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Jing et al. presented a scheme referred to as asynchronous sampling[21], which shifted sampling times of sensor nodes in order to reduce the sampling rate for each sensor node.
A quadrature scheme is adopted to reduce the sampling points in the inverse Fourier transformation.
The proposed approach is enhanced by a succession of additional techniques to reduce the sampling variability.
In addition it is shown by a parametric bootstrapping experiment how longer observation time series can reduce the sampling uncertainty.
It is demonstrated how a modulated demodulated structure may reduce the sampling rate in a hybrid control system.
The present approach is efficiently employed to reduce the sampling effort and computational cost when large number of input parameters is involved.
The sampling theorem is easier to show when applied to sampling-rate conversion in discrete-time, i.e., when simple downsampling of a discrete time signal is being used to reduce the sampling rate by an integer factor.
Compressed sensing exploits the sparsity of the signal to reduce the sampling rate while keeping the resolution fixed, and has been widely used.
Depending on the acceptable noise level, it may be necessary to reduce the sampling frequency below the bandwidth of the control loop.
Results highlighted a low predictive power with both methodologies, preventing the possibility to reduce the sampling efforts when estimating ΔG directly.
The paper presents a new method for the classification and parameter estimation of frequency-shift-keying (FSK) signals, which exploits compressive sampling, and thus, allows to reduce the sampling rate beyond the limit of the Shannon theorem.
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