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So far, depth video of natural scene can be obtained via Kinect sensors, depth camera systems, and depth estimation software [14 16].
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The model is relatively insensitive to realistic errors in sensor depth and thermal conductivity.
Moreover sensitivity to sensor depth is estimated and demonstrated to be not compromising.
However, given a priori knowledge of sensor depth and a relatively flat sea-bed, the 3-D MDS problem becomes reducible.
For further information on sensor depth and site, refer to [ 23].
The algorithm works with various limited sensing models, such as depth-limited boundary distance sensor, quadridirectional depth sensor, depth-limited gap sensor, and depth-limited radially-bounded depth senor.
The hydrodynamic pressures are normalized with the total hydrostatic pressure, and the sensor depths are normalized with reservoir depth.
The small but significant variation observed in baseline Vp values at the different sensor depths was likely associated with heterogeneous packing of the sediments within the column as mentioned above.
For K-NET sites we have S-wave velocity information of downhole P- and S-wave logging down to 20 m at most, while for KiK-net sites we have them down to the borehole sensor depths, typically 100 200 m.
Both sensors produce depth images that store the real depth value in each pixel.
Most TOF sensors acquire depth information by emitting continuous-wave (CW) modulated infra-red light and measuring the phase difference between the sent (reference) and received light signals.
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