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Principal component analysis (PCA) is a linear data analysis tool that aims to reduce the dimensionality of a dataset, while retaining most of the variation found in it. It transforms the variables of a dataset into a new set, called the principal components, using linear combinations of the original variables. PCA is a powerful statistical technique used in research for fault detection, classification...
The recently discovered family of generalized low-density (GLD) lattices brings new mathematical challenges to coding theorists and practitioners. Given the excellent performance of integer GLD lattices in high dimensions and motivated by the simple lattice structure used for fast iterative decoding, this paper is a first attempt to analyze GLD lattices for asymptotically large dimensions. Firstly,...
We propose the construction of a new family of lattice sphere packings. Given a small-dimensional lattice, we start by building a first lattice in a large dimension by the direct sum of the small lattice. Then, the coordinates of the first large lattice are permuted to yield a second large-dimensional lattice. Finally, our generalized low-density (GLD) lattice is the intersection of the first and...
Adaptive fuzzy control is used here to enforce a concentration level of some metabolite of a biological system representing a purine metabolism pathway model to track a reference trajectory in the presence of uncertainties. In contrast to the direct fuzzy controller, the adaptive fuzzy controller is able to reduce the variance of both the system's response and the controller's output. In this paper,...
In recent years, may researchers have been interested in modeling and developing therapeutic intervention strategies for biological systems. The objective of intervention strategies is to move an undesirable state of a diseased network towards a more desirable one. It is well known that biological phenomena are complex nonlinear processes that are impossible to perfectly represent using mathematical...
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