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GM tobacco samples and non-GM tobacco samples were clearly discriminated by the sensor method.
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The sensor methods use position sensors such as optical encoder.
In order to reduce the large dimensionality of the data set while keeping the relevant information provided by the sensors, two different methods of feature selection and data compression were used (the kernels method and Discrete Wavelet Transform feature extraction method).
The sensor nodes are randomly deployed by using the sensor density prediction method.
The surface contour and thickness of each layer was determined by analyzing the sensor using optical methods.
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The temperature performance of the body type sensor is clearly influenced by the sensor position.
By analyzing the sensor placement problem with EI method from the perspective of a new reduced system, the connection of MKE with EI method is revealed.
Detecting by electrochemical methods, the sensor was well characterized by electrochemical impedance spectroscopy (EIS) and cyclic voltammetry (CV).
Numerical results show that the optimal multi-type sensor placement determined by the proposed method can avoid redundant sensors and provide satisfactory results for structural damage detection.
As a model system the effect of electrostatically assembled polyelectrolyte layers deposited using layer-by-layer method on the sensor impedance was studied.
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