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Based on the filtering theory, we present a filtering based recursive least squares algorithm for a class of Wiener nonlinear systems. The basic idea is to use an estimated noise transfer function to filter the input-ouput data, to obtain two identification models containing the parameters of the system model and the noise model, respectively, and to present the filtering based recursive least squares...
A new combined video content protection approach is proposed and experimentally evaluated. In this approach reliability information obtained during watermark extraction process is used for improving the accuracy of traitor tracing procedure.
Baseline wander removal is an unavoidable step in ECG signal processing. The in-band nature of this noise makes its removal difficult without affecting the ECG, in particular the ST segment. This portion of the ECG has high clinical relevance, as it is related to the diagnosis of acute coronary syndromes. We have recently proposed a novel approach to baseline wander removal based on the notion of...
The traditional Richardson-Lucy (R-L) algorithm has a strong ability to realize super-resolution. However, it always suffers from noise amplification. In this paper, an improved R-L algorithm is proposed to solve the real-beam scanning radar angular super-resolution problem, which relies on both the traditional R-L deconvolution algorithm and the regularization term. We first describe the angular...
Finding roots of words is widely used in document classification and text mining. Computational methods of text similarity are intensely utilized on the English words and successful outcomes are obtained. On the other hand, applying the aforementioned methods on the Turkish words did not give the similar success. In this study, a novel similarity computation algorithm is developed. By using this algorithm...
Compressed Sensing (CS) is a new mathematical concept, which can reconstruct the original signal accurately with lower Nyquist sampling. Besides, multipath arrivals in an Ultra-wideband (UWB) channel have a long time intervals between clusters and rays where the signal takes on zero or negligible values. It is precisely this signal sparsity of the impulse response of the UWB channel that is suitable...
Computed Tomography as well as Magnetic Resonance or Positron Electron Tomography are currently the most commonly used medical imaging modalities for the analysis of human body complex structures and organs, where diseases must be recognized and identified. The image reconstruction process used in these tomography techniques is usually based on the Radon Transform (RT). In this paper, an algorithm...
In this paper, we propose robust set-membership filtering algorithms against impulsive noise. Firstly, we introduce set-membership normalized least absolute difference algorithm (SM-NLAD). This algorithm provides robustness against impulsive noise through pricing the absolute error instead of the square. Then, in order to achieve comparable convergence performance in the impulse-free noise environments,...
In this paper an adaptive noise cancelation (ANC) model is presented to remove baseline wander (BW) noise from mathematically modeled ECG signals. The ANC model is designed to have a trade-off between the correlation properties of noise and reference signals. Matlab is used to simulate ECG signals artificially, to represent different sinus rhythms and leads of ECG waveform. Furthermore contamination...
The recent advances in sparse representations of images have achieved outstanding results in terms of denoising and restoration; but removal of real and structured noise in digital video sequences remains a challenging problem. Based on this idea, the problem addressed in this paper proposes to improve the decision median filtering algorithm for denoising of video sequences corrupted with impulse...
Restoration of the image corrupted by impulse noise is proposed in this paper. Adaptive neuro fuzzy inference system (ANFIS) has been used to detect the impulse noisy pixels to keep preserve the fine details of the image. Feed-forward neural network with resilient backpropagation method is used to estimate the value of the pixel by which the corrupted pixel is replaced by the estimated value. Proposed...
Predominantly all signal processing algorithms are developed with floating-point arithmetic. However for low power and real-time applications they are finally implemented on embedded systems with fixed-point arithmetic. Implementation of signal processing algorithm in fixed-point arithmetic involves a floating-point to fixed-point conversion process. Wordlength optimization plays a significant role...
With an objective to improve the convergence characteristics of nonlinear active noise control (ANC) systems, this paper proposes a discrete cosine transform based adaptive algorithm for ANC. The performance of the new algorithm in terms of speed of convergence has been compared with that of the filtered-s least mean square algorithm. The improved convergence of the new algorithm is evident from the...
In this article we provide analyses of two low complexity LMS algorithmic variants as they typically appear in the context of FXLMS for active noise or vibration control in which the reference signal is not obtained by sensors but internally generated by the known engine speed. In particular we show that the algorithm with real valued error is robust and exhibits the same steady state quality as the...
This paper proposes a new adaptation algorithm named Normalized Recursive Least Moduli (NRLM) algorithm which employs “p-modulus” of error and “q-norm” of filter input. p-modulus and q-norm are generalization of the modulus and norm used in complex-domain adaptive filters. The NRLM algorithm with p-modulus and q-norm makes adaptive filters fast convergent and robust against two types of impulse noise:...
The ability of having a sparse representation for a certain class of signals has many applications in data analysis, image processing, and other research fields. Among sparse representations, the cosparse analysis model has recently gained increasing interest. Many signals exhibit a multidimensional structure, e.g. images or three-dimensional MRI scans. Most data analysis and learning algorithms use...
In this paper, the steady-state and tracking behavior of the complex signed regressor least mean square (SRLMS) algorithm are analyzed in stationary and nonstationary environments, respectively. Here, the SRLMS algorithm is analyzed in the presence of complex-valued white and correlated Gaussian input data. Moreover, a comparison between the convergence performance of the complex SRLMS algorithm and...
In recent years, image quality assessment has achieved great success. Many researchers have proposed more and more image quality assessment algorithms, which are being closer to human perception. However, some proposed algorithms could perform even better by doing some transformations on image. In this paper, a new image quality assessment based on contour let transformation is proposed. And experimental...
To analyze the characteristics of the blood vessels quantitatively is of great importance to the diagnosis of different diseases, e.g., stenosis, diabetic retinopathy, tumor and so on. However, the occurrence of imaging noise or illumination variation may include difficulty for image analysis, especially for accurate quantitative analysis. Therefore, more efficient algorithms for vessel image thresholding,...
Non-local Means(NLM) is increasingly popular in image denoising. In this paper, the nonlocal structure similarity of images obtained by the iteration is exploited. By combining the nonlocal similarity constraints with total variation regularization, an iterative regularized variational model is proposed, in which the nonlocal weight depends on local structure of patches. An effective algorithm is...
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