Articles — Two-column

Accurate prognosis and prediction of a patient's current disease state is critical in an ICU. The use of vast amounts of digital medical information can help in predicting the best course of action for the diagnosis and treatment of patients. The proposed technique investigates the strength of using a combination of latent variable models (latent dirichlet allocation) and structured data to transform the information streams into potentially actionable knowledge. In this project, I use Apache Spark to predict mortality among ICU patients so that it can be used as an acuity surrogate to help physicians identify the patients in need of immediate care.

En este documento se encuentra el desarrollo del primer laboratorio de la asignatura Vídeo Digital y Procesamiento de Imágenes el cual trata sobre el análisis de los sensores fotográficos y los metadatos de las imágenes tomadas, Ademas de la creación de algunos scripts en el programa Matlab para lo que se tiene que tener en cuenta la información de geolocalización o GPS de las imágenes. Keywords — GPS, Matlab, Metadatos

Recent work on information extraction has suggested that fast, interactive tools can be highly effective; however, creating a usable system is challenging, and few publically available tools exist. In this paper we present IKE, a new extraction tool that performs fast, interactive bootstrapping to develop high-quality extraction patterns for targeted relations, and provides novel solutions to these usability concerns. In particular, it uses a novel query language that is expressive, easy to understand, and fast to execute - essential requirements for a practical system - and is the first interactive extraction tool to seamlessly integrate symbolic and distributional methods for search. An initial evaluation suggests that relation tables can be populated substantially faster than by manual pattern authoring or using fully automated tools, while retaining accuracy, an important step towards practical knowledge-base construction.

Precision viticulture is a specialization of precision agriculture techniques applied to viticulture. Precision agriculture is the use of information system technologies applied to agricultural production. Some of the applicable technologies are; Wireless Sensor Networks (WSN), Global Positioning Systems (GPS), spectroscopy analysis of Near-Infrared (NIR), Geographic Information Systems (GIS). These systems provide means of observation, evaluation and control of agricultural activities. The farmers demand assistant systems to perform actions for saving time and avoiding risks. There are studies of maps crops and mesh-sampling techniques to predict the harvest volume in a vineyard with a certain varieties of grapes. The prediction is based on a previous study of crops over a period of three to four years. Along these three or four years a large volume of samples is taken to study several parameters. In this application area is where the wireless sensor networks technologies would have high incidence. In this context we intend to analyse, at first place, the specific characteristics of the operational environment of a vineyard. Second, we will analyse the most appropriate architecture for a sensor network in this environment. Application of wireless sensor networks technology can take many forms depending of environment, and implementation objectives. In this paper we discuss about the best procedure for deployment and the optimal topology of a wireless sensor network for viticulture.

Sjabloon Kampvoorbereiding volgens richtlijnen Scouts en Gidsen Vlaanderen

Projeto lab CE2

Comprensión de un estudio realizado en la mica-epoxi para placas de circuitos. El estudio consistió en pruebas de resistencia para medir el desgaste en el tiempo del material y así determinar su tiempo de vida aproximado.

Estudio de los procesos termodinámicos que conllevan una máquina de expreso. Se utiliza solo agua, sin el filtro de café para utilizar solo la densidad del agua. Se encuentra que es similar a un proceso de Carnot, pero como un ciclo abierto.

This research paper aims at exploiting efficient ways of implementing the N-Body problem. The N-Body problem, in the field of physics, predicts the movements and planets and their gravitational interactions. In this paper, the efficient execution of heavy computational work through usage of different cores in CPU and GPU is looked into; achieved by integrating the OpenMP parallelization API and the Nvidia CUDA into the code. The paper also aims at performance analysis of various algorithms used to solve the same problem. This research not only aids as an alternative to complex simulations but also for bigger data that requires work distribution and computationally expensive procedures.