Continuous patient monitoring has been evidenced as very beneficious for reducing degeneration1. Due to this, a POF specklegram sensor has been developed based on a previous work2. This work presents a comparative between analysis methods of the specklegram signal for achieving a precise and robust non-contact monitor system. Two different techniques have been used: one based on the Fast Fourier Transform (FFT) and the other based on the Hilbert Transform (HT). Each technique has been employed with two different methods, for heart rate and breath rhythm. The different algorithms are tested on 10 volunteers of different ages and sex.
An integral temperature sensor based on Brillouin laser ring that is feed by a Fourier Domain mode-locking (FDML)
laser is here proposed. The source FDML laser ring emits at 1532 nm within a range of 5 nm. The working wavelength is
given by tuning the offset voltage applied to a Fabry-Perot tunable filter (FFP-TF). In the present work, the FDML laser
linewidth is set at 0.136 nm. This linewidth allows a more efficient Brillouin response in the optical fiber without
increasing the Brillouin threshold. The FDML laser linewidth is controlled by setting the amplitude modulation of the
drive signal offset applied on the FFP-TF.
Chemical analysis of dangerous materials entails a safety issue for the researchers. Laser Induced
Breakdown Spectroscopy offers the possibility to analyze these materials away from them using Stand-Off Set-ups. To optimize the plasma induction, the remote focalization of the laser beam is of paramount
importance. A custom Fiber Bragg Grating sensor system able to correct the laser beam focalization
errors is proposed and experimentally checked. The optical transducer architecture and the preliminary
obtained results are reported in this paper.
Welding processes are one of the most widely spread industrial activities, and their quality control is an important area of
research. The presence of residual traces from the protective antioxidant coating, is a problematic issue since it causes a
significant reduction in the welding seam strength. In this work, a solution based on a Laser Induced Breakdown
Spectroscopy (LIBS) setup and a Support Vector Machines (SVMs) classifier to detect and discriminate antioxidant
coating residues in the welding area without destroying the sample before the welding procedure is proposed. This
system could be an interesting and fast tool to detect aluminium impurities.
In this paper, a method for the automatic qualitative discrimination of liquid samples based on their absorption spectrum
in the ultraviolet, visible and near-infrared regions is presented. An alternative implementation of conventional spectrum
matching methodologies is proposed working towards the improvement of the response time of the discrimination
system. The method takes advantage of not making assumptions on the probability density function of the data and it is
also capable of automatic outlier removal. Preliminary discrimination results have been evaluated on the classification of
different oil samples from seeds and olives. The system here proposed could be easily and efficiently implemented in
hardware platforms, improving in this way the system performance.
An erbium doped fiber ring laser (EDFRL) that incorporates four non-adiabatic concatenated single-mode fiber tapers
(acting as tunable filter in the laser cavity) is presented. These concatenated fiber tapers integrates a filter with a
narrower band-pass and a higher modulation depth than a single taper. The tuning of this filter was implemented
applying a controlled perturbation in the fiber taper. The proposed laser architecture was successfully demonstrated in
the laboratory in which a tuning range of 20.8nm (1544.5nm-1565.3nm) were measured.
A processing methodology based on Support Vector Machines is presented in this paper for the classification of
hyperspectral spectroscopic images. The accurate classification of the images is used to perform on-line material
identification in industrial environments. Each hyperspectral image consists of the diffuse reflectance of the material
under study along all the points of a line of vision. These images are measured through the employment of two imaging
spectrographs operating at Vis-NIR, from 400 to 1000 nm, and NIR, from 1000 to 2400 nm, ranges of the spectrum,
respectively. The aim of this work is to demonstrate the robustness of Support Vector Machines to recognise certain
spectral features of the target. Furthermore, research has been made to find the adequate SVM configuration for this
hyperspectral application. In this way, anomaly detection and material identification can be efficiently performed. A
classifier with a combination of a Gaussian Kernel and a non linear Principal Component Analysis, namely k-PCA is
concluded as the best option in this particular case. Finally, experimental tests have been carried out with materials
typical of the tobacco industry (tobacco leaves mixed with unwanted spurious materials, such as leathers, plastics, etc.)
to demonstrate the suitability of the proposed technique.
A non-intrusive and non-contact near infrared acquisition system based on a PGP spectrometer is presented. This work is an extension to the whole near infrared range of the spectrum, from 1000 to 2400 nm, of a previously designed system in the Vis-NIR range (400-1000 nm). The reason under this investigation is to improve material characterization and material classification performance. To our knowledge, no imaging spectroscopic system based on a PGP device working in this range has been previously reported. The components of the system, its assembling, alignment and calibration procedures will be described in detail.
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