That is, projecting the EEG and MEG signals back to their sources might remove, at least in part, differences in their manifestations that are due to their divergent projections from sources to sensors.
Supposing there are N sources and M sensors (M ≥ N), the signal captured by sensor m can be modeled as (1) [14], where ★ is the convolution operation, a mn (t) is the finite duration impulse response mixing filter from source n to sensor m. x m t = ∑ n = 1 N a mn t ★ s n t (1).
Currently, ant colony algorithm [3], EEABR [4], MACS [5], and EPACOR [6] are utilized to find the best path from source to sink for wireless sensor network.
Missing or delaying the process data may severely degrade the quality of control; thus at least two paths should be available from source to destination nodes in the wireless sensor networks (WSNs).
In modern supply chains, the entire journey from source to destination is tightly monitored through sensors, which continuously transmit data.
Here, z ∈ ℝ3 m are current moment vectors of unknown sources at m locations in the brain and F ∈ ℝ k × 3 m describes the mapping from sources to k sensors, which is determined by the shape of the head and the conductivities of brain, skull and skin tissues.
In challenging environment, sensory data must be stored inside the network in case of sink failures, we need to redistribute overflowing data items from the depleted storage source nodes to sensor nodes with available storage space and residual energy.
In the case of a linear antenna, the distance δ i,j from source i, i=1,⋯,P, to sensor j, j=1,⋯,N, is, according to Al-Kashi theorem, δ i, j = ρ i 2 + ( j − 1 ) d 2 + 2 ( j − 1 ) d ρ i sin ( θ i ), (27).
