Adapter module for self-learning production systems

Dissertação para obtenção do Grau de Mestre em Engenharia Electrotécnica, Sistemas e Computadores

Working towards the invisible repository

Presentation at Open Repositories 2014, Helsinki, Finland, June 9-13, 2014

Numerical precision requirements on the Multiband Ultra-Wideband system for practical consumer electronic devices

This paper discusses the requirements on the numerical precision for a practical Multiband Ultra-Wideband (UWB) consumer electronic solution. To this end we first present the possibilities that UWB has to offer to the consumer electronics market and the possible range of devices. We then show the performance of a model of the UWB baseband system implemented using floating point precision. Then, by simulation we find the minimal numerical precision required to maintain floating-point performance for each of the specific data types and signals present in the UWB baseband. Finally, we present a full description of the numerical requirements for both the transmit and receive components of the UWB baseband. The numerical precision results obtained in this paper can then be used by baseband designers to implement cost effective UWB systems using System-on-Chip (SoC), FPGA and ASIC technology solutions biased toward the competitive consumer electronics market(1).

Desenvolvimento formal de aplicações para smartcards

Smart card applications represent a growing market. Usually this kind of application manipulate and store critical information that requires some level of security, such as financial or confidential information. The quality and trustworthiness of smart card software can be improved through a rigorous development process that embraces formal techniques of software engineering. In this work we propose the BSmart method, a specialization of the B formal method dedicated to the development of smart card Java Card applications. The method describes how a Java Card application can be generated from a B refinement process of its formal abstract specification. The development is supported by a set of tools, which automates the generation of some required refinements and the translation to Java Card client (host) and server (applet) applications. With respect to verification, the method development process was formalized and verified in the B method, using the Atelier B tool [Cle12a]. We emphasize that the Java Card application is translated from the last stage of refinement, named implementation. This translation process was specified in ASF+SDF [BKV08], describing the grammar of both languages (SDF) and the code transformations through rewrite rules (ASF). This specification was an important support during the translator development and contributes to the tool documentation. We also emphasize the KitSmart library [Dut06, San12], an essential component of BSmart, containing models of all 93 classes/interfaces of Java Card API 2:2:2, of Java/Java Card data types and machines that can be useful for the specifier, but are not part of the standard Java Card library. In other to validate the method, its tool support and the KitSmart, we developed an electronic passport application following the BSmart method. We believe that the results reached in this work contribute to Java Card development, allowing the generation of complete (client and server components), and less subject to errors, Java Card applications.

Global distributions of pteropods (Gymnosomata, Thecosomata, Pseudothecosomata) abundance and biomass - Gridded data product (NetCDF) - Contribution to the MAREDAT World Ocean Atlas of Plankton Functional Types

Pteropods are a group of holoplanktonic gastropods for which global biomass distribution patterns remain poorly resolved. The aim of this study was to collect and synthesize existing pteropod (Gymnosomata, Thecosomata and Pseudothecosomata) abundance and biomass data, in order to evaluate the global distribution of pteropod carbon biomass, with a particular emphasis on its seasonal, temporal and vertical patterns. We collected 25 902 data points from several online databases and a number of scientific articles. The biomass data has been gridded onto a 360 x 180° grid, with a vertical resolution of 33 WOA depth levels. Data has been converted to NetCDF format. Data were collected between 1951-2010, with sampling depths ranging from 0-1000 m. Pteropod biomass data was either extracted directly or derived through converting abundance to biomass with pteropod specific length to weight conversions. In the Northern Hemisphere (NH) the data were distributed evenly throughout the year, whereas sampling in the Southern Hemisphere was biased towards the austral summer months. 86% of all biomass values were located in the NH, most (42%) within the latitudinal band of 30-50° N. The range of global biomass values spanned over three orders of magnitude, with a mean and median biomass concentration of 8.2 mg C l-1 (SD = 61.4) and 0.25 mg C l-1, respectively for all data points, and with a mean of 9.1 mg C l-1 (SD = 64.8) and a median of 0.25 mg C l-1 for non-zero biomass values. The highest mean and median biomass concentrations were located in the NH between 40-50° S (mean biomass: 68.8 mg C l-1 (SD = 213.4) median biomass: 2.5 mg C l-1) while, in the SH, they were within the 70-80° S latitudinal band (mean: 10.5 mg C l-1 (SD = 38.8) and median: 0.2 mg C l-1). Biomass values were lowest in the equatorial regions. A broad range of biomass concentrations was observed at all depths, with the biomass peak located in the surface layer (0-25 m) and values generally decreasing with depth. However, biomass peaks were located at different depths in different ocean basins: 0-25 m depth in the N Atlantic, 50-100 m in the Pacific, 100-200 m in the Arctic, 200-500 m in the Brazilian region and >500 m in the Indo-Pacific region. Biomass in the NH was relatively invariant over the seasonal cycle, but more seasonally variable in the SH. The collected database provides a valuable tool for modellers for the study of ecosystem processes and global biogeochemical cycles.

Global distributions of diazotrophs abundance, biomass and nitrogen fixation rates - Gridded data product (NetCDF) - Contribution to the MAREDAT World Ocean Atlas of Plankton Functional Types

The MAREDAT atlas covers 11 types of plankton, ranging in size from bacteria to jellyfish. Together, these plankton groups determine the health and productivity of the global ocean and play a vital role in the global carbon cycle. Working within a uniform and consistent spatial and depth grid (map) of the global ocean, the researchers compiled thousands and tens of thousands of data points to identify regions of plankton abundance and scarcity as well as areas of data abundance and scarcity. At many of the grid points, the MAREDAT team accomplished the difficult conversion from abundance (numbers of organisms) to biomass (carbon mass of organisms). The MAREDAT atlas provides an unprecedented global data set for ecological and biochemical analysis and modeling as well as a clear mandate for compiling additional existing data and for focusing future data gathering efforts on key groups in key areas of the ocean. This is a gridded data product about diazotrophic organisms . There are 6 variables. Each variable is gridded on a dimension of 360 (longitude) * 180 (latitude) * 33 (depth) * 12 (month). The first group of 3 variables are: (1) number of biomass observations, (2) biomass, and (3) special nifH-gene-based biomass. The second group of 3 variables is same as the first group except that it only grids non-zero data. We have constructed a database on diazotrophic organisms in the global pelagic upper ocean by compiling more than 11,000 direct field measurements including 3 sub-databases: (1) nitrogen fixation rates, (2) cyanobacterial diazotroph abundances from cell counts and (3) cyanobacterial diazotroph abundances from qPCR assays targeting nifH genes. Biomass conversion factors are estimated based on cell sizes to convert abundance data to diazotrophic biomass. Data are assigned to 3 groups including Trichodesmium, unicellular diazotrophic cyanobacteria (group A, B and C when applicable) and heterocystous cyanobacteria (Richelia and Calothrix). Total nitrogen fixation rates and diazotrophic biomass are calculated by summing the values from all the groups. Some of nitrogen fixation rates are whole seawater measurements and are used as total nitrogen fixation rates. Both volumetric and depth-integrated values were reported. Depth-integrated values are also calculated for those vertical profiles with values at 3 or more depths.

Integration and visualization of systems biology data in context of the genome

Background: High-density tiling arrays and new sequencing technologies are generating rapidly increasing volumes of transcriptome and protein-DNA interaction data. Visualization and exploration of this data is critical to understanding the regulatory logic encoded in the genome by which the cell dynamically affects its physiology and interacts with its environment. Results: The Gaggle Genome Browser is a cross-platform desktop program for interactively visualizing high-throughput data in the context of the genome. Important features include dynamic panning and zooming, keyword search and open interoperability through the Gaggle framework. Users may bookmark locations on the genome with descriptive annotations and share these bookmarks with other users. The program handles large sets of user-generated data using an in-process database and leverages the facilities of SQL and the R environment for importing and manipulating data. A key aspect of the Gaggle Genome Browser is interoperability. By connecting to the Gaggle framework, the genome browser joins a suite of interconnected bioinformatics tools for analysis and visualization with connectivity to major public repositories of sequences, interactions and pathways. To this flexible environment for exploring and combining data, the Gaggle Genome Browser adds the ability to visualize diverse types of data in relation to its coordinates on the genome. Conclusions: Genomic coordinates function as a common key by which disparate biological data types can be related to one another. In the Gaggle Genome Browser, heterogeneous data are joined by their location on the genome to create information-rich visualizations yielding insight into genome organization, transcription and its regulation and, ultimately, a better understanding of the mechanisms that enable the cell to dynamically respond to its environment.

Data Extraction Tool to Analyse, Transform and Store Real Data from Electricity Markets

The study of electricity markets operation has been gaining an increasing importance in the last years, as result of the new challenges that the restructuring process produced. Currently, lots of information concerning electricity markets is available, as market operators provide, after a period of confidentiality, data regarding market proposals and transactions. These data can be used as source of knowledge to define realistic scenarios, which are essential for understanding and forecast electricity markets behavior. The development of tools able to extract, transform, store and dynamically update data, is of great importance to go a step further into the comprehension of electricity markets and of the behaviour of the involved entities. In this paper an adaptable tool capable of downloading, parsing and storing data from market operators’ websites is presented, assuring constant updating and reliability of the stored data.

An automatic tool to Extract, Transform and Load data from real electricity markets

The study of Electricity Markets operation has been gaining an increasing importance in the last years, as result of the new challenges that the restructuring produced. Currently, lots of information concerning Electricity Markets is available, as market operators provide, after a period of confidentiality, data regarding market proposals and transactions. These data can be used as source of knowledge, to define realistic scenarios, essential for understanding and forecast Electricity Markets behaviour. The development of tools able to extract, transform, store and dynamically update data, is of great importance to go a step further into the comprehension of Electricity Markets and the behaviour of the involved entities. In this paper we present an adaptable tool capable of downloading, parsing and storing data from market operators’ websites, assuring actualization and reliability of stored data.

Prevalence of human papillomavirus types in women with pre-neoplastic and neoplastic cervical lesions in the Federal District of Brazil

As a contribution to the public health authorities in planning prophylactic and therapeutic vaccine strategies, we describe the prevalence of human papillomavirus (HPV) types in women presenting abnormal cytological results in Pap smear screening tests in the Federal District, Central Brazil. We studied 129 cervical scraping samples from women whose cytological tests showed either pre-neoplastic or neoplastic lesions. Amplification of HPV DNA was performed by polymerase chain reaction using consensus primers MY09 and MY11 followed by identification of isolates by restriction fragment length polymorphism. We detected HPV DNA in 62% of the samples, including HPV-16 in 43.8%, HPV-58 in 12.5%, HPV-31 in 10%, HPV-53 in 6.3%, each of HPV-18 and HPV-33 in 3.8% of the isolates. Other types (HPV-35, -52, -66, -CP8304, -6, -11, and -CP8061) were less frequent (= or < 2.5% each). The prevalence of HPV-58 was relatively higher in this population than in data in South America, but similar to results obtained in other studies in Latin America, Europe, and Eastern Asia. Case-control studies need to be carried out to establish the association between the prevalence of HPV types specially the less frequent high-risk types and cervical cancer.

Sociodemographic and occupational risk factors associated with the development of different burnout types: the cross-sectional University of Zaragoza study

BACKGROUND: Three different burnout types have been described: The "frenetic" type describes involved and ambitious subjects who sacrifice their health and personal lives for their jobs; the "underchallenged" type describes indifferent and bored workers who fail to find personal development in their jobs, and the "worn-out" in type describes neglectful subjects who feel they have little control over results and whose efforts go unacknowledged. The study aimed to describe the possible associations between burnout types and general sociodemographic and occupational characteristics. METHODS: A cross-sectional study was carried out on a multi-occupational sample of randomly selected university employees (n = 409). The presence of burnout types was assessed by means of the "Burnout Clinical Subtype Questionnaire (BCSQ-36)", and the degree of association between variables was assessed using an adjusted odds ratio (OR) obtained from multivariate logistic regression models. RESULTS: Individuals working more than 40 hours per week presented with the greatest risk for "frenetic" burnout compared to those working fewer than 35 hours (adjusted OR = 5.69; 95% CI = 2.52-12.82; p < 0.001). Administration and service personnel presented the greatest risk of "underchallenged" burnout compared to teaching and research staff (adjusted OR = 2.85; 95% CI = 1.16-7.01; p = 0.023). Employees with more than sixteen years of service in the organisation presented the greatest risk of "worn-out" burnout compared to those with less than four years of service (adjusted OR = 4.56; 95% CI = 1.47-14.16; p = 0.009). CONCLUSIONS: This study is the first to our knowledge that suggests the existence of associations between the different burnout subtypes (classified according to the degree of dedication to work) and the different sociodemographic and occupational characteristics that are congruent with the definition of each of the subtypes. These results are consistent with the clinical profile definitions of burnout syndrome. In addition, they assist the recognition of distinct profiles and reinforce the idea of differential characterisation of the syndrome for more effective treatment.

Bgee: integrating and comparing heterogeneous transcriptome data among species

Gene expression patterns are a key feature in understanding gene function, notably in development. Comparing gene expression patterns between animals is a major step in the study of gene function as well as of animal evolution. It also provides a link between genes and phenotypes. Thus we have developed Bgee, a database designed to compare expression patterns between animals, by implementing ontologies describing anatomies and developmental stages of species, and then designing homology relationships between anatomies and comparison criteria between developmental stages. To define homology relationships between anatomical features we have developed the software Homolonto, which uses a modified ontology alignment approach to propose homology relationships between ontologies. Bgee then uses these aligned ontologies, onto which heterogeneous expression data types are mapped. These already include microarrays and ESTs.

Analysis of warm season thunderstorms using an object-oriented tracking method based on radar and total lightning data

Monitoring thunderstorms activity is an essential part of operational weather surveillance given their potential hazards, including lightning, hail, heavy rainfall, strong winds or even tornadoes. This study has two main objectives: firstly, the description of a methodology, based on radar and total lightning data to characterise thunderstorms in real-time; secondly, the application of this methodology to 66 thunderstorms that affected Catalonia (NE Spain) in the summer of 2006. An object-oriented tracking procedure is employed, where different observation data types generate four different types of objects (radar 1-km CAPPI reflectivity composites, radar reflectivity volumetric data, cloud-to-ground lightning data and intra-cloud lightning data). In the framework proposed, these objects are the building blocks of a higher level object, the thunderstorm. The methodology is demonstrated with a dataset of thunderstorms whose main characteristics, along the complete life cycle of the convective structures (development, maturity and dissipation), are described statistically. The development and dissipation stages present similar durations in most cases examined. On the contrary, the duration of the maturity phase is much more variable and related to the thunderstorm intensity, defined here in terms of lightning flash rate. Most of the activity of IC and CG flashes is registered in the maturity stage. In the development stage little CG flashes are observed (2% to 5%), while for the dissipation phase is possible to observe a few more CG flashes (10% to 15%). Additionally, a selection of thunderstorms is used to examine general life cycle patterns, obtained from the analysis of normalized (with respect to thunderstorm total duration and maximum value of variables considered) thunderstorm parameters. Among other findings, the study indicates that the normalized duration of the three stages of thunderstorm life cycle is similar in most thunderstorms, with the longest duration corresponding to the maturity stage (approximately 80% of the total time).

Analysis, modelling and classification of geospatial data using machine learning

The research considers the problem of spatial data classification using machine learning algorithms: probabilistic neural networks (PNN) and support vector machines (SVM). As a benchmark model simple k-nearest neighbor algorithm is considered. PNN is a neural network reformulation of well known nonparametric principles of probability density modeling using kernel density estimator and Bayesian optimal or maximum a posteriori decision rules. PNN is well suited to problems where not only predictions but also quantification of accuracy and integration of prior information are necessary. An important property of PNN is that they can be easily used in decision support systems dealing with problems of automatic classification. Support vector machine is an implementation of the principles of statistical learning theory for the classification tasks. Recently they were successfully applied for different environmental topics: classification of soil types and hydro-geological units, optimization of monitoring networks, susceptibility mapping of natural hazards. In the present paper both simulated and real data case studies (low and high dimensional) are considered. The main attention is paid to the detection and learning of spatial patterns by the algorithms applied.

A comparison of mean phase difference and generalized least squares for analyzing single-case data

The present study focuses on single-case data analysis and specifically on two procedures for quantifying differences between baseline and treatment measurements The first technique tested is based on generalized least squares regression analysis and is compared to a proposed non-regression technique, which allows obtaining similar information. The comparison is carried out in the context of generated data representing a variety of patterns (i.e., independent measurements, different serial dependence underlying processes, constant or phase-specific autocorrelation and data variability, different types of trend, and slope and level change). The results suggest that the two techniques perform adequately for a wide range of conditions and researchers can use both of them with certain guarantees. The regression-based procedure offers more efficient estimates, whereas the proposed non-regression procedure is more sensitive to intervention effects. Considering current and previous findings, some tentative recommendations are offered to applied researchers in order to help choosing among the plurality of single-case data analysis techniques.