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We consider the estimation of the state transition matrix in vector autoregressive models when the time sequence data is limited but nonsequence steady-state data is abundant. To leverage both sources of data, we formulate the problem as the least-squares minimization regularized by a Lyapunov penalty. Explicit cardinality or rank constraints are imposed to reduce the complexity of the model. The...
Due to the inherent physical characteristics of systems under investigation, non-negativity is one of the most interesting constraints that can usually be imposed on the parameters to estimate. The Non-Negative Least-Mean-Square algorithm (NNLMS) was proposed to adaptively find solutions of a typical Wiener filtering problem but with the side constraint that the resulting weights need to be non-negative...
Explicit feedback based congestion control schemes can capture network congestion status more accurately than pure end-to-end schemes. However, some of such schemes require modifying IP header in order to achieve near optimal performance, which incurs complicated computation in routers as well as makes them difficult to deploy in real networks. In contrast, the VCP protocol achieves good performance...
Orthogonal Frequency Division Multi-plexing (OFDM) is a significant technology to provide a high-rate wireless transmissions. In this paper, a scheme called as a step size delta - least mean square (SSD-LMS) is used for phase error cancellation. The results show that SSD-LMS algorithm can provide a rapid convergence with a low steady-state fluctuation error than that with the conventional LMS algorithm.
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