This paper proposes the application of a monochromatic wavelength dispersive X-ray fluorescence (MWDXRF)
technique developed in the X-ray Optical Systems laboratory Inc. The technique measures low-level sulfur (uls)
in fuel. Data for ultra low sulfur in diesel were collected and analyzed using the combination of the mentioned
technique and the usage of engineering tools as a fastloop array and a measurement technique. This provides
a qualitative method for Diesel sulfur analysis of the Refinery Ing. Antonio M Amor (RIAMA) in Salamanca,
Guanajuato. The pooled limit of quantification (PLOQ) for ultra-low-sulfur diesel was found to be less than 1.5
ppm in this study. The reproducibility of 15-ppm sulfur diesel fuel was determined to be better than 3 ppm (95
% confident level). This work shows the performance of the production of Diesel with less than 15-ppm in sulfur
lines in the Hydrodesulfurizer Unit of Diesel (HDD) of the refinery. Results and conclusions discusses the better
and cheaper method for the production of ultra low sulfur Diesel in the refinery.
We discuss a structured 3D Dielectric Photonic Crystal with both a metallic core and a metallic shell. We
discuss the role of each one, the stack, the core as well as the cavity formed between the core and the
shell. The low frequency metallic core features becomes much more significant as it gets smaller and get
diluted by the cavity.
Our work uses 1080 images sequence obtained from "in vitro" samples taken every 4 min from a microscope under
phase contrast technique. These images are in JPEG format and are 500×700 pixels size with a compression rate of 3:1.
We developed an algorithm and characterize it over several image operations against the tracking effectiveness and its
robustness respect mitosis and cell shape change. Image equalization, dilation and erosion were the image processing
procedures founded to provide best tracking results. Equalization procedure, for example, required a time delay of 5 sec
for a size target of 60×90 pixels and 9 sec for size target of 89×100 pixels. This algorithm was implemented into a FPGA
which controlled our optical correlator in order to performance all Fourier operations by optical method. Our results
showed that the use of the optical correlator can reduce the time consuming in the image process until for 90% which
able us to track cells in vascular structure.
This work implements a novel hybrid method for detection and tracking of biological cells of "in vitro" samples
(Goobic,1 2005). The method is able to detect and track cells based on image processing, nonlinear filters and
normalized cross correlation (ncc) and it is tested on a full sequence of 1080 images of cell cultures. In addition of
the cell speed, Cell tracking differentiate itself from tracking other kinds of tracking because cells show: mitosis,
apthosis, overlapping and migration (Liao,2 1995). Image processing provides an excellent tool to improve cell
recognition and background elimination, set as a priori task on this work and conveniently implemented by a
Fourier analysis. The normal cross correlation was developed in the Fourier space to reduce time processing. The
problem of the target detection was formulated as a nonlinear joint detection/estimation problem on the position
parameters. A bank of spatially and temporally localized nonlinear filters is used to estimate the a posteriori
likelihood of the existence of the target in a given space-time resolution cell. The shapes of the targets are random
and according to the sequence, the targets change of shape almost every frame. However, the cross correlation
result is based on the target shape matching, not in the position; and the system is invariant to rotation.
Nonlinear filter makes a robust cell tracking method by producing a sharper correlation peak and reducing the
false positives in the correlation. These false positives may also be reduced by using image preprocessing. Fourier
and nonlinear filtering implementation showed the best results for the proposed cell tracking method presenting
the best time consumption and the best cell localization.
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