Real-time traffic-based routing, based on open data and open-source software

2016 ◽  
Author(s):  
Diego Serrano ◽  
Teresa Baldassarre ◽  
Eleni Stroulia
Keyword(s):  
Open Source ◽  
Real Time ◽  
Open Source Software ◽  
Open Data ◽  
Real Time Traffic

scholarly journals Using Open Source, Open Data, and Civic Technology to Address the COVID-19 Pandemic and Infodemic

2021 ◽  
Author(s):  
Shinji Kobayashi ◽  
Luis Falcón ◽  
Hamish Fraser ◽  
Jørn Braa ◽  
Pamod Amarakoon ◽  
...  
Keyword(s):  
Open Source ◽  
Medical Informatics ◽  
Open Source Software ◽  
Collective Intelligence ◽  
Collaborative Work ◽  
Open Data ◽  
Theme Issue ◽  
Health Organizations ◽  
The World ◽  
Civic Technology

Objectives: The emerging COVID-19 pandemic has caused one of the world’s worst health disasters compounded by social confusion with misinformation, the so-called “Infodemic”. In this paper, we discuss how open technology approaches - including data sharing, visualization, and tooling - can address the COVID-19 pandemic and infodemic. Methods: In response to the call for participation in the 2020 International Medical Informatics Association (IMIA) Yearbook theme issue on Medical Informatics and the Pandemic, the IMIA Open Source Working Group surveyed recent works related to the use of Free/Libre/Open Source Software (FLOSS) for this pandemic. Results: FLOSS health care projects including GNU Health, OpenMRS, DHIS2, and others, have responded from the early phase of this pandemic. Data related to COVID-19 have been published from health organizations all over the world. Civic Technology, and the collaborative work of FLOSS and open data groups were considered to support collective intelligence on approaches to managing the pandemic. Conclusion: FLOSS and open data have been effectively used to contribute to managing the COVID-19 pandemic, and open approaches to collaboration can improve trust in data.

Prospects of Open Source Software for Maximizing the User Expectations in Heterogeneous Network

2021 ◽  
pp. 257-271
Author(s):  
Pushpa Singh ◽  
Rajeev Agrawal
Keyword(s):  
Machine Learning ◽  
Open Source ◽  
Real Time ◽  
Open Source Software ◽  
Heterogeneous Networks ◽  
Heterogeneous Network ◽  
Research Work ◽  
Software Tool ◽  
Knn Classifier ◽  
User Expectations

This article focuses on the prospects of open source software and tools for maximizing the user expectations in heterogeneous networks. The open source software Python is used as a software tool in this research work for implementing machine learning technique for the categorization of the types of user in a heterogeneous network (HN). The KNN classifier available in Python defines the type of user category in real time to predict the available users in a particular category for maximizing profit for a business organization.

scholarly journals Development of an All-Fiber Coherent Doppler Lidar in the IAO SB RAS

2020 ◽  
Vol 237 ◽  
pp. 06005
Author(s):  
Artem Sherstobitov ◽  
Viktor Banakh ◽  
Alexander Nadeev ◽  
Igor Razenkov ◽  
Igor Smalikho ◽  
...  
Keyword(s):  
Signal Processing ◽  
Radial Velocity ◽  
Open Source ◽  
Real Time ◽  
Open Source Software ◽  
Stream Line ◽  
Doppler Lidar ◽  
Hardware System ◽  
Coherent Doppler Lidar

Paper presents a model of the all-fiber pulsed coherent Doppler lidar (IAO-lidar) build in the IAO SB RAS. Here is described lidar design, the algorithm for processing of lidar signals and the software-hardware system that implements signal processing in real time, created with the use of open source software. The results of joint measurements of the radial velocity by the IAO-lidar and the HALO Photonics (Stream Line) lidar are given.

Embedded Real-Time Linux for Cable Robot Control

2002 ◽  
Author(s):  
Frederick M. Proctor ◽  
William P. Shackleford
Keyword(s):  
Open Source ◽  
Real Time ◽  
Open Source Software ◽  
Hard Disk Drives ◽  
Source Model ◽  
Disk Drives ◽  
Deterministic Scheduling ◽  
Cable Robot ◽  
Real Time Computing ◽  
Embedded Applications

Linux is a version of the Unix operating system distributed according to the open source model. Programmers are free to adapt the source code for their purposes, but are required to make their modifications or enhancements available as open source software as well. This model has fostered the widespread adoption of Linux for typical Unix server and workstation roles, and also in more arcane applications such as embedded or real-time computing. Embedded applications typically run in small physical and computing footprints, usually without fragile peripherals like hard disk drives. Special configurations are required to support these limited environments. Real-time applications require guarantees that tasks will execute within their deadlines, something not possible in general with the normal Linux scheduler. Real-time extensions to Linux enable deterministic scheduling, at task periods at tens of microseconds. This paper describes embedded and real-time Linux, and an application for distributed control of a Stewart Platform cable robot. Special Linux configuration requirements are detailed, and the architecture for teleoperated control of the cable robot is presented, with emphasis on the resolved-rate control of the suspended platform.

scholarly journals HARVESTING, INTEGRATING AND DISTRIBUTING LARGE OPEN GEOSPATIAL DATASETS USING FREE AND OPEN-SOURCE SOFTWARE

2016 ◽  
Vol XLI-B7 ◽  
pp. 939-940
Author(s):  
Ricardo Oliveira ◽  
Rafael Moreno
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Spatial Information ◽  
Open Data ◽  
Federal State ◽  
Data Sets ◽  
Data Set ◽  
Geospatial Datasets ◽  
State And Local ◽  
The City

Federal, State and Local government agencies in the USA are investing heavily on the dissemination of Open Data sets produced by each of them. The main driver behind this thrust is to increase agencies’ transparency and accountability, as well as to improve citizens’ awareness. However, not all Open Data sets are easy to access and integrate with other Open Data sets available even from the same agency. The City and County of Denver Open Data Portal distributes several types of geospatial datasets, one of them is the city parcels information containing 224,256 records. Although this data layer contains many pieces of information it is incomplete for some custom purposes. Open-Source Software were used to first collect data from diverse City of Denver Open Data sets, then upload them to a repository in the Cloud where they were processed using a PostgreSQL installation on the Cloud and Python scripts. Our method was able to extract non-spatial information from a ‘not-ready-to-download’ source that could then be combined with the initial data set to enhance its potential use.

scholarly journals HARVESTING, INTEGRATING AND DISTRIBUTING LARGE OPEN GEOSPATIAL DATASETS USING FREE AND OPEN-SOURCE SOFTWARE

2016 ◽  
Vol XLI-B7 ◽  
pp. 939-940 ◽  
Author(s):  
Ricardo Oliveira ◽  
Rafael Moreno
Keyword(s):  
Open Source ◽  
Open Source Software ◽  
Spatial Information ◽  
Open Data ◽  
Federal State ◽  
Data Sets ◽  
Data Set ◽  
Geospatial Datasets ◽  
State And Local ◽  
The City

Federal, State and Local government agencies in the USA are investing heavily on the dissemination of Open Data sets produced by each of them. The main driver behind this thrust is to increase agencies’ transparency and accountability, as well as to improve citizens’ awareness. However, not all Open Data sets are easy to access and integrate with other Open Data sets available even from the same agency. The City and County of Denver Open Data Portal distributes several types of geospatial datasets, one of them is the city parcels information containing 224,256 records. Although this data layer contains many pieces of information it is incomplete for some custom purposes. Open-Source Software were used to first collect data from diverse City of Denver Open Data sets, then upload them to a repository in the Cloud where they were processed using a PostgreSQL installation on the Cloud and Python scripts. Our method was able to extract non-spatial information from a ‘not-ready-to-download’ source that could then be combined with the initial data set to enhance its potential use.

scholarly journals A brief introduction to Open Data, Open Source Software and Collective Intelligence for environmental data creators and users

2018 ◽  
Author(s):  
Tomislav Hengl ◽  
Ichsani Wheeler ◽  
Robert A MacMillan
Keyword(s):  
Open Access ◽  
Open Source ◽  
Open Source Software ◽  
Collective Intelligence ◽  
Open Data ◽  
Open Science ◽  
Environmental Data ◽  
Key Aspects

Using the term "Open data" has become a bit of a fashion, but using it without clear specifications is misleading i.e. it can be considered just an empty phrase. Probably even worse is the term "Open Science" — can science be NOT open at all? Are we reinventing something that should be obvious from start? This guide tries to clarify some key aspects of Open Data, Open Source Software and Crowdsourcing using examples of projects and business. It aims at helping you understand and appreciate complexity of Open Data, Open Source software and Open Access publications. It was specifically written for producers and users of environmental data, however, the guide will likely be useful to any data producers and user.

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