"T" and "H" are the matrix transpose and Hermitian transpose.
We denote by the matrix transpose, and by the matrix conjugate transpose.
The chapter concludes by presenting the matrix transpose and the definition of a symmetric matrix.
Here, denotes matrix transpose, and for complex conjugation and conjugate transposition, notations and are used, respectively.
A*, A T, and A H denote the conjugate matrix, matrix transpose, and conjugate (Hermitian) transpose of A, respectively.
To utilize the spatial data correlation, we will jointly estimate the data from all the nodes, (textbf {x}_{k} = [x_{1k}, cdots, x_{textit {nk}}]^{T} in {mathcal {B}}^{N times 1}), based on the received signal vector, (textbf {y}_{k} = [y_{1k}, cdots, y_{textit {Nk}}]^{T} in {mathcal {C}}^{Ntimes 1}), where T represents matrix transpose, and is the set of complex numbers.
A common approach is to adopt the "correlation" term defined as follows: {alpha}_i=left|{boldsymbol{Omega}}_i{boldsymbol{M}}^Tboldsymbol{y}right|,kern1em iin left{1,2,cdots, pright} (6 where M T y resembles the original signal, Ω i denotes the i-th row of Ω, the superscript T denotes matrix transpose, and | ⋅ | takes the absolute value of its argument.
We want to linearly combine the PC signals to best predict the velocity: (60) β = argmin 〈 (v − β T x ) 2 〉, where β is a four-dimensional column vector of weights, v denotes the velocity, the superscript T denotes the matrix transpose, and x is the 4-vector of PC signals.
If λij t) denotes the transition intensity from state i to state j at time t, then λij t) may be modelled as follows: where λ0ij t) denotes a baseline state i to state j transition intensity at time t, T denotes matrix transpose, and X t) is a vector of explanatory variables with associated explanatory variable effects on the state i to state j intensity denoted by βij.
Subsequently, the data matrix was transposed and clustering of variables performed in "R" mode (See details in Legendre and Legendre [ 54]).
