Evaluation of geostatistical techniques to estimate the spatial distribution of aboveground biomass in the Amazon rainforest using high-resolution remote sensing data

ABSTRACT The spatial distribution of forest biomass in the Amazon is heterogeneous with a temporal and spatial variation, especially in relation to the different vegetation types of this biome. Biomass estimated in this region varies significantly depending on the applied approach and the data set used for modeling it. In this context, this study aimed to evaluate three different geostatistical techniques to estimate the spatial distribution of aboveground biomass (AGB). The selected techniques were: 1) ordinary least-squares regression (OLS), 2) geographically weighted regression (GWR) and, 3) geographically weighted regression - kriging (GWR-K). These techniques were applied to the same field dataset, using the same environmental variables derived from cartographic information and high-resolution remote sensing data (RapidEye). This study was developed in the Amazon rainforest from Sucumbíos - Ecuador. The results of this study showed that the GWR-K, a hybrid technique, provided statistically satisfactory estimates with the lowest prediction error compared to the other two techniques. Furthermore, we observed that 75% of the AGB was explained by the combination of remote sensing data and environmental variables, where the forest types are the most important variable for estimating AGB. It should be noted that while the use of high-resolution images significantly improves the estimation of the spatial distribution of AGB, the processing of this information requires high computational demand.

Text mining na análise de sentimentos em contextos de big data

Dissertação de mestrado integrado em Engenharia e Gestão de Sistemas de Informação

Hazard assessment of storm events for the battery, New York

The environmental and socio-economic importance of coastal areas is widely recognized, but at present these areas face severe weaknesses and high-risk situations. The increased demand and growing human occupation of coastal zones have greatly contributed to exacerbating such weaknesses. Today, throughout the world, in all countries with coastal regions, episodes of waves overtopping and coastal flooding are frequent. These episodes are usually responsible for property losses and often put human lives at risk. The floods are caused by coastal storms primarily due to the action of very strong winds. The propagation of these storms towards the coast induces high water levels. It is expected that climate change phenomena will contribute to the intensification of coastal storms. In this context, an estimation of coastal flooding hazards is of paramount importance for the planning and management of coastal zones. Consequently, carrying out a series of storm scenarios and analyzing their impacts through numerical modeling is of prime interest to coastal decision-makers. Firstly, throughout this work, historical storm tracks and intensities are characterized for the northeastern region of United States coast, in terms of probability of occurrence. Secondly, several storm events with high potential of occurrence are generated using a specific tool of DelftDashboard interface for Delft3D software. Hydrodynamic models are then used to generate ensemble simulations to assess storms' effects on coastal water levels. For the United States’ northeastern coast, a highly refined regional domain is considered surrounding the area of The Battery, New York, situated in New York Harbor. Based on statistical data of numerical modeling results, a review of the impact of coastal storms to different locations within the study area is performed.

An R package for the integrated analysis of metabolomics and spectral data

Recently, there has been a growing interest in the field of metabolomics, materialized by a remarkable growth in experimental techniques, available data and related biological applications. Indeed, techniques as Nuclear Magnetic Resonance, Gas or Liquid Chromatography, Mass Spectrometry, Infrared and UV-visible spectroscopies have provided extensive datasets that can help in tasks as biological and biomedical discovery, biotechnology and drug development. However, as it happens with other omics data, the analysis of metabolomics datasets provides multiple challenges, both in terms of methodologies and in the development of appropriate computational tools. Indeed, from the available software tools, none addresses the multiplicity of existing techniques and data analysis tasks. In this work, we make available a novel R package, named specmine, which provides a set of methods for metabolomics data analysis, including data loading in different formats, pre-processing, metabolite identification, univariate and multivariate data analysis, machine learning, and feature selection. Importantly, the implemented methods provide adequate support for the analysis of data from diverse experimental techniques, integrating a large set of functions from several R packages in a powerful, yet simple to use environment. The package, already available in CRAN, is accompanied by a web site where users can deposit datasets, scripts and analysis reports to be shared with the community, promoting the efficient sharing of metabolomics data analysis pipelines.

Towards a pervasive data mining engine: architecture overview

Current data mining engines are difficult to use, requiring optimizations by data mining experts in order to provide optimal results. To solve this problem a new concept was devised, by maintaining the functionality of current data mining tools and adding pervasive characteristics such as invisibility and ubiquity which focus on their users, providing better ease of use and usefulness, by providing autonomous and intelligent data mining processes. This article introduces an architecture to implement a data mining engine, composed by four major components: database; Middleware (control); Middleware (processing); and interface. These components are interlinked but provide independent scaling, allowing for a system that adapts to the user’s needs. A prototype has been developed in order to test the architecture. The results are very promising and showed their functionality and the need for further improvements.

Pervasive adaptive data acquisition gateway for critical healthcare

The data acquisition process in real-time is fundamental to provide appropriate services and improve health professionals decision. In this paper a pervasive adaptive data acquisition architecture of medical devices (e.g. vital signs, ventilators and sensors) is presented. The architecture was deployed in a real context in an Intensive Care Unit. It is providing clinical data in real-time to the INTCare system. The gateway is composed by several agents able to collect a set of patients’ variables (vital signs, ventilation) across the network. The paper shows as example the ventilation acquisition process. The clients are installed in a machine near the patient bed. Then they are connected to the ventilators and the data monitored is sent to a multithreading server which using Health Level Seven protocols records the data in the database. The agents associated to gateway are able to collect, analyse, interpret and store the data in the repository. This gateway is composed by a fault tolerant system that ensures a data store in the database even if the agents are disconnected. The gateway is pervasive, universal, and interoperable and it is able to adapt to any service using streaming data.

Oligochaetes (Annelida, Clitellata) in a neotropical stream: a mesohabitat approach

This paper analyses the relationship among mesohabitat and aquatic oligochaete species in the Galharada Stream (Campos do Jordão State Park, state of São Paulo, Brazil). Between August 2005 and May 2006 a total of 192 samples were obtained in areas of four different mesohabitats: riffle leaf litter (RL), pool leaf litter (PL), pool sediment (PS) and interstitial sediment from rocky beds in riffle areas (IS). In the mesohabitats sampled, 2007 specimens were identified, belonging to two families (Naididae and Enchytraeidae). Among the oligochaetes identified Naididae was represented by six genera (Allonais, Chaetogaster, Nais, Pristina, Aulodrilus and Limnodrilus). Principal components analysis (PCA) revealed the first two axes explained 85.1% of the total variance of the data. Limnodrilus hoffmeisteri Claparede, 1862 and Aulodrilus limnobius Bretscher, 1899 were associated with the pool areas (PL and PS). Most species of genera Pristina and Nais demonstrated apparent affinity with the riffle mesohabitats. The Indicator Species Analysis (IndVal) revealed that Nais communis Piguet, 1906, Pristina leidyi Smith, 1896 and Pristina (Pristinella) jenkinae (Stephenson, 1931) are indicative of RL mesohabitat, while family Enchytraeidae was considered indicative of PL mesohabitat.

Enhancing the vertical resolution of surface georadar data

There are far-reaching conceptual similarities between bi-static surface georadar and post-stack, "zero-offset" seismic reflection data, which is expressed in largely identical processing flows. One important difference is, however, that standard deconvolution algorithms routinely used to enhance the vertical resolution of seismic data are notoriously problematic or even detrimental to the overall signal quality when applied to surface georadar data. We have explored various options for alleviating this problem and have tested them on a geologically well-constrained surface georadar dataset. Standard stochastic and direct deterministic deconvolution approaches proved to be largely unsatisfactory. While least-squares-type deterministic deconvolution showed some promise, the inherent uncertainties involved in estimating the source wavelet introduced some artificial "ringiness". In contrast, we found spectral balancing approaches to be effective, practical and robust means for enhancing the vertical resolution of surface georadar data, particularly, but not exclusively, in the uppermost part of the georadar section, which is notoriously plagued by the interference of the direct air- and groundwaves. For the data considered in this study, it can be argued that band-limited spectral blueing may provide somewhat better results than standard band-limited spectral whitening, particularly in the uppermost part of the section affected by the interference of the air- and groundwaves. Interestingly, this finding is consistent with the fact that the amplitude spectrum resulting from least-squares-type deterministic deconvolution is characterized by a systematic enhancement of higher frequencies at the expense of lower frequencies and hence is blue rather than white. It is also consistent with increasing evidence that spectral "blueness" is a seemingly universal, albeit enigmatic, property of the distribution of reflection coefficients in the Earth. Our results therefore indicate that spectral balancing techniques in general and spectral blueing in particular represent simple, yet effective means of enhancing the vertical resolution of surface georadar data and, in many cases, could turn out to be a preferable alternative to standard deconvolution approaches.

Coronary MR angiography: comparison of quantitative and qualitative data from four techniques.

OBJECTIVE: The optimal coronary MR angiography sequence has yet to be determined. We sought to quantitatively and qualitatively compare four coronary MR angiography sequences. SUBJECTS AND METHODS. Free-breathing coronary MR angiography was performed in 12 patients using four imaging sequences (turbo field-echo, fast spin-echo, balanced fast field-echo, and spiral turbo field-echo). Quantitative comparisons, including signal-to-noise ratio, contrast-to-noise ratio, vessel diameter, and vessel sharpness, were performed using a semiautomated analysis tool. Accuracy for detection of hemodynamically significant disease (> 50%) was assessed in comparison with radiographic coronary angiography. RESULTS: Signal-to-noise and contrast-to-noise ratios were markedly increased using the spiral (25.7 +/- 5.7 and 15.2 +/- 3.9) and balanced fast field-echo (23.5 +/- 11.7 and 14.4 +/- 8.1) sequences compared with the turbo field-echo (12.5 +/- 2.7 and 8.3 +/- 2.6) sequence (p < 0.05). Vessel diameter was smaller with the spiral sequence (2.6 +/- 0.5 mm) than with the other techniques (turbo field-echo, 3.0 +/- 0.5 mm, p = 0.6; balanced fast field-echo, 3.1 +/- 0.5 mm, p < 0.01; fast spin-echo, 3.1 +/- 0.5 mm, p < 0.01). Vessel sharpness was highest with the balanced fast field-echo sequence (61.6% +/- 8.5% compared with turbo field-echo, 44.0% +/- 6.6%; spiral, 44.7% +/- 6.5%; fast spin-echo, 18.4% +/- 6.7%; p < 0.001). The overall accuracies of the sequences were similar (range, 74% for turbo field-echo, 79% for spiral). Scanning time for the fast spin-echo sequences was longest (10.5 +/- 0.6 min), and for the spiral acquisitions was shortest (5.2 +/- 0.3 min). CONCLUSION: Advantages in signal-to-noise and contrast-to-noise ratios, vessel sharpness, and the qualitative results appear to favor spiral and balanced fast field-echo coronary MR angiography sequences, although subjective accuracy for the detection of coronary artery disease was similar to that of other sequences.

Diseño y Desarrollo de un cliente Web Processing Services (WPS) para gvSIG

Configuració d'un entorn de desenvolupament en el IDE Eclipse. Introducció als SIG. Usos, utilitats i exemples. Conèixer la eina gvSIG. Conèixer els estàndards més estesos de l'Open Geospatial Consortium (OGC) i en especial del Web Processing Services. Analitzar, dissenyar i desenvolupar un client capaç de consumir serveis wps.

Diseño y Desarrollo de un cliente Web Processing Service (WPS) para gvSIG

Estudi dels estàndards definits per l'Open Geospatial Consortium, i més concretament en l'estàndard Web Processing Service (wps). Així mateix, ha tingut una component pràctica que ha consistit en el disseny i desenvolupament d'un client capaç de consumir serveis Web creats segons wps i integrat a la plataforma gvSIG.

Auditory spatial deficits following hemispheric lesions: Dissociation of explicit and implicit processing.

Auditory spatial deficits occur frequently after hemispheric damage; a previous case report suggested that the explicit awareness of sound positions, as in sound localisation, can be impaired while the implicit use of auditory cues for the segregation of sound objects in noisy environments remains preserved. By assessing systematically patients with a first hemispheric lesion, we have shown that (1) explicit and/or implicit use can be disturbed; (2) impaired explicit vs. preserved implicit use dissociations occur rather frequently; and (3) different types of sound localisation deficits can be associated with preserved implicit use. Conceptually, the dissociation between the explicit and implicit use may reflect the dual-stream dichotomy of auditory processing. Our results speak in favour of systematic assessments of auditory spatial functions in clinical settings, especially when adaptation to auditory environment is at stake. Further, systematic studies are needed to link deficits of explicit vs. implicit use to disability in everyday activities, to design appropriate rehabilitation strategies, and to ascertain how far the explicit and implicit use of spatial cues can be retrained following brain damage.

Intelligence-led crime scene processing. Part II : Intelligence and crime scene examination

A better integration of the information conveyed by traces within intelligence-led framework would allow forensic science to participate more intensively to security assessments through forensic intelligence (part I). In this view, the collection of data by examining crime scenes is an entire part of intelligence processes. This conception frames our proposal for a model that promotes to better use knowledge available in the organisation for driving and supporting crime scene examination. The suggested model also clarifies the uncomfortable situation of crime scene examiners who must simultaneously comply with justice needs and expectations, and serve organisations that are mostly driven by broader security objectives. It also opens new perspective for forensic science and crime scene investigation, by the proposal to follow other directions than the traditional path suggested by dominant movements in these fields.

Exploration of alpine orographic precipitation patterns with radar image processing and clustering techniques

A methodology of exploratory data analysis investigating the phenomenon of orographic precipitation enhancement is proposed. The precipitation observations obtained from three Swiss Doppler weather radars are analysed for the major precipitation event of August 2005 in the Alps. Image processing techniques are used to detect significant precipitation cells/pixels from radar images while filtering out spurious effects due to ground clutter. The contribution of topography to precipitation patterns is described by an extensive set of topographical descriptors computed from the digital elevation model at multiple spatial scales. Additionally, the motion vector field is derived from subsequent radar images and integrated into a set of topographic features to highlight the slopes exposed to main flows. Following the exploratory data analysis with a recent algorithm of spectral clustering, it is shown that orographic precipitation cells are generated under specific flow and topographic conditions. Repeatability of precipitation patterns in particular spatial locations is found to be linked to specific local terrain shapes, e.g. at the top of hills and on the upwind side of the mountains. This methodology and our empirical findings for the Alpine region provide a basis for building computational data-driven models of orographic enhancement and triggering of precipitation. Copyright (C) 2011 Royal Meteorological Society .

Using free data mining software and clustering algorithms to find predictors from student qualifications

In this project a research both in finding predictors via clustering techniques and in reviewing the Data Mining free software is achieved. The research is based in a case of study, from where additionally to the KDD free software used by the scientific community; a new free tool for pre-processing the data is presented. The predictors are intended for the e-learning domain as the data from where these predictors have to be inferred are student qualifications from different e-learning environments. Through our case of study not only clustering algorithms are tested but also additional goals are proposed.