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Exact(5)
In this method, each sensor first determines a local set which approximates the local posterior density.
In the proposed method, each sensor node only needs to deal with a portion of the system state, which has significantly reduced the communication and computation requirements.
Through the proposed heuristic method, each sensor node is enabled to more favor paths excluding the most overloaded node as its shortest hop distance to a sink decreases.
In our proposed method, each sensor node N i has to store its own private seed, e.g. S N i, and the seeds of its neighboring sensor nodes, e.g. S N k ″″.
In their method, each sensor transmits 1 bit per observation; hence, the energy consumed on communication is conserved while the estimation accuracy can be guaranteed through the Cramer‐Rao lower bound.
Similar(55)
In this paper, a sensor signal is modeled as the Gaussian distribution based signal points group, and signal points in overlapping region between two different sensors are classified by e-SVM (support vector machine) method as each sensor group.
In selecting cluster-heads, a probabilistic method where each sensor node selects itself as a cluster-head with a given probability is often used in large-scale and dense WSNs because it enables all nodes to independently decide their roles while keeping the signaling overhead low.
Topology control methods allow each sensor node to locally adjust its transmission range and select certain neighbors for communication, while maintaining a structure that can support energy efficient routing and improve the overall network performance.
Herein, we use a decision-level data fusion method which combines sensor information after each sensor has made a preliminary decision.
In the proposed method, each robot iteratively uses sensors to measure the working environment and execute the rearrangement task.
In their method, wireless sensor networks are divided into several cells, and the location claim of each node is mapped to a cell and broadcasted within the cell.
Related(20)
approaches each sensor
method each composition
method each slave
method each band
method each candidate
method each link
method each feature
method each router
method each parameter
method each plant
method each deadlock
method each element
method each quarter
method each station
method each group
method each decomposition
method each classifier
method each module
method each image
method each player
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