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We present a generic method for analyzing the effect of process variability in nanoscale circuits. The proposed framework uses kernel and a generic tail probability estimator to eliminate the need for a-priori density choice for the nature of circuit variation. This allows capturing the true nature of the circuit variation from a few random samples of its observed responses. The data-driven, non-parametric,...
In this paper, a new method is proposed for predicting the future evolution of a time series of spacecraft telemetry data. Because such a time series has usually a non-stationary trend, nonlinear functional relationship between inputs and outputs and other uncertainties, an appropriate prediction model for spacecraft data is needed to set up. To this end, we analyze the characteristics of the Probability...
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