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Additive Noise (additive + noise)
Selected AbstractsIterative channel estimation and data detection in frequency-selective fading MIMO channels,EUROPEAN TRANSACTIONS ON TELECOMMUNICATIONS, Issue 5 2004Maja Lon Signals transmitted through multiple-input multiple-output (MIMO) wireless channels suffer from multiple-access interference (MAI), multipath propagation and additive noise. Iterative multiuser receiver algorithms mitigate these signal impairments, while offering a good tradeoff between performance and complexity. The receiver presented in this paper performs channel estimation, multiuser detection and decoding in an iterative manner. The estimation of the frequency selective, block-fading channel is initiated with the pilot symbols. In subsequent iterations, soft decisions of all the data symbols are used in an appropriate way to improve the channel estimates. This approach leads to significant improvement of the overall receiver performance, compared to other schemes. The bit-error-rate (BER) performance of the receiver is evaluated by simulations for different parameter setups. Copyright © 2004 AEI. [source] Identification of autoregressive models in the presence of additive noiseINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 5 2008Roberto Diversi Abstract A common approach in modeling signals in many engineering applications consists in adopting autoregressive (AR) models, consisting in filters with transfer functions having a unitary numerator, driven by white noise. Despite their wide application, these models do not take into account the possible presence of errors on the observations and cannot prove accurate when these errors are significant. AR plus noise models constitute an extension of AR models that consider also the presence of an observation noise. This paper describes a new algorithm for the identification of AR plus noise models that is characterized by a very good compromise between accuracy and efficiency. This algorithm, taking advantage of both low and high-order Yule,Walker equations, also guarantees the positive definiteness of the autocorrelation matrix of the estimated process and allows to estimate the equation error and observation noise variances. It is also shown how the proposed procedure can be used for estimating the order of the AR model. The new algorithm is compared with some traditional algorithms by means of Monte Carlo simulations. Copyright © 2007 John Wiley & Sons, Ltd. [source] An efficient adaptive algorithm for edge detection based on the likelihood ratio testINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 4 2002A. De Santis Abstract The edge detection problem in blurred and noisy 2-D signals is dealt with. An adaptive signal processing algorithm is proposed which marks edge points according to an hypothesis test which compares the likelihoods of two models describing the local signal behaviour in the two cases of absence/presence of an edge. The two models are identified by a regularized least squares estimation algorithm, obtaining a numerically efficient procedure, quite robust with respect to additive noise and blurr perturbation. No global thresholding or data prefiltering is required. Copyright © 2002 John Wiley & Sons, Ltd. [source] Spatially adaptive color filter array interpolation for noiseless and noisy dataINTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, Issue 3 2007Dmitriy Paliy Abstract Conventional single-chip digital cameras use color filter arrays (CFA) to sample different spectral components. Demosaicing algorithms interpolate these data to complete red, green, and blue values for each image pixel, to produce an RGB image. In this article, we propose a novel demosaicing algorithm for the Bayer CFA. For the algorithm design, we assume that, following the concept proposed in (Zhang and Wu, IEEE Trans Image Process 14 (2005), 2167,2178), the initial interpolation estimates of color channels contain two additive components: the true values of color intensities and the errors that are considered as an additive noise. A specially designed signal-adaptive filter is used to remove this so-called demosaicing noise. This filter is based on the local polynomial approximation (LPA) and the paradigm of the intersection of confidence intervals applied to select varying scales of LPA. This technique is nonlinear and spatially-adaptive with respect to the smoothness and irregularities of the image. The presented CFA interpolation (CFAI) technique takes significant advantage from assuming that the original data is noise-free. Nevertheless, in many applications, the observed data is noisy, where the noise is treated as an important intrinsic degradation of the data. We develop an adaptation of the proposed CFAI for noisy data, integrating the denoising and CFAI into a single procedure. It is assumed that the data is given according to the Bayer pattern and corrupted by signal-dependant noise common for charge-coupled device and complementary-symmetry/metal-oxide semiconductor sensors. The efficiency of the proposed approach is demonstrated by experimental results with simulated and real data. © 2007 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 17, 105,122, 2007 [source] A novel blind super-resolution technique based on the improved Poisson maximum a posteriori algorithmINTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, Issue 6 2002Min-Cheng Pan Abstract Image restoration has received considerable attention. In many practical situations, unfortunately, the blur is often unknown, and little information is available about the true image. Therefore, the true image is identified directly from the corrupted image by using partial or no information about the blurring process and the true image. In addition, noise will be amplified to induce severely ringing artifacts in the process of restoration. This article proposes a novel technique for the blind super-resolution, whose mechanism alternates between de-convolution of the image and the point spread function based on the improved Poisson maximum a posteriori super-resolution algorithm. This improved Poisson MAP super-resolution algorithm incorporates the functional form of a Wiener filter into the Poisson MAP algorithm operating on the edge image further to reduce noise effects and speed restoration. Compared with that based on the Poisson MAP, the novel blind super-resolution technique presents experimental results from 1-D signals and 2-D images corrupted by Gaussian point spread functions and additive noise with significant improvements in quality. © 2003 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 12, 239,246, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ima.10032 [source] Pseudoparabolic equations with additive noise and applicationsMATHEMATICAL METHODS IN THE APPLIED SCIENCES, Issue 8 2009K. B. Liaskos Abstract In this work we present some results on the Cauchy problem for a general class of linear pseudoparabolic equations with additive noise. We consider questions of existence and uniqueness of mild and strong solutions and well posedness for this problem. We also prove the existence and uniqueness of mild and strong solutions for a related perturbed Cauchy problem and we investigate the continuity of the solution with respect to a small parameter. The abstract results are illustrated using examples from electromagnetics and heat conduction. Copyright © 2008 John Wiley & Sons, Ltd. [source] | ||||||||||