Rajib Lochan Das

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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)
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)
We have devised a new technique to segment an diseased MRI image wherein the diseased part is segregated using a masking based thresholding technique together with entropy maximization. The particle swarm optimization technique (PSO) is used to get the region of interest (ROI) of the MRI image. The mask used is a variable mask. The(More)
The generalized Orthogonal Matching Pursuit (gOMP) is a recently proposed compressive sensing greedy recovery algorithm which generalizes the OMP algorithm by selecting N( ≥ 1) atoms in each iteration. In this letter, we demonstrate that the gOMP can successfully reconstruct a K-sparse signal from a compressed measurement y=Φx by a maximum of(More)
The proportionate normalized least mean square (PNLMS) algorithm, a popular tool for sparse system identification, achieves fast initial convergence by assigning independent step sizes to the different taps, each being proportional to the magnitude of the respective tap weight. However, once the active (i.e., non-zero) taps converge, the speed of(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)
In the quantitative analysis of brain tissues, in magnetic resonance (MR) brain images, segmentation is the preliminary step. In this paper first we analyzed and compared various techniques used for Brain Image segmentation. Further it introduces an automatic model based technique for brain tissue segmentation from cerebral magnetic resonance (MR) images by(More)