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In general, the relationship between the measurement vector y (measured spectrum) and the state vector x to be retrieved can be expressed by a nonlinear forward model F describing the radiative transfer in the atmosphere: {mathbf{y}} = {mathbf{F}}({mathbf{x}},{mathbf{b}}) + {varvec{upvarepsilon}}, (3)where b is the additional not-retrieved state vector and ε the measurement error vector.
Under some conditions, the solution vector can be recovered accurately from the measurement vector y [10].
The updated processing of BF for the MF-based measurement vector y n has been derived from eq. (10).
The measurement vector y∈ ℂ m thus reads as y = A Ω α with A Ω = Φ Ω * Ψ ∈ ℂ m × N. (1).
We concatenate all of the measurement vectors { y j, s i } i = 1 T into the length-M j measurement vector y j = Φ j x j.
2. Update step The updated processing of BF for the MF-based measurement vector y n has been derived from eq. (10).
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After we obtain the estimate x ^, we refine the camera positions by registering the measurement vectors y j with respect to this manifold.
Given this estimate, we may then re-estimate the camera positions by projecting the measurement vectors y j, s onto the manifold ℳ ( x ^ ).
When the measurement vectors y i and the dictionary parameters μ i ∗ are derived, the two speech parts can be recovered using OMP algorithm [22].
Using an overcomplete basis A ∈ ℂM×K(M ≪ K) and the sparsity prior on signal x, the sparse representation problem of the noiseless measurements with single measurement vector (SMV) y = Ax can be solved by a combinatorial ℓ0 problem min x 0 s.t. y = Ax, (1).
The algorithm used to generate the s - th (s = 1,..., 1000) sample can be summarised in the following steps: 1. set δ and and set values for β and ; 2. generate each measurement data vector y s from the multivariate normal distribution MV N(X t, V; 3. fit the mixed model for each data set using GEE when Lin's approach is applied, MLE for Choudhary's approach and REML for our proposal.
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