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Determining sensor parameters is a prerequisite for quantitative force measurement.
For each sensor, parameters were set to express the robot's personality.
Sensor parameters and target coverage requirement are specified in Table 2.
The digital elevation model, target model, and the sensor parameters are the inputs of the simulator.
Influence of a superconducting sample on the sensor parameters is analyzed for the two configurations.
The overall performances of these architectures are analyzed with respect to coupling factor and sensor parameters.
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The relative change of Rct was used as sensor parameter.
Neural network based classification of material type even with the variation in the sensor parameter is investigated in this paper.
It is found that the sensor system is intelligent due to its ability to classify the material type even with the variation in the sensor parameter (distance between the sensor probe and plain objects).
According to the literature [40], parameter μ denotes the sensitivity of the sensor at the unit distance or the direction of directional sensor, parameter τ denotes the attenuation coefficient of sensor sensitivity on distance, and parameter γ denotes the attenuation coefficient of the directed sensor sensitivity on deviating from the central direction.
Sensor URNs identify sensors deployed at a platform, and consist of the namespace ID followed by the classifier "sensor", the organization, suborganization, and station IDs, a data quality status indicator ("raw", provisional" or "final"), and the sensor parameter: urn:x-epaiwpp:sensor:ohclecty:wrd:efrm34.8 raw:temperature.
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