Topic
White noise
About: White noise is a research topic. Over the lifetime, 16496 publications have been published within this topic receiving 318633 citations.
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TL;DR: In this article, the spatial interaction is described by a Markov Random Field whose energy is chosen in order to preserve the edges, and an estimation method of the parameters is proposed that is performed before the restoration.
79 citations
TL;DR: The a posteriori probability for the location of bursts of noise additively superimposed on a Gaussian AR process is derived to give a sequentially based restoration algorithm suitable for real-time applications.
Abstract: In this paper we derive the a posteriori probability for the location of bursts of noise additively superimposed on a Gaussian AR process. The theory is developed to give a sequentially based restoration algorithm suitable for real-time applications. The algorithm is particularly appropriate for digital audio restoration, where clicks and scratches may be modelled as additive bursts of noise. Experiments are carried out on both real audio data and synthetic AR processes and significant improvements are demonstrated over existing restoration techniques. >
79 citations
78 citations
TL;DR: This work derives a necessary condition on the chosen precoding matrices for minimizing error probability of the OFDM system in the additive white Gaussian noise (AWGN) channel and proves that the precoding matrix with all the singular values equal to 1 is one of the optimal solutions.
Abstract: The precoding technique is an effective and flexible way for reducing the peak-to-average power ratio (PAPR) of orthogonal frequency division multiplexing (OFDM) signals. However, different precoding schemes will increase error probabilities of the system. With the knowledge of the channel information and the receiver filter, we derive a necessary condition on the chosen precoding matrices for minimizing error probability of the OFDM system in the additive white Gaussian noise (AWGN) channel. A systematic procedure in designing such an optimal precoding matrix is provided. With a proper selection, the optimal precoding matrix can meet the requirements of PAPR reduction and achieve the minimum error probability in white Gaussian noise. Our simulation results show that the chosen precoding matrix notably outperforms other general precoding matrices in both AWGN and multipath fading channels. We also proved that the precoding matrix with all the singular values equal to 1 is one of the optimal solutions.
78 citations
TL;DR: A time and frequency analysis method based on the complete ensemble empirical mode decomposition (CEEMD) method in ground-penetrating radar (GPR) signal processing demonstrates that CEEMD promises higher spectral-spatial resolution than the other two EMD methods in GPR signal denoising and target extraction.
Abstract: In this letter, we apply a time and frequency analysis method based on the complete ensemble empirical mode decomposition (CEEMD) method in ground-penetrating radar (GPR) signal processing. It decomposes the GPR signal into a sum of oscillatory components, with guaranteed positive and smoothly varying instantaneous frequencies. The key idea of this method relies on averaging the modes obtained by empirical mode decomposition (EMD) applied to several realizations of Gaussian white noise added to the original signal. It can solve the mode-mixing problem in the EMD method and improve the resolution of ensemble EMD (EEMD) when the signal has a low signal-to-noise ratio. First, we analyze the difference between the basic theory of EMD, EEMD, and CEEMD. Then, we compare the time and frequency analysis with Hilbert–Huang transform to test the results of different methods. The synthetic and real GPR data demonstrate that CEEMD promises higher spectral–spatial resolution than the other two EMD methods in GPR signal denoising and target extraction. Its decomposition is complete, with a numerically negligible error.
78 citations