Free us department software for linux

PatientOS is a free clinical information management system designed for hospitals and healthcare practitioners.

PatientOS project is for physicians, nursing, pharmacy, the laboratory and eventually all hospital departments. An extensive sofware architecture has been designed to create the highest quality system.

Eventum provides a user-friendly and flexible issue/bug tracking system.

Eventum is a user-friendly and flexible issue tracking system that can be used by a support department to track incoming technical support requests or by a software development team to quickly organize tasks and bugs.

Eventum is used by the MySQL AB Technical Support team, and has allowed them to dramatically improve response times.

CyberTester is a Web-based application originally designed to allow teachers to create tests/exams/assignments (consisting of single-choice, multiple-choices or keywords-based questions) and assign them to students to conduct online tests. Students can login to take these tests and review their results.
CyberTester project is suitable to be integrated into online-education services or used as a standalone Web application for a school, college or university.
This latest version of CyberTester (CyberTester2) is implemented using Java Enterprise Edition (with EJB 3), running on JBoss as it application server and MySQL as the database. CyberTester relies on JBoss to manage persistence, relationships and security.
Main features:
- Highly configurable hierarchy tree to group tests, e.g. a hierarchy of Department-Class-Subject-Topic .
- Administrator tools to create/manage users/groups and assign tests to users/groups.
- Administrator tools to create/manage single-choice, multiple-choices and keywords-matching questions using a rich text editor.
- Administrator tools to create/manage tests that contain questions. A test may have a time-limit and a time window in which it can be taken.
- Student test-taking functionality that can be presented either one question at a time or multiple questions in a page.
- Student test results that can be optionally suppressed or be viewable once a test is completed.
- Charting of students performance (very rudimentary at this stage).
- Time limits on questions and/or tests.
- Support for images and external links (via rich text editor) in questions and answer choices.
- Anonymous users who can take the same test multiple times.

jSVR is a java implementation of a semi-automatic process for identifying and exporting three-dimensional information from a single un-calibrated image. The project is based on previous publications of A. Criminisi, A. Zisserman and others. svrIn the section "svr theory" there is a brief presentation of the principals behind single view reconstruction, using perspective information of an image.

jSVR was originally developed as my degree project in the computer science department of the university of Crete, in 2004. Most of the initial work was done in the CVRL lab of ics-forth, under the supervision of professors A. Argyros and M. Lourakis. Many documents in this site come from my report on svr, and where initially converted from latex using latex2html and then modified. If you find any broken links or other problems please let me know.

I recently decided to try and continue that work, as an open source project.

Single View reconstruction is a technique that can have various applications in different areas of interest. From acquiring metric information from low quality images, to reconstructing scenes from paintings. The process of reconstructing an image is semi-automated and can be really tricky for the user, who is requested to define and/or fine tune a set of difficult to understand parameters, (see the "svr theory" section for some examples).

Depending on the goal, the demands to the reconstruction technique differ. In the original work the goal was to test the proposed methodology in the relevant literature and to implement the techniques into an application that could reconstruct an image. The new goal of svr is to provide a user-friendly and intuitive way for reconstructing images, as well as to improve the existing implementation to produce better results. Determining the correct vanishing points with an automatic algorithm would be a great improvement to the whole process.

One step towards this direction would be an MLE estimation for the best intersection point. Also it is probable that suitable heuristics could be used to exclude intersections (false vanishing points) in areas that vanishing points are not expected (i.e. the center of the image). In order to exclude as many false VP detections as possible, appropriate filters could be applied on the image before the reconstruction (i.e. noise reduction, removing the radial distortion, and sharpen edges).

The phase of manual reconstruction could also be made semi-automatic by implementing object identification algorithms. This would help by automatically identifying the bigger structures in the image (i.e the vertical planes). In addition smaller structures can be interactively traced. This would also allow better texture extraction from the objects in the image.

ParaView project is an application designed with the need to visualize large data sets in mind. The goals of the ParaView project include the following:
- Develop an open-source, multi-platform visualization application.
- Support distributed computation models to process large data sets.
- Create an open, flexible, and intuitive user interface.
- Develop an extensible architecture based on open standards.
ParaView runs on distributed and shared memory parallel as well as single processor systems and has been succesfully tested on Windows, Linux and various Unix workstations and clusters. Under the hood, ParaView uses the Visualization Toolkit as the data processing and rendering engine and has a user interface written using a unique blend of Tcl/Tk and C++. Please go here for a detailed list of features.
ParaView was created by Kitware in conjunction with Jim Ahrens of the Advanced Computing Laboratory at Los Alamos National Laboratory (LANL). Contributors and developers of ParaView currently include: Kitware, LANL, Sandia National Laboratories, and Army Research Laboratory. ParaView is funded by the US Department of Energy ASCI Views program as part of a three-year contract awarded to Kitware, Inc. by a consortium of three National Labs - Los Alamos, Sandia, and Livermore. The goal of the project is to develop scalable parallel processing tools with an emphasis on distributed memory implementations. The project includes parallel algorithms, infrastructure, I/O, support, and display devices. One significant feature of the contract is that all software developed is to be delivered open source. Hence ParaView is available as an open-source system.
Main features:
- Handles structured (uniform rectilinear, non-uniform rectilinear, and curvilinear grids), unstructured, polygonal and image data.
- All processing operations (filters) produce datasets. This allows the user to either further process or save as a data file the result of every operation. For example, the user can extract a cut surface, reduce the number of points on this surface by masking, and apply glyphs (for example, vector arrows) to the result.
- Contours and isosurfaces can be extracted from all data types using scalars or vector components. The results can be colored by any other variable or processed further. When possible, structured data contours/isosurfaces are extracted with fast and efficient algorithms which make use of the special data layout.
- Vectors fields can be inspected by applying glyphs (arrows, cones, lines, spheres, and various 2D glyphs) to the points in a dataset. The glyphs can be scaled by scalars, vector component or vector magnitude and can be oriented using a vector field.
- A sub-region of a dataset can be extracted by cutting or clipping with an arbitrary plane (all data types), specifying a threshold criteria to exclude cells (all data types) and/or specifying a VOI (volume of interest - structured data types only)
- Streamlines can be generated using constant step or adaptive integrators. The results can be displayed as points, lines, tubes, ribbons, etc., and can be processed by a multitude of filters.
- The points in a dataset can be warped (displaced) with scalars (given a user defined displacement vector) or with vectors (unavailable for non-linear rectilinear grids).
- With the array calculator, new variables can be computed using existing point or cell field arrays. A multitude of scalar and vector operations are supported.
- Data can be probed at a point or along a line. The results are displayed either graphically or as text and can be exported for further analysis.
- ParaView provides many other data sources and filters by default (edge extraction, surface extraction, reflection, decimation, extrusion, smoothing...) and any VTK filter can be added by providing a simple XML description (VTK provides hundreds of sources and filters, see VTK documentation for a complete list).
Enhancements:
- This release adds parallel uniform rectilinear grid volume rendering (vtkImageData).
- It introduces new algorithms for parallel unstructured grid volume rendering.
- Support for hardware accelerated offscreen rendering using OpenGL framebuffers.
- Improved multi-block support.
- Improved AMR support.
- Animation saving with ffmpeg.
- Filters have been added for FLUENT, OpenFOAM, MFIX, LSDyna, and AcuSolve.
- A gradient filter for unstructured data.
- Many other enhancements and bugfixes.

This documentation from Linux Holdings will help you to prepair for the Linux LPI 102 exam.

Preparation guide LPI 102 is not a training manual but rather a summary of what you will need to know for this exam.

Linux Holdings is a linux and Open Source training company, training Linux Administration as the core focus.

These are not the only Open Source courses we offer, but the most popular.

Training info

Linux Holdings offer a full spectrum of Linux and open source courses. These start from basic low-level courses to the very advanced administration courses.

All our administration courses are based on the international LPI certification. Our trainers are the best available in South Africa. They are not just excellent Linux administrators with many years experience; they are also the best trainers in the industry. Good administrators are not necessarily good trainers; we give you the best of both sides.

Our entry-level and intermediate courses include the ICDL for OpenOffice.org. We have a full marketing and development department constantly developing and updating courses. Linux Holdings invests huge amounts of time and resources in development, testing and research to give the best training available today.

Our quality department ensures that all students are trained to the highest standard possible. It is also the quality departments duty to ensure that students start with the right course so the learning gradient is not to high or to low. Speak to us so that we can suggest the proper courses for your IT experience.

PyDespike is a program to graphically process (ie. despike) Raman and other spectroscopic data. Removing spikes in data due to artificial background fluctuations can be a tedious and time-consuming process, and since no other software with these requirements was known to exist, PyDespike was created to make Raman data processing more efficient.

PyDespike is the brain child of Michael Vance (Department of Chemistry, Stanford University) and much thanks is given to him for supporting the development of a scientifically useful project and providing valuable feedback to the graphical interface of PyDespike in its beta stages.

It was written using python and the python extensions to Qt and Qwt (PyQt and PyQwt), and thus, is cross-platform. PyDespike application has only been tested on Gentoo Linux and Windows XP, but there is no reason for it not to be supported on any platform with python, PyQwt, and PyQt.