspectral image denoising using the robust low-rank tensor recovery.

Abstract: Denoising is an important preprocessing step to further analyze the hyperspectral image (HSI), and many denoising methods have been used for the denoising of the HSI data cube. However, the traditional denoising methods are sensitive to outliers and non-Gaussian noise. In this paper, by utilizing the underlying low-rank tensor property of the clean HSI data and the sparsity property of the outliers and non-Gaussian noise, we propose a new model based on the robust low-rank tensor recovery, which can preserve the global structure of HSI and simultaneously remove the outliers and different types of noise: Gaussian noise, impulse noise, dead lines, and so on. The proposed model can be solved by the inexact augmented Lagrangian method, and experiments on simulated and real hyperspectral images demonstrate that the proposed method is efficient for HSI denoising.

Successive impulsive noise suppression in OFDM

Abstract: We consider orthogonal frequency division multiplexing (OFDM) for high data rate narrowband power line communication (PLC) in the frequency bands up to 500 kHz. In narrowband PLC, the performance is strongly influenced by the impulsive noise with very large amplitudes with short durations. Simple iterative impulsive noise suppression algorithms can effectively improve the error rate performance in OFDM systems. However, the convergence speed depends on the number of subcarriers, N. For N ≤ 256, the algorithms converge slowly or not even at all. In this paper, we extend the iterative receiver design to enable a fast convergence for N ≫ 64 and to improve the error rate performance for N ≤ 64. These extensions include 1) a clipping and nulling technique at the input of the iterative algorithm 2) a novel low complexity syndrome decoder which uses the redundancy that is transmitted for synchronization or other purposes. Simulation results are provided to show the improvement in error rate

New Sequential Partial-Update Least Mean M-Estimate Algorithms for Robust Adaptive System Identification in Impulsive Noise

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.

Diffusion least-mean P-power algorithms for distributed estimation in alpha-stable noise environments

Abstract: A diffusion least-mean P-power (LMP) algorithm is proposed for distributed estimation in alpha-stable noise environments, which is one of the widely used models that appears in various environments. Compared with the diffusion least-mean squares algorithm, better performance is obtained for the diffusion LMP methods when the noise is with alpha-stable distribution.