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with F the N × K precoding matrix, and s the K × 1 symbol vector.
Actually, W = P D H−1, therefore, s ̂ = W x = P D s, where P is a permutation matrix, D is a scaling diagonal matrix, and s ̂ is the estimation of the source signal vector s.
where W[ q,n], with dimensions N T ×N s, represents the precoding matrix and s [ q, n ] = s 1 [ q, n ] ⋯ s N s [ q, n ] T, with s i [ q,n] denoting the symbol corresponding to the i th stream to be transmitted on the q th subcarrier at time instant n.
The precoded transmit signal vector x k of MS is given by Fig. 1 Block diagram of multicell downlink system {boldsymbol{x}}_k={boldsymbol{T}}_k{boldsymbol{s}}_k (1 where T k is the precoding matrix and s k is the data for user k.
where x ( t ) ∈ ℜ 12 × 1 is the electrical signal recorded at the standard 12 leads in an ECG recording, A ∈ ℜ 12 × M is the unknown full column rank mixing matrix, and s ( t ) ∈ ℜ M × 1 is the source vector that assembles all the possible M sources involved in the ECG, including the interesting atrial component.
where T j =[t j,1,t j,K,t j,3···t j,K ]∈C N×K is the precoding matrix, and s j =[s j,1,s j,K,s j,3···s j,K ] T ∈C K×1 is the transmitted signal vector for K users.
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T refers to the transpose of the matrix, and S- refers to the Moore-Penrose generalized inverse of the S matrix.
The matrix Γ ̃ Y is an M × M matrix and P s and Y are K × K diagonal matrices.
Biomonitoring studies should follow validated protocols that are appropriate to the matrix and analyte(s) of interest.
Here, T and R are the shear mapping transformation matrix and the rotation matrix respectively, and S represents the scaling matrix.
where M s is a generalized mass matrix and h s is a generalized force vector.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com