Fracture imaging within a granitic rock aquifer using multiple-offset single-hole and cross-hole GPR reflection data

The sparsely spaced highly permeable fractures of the granitic rock aquifer at Stang-er-Brune (Brittany, France) form a well-connected fracture network of high permeability but unknown geometry. Previous work based on optical and acoustic logging together with single-hole and cross-hole flowmeter data acquired in 3 neighbouring boreholes (70-100 m deep) has identified the most important permeable fractures crossing the boreholes and their hydraulic connections. To constrain possible flow paths by estimating the geometries of known and previously unknown fractures, we have acquired, processed and interpreted multifold, single- and cross-hole GPR data using 100 and 250 MHz antennas. The GPR data processing scheme consisting of timezero corrections, scaling, bandpass filtering and F-X deconvolution, eigenvector filtering, muting, pre-stack Kirchhoff depth migration and stacking was used to differentiate fluid-filled fracture reflections from source generated noise. The final stacked and pre-stack depth-migrated GPR sections provide high-resolution images of individual fractures (dipping 30-90°) in the surroundings (2-20 m for the 100 MHz antennas; 2-12 m for the 250 MHz antennas) of each borehole in a 2D plane projection that are of superior quality to those obtained from single-offset sections. Most fractures previously identified from hydraulic testing can be correlated to reflections in the single-hole data. Several previously unknown major near vertical fractures have also been identified away from the boreholes.

Reconeixement de veu mitjançant xarxes neuronals

La interacció home-màquina per mitjà de la veu cobreix moltes àrees d’investigació. Es destaquen entre altres, el reconeixement de la parla, la síntesis i identificació de discurs, la verificació i identificació de locutor i l’activació per veu (ordres) de sistemes robòtics. Reconèixer la parla és natural i simple per a les persones, però és un treball complex per a les màquines, pel qual existeixen diverses metodologies i tècniques, entre elles les Xarxes Neuronals. L’objectiu d’aquest treball és desenvolupar una eina en Matlab per al reconeixement i identificació de paraules pronunciades per un locutor, entre un conjunt de paraules possibles, i amb una bona fiabilitat dins d’uns marges preestablerts. El sistema és independent del locutor que pronuncia la paraula, és a dir, aquest locutor no haurà intervingut en el procés d’entrenament del sistema. S’ha dissenyat una interfície que permet l’adquisició del senyal de veu i el seu processament mitjançant xarxes neuronals i altres tècniques. Adaptant una part de control al sistema, es podria utilitzar per donar ordres a un robot com l’Alfa6Uvic o qualsevol altre dispositiu.

High-performance payload data handling system for Gaia

Gaia is the most ambitious space astrometry mission currently envisaged and is a technological challenge in all its aspects. We describe a proposal for the payload data handling system of Gaia, as an example of a high-performance, real-time, concurrent, and pipelined data system. This proposal includes the front-end systems for the instrumentation, the data acquisition and management modules, the star data processing modules, and the payload data handling unit. We also review other payload and service module elements and we illustrate a data flux proposal.

Kernel-PCA data integration with enhanced interpretability

Background Nowadays, combining the different sources of information to improve the biological knowledge available is a challenge in bioinformatics. One of the most powerful methods for integrating heterogeneous data types are kernel-based methods. Kernel-based data integration approaches consist of two basic steps: firstly the right kernel is chosen for each data set; secondly the kernels from the different data sources are combined to give a complete representation of the available data for a given statistical task. Results We analyze the integration of data from several sources of information using kernel PCA, from the point of view of reducing dimensionality. Moreover, we improve the interpretability of kernel PCA by adding to the plot the representation of the input variables that belong to any dataset. In particular, for each input variable or linear combination of input variables, we can represent the direction of maximum growth locally, which allows us to identify those samples with higher/lower values of the variables analyzed. Conclusions The integration of different datasets and the simultaneous representation of samples and variables together give us a better understanding of biological knowledge.

DnaSP v5: A software for comprehensive analysis of DNA polymorphism data

DnaSP is a software package for a comprehensive analysis of DNA polymorphism data. Version 5 implements a number of new features and analytical methods allowing extensive DNA polymorphism analyses on large datasets. Among other features, the newly implemented methods allow for: (i) analyses on multiple data files; (ii) haplotype phasing; (iii) analyses on insertion/deletion polymorphism data; (iv) visualizing sliding window results integrated with available genome annotations in the UCSC browser.

Threading libraries performance when applied to image acquisition and processing in a forensic application

Peer-reviewed

High-performance payload data handling system for Gaia

Gaia is the most ambitious space astrometry mission currently envisaged and is a technological challenge in all its aspects. We describe a proposal for the payload data handling system of Gaia, as an example of a high-performance, real-time, concurrent, and pipelined data system. This proposal includes the front-end systems for the instrumentation, the data acquisition and management modules, the star data processing modules, and the payload data handling unit. We also review other payload and service module elements and we illustrate a data flux proposal.

Kernel-PCA data integration with enhanced interpretability

Background Nowadays, combining the different sources of information to improve the biological knowledge available is a challenge in bioinformatics. One of the most powerful methods for integrating heterogeneous data types are kernel-based methods. Kernel-based data integration approaches consist of two basic steps: firstly the right kernel is chosen for each data set; secondly the kernels from the different data sources are combined to give a complete representation of the available data for a given statistical task. Results We analyze the integration of data from several sources of information using kernel PCA, from the point of view of reducing dimensionality. Moreover, we improve the interpretability of kernel PCA by adding to the plot the representation of the input variables that belong to any dataset. In particular, for each input variable or linear combination of input variables, we can represent the direction of maximum growth locally, which allows us to identify those samples with higher/lower values of the variables analyzed. Conclusions The integration of different datasets and the simultaneous representation of samples and variables together give us a better understanding of biological knowledge.

Reconeixement de veu mitjançant xarxes neuronals

La interacció home-màquina per mitjà de la veu cobreix moltes àrees d’investigació. Es destaquen entre altres, el reconeixement de la parla, la síntesis i identificació de discurs, la verificació i identificació de locutor i l’activació per veu (ordres) de sistemes robòtics. Reconèixer la parla és natural i simple per a les persones, però és un treball complex per a les màquines, pel qual existeixen diverses metodologies i tècniques, entre elles les Xarxes Neuronals. L’objectiu d’aquest treball és desenvolupar una eina en Matlab per al reconeixement i identificació de paraules pronunciades per un locutor, entre un conjunt de paraules possibles, i amb una bona fiabilitat dins d’uns marges preestablerts. El sistema és independent del locutor que pronuncia la paraula, és a dir, aquest locutor no haurà intervingut en el procés d’entrenament del sistema. S’ha dissenyat una interfície que permet l’adquisició del senyal de veu i el seu processament mitjançant xarxes neuronals i altres tècniques. Adaptant una part de control al sistema, es podria utilitzar per donar ordres a un robot com l’Alfa6Uvic o qualsevol altre dispositiu.

Segmental Durations of Speech

This dissertation considers the segmental durations of speech from the viewpoint of speech technology, especially speech synthesis. The idea is that better models of segmental durations lead to higher naturalness and better intelligibility. These features are the key factors for better usability and generality of synthesized speech technology. Even though the studies are based on a Finnish corpus the approaches apply to all other languages as well. This is possibly due to the fact that most of the studies included in this dissertation are about universal effects taking place on utterance boundaries. Also the methods invented and used here are suitable for any other study of another language. This study is based on two corpora of news reading speech and sentences read aloud. The other corpus is read aloud by a 39-year-old male, whilst the other consists of several speakers in various situations. The use of two corpora is twofold: it involves a comparison of the corpora and a broader view on the matters of interest. The dissertation begins with an overview to the phonemes and the quantity system in the Finnish language. Especially, we are covering the intrinsic durations of phonemes and phoneme categories, as well as the difference of duration between short and long phonemes. The phoneme categories are presented to facilitate the problem of variability of speech segments. In this dissertation we cover the boundary-adjacent effects on segmental durations. In initial positions of utterances we find that there seems to be initial shortening in Finnish, but the result depends on the level of detail and on the individual phoneme. On the phoneme level we find that the shortening or lengthening only affects the very first ones at the beginning of an utterance. However, on average, the effect seems to shorten the whole first word on the word level. We establish the effect of final lengthening in Finnish. The effect in Finnish has been an open question for a long time, whilst Finnish has been the last missing piece for it to be a universal phenomenon. Final lengthening is studied from various angles and it is also shown that it is not a mere effect of prominence or an effect of speech corpus with high inter- and intra-speaker variation. The effect of final lengthening seems to extend from the final to the penultimate word. On a phoneme level it reaches a much wider area than the initial effect. We also present a normalization method suitable for corpus studies on segmental durations. The method uses an utterance-level normalization approach to capture the pattern of segmental durations within each utterance. This prevents the impact of various problematic variations within the corpora. The normalization is used in a study on final lengthening to show that the results on the effect are not caused by variation in the material. The dissertation shows an implementation and prowess of speech synthesis on a mobile platform. We find that the rule-based method of speech synthesis is a real-time software solution, but the signal generation process slows down the system beyond real time. Future aspects of speech synthesis on limited platforms are discussed. The dissertation considers ethical issues on the development of speech technology. The main focus is on the development of speech synthesis with high naturalness, but the problems and solutions are applicable to any other speech technology approaches.

Geological image processing of petrographic thin sections using the rotating polarizer stage

One of the fundamental problems with image processing of petrographic thin sections is that the appearance (colour I intensity) of a mineral grain will vary with the orientation of the crystal lattice to the preferred direction of the polarizing filters on a petrographic microscope. This makes it very difficult to determine grain boundaries, grain orientation and mineral species from a single captured image. To overcome this problem, the Rotating Polarizer Stage was used to replace the fixed polarizer and analyzer on a standard petrographic microscope. The Rotating Polarizer Stage rotates the polarizers while the thin section remains stationary, allowing for better data gathering possibilities. Instead of capturing a single image of a thin section, six composite data sets are created by rotating the polarizers through 900 (or 1800 if quartz c-axes measurements need to be taken) in both plane and cross polarized light. The composite data sets can be viewed as separate images and consist of the average intensity image, the maximum intensity image, the minimum intensity image, the maximum position image, the minimum position image and the gradient image. The overall strategy used by the image processing system is to gather the composite data sets, determine the grain boundaries using the gradient image, classify the different mineral species present using the minimum and maximum intensity images and then perform measurements of grain shape and, where possible, partial crystallographic orientation using the maximum intensity and maximum position images.

Integrated Environment for Launch Vehicle Telemetry Data Management

The telemetry data processing operation intended for a given mission are pre-defined by an onboard telemetry configuration, mission trajectory and overall telemetry methodology have stabilized lately for ISRO vehicles. The given problem on telemetry data processing is reduced through hierarchical problem reduction whereby the sequencing of operations evolves as the control task and operations on data as the function task. The function task Input, Output and execution criteria are captured into tables which are examined by the control task and then schedules when the function task when the criteria is being met.

Learning analysis by reduction from positive data

Analysis by reduction is a linguistically motivated method for checking correctness of a sentence. It can be modelled by restarting automata. In this paper we propose a method for learning restarting automata which are strictly locally testable (SLT-R-automata). The method is based on the concept of identification in the limit from positive examples only. Also we characterize the class of languages accepted by SLT-R-automata with respect to the Chomsky hierarchy.

Evolving Classifiers - Evolutionary Algorithms in Data Mining

Data mining means to summarize information from large amounts of raw data. It is one of the key technologies in many areas of economy, science, administration and the internet. In this report we introduce an approach for utilizing evolutionary algorithms to breed fuzzy classifier systems. This approach was exercised as part of a structured procedure by the students Achler, Göb and Voigtmann as contribution to the 2006 Data-Mining-Cup contest, yielding encouragingly positive results.

Global Optimization Algorithms and their Application to Distributed Systems

In this report, we discuss the application of global optimization and Evolutionary Computation to distributed systems. We therefore selected and classified many publications, giving an insight into the wide variety of optimization problems which arise in distributed systems. Some interesting approaches from different areas will be discussed in greater detail with the use of illustrative examples.