Daniel Weingaertner

Role of OpenEHR as an open source solution for the regional modelling of patient data in obstetrics

This work investigates, whether openEHR with its reference model, archetypes and templates is suitable for the digital representation of demographic as well as clinical data. Moreover, it elaborates openEHR as a tool for modelling Hospital Information Systems on a regional level based on a national logical infrastructure. OpenEHR is a dual model approach developed for the modelling of Hospital Information Systems enabling semantic interoperability. A holistic solution to this represents the use of dual model based Electronic Healthcare Record systems. Modelling data in the field of obstetrics is a challenge, since different regions demand locally specific information for the process of treatment. Smaller health units in developing countries like Brazil or Malaysia, which until recently handled automatable processes like the storage of sensitive patient data in paper form, start organizational reconstruction processes. This archetype proof-of-concept investigation has tried out some elements of the openEHR methodology in cooperation with a health unit in Colombo, Brazil. Two legal forms provided by the Brazilian Ministry of Health have been analyzed and classified into demographic and clinical data. LinkEHR-Ed editor was used to read, edit and create archetypes. Results show that 33 clinical and demographic concepts, which are necessary to cover data demanded by the Unified National Health System, were identified. Out of the concepts 61% were reused and 39% modified to cover domain requirements. The detailed process of reuse, modification and creation of archetypes is shown. We conclude that, although a major part of demographic and clinical patient data were already represented by existing archetypes, a significant part required major modifications. In this study openEHR proved to be a highly suitable tool in the modelling of complex health data. In combination with LinkEHR-Ed software it offers user-friendly and highly applicable tools, although the complexity built by the vast specifications requires expert networks to define generally excepted clinical models. Finally, this project has pointed out main benefits enclosing high coverage of obstetrics data on the Clinical Knowledge Manager, simple modelling, and wide network and support using openEHR. Moreover, barriers described are enclosing the allocation of clinical content to respective archetypes, as well as stagnant adaption of changes on the Clinical Knowledge Manager leading to redundant efforts in data contribution that need to be addressed in future works.

Efficient Implementation of Canny Edge Detection Filter for ITK Using CUDA

This work presents an efficient CUDA implementation of the Canny edge detection Filter for the Insight Segmentation and Registration Toolkit (ITK). The algorithm is tested on three generations of NVidia GPGPUs, showing performance gains of 3.6 to 50 times when compared to the standard ITK Canny running on two CPU models. The CUDA-enabled Canny is also compared to a more efficient Canny implementation from the OpenCV library. Examples of coding strategies to avoid warp serialization in CUDA are shown on a smart implementation of the Sobel filter, as well as on other algorithms.

LIBRAS Sign Language Hand Configuration Recognition Based on 3D Meshes

This paper presents a method for recognizing hand configurations of the Brazilian sign language (LIBRAS) using 3D meshes and 2D projections of the hand. Five actors performing 61 different hand configurations of the LIBRAS language were recorded twice, and the videos were manually segmented to extract one frame with a frontal and one with a lateral view of the hand. For each frame pair, a 3D mesh of the hand was constructed using the Shape from Silhouette method, and the rotation, translation and scale invariant Spherical Harmonics method was used to extract features for classification. A Support Vector Machine (SVM) achieved a correct classification of Rank1 = 86.06% and Rank3 = 96.83% on a database composed of 610 meshes. SVM classification was also performed on a database composed of 610 image pairs using 2D horizontal and vertical projections as features, resulting in Rank1 = 88.69% and Rank3 = 98.36%. Results encourage the use of 3D meshes as opposed to videos or images, given that their direct, real time acquisition is becoming possible due to devices like Leap Motion® or high resolution depth cameras.

Universal remote boot and administration service

Booting operating systems over the network is a well established method to simplify system administration in medium to large computer environments. Remote booting Linux and using PXE based environments for large scale system deployments are widely adopted by many system administrators. While scaling very well in controlled subnets they can not easily cross sub-network boundaries or co-exist with each other. Additionally it would be desirable to offer a wider range of standard services not only in dedicated sub-networks but on the whole enterprise infrastructure. This paper discusses advanced network booting methods overcoming the restrictions of traditional PXE/DHCP/TFTP setups. It suggests a new approach combining existing well established boot services into a more flexible framework for remote booting a wide range of operating systems and maintanance tools. A centralized boot configuration web-service is the universal, multi-purpose entry point of the architecture offering configurable, flexible boot menus for directly managed and arbitrary computers of unregistered users.

Estimating stop conditions of swarm based stochastic metaheuristic algorithms

When dealing with metaheuristics, one important question is how many evaluations are worth spending in the search for better results. This work proposes a method to estimate the best moment to stop swarm iterations based on the analysis of the convergence behavior presented during optimization, aiming to provide an effective balance between saving fitness evaluations and keeping the optimization quality. An automated Convergence Stabilization Modeling operating in Online mode (CSMOn) is proposed based on a sequence of linear regressions using exponential and log-like curves. The method was tested on the CEC13 benchmark with CCPS02-IP E algorithm and on 30 random Max-Set functions with the swarm algorithms PSO, ABC and CCPS02-IP E. CEC13 results show that up to 90% less fitness evaluations are performed for functions where CCPS02-IP E has a steady convergence, and up to 49% for functions where convergence is erratic, while penalties for fitness are kept small. Max-Set results demonstrates the robustness of CSMOn for the search algorithms tested. We conclude that CSMOn is capable of adapting to an optimization in progress, producing a good trade-off between result quality and evaluation savings.

A Case Study of the Aggregation Query Model in Read-Mostly NoSQL Document Stores

In this paper we focus on the aggregate query model implemented over NoSQL document-stores for read-mostly data bases. We discuss that the aggregate query model can be a good fit for read-mostly databases if the following design requirements are met: on-line time range queries, aggregates with predefined filters, frequent schema evolution and no ad-hoc. In our model, we present a composite object schema implementation over NoSQL document-stores, in which data associations are nested in a document under the same search key. We present the design choices to obtain a model adapted to our needs. Our schema is inspired by the star schema of Data Warehouses to reduce accessing data associations in many different documents and computing aggregates within the same composite. We present performance results of our empirical study over a 300 million records database that serves in production for the Ministry of Communications of Brazil. Results show the performance gains and penalties of our star composite schema when compared to the traditional multidimensional schema.

Automated Iterative Partitioning for Cooperatively Coevolving Particle Swarms in Large Scale Optimization

Particle Swarm Optimization (PSO) is a relatively recent meta-heuristic inspired by the swarming or collaborative behaviour of biological populations. It is known by its capacity of obtaining important fitness improvements on a short period of time. A cooperative version named CPSO has been used to deal with high dimensional search spaces and CCPSO2 is one of its variants that has achieved high performances in large scale optimization problems (above 500 dimensions). This paper proposes an Iterative Partitioning (IP) method for CCPSO2 that takes advantage of the CCPSO2 characteristics. The resulting approach, named CCPSO2-IP, also joins some well known good practices into one single algorithm. Boost functions are included to fine tune the search steps. The competition benchmark CEC13 for large scale global optimization (LSGO) is used to validate the proposed method. Results show that the IP-based method outperforms the standard CCPSO2 and the single swarm PSO, where the exponential boost function presents the highest performance.

Blockwise Classification of Lung Patterns in Unsegmented CT Images

Diagnosis of lung diseases is usually accomplished by detecting abnormal characteristics in Computed Tomography (CT) scans. We report an initial study for classifying texture patterns in High-Resolution lung CTs using the Completed Local Binary Pattern (CLBP) descriptor with a Support Vector Machine (SVM). The main contribution of the proposed method is that it does not depend on a previously segmented lung, as it performs a coarse segmentation by classifying body areas outside the lungs. The classified patterns are: non lung, normal lung tissue, emphysema, ground-glass opacity, fibrosis and micronodules. Using image blocks of 32x32 pixels, extracted from a public dataset with 113 patients, correct block wise classification of non lung patterns was achieved with an accuracy of 98.91%. Regarding normal and pathological lung patterns, a mean accuracy of 91.81% was obtained. This is similar to the reported results in literature which used a presegmented lung.

PROINFODATA: Monitoring a Large Park of Computational Laboratories

This paper briefly presents a model for monitoring a large, heterogeneous and geographically scattered computer park. The data collection is performed by a software agent. The collected data are sent to the central server over the Internet, and stored by the storage system. An on-line portal makes up the visualization system, featuring charts, reports, and other tools for assessing the state of the park. This system is currently monitoring circa 150,000 machines.

Laboratórios de Informática com Software Livre para Atender Políticas Estaduais do Ensino Escolar

Resumo: Este trabalho avalia as iniciativas de combate a brecha digital por meio da introducao de computadores e do acesso a internet nas escolas da rede publica de ensino fundamental das cidades medias fluminenses. A analise divide o processo de informatizacao em tres estagios: a aquisicao de computadores , a implantacao de laboratorios de informatica e o acesso a internet com objetivo de identificar em que estagio se encontramos municipios em estudo. Abstract: This paper aimed at the medium size cities in Rio de Janeiro and their politics to control the digital divide through the introduction of computers and Internet on public schools. The analysis divided this process in three different stages from the acquisition of computers to Internet access. The appraisal pointed out the necessity of public policies to overcome the stages as the majority of these cities are still acquiring computers instead of implementing Internet access and combating the digital divide.