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A performance of detection system is evaluated by calculating its reliability (Kononenko 1991).
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Therefore, the performance of detection in TTSD enhances.
Benchmarking the performance of detection algorithms requires training and validation data.
The biosensor constructed with six bilayers of SCGNPs/TH showed a good performance of glucose detection with a fast response less than 20 s, acceptable sensitivity of 3.8 μA mM−1 cm−2 and the detection limit of 3.5 × 10−5 M.
This obviously leads to a better performance of the detection systems especially in the UV range and allows some fine tuning of the intensity for the wavelength range most suitable for the experiment at hand by adjusting the amount of light via the iris.
Furthermore, this method provides better performances of detection when an iterative process is considered in order to increase the probability to obtain non-erroneous pivots during the Gauss elimination.
The Au@ZnO@ZIF-8 Janus nanostructure exhibited an impressive performance of selective detection and oxidation of HCHO, where Au NRs helped to generate charge carriers on ZnO surface under visible light irradiation.
Here we present a calibration design that integrates detection limit theory and linear dynamic range to obtain a performance index of microarray detection platform using oligonucleotide arrays as a model system.
The performance of a detection algorithm is, usually, characterized by two types of probabilities: the false alarm probability (P fa) and the detection probability (P d).
The performance of a detection system is characterized by its miss and false alarm probabilities.
The target Doppler frequency, f D, influences heavily the performance of a detection algorithm, as if the Range-Doppler bin of the target is located within an area of low clutter power, the detection performance will be high.
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