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Before the visualization stage, sensor network layer was made above the floor plan.
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In the preprocessing stage, sensor-specific algorithms are applied to correct for both geometric and intensity/radiometric distortions introduced by the sensor.
Discharge and suspended sediment concentration data were monitored during the rainy season of 2014 and 2015 using automatic flow stage sensors, manual staff gauges and a depth-integrated sediment sampler.
The thermal compensation and independent programmability methods are used in all the main channel stages: sensor biasing, high-pass filtering and pre-amplification, blind cancellation, lock-in demodulation and A/D conversion.
In this stage, D sensor could receive the ( frac{m}{n}{N}_R{N}_M ) packets from S sensor and N R relay sensors.
At the first stage, each sensor collects its own measurements and generates the local optimal estimates of the state and the bias which are later used to compute the local estimate of the UI via the least squares method.
At the first stage, every sensor in the WSN collects measurements from its neighbors to generate a local estimate, then local estimates in the neighbors are further collected at the second stage to form a fused estimate to improve estimation performance and reduce disagreements among local estimates at different sensors.
Local communication stage: Each sensor collects the current estimates of the coordinates from its neighbors.
There are two stages in the algorithm: 1. Local communication stage: Each sensor collects the current estimates of the coordinates from its neighbors.
In the final stage, the sensor was washed for 30 min with fresh portions of 5 mM KH2PO4 Na2HPO4 buffer at constant stirring.
In the WSN's initial stage, each sensor node will transmit on the basis of these three power discover messages in order to collect neighboring nodes' information.
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