Victor Sreeram

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In this paper, a new frequency-weighted model reduction method with an a priori error bound is proposed. The method is a generalization of Enns technique and yields stable models even when both input and output weightings are included. The proposed method is compared with other existing methods using numerical examples.
In sensor networks, linear regression has been applied over a set of sending-receiving timestamps to estimate the clock-skew, assuming a time-invariant propagation delay of the beacons exchanged between the nodes. Due to ocean currents and dispersion, propagation delay between underwater nodes is time-varying. In this paper, we first analyze the effect of(More)
An improved frequency domain interval Gramianbased model reduction scheme for discrete time systems is presented. It is first shown that two of the main results presented in the model reduction method of [20] are incorrect. Improved methods which overcomes these shortcomings are then presented. Improved methods not only yields stable reduced-order models(More)