The analysis of chaotic time series requires proper reconstruction of the state space from the available data in order to successfully estimate invariant properties of the embedded attractor. Using the correlation dimension, we discuss the applicability of the two most common methods of reconstruction, the method of delays (MOD) and the Singular Spectrum Approach (SSA). Contrary to previous dis...

Neural data are inevitably contaminated by noise. When such noisy data are subjected to statistical analysis, misleading conclusions can be reached. Here we attempt to address this problem by applying a state-space smoothing method, based on the combined use of the Kalman filter theory and the Expectation-Maximization algorithm, to denoise two datasets of local field potentials recorded from mo...

Kalman filtering has been widely considered for dynamic state estimation in smart grids. Despite its unique merits, the Kalman Filter (KF)-based dynamic state estimation can be undesirably influenced by cyber adversarial attacks that can potentially be launched against the communication links in the Cyber-Physical System (CPS). To enhance the security of KF-based state estimation, in this paper...