Rajib Lochan Das

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Recognition rate of handwritten character is still limited around 90 percent due to the presence of large variation of shape, scale and format in hand written characters. A sophisticated hand written character recognition system demands a better feature extraction technique that would take care of such variation of hand writing. In this paper, we propose a(More)
—The generalized Orthogonal Matching Pursuit (gOMP) is a recently proposed compressive sensing greedy recovery algorithm which generalizes the OMP algorithm by selecting atoms in each iteration. In this letter, we demonstrate that the gOMP can successfully reconstruct a-sparse signal from a compressed measurement by a maximum of iterations if the sensing(More)
—In this paper, a new convergence analysis is presented for a well-known sparse adaptive filter family, namely, the proportionate type normalized least mean square (PtNLMS) algorithms, where, unlike all the existing approaches, no assumption of whiteness is made on the input. The analysis relies on a " transform " domain based model of the PtNLMS algorithms(More)
The proportionate normalized least mean square (PNLMS) algorithm and its variants are by far the most popular adaptive filters that are used to identify sparse systems. The convergence speed of the PNLMS algorithm, though very high initially, however, slows down at a later stage, even becoming worse than sparsity agnostic adaptive filters like the NLMS. In(More)
The proportionate normalized least mean square (PNLMS) algorithm and its variants are by far the most popular adaptive filters that are used to identify sparse systems. The convergence speed of the PNLMS algorithm, though very high initially, however, slows down at a later stage, even becoming worse than sparsity agnostic adaptive filters like the NLMS. In(More)