The second we define as the "normalized magnitude error" calculated according to This element-by-element summation is sensitive to the shape and position of the reconstructed source and is a more stringent metric than the total power.
To explain the relatively large errors of the internal models in the human nervous system, Maeda et al. (Proceedings of the 1993 International Joint Conference on Neural Networks (IJCNN'93 Nagoya), 1993, pp. 1317 1320) proposed a neural network architecture called independent scalar learning elements summations (ISLES) model.
The vector Δ a b 1 θ, ϕ represents the truncation errors term and its element is the summation of j m J m (kr sin θ e jmϕ with M < |m| ≤ ∞.
It represents a kind of hybrid, which incorporates an important element of the summation hypothesis.
Therefore, the limitation done in formula (3), in terms of number of included summation elements, is accurate and does not have any influence on the performance of the receiver.
G c is the complement of G, that is G c ={1, 2, 3,…, n}\ G. F k will contain m!/(m− k)! k! elements, over which summation is infeasible in practice unless m is small.
The elements of matrix Z are evaluated with the condition that for each row, the summation of elements of K t) is 1.
where ω is a decay parameter, η a coefficient of diffusion and the a ij ′ in the summation are elements of A′.
Thus, by carefully designing a codeword to be the summation of elements from the four defined sets in (2), the communication system is decomposed into parallel independent virtual channels: the private (unicast) channel to R1, the private channel to R2, the common (broadcast) channel to both R1 and R2 and the fourth channel to any receiver but R1 and R2.
For each probe i, we calculate a threshold t i such that probe i has probability α i = min 1, λπ i /|π|) to be a FP by chance after de-clumping, where |π| denotes to the summation of elements in π.
