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The instruments are first segmented and pose is estimated, then the RSN system picks up the unknown instruments and presents them to the optical sensor in the determined pose.
In this article, we propose and investigate a totally different model of CS-based RSN system.
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Deng [6] introduced a distributed RSNs systems for effective target detection and the detection performance of this system improves dramatically with the increase of the number of radar sensors (RSs) in the system.
In the end, the proposed ADN-RSN-PSO is applied to the radar system design problem to demonstrate its potential in real-life applications.
Simulation results show that our RSN vastly reduces the ATR error compared to a single radar system in ATR with delay-Doppler uncertainty.
The sensorimotor component RSN 6 reported by Smith et al. (2009) split up into two components, termed RSN 6a and RSN 6b.
Different from previous CS-based radar system, we proposed a totally new model for CS-based RSN.
Information about the spatial relation of EEG spectral fluctuations and RSN dynamics might provide a missing link between the known functional domains of specific RSNs (e.g. attention, vision, motor system) and the existing functional interpretations of localized or global changes in EEG spectra.
These RSNs included the sensorimotor areas, the primary and secondary visual systems, the auditory system, as well as attention and memory related networks.
With the help of HELPNI system and the implemented HAFNI computational framework, we successfully identified 10 meaningful and consistent resting state networks (RSNs) which are in agreement with previous studies across all individuals and datasets in 1000FC database.
It is RCN, not RSN.
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