V. Vivekanand

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The Compressed Sensing (CS) is a new signal acquisition technique that enables the reduction in the number of measurements required for recovery of sparse signal and the signal recovery is done using optimization techniques. The recovery of the original signal from CS measurements becomes difficult when the CS data acquisition is noisy. Here we are(More)
Many critical parameters of rockets and satellite are obtained by means of telemetry system present onboard. For meaningful analysis of data these parameters are to be time stamped. This is achieved by means IRIG B time code proposed by Inter-range instrumentation group. The data obtained need to be transmitted to various sub systems present in ground. Bus(More)
The development of a computationally optimal Compressed Sensing recovery algorithm based on polynomial approximated L<sub>0</sub> minimization of the objective signal x and the error projection, is discussed in this paper. The proposed algorithm X-L0 E-L0 minimization (XEL0) using continuous function approximation for sparse signal recovery, minimizes the(More)
Analysis of cascade network consisting of RBF nodes and least square error minimization block for compressed sensing recovery of sparse signals is presented in this paper. The proposed algorithm radial basis function cascade network for sparse signal recovery uses the L<sub>0</sub> norm optimization, L<sub>2</sub> least square method and feedback network(More)
Optimization of the fermentation conditions for chitin deacetylase (CDA) production by Penicillium oxali-cum SAE M-51 was undertaken in the present study using central composite design (CCD) under submerged condition. CDA is widely employed for bio-catalytic conversion of chitin to chitosan. Chitosan is a biopolymer with immense commercial potential in(More)
T h e theoretical available instruction level parallelism in m o s t benchmarks is very high. Vulnerability i s related t o the difficulty with which w e can extract t h i s parallelism with finite resources. This study characterizes the vulnerability of parallelism t o resource constraints by scheduling d y n a m i c dependence graphs (D D G s) f r o m(More)
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