The potassium LIDAR at Arecibo Observatory utilizes the Light Age alexandrite high power laser which requires a well synchronized system and steady trigger repetition rate to achieve correct height determination and to extend the lifetime of the equipment. The system includes an optical chopper that prevents the detector from saturating before the altitude of interest is reached and a sequence of delayed pulses that execute the laser trigger which are generated by an external pulse/delay generator. The accuracy of the optical chopper limits the accuracy of the laser repetition rate as well as other equipment in the synchronized system. This work describes the implementation of a new microcontroller based single instrument optical chopper and laser trigger controller to improve stability and functionality. By programming a unifying USB user interface, the new capability of monitoring the system and manipulating relevant variables was achieved. This includes changing the repetition rate, moving the optical chopper edge to block out different altitudes, tuning PID constants, and more. The new system centralizes control, increasing ease of operation and allowing more flexible and efficient use. Furthermore, a laser only mode for testing has been implemented to send out a laser trigger sequence to the rest of the system without the need of an optical chopper. The new implementation has reduced steady state frequency jitter of the laser trigger by 60% and startup time by 77%.
Noise reduction algorithms for improving Raman spectroscopy signals while preserving signal information were
implemented. Algorithms based on Wavelet denoising and Kalman filtering are presented in this work as alternatives
to the well-known Savitky-Golay algorithm. The Wavelet and Kalman algorithms were designed based on
the noise statistics of real signals acquired using CCD detectors in dispersive spectrometers. Experimental results
show that the random noise generated in the data acquisition is governed by sub-Poisson statistics. The proposed
algorithms have been tested using both real and synthetic data, and were compared using Mean Squared Error
(MSE) and Infinity Norm (L∞) to each other and to the standard Savitky-Golay algorithm. Results show that
denoising based on Wavelets performs better in both the MSE and (L∞) the sense.
Charge-Coupled Device (CCD) detectors are becoming more popular in spectroscopy instrumentation. In spite of
technological advances, spurious signals and noise are unavoidable in Raman spectroscopes. In general, the noise
comes from two major sources, impulsive noise caused by high energy radiation from local or extraterrestrial
sources (cosmic rays), and noise produced in Raman backscattering estimation. In this work, two algorithms
for impulsive noise removal are presented, based in spectral and spatial features of the noise. The algorithms
combine pattern recognition and classical filtering techniques to identify the impulses. Once an impulse has been
identified, it is removed and substituted with data points having similar statistical properties as the surrounding
data.
Raman spectroscopy, in combination with optical microscopy provides a new non-invasive method to asses and image
cellular processes. Based on the spectral signatures of a cell's components, it is possible to image cellular organelles
such as the nucleus, chromatin, mitochondria or lipid bodies, at the resolution of conventional microscopy. Several
multivariate algorithms, for example hierarchical cluster analysis or orthogonal subspace projection, may be used to
reconstruct an image of a cell. The noninvasive character of the technique, as well as the associated chemical
information, may offer advantages over other imaging techniques such as fluorescence microscopy. Currently of
particular interest are uptake and intracellular fate of various pharmaceutical nanocarriers, which are widely used for
drug delivery purposes, including intracellular drug and gene delivery. We have imaged the uptake and distribution
patterns of several carrier systems over time. In order to distinguish the species of interest from their cellular
environment spectroscopically, the carrier particles or the drug load itself may be labeled with deuterium. Here, we
introduce the concept of Raman imaging in combination with vertex component data analysis to follow the uptake of
nanocarriers based on phospholipids as well as biodegradable polymers.
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