Abstract

Abstract

Abstract
In this work a new nano-biofilm is proposed for the detection of celiac disease (CD). A long-period fiber grating (LPFG) is used as a transducer and the surface of the fiber is coated with a precursor layer of SiO2-nanospheres using the electrostatic self-assembly technique (ESA). This layer has been designed in order to create a substrate of high porosity where the gliadins could be deposited. Under the presence of specific antibodies antigliadin antibodies (AGA) the refractive index of the overlay changes giving a detectable shift in the resonance wavelength of the LPFG. Concentrations as low as 5 ppm were detected.

Abstract
This work addresses a humidity sensor using long-period fiber gratings (LPG) coated with silica nanospheres film. SiO2-nanospheres coating is deposited onto the LPG using the electrostatic self-assembly technique (ESA). The polymeric overlay changes its optical properties when exposed to different humidity levels, resulting in a shift of the resonance wavelength of the LPG. The obtained results are accordant with the theoretical simulations. Wavelength shifts up to 12nm in a humidity range from 20% to 80% are reported, maintaining the same dependence at different temperatures.

Abstract
In this article, we introduce an iterative subspace system identification algorithm for MIMO linear parameter-varying systems with innovation-type noise models driven by general inputs and a measurable white noise time-varying parameter vector. The new algorithm is based on a convergent sequence of linear deterministic-stochastic state-space approximations, thus considered a Picard-based method. Such methods have proven to be convergent for the bilinear state-space system identification problem. Their greatest strength lies on the dimensions of the data matrices that are comparable to those of a linear subspace algorithm, thus avoiding the curse of dimensionality.

Abstract
In this paper a successive approximation approach for MIMO linear parameter varying (LPV) systems with affine parameter dependence is proposed. This new approach is based on an algorithm previously introduced by the authors, which elaborates on a convergent sequence of linear deterministic-stochastic state-space approximations. In the previous algorithm the bilinear term between the time varying parameter vector and the state vector is allowed to behave as a white noise process when the scheduling parameter is a white noise sequence. However, this is a strong limitation in practice since, most often than not, the scheduling parameter is imposed by the process itself and it is typically a non white noise signal. In this paper, the bilinear term is analysed for non white noise scheduling sequences. It is concluded that its behaviour depends on the input sequence itself and it ranges from acting as an independent colored noise source, mostly removed by the identification algorithm, down to a highly input correlated signal that may be incorrectly assumed as being part of the system subspace. Based on the premise that the algorithm performance can be improved by the noise energy reduction, the bilinear term is expressed as a function of past inputs, scheduling parameters, outputs, and states, and the linear terms are included in a new extended input.