Gallery — 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.

Template for use in the University of Iowa course MATH:3900 Introduction to Math Research.

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.

For an electron moving in a circular path in a magnetic field, if we know the magnetic field strength, accelerating voltage, and radius of the electron's trajectory, then we can make an estimation of the electron's charge to mass ratio. We calculated an average charge to mass ratio of \(2.08 \times 10^{11} \pm 1.81 \times 10^8\) Coulombs per kilogram.

Template for the the final project of the course.

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.

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

This paper implements Simultaneous Localization and Mapping (SLAM) technique to construct a map of a given environment. A Real Time Appearance Based Mapping (RTAB-Map) approach was taken for accomplishing this task. Initially, a 2d occupancy grid and 3d octomap was created from a provided simulated environment. Next, a personal simulated environment was created for mapping as well. In this appearance based method, a process called Loop Closure is used to determine whether a robot has seen a location before or not. In this paper, it is seen that RTAB-Map is optimized for large scale and long term SLAM by using multiple strategies to allow for loop closure to be done in real time and the results depict that it can be an excellent solution for SLAM to develop robots that can map an environment in both 2d and 3d.

The viscosity of a particular fluid is an interesting parameter that plays an important role in fluid dynamics of that fluid. We chose the common household cooking item canola oil. Using a ball drop, we set out to measure viscosity at various temperatures and create a model for the viscosity of canola oil as a function of temperature, as well as an accurate measurement for viscosity at room temperature. It was found that the viscosity between 0 and 40 degrees Celsius can be approximated using an exponential function and that an estimation for viscosity at room temperature was not very difficult to obtain. The precision of this measurement was limited by uncertainty in lab equipment used to measure various quantities as well as the image analysis software we used and the limited frame-rate of our camera.