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Charge identification of the incident cosmic ray is accomplished, primarily, by a pixilated Silicon Matrix detector located at the very top of the instrument.
While it has been shown that the Silicon Matrix detector provides good charge identification even in the presence of electromagnetic shower backscatter from the calorimeter, the detector only measures the charge once.
The instrument is composed of a Silicon matrix detector followed by a carbon target interleaved with scintillator tracking layers and a segmented BGO calorimeter composed of 320 individual crystals totaling 18 radiation lengths to determine the particle energy.
Signals from a diode matrix detector were integrated and analysed with Agilent Chem Station B.04.01 software (Agilent Technologies, Waldbronn, Germany).
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One group has detector elements of equal width along the z-axis (matrix detectors) and the second has detector elements of unequal width (adaptive or hybrid array detectors).
Compared to the covariance matrix based detector, the proposed model-based adaptive detectors have significantly decreased the training requirements and the computation complexity.
Compared to the covariance matrix based detector, all of the proposed adaptive detectors can handle the training-limited case and significantly decreased the computation complexity in compound-Gaussian environment.
Indeed, the parametric detectors are significantly less dependent on training than the covariance matrix based detector.
Relative recoveries were determined by comparing the % of drug recovered from serum matrix against detector response for extracted standards.
Relative recoveries were determined by comparing the % amount of drug recovered from hair matrix against detector response for extracted standards, utilising three different concentrations of quality controls.
Numerical results show that the proposed adaptive CG-PGLRT detectors have dramatically ease the training and computational burden compared to the generalized likelihood ratio test-linear quadratic (GLRT-LQ) which is referred to as covariance matrix based detector and relies more heavily on training.
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