Meanwhile, descriptions to the proposed fusion techniques, data data (D D) fusion, algorithm algorithm (A A) fusion, feature feature (F F) fusion, data and algorithm (D-A) fusion, data and feature (D-F) fusion, and algorithm-feature (A-F) fusion, and data-algorithm-feature (D-A-F) fusion, will be elaborated.
Work through this Fusion Table tutorial, which shows how import some data, create a Fusion Table, and view the data on a map with your browser.
For multiple sensor systems, sensors send their sense data to a fusion center, and the fusion center makes the final decision to improve the global probability of detection.
Consensus algorithm is acted as a way to achieve globally optimal decision in a totally decentralized way, without sending all the sensors data to a fusion center [3].
We consider sensor-based distributed source localization applications, where sensors transmit quantized data to a fusion node, which then produces an estimate of the source location.
Instead of processing the data in a fusion center, our solution is letting each agent autonomously make decisions aided by limited communication with its neighbors.
Fig. 1 MSL scenario with two sources and four stations that forward the measured data to a fusion center that localizes the sources.
Palauqui and B. Dubreucq, Institute Jean-Pierre Bourgin, INRA, Versailles, France, unpublished data) as a fusion with the GUS reporter gene.
Each sensor node collects samples of the field, and forwards the information to a data fusion center (FC) through an one-hop AWGN or fading channel.
The proposed framework includes a multi-dimensional nonlinear interpolation (Kriging), a data fusion (co-Kriging) and a sampling method (Latin Hypercube Sampling) in an integrated structure coupled with aerodynamic solvers.
