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The sensor response patterns for B. licheniformis biofilm-3T3 cells, E. coli DH5α-biofilm-3T3 cells, and noninfected 3T3 cells were different, with LDA demonstrating 100% discrimination of the two cocultures and 3T3 cells.
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Sensor array response patterns are transformed into a vector containing values for solvation parameter descriptors of the detected vapor's properties.
These materials exhibited excellent sensing characteristics; moreover, the two sensors exhibited cross-reactive response patterns, making these compounds very promising candidates for array-based vapor-sensing applications.
The response patterns of the sensors were recorded and further analyzed by chemometrics methods.
Within these 2 time windows, we predicted/classified the MEG signals for the left-out objects based on the spatiotemporal response patterns across all sensors and all time points within the time window.
Figure 8 Sensor response and response time.
The response patterns of a temperature-modulated chemoresistive gas sensor were transformed to multi-exponential functions which facilitated the extraction of their discriminative features for gas diagnosis.
An array of SnO2-based thick-film gas sensors doped with Pd, Pt and Au is used to generate the response patterns and backpropagation neural network is used for the identification.
A new approach is presented for analyzing gas mixtures by transforming the problem into a pattern classification one to reduce the effect of the poor repeatability of sensor response on the prediction of gas concentration.
Maximum percentage sensor response of the sensor was 143.
The sensor response time is 2.5 s.
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