The Recursive Least Squares Minimum Distance Error algorithm presented here seems to satisfy this condition.
The SOM-hybrid ANN classifier produced the most accurate map from the 194-band hyperspectral data with 23 cover classes (89%), followed by the minimum Euclidean distance algorithm (82%) and the spectral angle mapper (79%).
For the general case of having L relays we get similarly the squared minimum distance of the proposed algorithm for single-antenna relays given by Figure 5 Trellis code for the RA precoded algorithm between symbols u (1) and u (i), for L = 2. d min PRA 2 = μ 2 d min QPSK 2 min j ∈ J ∑ i = 0 L - 1 4 - i h r u _ i + L _ 1, k 2, (4).
When N R = 2 the modulation used is given by M A = 4 L. The squared minimum distance for the DCA algorithm, obtained by application of Euclidean distance definition, is then given by d min RA-DCA 2 = α M A - QAM 2 2 ∑ i = 0 L - 1 g N R, i, k. (A.6).
Derivation of the gain and the minimum distance expressions, for the distributed algorithm, is detailed in Appendix 1.
According to this, the squared minimum distance expression for the precoded algorithm is given by d min RA-SFBC 2 ≈ d min 16 - QAM 2 N R L ∑ i = 0 L - 1 g N R, i, k, (A.1).
Labeled nuclei (spots) were tracked using a Brownian motion algorithm (the minimum distance between each spot across the time frame was 20 µm).
The following is a summary of the steps to be followed in the proposed control points selection algorithm with a minimum distance, Dmin, with respect to each other.
BaobabLuna computes the minimum distance of inversions using the same algorithm GRIMM uses, which is based on an analysis of the breakpoint graph (Tesler 2002).
The asymptotic lower power gain from the proposed algorithm considered relatively to the SFBC is obtained through the ratio of the minimum distances of both algorithms in Equations (8) and (A.4), and by using (A.3), what results in G L ≈ 10 log 1 ∑ i = 0 L - 1 4 i ∑ i = 1 L - 1 4 i ρ m i + 1 2 5 L N R ∑ i = 1 L - 1 ρ m i + 1. (A.5).
