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In this paper we revisit the well known and popular Normalized Subband Adaptive Filter (NSAF). Based on an analysis of the algorithm in the mean and using an analysis strategy presented in [1], we find that the NSAF can be seen as a Richardson iteration applied to a preconditioned augmented Wiener-Hopf equation. This equation is formulated in such a way that its convergence speed can be predicted...
We investigate the problem of finding the real-valued vectors h, of size L, and x, of size P, from M independent measurements ym = 〈am, h〉〈bm, x〉, where am and bm are known random vectors. Recovery of the unknowns entails solving a set of bilinear equations, a challenging problem encountered in signal processing tasks such as blind deconvolution for channel equalization or image deblurring. Inspired...
In this paper we propose an extension of a blind source separation algorithm that can be used to process the data obtained by an array of ion-selective electrodes to measure the ionic activity of different ions in an aqueous solution. While a previous algorithm used a polynomial approximation of the mixing model and mutual information as means of estimating the mixture coefficients, it only worked...
In this paper, demodulation method by using adaptive filter with initial condition is proposed. In the proposed method, message signal is recovered from amplitude and phase of reference carrier signal. The initial weights for adaptive filter are determined by representing the adapted weight equation in the form of the difference equation. The results obtained from both computer and TMS320C6713 digital...
This paper presents an approach for parallel implementation of cross-correlation using the graphics processing unit (GPU). Cross-correlation is a central digital signal processing (DSP) algorithm with applications in many areas. In many cases in real time systems, a sequential implementation of the cross-correlation creates a performance bottleneck and prevents the systems from reaching the real time...
We analyze the behaviors of active noise control with a time-varying primary path using a statistical-mechanical method. The principal assumption used in the analysis is that the impulse responses of the primary path and adaptive filter are sufficiently long. We analyze a novel model in which the reference signal is not necessarily white and the primary path is time-varying while its norm is kept...
Over the last decades, machine learning techniques have been an important asset for detecting nonlinear relations in data. In particular, one-class classification has been very popular in many fields, specifically in applications where the available data refer to a unique class only. In this paper, we propose a sparse approach for one-class classification problems. We define the one-class by the hypersphere...
This paper explores the application of the Particle Swarm Optimization (PSO) algorithm for parameter estimation of a Nonlinear Auto-Regressive with Exogeneous Model (NARX) of a Direct Current (DC) motor. The two-step identification step consists of structure selection and parameter estimation. The structure selection process was based on methods from our previous works, while the parameters were estimated...
The adaptive filter is an important workhorse of digital signal processing having a vast variety of applications in almost every branch of modern electronics. Today there is an abundance of algorithms with different performance and computational characteristics. In this paper we address the class of delayless subband adaptive filters (SAF). Although this class of adaptive filters have been popular...
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...
In this paper, we propose a new algorithm of cyclic spectrum based on time-varying ARV (Vector Auto-regressive) model that aims to deal with the disadvantages of the cycle spectrum made by the traditional periodogram such as large estimated variance and low resolution. This paper uses the algorithm to get cyclic spectrum by building a cyclostationary time-varying ARV model for communication signal...
Based on spectrum correlation of Multi-section Sinusoids with the Known Frequency-Ratio (hereinafter referred as MSKFR), a frequency estimation algorithm was proposed. This algorithm aims at improving frequency estimation of the short sinusoid at low Signal-to-Noise Ratio(SNR), and extending the applicable range of the multi-section signals fusion method. Firstly, an easy way to get MSKFR in application...
With the development of information processing technology, DCT has been used more and more widely and the research on DCT algorithm is very much too. But the physical performance of DCT is unclear. To overcome such drawbacks, the paper has proposed 1-D SDCT operation method. First, some new 1-D matrix operation principles are defined. Then, the transform basic matrix used for 1-D SDCT operation was...
The purpose of channel shortening is to condense the channel in a shorter span to make the Multi Carrier Communication Systems bandwidth and power efficient. The autocorrelation minimization based channel shortening algorithms are investigated in this paper. If a white signal is input to a filter having a short span, the autocorrelation introduced in the output signal is small. The SAM algorithm expects...
Based on a novel two-dimensional autoregressive moving average (2D-ARMA) parameter estimate, this paper develops a neural network algorithm for fast blind image restoration. The point spread function of degraded image is reformulated as an optimal solution of a quadratic convex programming problem and it is well solved by a neural network. Compared with existing ARMA parametric methods, the proposed...
A modified Cao method has been proposed in this paper, which is a new phase space reconstruction algorithm using multiple delay embedded. Through the proposed method, the minimum embedding dimension and the best time delay could be determined under a unified criterion, so that the applications of Cao method can be expanded. The numeral simulation demonstrates that, compared with the single-delay embedded...
In this work, we propose an adaptive filter based on a non linear function, namely Recursive Non Linear (RNL) algorithm, which is inspired in the Recursive Lest Square (RLS) algorithm. We derive equations based on a nonlinear function in order to obtain criterions that guarantee convergence. We also make a study about the covariance of the weight vector on steady state and determine equations that...
A novel method termed differential joint diagonalization (DJD) is introduced to find good initial values for joint diagonalization of time-varying correlation matrices. A key point of the method is finding a matrix which satisfies several conditions in the form of the matrix Riccati equation simultaneously. An alternate algorithm of differential joint diagonalization and joint diagonalization is proposed...
This paper studies the potential for passive steganalysis in correlated image frames using non-classical detection theory.In particular,an algorithm for digital video steganalysis,named MoViSteg for Motion-based Video Steganalysis, is developed that exploits the temporal correlation among individual image frames in video signals to enhance steganalysis performance. The method differs from prior art...
In this paper, a new FIR adaptive filtering algorithm is introduced. This algorithm is based on the Quasi-Newton (QN) optimization algorithm. The approach uses a variable step-size in the coefficient update equation that leads to an improved performance. The simulation results show that the algorithm has very similar performance to the robust recursive least squares algorithm (RRLS) while performing...
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