Abstract: In this paper we describe the Levenvberg-Marquardt
(LM) algorithm for identification and equalization of CDMA
signals received by an antenna array in communication channels.
The synthesis explains the digital separation and equalization of
signals after propagation through multipath generating intersymbol
interference (ISI). Exploiting discrete data transmitted and three
diversities induced at the reception, the problem can be composed
by the Block Component Decomposition (BCD) of a tensor of
order 3 which is a new tensor decomposition generalizing the
PARAFAC decomposition. We optimize the BCD decomposition by
Levenvberg-Marquardt method gives encouraging results compared to
classical alternating least squares algorithm (ALS). In the equalization
part, we use the Minimum Mean Square Error (MMSE) to perform
the presented method. The simulation results using the LM algorithm
are important.
Abstract: Multi-dimensional principal component analysis
(PCA) is the extension of the PCA, which is used widely as the
dimensionality reduction technique in multivariate data analysis, to
handle multi-dimensional data. To calculate the PCA the singular
value decomposition (SVD) is commonly employed by the reason of
its numerical stability. The multi-dimensional PCA can be calculated
by using the higher-order SVD (HOSVD), which is proposed by
Lathauwer et al., similarly with the case of ordinary PCA. In this
paper, we apply the multi-dimensional PCA to the multi-dimensional
medical data including the functional independence measure (FIM)
score, and describe the results of experimental analysis.