The framework of the ROC based signal detection analysis utilized in this paper provides a non-biased and completely data driven method to identify regions and cut-points for categorization of treatment outcome.
ROC analyses, based on signal detection methods (Kraemer, 1992) were used to identify which MRI measures (GM region/white matter tract), and at what level (volume or FA), optimally discriminate non-remitters and remitters.
Aiming at detecting the weak signal in a strong noise background, an enhanced weak signal detection method based on adaptive parameter-induced tri-stable stochastic resonance is proposed.
Aimed at the detection problem of multi-frequency signal under colored noise background, a novel weak signal detection method based on stochastic resonance (SR) tuning by multi-scale noise is proposed.
Therefore, the paper proposes a novel weak signal detection method based on time-delayed feedback monostable stochastic resonance (TFMSR) system and adaptive minimum entropy deconvolution (MED) to realize the fault diagnosis of rolling bearings.
Yan et al. [16] presented a weak signal detection method based on the improved Hilbert-Huang Transform (HHT) by restraining the end effect of the traditional EMD method.
This paper has introduced a joint scheme for low complexity cyclic spectrum estimation and robust multiband signal detection method based on compressive sampling.
Based on this ontology, we design and implement software interfaces to mediate between existing signal detection method implementations and to aggregate their outcomes, facilitating their exploitation under a common integrated framework.
It is important to note that the current signal detection method cannot distinguish axon terminals from passing fibers.
SWAE enhances the SNR of output signal by using signal detection method.
