Evaluation of APD and SiPM Matrices as Sensors for Monolithic PET Detector Block

Gamma detectors based on monolithic scintillator blocks coupled to APDs matrices have proved to be a good alternative to pixelated ones for PET scanners. They provide comparable spatial resolution, improve the sensitivity and make easier the mechanical design of the system. In this study we evaluate by means of Geant4-based simulations the possibility of replacing the APDs by SiPMs. Several commercial matrices of light sensors coupled to LYSO:Ce monolithic blocks have been simulated and compared. Regarding the spatial resolution and linearity of the detector, SiPMs with high photo detection efficiency could become an advantageous replacement for the APDs

Evaluation of a PET Prototype Using LYSO:Ce Monolithic Detector Block

We have analyzed the performance of a PET demonstrator formed by two sectors of four monolithic detector blocks placed face-to-face. Both front-end and read-out electronics have been evaluated by means of coincidence measurements using a rotating 22Na source placed at the center of the sectors in order to emulate the behavior of a complete full ring. A continuous training method based on neural network (NN) algorithms has been carried out to determine the entrance points over the surface of the detectors. Reconstructed images from 1 MBq 22Na point source and 22Na Derenzo phantom have been obtained using both filtered back projection (FBP) analytic methods and the OSEM 3D iterative algorithm available in the STIR software package [1]. Preliminary data on image reconstruction from a 22Na point source with Ø = 0.25 mm show spatial resolutions from 1.7 to 2.1 mm FWHM in the transverse plane. The results confirm the viability of this design for the development of a full-ring brain PET scanner compatible with magnetic resonance imaging for human studies.

Reduction in Calculated Uncertainty of a Noise Map by Improving the Traffic Model Data Through Two Phases

The influence of applying European default traffic values to the making of a noise map was evaluated in a typical environment like Palma de Mallorca. To assess these default traffic values, a first model has been created and compared with measured noise levels. Subsequently a second traffic model, improving the input data used for the first one, has been created and validated according to the deviations. Different methodologies were also examined for collecting model input data that would be of higher quality, by analysing the improvement generated in the reduction in the uncertainty of the noise map introduced by the road traffic noise emission

Multifractal analysis of the pore- and solid-phases in binary two-dimensional images of natural porous structures

We use multifractal analysis (MFA) to investigate how the Rényi dimensions of the solid mass and the pore space in porous structures are related to each other. To our knowledge, there is no investigation about the relationship of Rényi or generalized dimensions of two phases of the same structure.

High-peak-power pulse generation from a monolithic master oscillator power amplifier at 1.5 μm

We present an experimental study on the generation of high-peak-power short optical pulses from a fully integrated master-oscillator power-amplifier emitting at 1.5 μm. High-peak-power (2.7 W) optical pulses with short duration (100 ps) have been generated by gain switching the master oscillator under optimized driving conditions. The static and dynamic characteristics of the device have been studied as a function of the driving conditions. The ripples appearing in the power-current characteristics under cw conditions have been attributed to mode hopping between the master oscillator resonant mode and the Fabry-Perot modes of the entire device cavity. Although compound cavity effects have been evidenced to affect the static and dynamic performance of the device, we have demonstrated that trains of single-mode short optical pulses at gigahertz frequencies can be conveniently generated in these devices.

Time-Frequency based Feature Selection for Discrimination of non stationary Biosignals.

This research proposes a generic methodology for dimensionality reduction upon time-frequency representations applied to the classification of different types of biosignals. The methodology directly deals with the highly redundant and irrelevant data contained in these representations, combining a first stage of irrelevant data removal by variable selection, with a second stage of redundancy reduction using methods based on linear transformations. The study addresses two techniques that provided a similar performance: the first one is based on the selection of a set of the most relevant time?frequency points, whereas the second one selects the most relevant frequency bands. The first methodology needs a lower quantity of components, leading to a lower feature space; but the second improves the capture of the time-varying dynamics of the signal, and therefore provides a more stable performance. In order to evaluate the generalization capabilities of the methodology proposed it has been applied to two types of biosignals with different kinds of non-stationary behaviors: electroencephalographic and phonocardiographic biosignals. Even when these two databases contain samples with different degrees of complexity and a wide variety of characterizing patterns, the results demonstrate a good accuracy for the detection of pathologies, over 98%.The results open the possibility to extrapolate the methodology to the study of other biosignals.

Electrooptic effect in cholesteric blue phases with small positive dielectric anisotropy

An electrooptic effect in the blue phase of the cholesteric mixture S811-and the nematic mixture N5 is reported. To demonstrate this effect ac voltages (ƒ = 1000 Hz) between 0 and 150 νwere applied. Wavelength shifts of 70 nm were obtained.

Bare-tether sheath and current: comparison of asymptotic theory and kinetic simulations in stationary plasma

Analytical expressions for current to a cylindrical Langmuir probe at rest in unmagnetized plasma are compared with results from both steady-state Vlasov and particle-in-cell simulations. Probe bias potentials that are much greater than plasma temperature (assumed equal for ions and electrons), as of interest for bare conductive tethers, are considered. At a very high bias, both the electric potential and the attracted-species density exhibit complex radial profiles; in particular, the density exhibits a minimum well within the plasma sheath and a maximum closer to the probe. Excellent agreement is found between analytical and numerical results for values of the probe radiusR close to the maximum radius Rmax for orbital-motion-limited (OML) collection at a particular bias in the following number of profile features: the values and positions of density minimum and maximum, position of sheath boundary, and value of a radius characterizing the no-space-charge behavior of a potential near the high-bias probe. Good agreement between the theory and simulations is also found for parametric laws jointly covering the following three characteristic R ranges: sheath radius versus probe radius and bias for Rmax; density minimum versus probe bias for Rmax; and (weakly bias-dependent) current drop below the OML value versus the probe radius for R > Rmax.

Multi-dimensional classification using Bayesian networks for stationary and evolving streaming data

Hoy en día, con la evolución continua y rápida de las tecnologías de la información y los dispositivos de computación, se recogen y almacenan continuamente grandes volúmenes de datos en distintos dominios y a través de diversas aplicaciones del mundo real. La extracción de conocimiento útil de una cantidad tan enorme de datos no se puede realizar habitualmente de forma manual, y requiere el uso de técnicas adecuadas de aprendizaje automático y de minería de datos. La clasificación es una de las técnicas más importantes que ha sido aplicada con éxito a varias áreas. En general, la clasificación se compone de dos pasos principales: en primer lugar, aprender un modelo de clasificación o clasificador a partir de un conjunto de datos de entrenamiento, y en segundo lugar, clasificar las nuevas instancias de datos utilizando el clasificador aprendido. La clasificación es supervisada cuando todas las etiquetas están presentes en los datos de entrenamiento (es decir, datos completamente etiquetados), semi-supervisada cuando sólo algunas etiquetas son conocidas (es decir, datos parcialmente etiquetados), y no supervisada cuando todas las etiquetas están ausentes en los datos de entrenamiento (es decir, datos no etiquetados). Además, aparte de esta taxonomía, el problema de clasificación se puede categorizar en unidimensional o multidimensional en función del número de variables clase, una o más, respectivamente; o también puede ser categorizado en estacionario o cambiante con el tiempo en función de las características de los datos y de la tasa de cambio subyacente. A lo largo de esta tesis, tratamos el problema de clasificación desde tres perspectivas diferentes, a saber, clasificación supervisada multidimensional estacionaria, clasificación semisupervisada unidimensional cambiante con el tiempo, y clasificación supervisada multidimensional cambiante con el tiempo. Para llevar a cabo esta tarea, hemos usado básicamente los clasificadores Bayesianos como modelos. La primera contribución, dirigiéndose al problema de clasificación supervisada multidimensional estacionaria, se compone de dos nuevos métodos de aprendizaje de clasificadores Bayesianos multidimensionales a partir de datos estacionarios. Los métodos se proponen desde dos puntos de vista diferentes. El primer método, denominado CB-MBC, se basa en una estrategia de envoltura de selección de variables que es voraz y hacia delante, mientras que el segundo, denominado MB-MBC, es una estrategia de filtrado de variables con una aproximación basada en restricciones y en el manto de Markov. Ambos métodos han sido aplicados a dos problemas reales importantes, a saber, la predicción de los inhibidores de la transcriptasa inversa y de la proteasa para el problema de infección por el virus de la inmunodeficiencia humana tipo 1 (HIV-1), y la predicción del European Quality of Life-5 Dimensions (EQ-5D) a partir de los cuestionarios de la enfermedad de Parkinson con 39 ítems (PDQ-39). El estudio experimental incluye comparaciones de CB-MBC y MB-MBC con los métodos del estado del arte de la clasificación multidimensional, así como con métodos comúnmente utilizados para resolver el problema de predicción de la enfermedad de Parkinson, a saber, la regresión logística multinomial, mínimos cuadrados ordinarios, y mínimas desviaciones absolutas censuradas. En ambas aplicaciones, los resultados han sido prometedores con respecto a la precisión de la clasificación, así como en relación al análisis de las estructuras gráficas que identifican interacciones conocidas y novedosas entre las variables. La segunda contribución, referida al problema de clasificación semi-supervisada unidimensional cambiante con el tiempo, consiste en un método nuevo (CPL-DS) para clasificar flujos de datos parcialmente etiquetados. Los flujos de datos difieren de los conjuntos de datos estacionarios en su proceso de generación muy rápido y en su aspecto de cambio de concepto. Es decir, los conceptos aprendidos y/o la distribución subyacente están probablemente cambiando y evolucionando en el tiempo, lo que hace que el modelo de clasificación actual sea obsoleto y deba ser actualizado. CPL-DS utiliza la divergencia de Kullback-Leibler y el método de bootstrapping para cuantificar y detectar tres tipos posibles de cambio: en las predictoras, en la a posteriori de la clase o en ambas. Después, si se detecta cualquier cambio, un nuevo modelo de clasificación se aprende usando el algoritmo EM; si no, el modelo de clasificación actual se mantiene sin modificaciones. CPL-DS es general, ya que puede ser aplicado a varios modelos de clasificación. Usando dos modelos diferentes, el clasificador naive Bayes y la regresión logística, CPL-DS se ha probado con flujos de datos sintéticos y también se ha aplicado al problema real de la detección de código malware, en el cual los nuevos ficheros recibidos deben ser continuamente clasificados en malware o goodware. Los resultados experimentales muestran que nuestro método es efectivo para la detección de diferentes tipos de cambio a partir de los flujos de datos parcialmente etiquetados y también tiene una buena precisión de la clasificación. Finalmente, la tercera contribución, sobre el problema de clasificación supervisada multidimensional cambiante con el tiempo, consiste en dos métodos adaptativos, a saber, Locally Adpative-MB-MBC (LA-MB-MBC) y Globally Adpative-MB-MBC (GA-MB-MBC). Ambos métodos monitorizan el cambio de concepto a lo largo del tiempo utilizando la log-verosimilitud media como métrica y el test de Page-Hinkley. Luego, si se detecta un cambio de concepto, LA-MB-MBC adapta el actual clasificador Bayesiano multidimensional localmente alrededor de cada nodo cambiado, mientras que GA-MB-MBC aprende un nuevo clasificador Bayesiano multidimensional. El estudio experimental realizado usando flujos de datos sintéticos multidimensionales indica los méritos de los métodos adaptativos propuestos. ABSTRACT Nowadays, with the ongoing and rapid evolution of information technology and computing devices, large volumes of data are continuously collected and stored in different domains and through various real-world applications. Extracting useful knowledge from such a huge amount of data usually cannot be performed manually, and requires the use of adequate machine learning and data mining techniques. Classification is one of the most important techniques that has been successfully applied to several areas. Roughly speaking, classification consists of two main steps: first, learn a classification model or classifier from an available training data, and secondly, classify the new incoming unseen data instances using the learned classifier. Classification is supervised when the whole class values are present in the training data (i.e., fully labeled data), semi-supervised when only some class values are known (i.e., partially labeled data), and unsupervised when the whole class values are missing in the training data (i.e., unlabeled data). In addition, besides this taxonomy, the classification problem can be categorized into uni-dimensional or multi-dimensional depending on the number of class variables, one or more, respectively; or can be also categorized into stationary or streaming depending on the characteristics of the data and the rate of change underlying it. Through this thesis, we deal with the classification problem under three different settings, namely, supervised multi-dimensional stationary classification, semi-supervised unidimensional streaming classification, and supervised multi-dimensional streaming classification. To accomplish this task, we basically used Bayesian network classifiers as models. The first contribution, addressing the supervised multi-dimensional stationary classification problem, consists of two new methods for learning multi-dimensional Bayesian network classifiers from stationary data. They are proposed from two different points of view. The first method, named CB-MBC, is based on a wrapper greedy forward selection approach, while the second one, named MB-MBC, is a filter constraint-based approach based on Markov blankets. Both methods are applied to two important real-world problems, namely, the prediction of the human immunodeficiency virus type 1 (HIV-1) reverse transcriptase and protease inhibitors, and the prediction of the European Quality of Life-5 Dimensions (EQ-5D) from 39-item Parkinson’s Disease Questionnaire (PDQ-39). The experimental study includes comparisons of CB-MBC and MB-MBC against state-of-the-art multi-dimensional classification methods, as well as against commonly used methods for solving the Parkinson’s disease prediction problem, namely, multinomial logistic regression, ordinary least squares, and censored least absolute deviations. For both considered case studies, results are promising in terms of classification accuracy as well as regarding the analysis of the learned MBC graphical structures identifying known and novel interactions among variables. The second contribution, addressing the semi-supervised uni-dimensional streaming classification problem, consists of a novel method (CPL-DS) for classifying partially labeled data streams. Data streams differ from the stationary data sets by their highly rapid generation process and their concept-drifting aspect. That is, the learned concepts and/or the underlying distribution are likely changing and evolving over time, which makes the current classification model out-of-date requiring to be updated. CPL-DS uses the Kullback-Leibler divergence and bootstrapping method to quantify and detect three possible kinds of drift: feature, conditional or dual. Then, if any occurs, a new classification model is learned using the expectation-maximization algorithm; otherwise, the current classification model is kept unchanged. CPL-DS is general as it can be applied to several classification models. Using two different models, namely, naive Bayes classifier and logistic regression, CPL-DS is tested with synthetic data streams and applied to the real-world problem of malware detection, where the new received files should be continuously classified into malware or goodware. Experimental results show that our approach is effective for detecting different kinds of drift from partially labeled data streams, as well as having a good classification performance. Finally, the third contribution, addressing the supervised multi-dimensional streaming classification problem, consists of two adaptive methods, namely, Locally Adaptive-MB-MBC (LA-MB-MBC) and Globally Adaptive-MB-MBC (GA-MB-MBC). Both methods monitor the concept drift over time using the average log-likelihood score and the Page-Hinkley test. Then, if a drift is detected, LA-MB-MBC adapts the current multi-dimensional Bayesian network classifier locally around each changed node, whereas GA-MB-MBC learns a new multi-dimensional Bayesian network classifier from scratch. Experimental study carried out using synthetic multi-dimensional data streams shows the merits of both proposed adaptive methods.

Optical behaviour of cholesteric blue phases in electric fields

The optical behaviour of cholesteric mixtures of negative dielectric anisotrony under electric fields is reported. A mixture of S 311~ (31.35 %) + N 5 was employed. AC voltages (f = 1000 Hz) betweeen 0 and 150 volts were applied. Cells 23 micron thick, with internal SnO2 electrodes, were used.

Effects of forage type on diversity in bacterial pellets isolated from liquid and solid phases of the rumen content in sheep and goats.

Two sheep and two goats, fitted with a ruminal cannula, received two diets composed of 30% concentrate and 70% of either alfalfa hay (AL) or grass hay (GR) as forage in a two-period crossover design. Solid and liquid phases of the rumen were sampled from each animal immediately before feeding and 4 h post-feeding. Pellets containing solid associated bacteria (SAB) and liquid associated bacteria (LAB) were isolated from the corresponding ruminal phase and composited by time to obtain 2 pellets per animal (one SAB and one LAB) before DNA extraction. Denaturing gradient gel electrophoresis (DGGE) analysis of 16S ribosomal DNA was used to analyze bacterial diversity. A total of 78 and 77 bands were detected in the DGGE gel from sheep and goats samples, respectively. There were 18 bands only found in the pellets from sheep fed AL-fed sheep and 7 found exclusively in samples from sheep fed the GR diet. In goats, 21 bands were found only in animals fed the AL diet and 17 were found exclusively in GR-fed ones. In all animals, feeding AL diet tended (P < 0.10) to promote greater NB and SI in LAB and SAB pellets compared with the GR diet. The dendrogram generated by the cluster analysis showed that in both animal species all samples can be included in two major clusters. The four SAB pellets within each animal species clustered together and the four LAB pellets grouped in a different cluster. Moreover, SAB and LAB clusters contained two clear subclusters according to forage type. Results show that in all animals bacterial diversity was more markedly affected by the ruminal phase (solid vs. liquid) than by the type of forage in the diet.

Dynamic response of a monolithic master-oscillator power-amplifier at 1.5 µm

We study experimentally the dynamic properties of a fully integrated high power master-oscillator power-amplifier emitting at 1.5 μm under continuous wave and gain-switching conditions. High peak power (2.7 W) optical pulses with short duration (~ 110 ps) have been generated by gain switching the master-oscillator. We show the existence of working points at very close driving conditions with stable or unstable regimes caused by the compound cavity effects. The optical and radio-frequency spectra of stable and unstable operating points are analyzed.

Dynamical characterization of monolithic MOPAs emitting at 1.5 μ�m

Eye-safety requirements in important applications like LIDAR or Free Space Optical Communications make specifically interesting the generation of high power, short optical pulses at 1.5 um. Moreover, high repetition rates allow reducing the error and/or the measurement time in applications involving pulsed time-of-flight measurements, as range finders, 3D scanners or traffic velocity controls. The Master Oscillator Power Amplifier (MOPA) architecture is an interesting source for these applications since large changes in output power can be obtained at GHz rates with a relatively small modulation of the current in the Master Oscillator (MO). We have recently demonstrated short optical pulses (100 ps) with high peak power (2.7 W) by gain switching the MO of a monolithically integrated 1.5 um MOPA. Although in an integrated MOPA the laser and the amplifier are ideally independent devices, compound cavity effects due to the residual reflectance at the different interfaces are often observed, leading to modal instabilities such as self-pulsations.

Analysis of mode competition in a monolithic master- oscillator power‐amplifier emitting at 1.5 μm

The optical and radio-frequency spectra of a monolithic master-oscillator power-amplifier emitting at 1.5 ?m have been analyzed in a wide range of steady-state injection conditions. The analysis of the spectral maps reveals that, under low injection current of the master oscillator, the device operates in two essentially different operation modes depending on the current injected into the amplifier section. The regular operation mode with predominance of the master oscillator alternates with lasing of the compound cavity modes allowed by the residual reflectance of the amplifier front facet. The quasi-periodic occurrence of these two regimes as a function of the amplifier current has been consistently interpreted in terms of a thermally tuned competition between the modes of the master oscillator and the compound cavity modes.

Aerodynamic Analysis of Cup Anemometers Performance: The Stationary Harmonic Response

The effect of cup anemometer shape parameters, such as the cups’ shape, their size, and their center rotation radius, was experimentally analyzed.This analysis was based on both the calibration constants of the transfer function and the most important harmonic termof the rotor’smovement,which due to the cup anemometer design is the third one.This harmonic analysis represents a new approach to study cup anemometer performances. The results clearly showed a good correlation between the average rotational speed of the anemometer’s rotor and the mentioned third harmonic term of its movement.