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In this paper, a large number of features are extracted from raw EEG data and then feature selection and classification are performed ,for brain computer interface (BCI) applications using motor imaginary movements. As the feature selection method, mRMR (minimum Redundancy Maximum Relevance) method, which is a fast method to select relevant and non redundant feature set, is chosen. Using a number...
A novel variable step size least mean squares (VSS-LMS) algorithm employing cross correlation between channel output and error signal has been proposed as a solution to disadvantage of slow convergence of LMS algorithm. The new algorithm resolves the conflict between the convergence rate and precise of the fixed step-size conventional LMS algorithm. Computer simulations have been performed to verify...
As an alternative technique to well-known constant modulus algorithm (CMA), a Decision Feedback Equalizer via Channel Matched Filter (CMF-DFE) based blind channel estimation and equalization algorithm is proposed in this paper. The proposed technique employs Particle Swarm Optimization (PSO) in training, where the conventional CMA and least mean squares (LMS) based training algorithms are found slow...
In this study, a novel nonlinear adaptive filter algorithm is proposed guaranteeing the asymptotic stability in the sense of Lyapunov. The tracking capability of the proposed filter is tested by using a created artificial signal having a finite number of discontinuities. The proposed filter shows high performance both in Matlab environment and its FPGA realization. As a result, realization of the...
In this paper, the problem of estimating the impulse responses of individual nodes in a network of nodes is dealt. It was shown by the previous work in literature that when the nodes can interact with each other, fusion based adaptive filtering approaches are more effective than handling nodes independently. Here we are proposing the use of entropy functional based optimization in the adaptive filtering...
A new 2D frequency-response-shaped least mean square (2D FRS-LMS) adaptive filter is proposed by developing the 1D FRS-LMS. The new algorithm reuses data in both horizontal and vertical directions within the space plane to update the weight vector of the filter. Further, the proposed algorithm involves the multiplication of the filter coefficient vector by a variable matrix in the coefficient updating...
The recursive inverse (RI) adaptive algorithm, was shown to have comparable performance to that of the well-known recursive-least-squares (RLS) algorithms but with reduced computational complexity. Although the RI algorithm provides significant performance, it suffers from low convergence rate in some situations where a relatively low initial step-size is required. In this paper, we propose a new...
The amplifiers that are used on communication systems in order to increase the productivity, are worked on the nonlinear region. Therefore, digital communication channel can be defined as a Wiener block structure that contains a linear dynamic system and a non-linear static block. In this study, the nonlinear channel equalization problem, a Wiener block structured communication channel is tried to...
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