Community based software development: The GRASS GIS projectMarkus Neteler
The document summarizes the GRASS GIS open source project. It discusses the project's objectives of developing free GIS software and algorithms. It describes the international development team and communication structures used, including mailing lists, wikis and bug trackers. Legal aspects of code contributions and licensing are also briefly covered.
GRASS GIS 7: your reliable geospatial number cruncherMarkus Neteler
GRASS GIS (Geographic Resources Analysis Support System) looks back to the longest development history in the FOSS4G community. Having been available for 30 years, a lot of innovation has been put into the new GRASS GIS 7 release. After six years of development it offers a lot of new functionality, e.g. enhanced vector network analysis, voxel processing, a completely new engine for massive time series management, an animation tool for raster and vector map time series, a new graphic image classification tool, a "map swiper" for interactive maps comparison, and major improvements for massive data analysis (see also http://grass.osgeo.org/grass7/). The development was driven by the rapidly increasing demand for robust and modern free analysis tools, especially in terms of massive spatial data processing and processing on high-performance computing systems. With respect to GRASS GIS 6.4 more than 10,000 source code changes have since been made.
Markus Neteler presented on GRASS GIS and Sextante at the Quarte Giornate Italiane di gvSIG conference in Udine, Italy. He discussed how Sextante integrates algorithms from GRASS GIS and other sources and can be used as an extension in open source Java GIS applications. He also provided an overview of GRASS GIS functionality for geospatial analysis, modeling, and visualization and demonstrated some examples including lidar data analysis, viewshed modeling, and reconstructing land surface temperature (LST) from satellite data.
News in GRASS GIS7. Plenary talk at FOSS4G-CEE 2013, RomaniaMarkus Neteler
GRASS GIS, commonly referred to as GRASS (Geographic Resources Analysis Support System), is the free Geographic Information System (GIS) software with the longest record of development as FOSS4G community project. The increasing demand for a robust and modern analytical free GIS led to the start of GRASS GIS 7 development in April 2008. Since GRASS 6 more than 10,000 changes have been implemented with a series of new modules for vector network analysis, image processing, voxel analysis, time series management and improved graphical user interface (http://trac.osgeo.org/grass/wiki/Grass7/NewFeatures). The core system offers a new Python API and large file support for massive data analysis. Many modules have been undergone major optimization also in terms of speed. The presentation will highlight the advantages for users to migrate to the upcoming GRASS GIS 7 release.
Vom Laptop zum Großrechner: Neues in GRASS GIS 7Markus Neteler
GRASS GIS 7 bietet neue Module zur Vektornetzwerk-, Voxelanalyse, Zeitreihenspeicherung und -management, dazu ein Animationstool für Raster-und Vektorkartenzeitreihen, ein graphisches Bildklassifikationtool, "Map Swiper" zum interaktiven Kartenvergleich nebst verbesserter massiver Datenanalyse.
GRASS GIS (Geographic Ressourcen Analysis Support System) blickt mit nun 30 Jahren auf die längste Entwicklungsgeschichte in der FOSSGIS Community zurück. Die stark ansteigende Nachfrage nach robusten und modernen freien Analysewerkzeugen, v.a. im Hinblick auf die heutzutage enormen räumlichen Datenmengen führte 2008 zum Beginn der GRASS GIS 7 Entwicklung. In Bezug auf GRASS GIS 6.4 wurden inzwischen mehr als 10.000 Verbesserungen vorgenommen.
Die Entwicklercommunity hat eine Reihe von neuen Modulen für Vektornetzwerkanalyse, Bildverarbeitung, Voxelanalyse, Zeitreihenspeicherung (Raster, Vektor, Voxel) und eine verbesserte grafische Benutzeroberfläche integriert (http://trac.osgeo.org/grass/wiki/Grass7/NewFeatures). GRASS GIS 7 bietet eine neue Python Schnittstelle, die auf einfache Weise ermöglicht, neue Anwendungen zu erstellen, die leistungsfähig und effizient sind. In der Benutzeroberfläche gibt es nun ein neues Werkzeug für die Animation von Raster-und Vektorkartenzeitreihen, einen verbesserten Georektifier, ein neues Werkzeug zur überwachten Bildklassifikation, einen "map swiper" zum interaktiven Vergleich zweier Karten (z.B. für Katastrophen) und ein visuelles Zeitreihenmanagement.
Darüber hinaus wurde insbesondere die topologische Vektorbibliothek in Bezug auf die Unterstützung von großen Dateien verbessert. Des weiteren gibt es eine Reihe von neuen Analysefunktionen und auch im Raster-/Bildbereich die Unterstützung für massive Datenanalyse. Auch werden nun Projektionen andere Planeten unterstützt. Viele Module wurden in Bezug auf Geschwindigkeit signifikant optimiert. Der Vortrag illustriert die interessantesten Neuerungen und zeigt, wie Benutzer auf einfache Weise auf die kommende GRASS GIS 7 Version migrieren können. Testversionen stehen für alle üblichen Betriebssysteme zur Verfügung (http://grass.osgeo.org/download/software/).
Community based software development: The GRASS GIS projectMarkus Neteler
The document summarizes the GRASS GIS open source project. It discusses the project's objectives of developing free GIS software and algorithms. It describes the international development team and communication structures used, including mailing lists, wikis and bug trackers. Legal aspects of code contributions and licensing are also briefly covered.
GRASS GIS 7: your reliable geospatial number cruncherMarkus Neteler
GRASS GIS (Geographic Resources Analysis Support System) looks back to the longest development history in the FOSS4G community. Having been available for 30 years, a lot of innovation has been put into the new GRASS GIS 7 release. After six years of development it offers a lot of new functionality, e.g. enhanced vector network analysis, voxel processing, a completely new engine for massive time series management, an animation tool for raster and vector map time series, a new graphic image classification tool, a "map swiper" for interactive maps comparison, and major improvements for massive data analysis (see also http://grass.osgeo.org/grass7/). The development was driven by the rapidly increasing demand for robust and modern free analysis tools, especially in terms of massive spatial data processing and processing on high-performance computing systems. With respect to GRASS GIS 6.4 more than 10,000 source code changes have since been made.
Markus Neteler presented on GRASS GIS and Sextante at the Quarte Giornate Italiane di gvSIG conference in Udine, Italy. He discussed how Sextante integrates algorithms from GRASS GIS and other sources and can be used as an extension in open source Java GIS applications. He also provided an overview of GRASS GIS functionality for geospatial analysis, modeling, and visualization and demonstrated some examples including lidar data analysis, viewshed modeling, and reconstructing land surface temperature (LST) from satellite data.
News in GRASS GIS7. Plenary talk at FOSS4G-CEE 2013, RomaniaMarkus Neteler
GRASS GIS, commonly referred to as GRASS (Geographic Resources Analysis Support System), is the free Geographic Information System (GIS) software with the longest record of development as FOSS4G community project. The increasing demand for a robust and modern analytical free GIS led to the start of GRASS GIS 7 development in April 2008. Since GRASS 6 more than 10,000 changes have been implemented with a series of new modules for vector network analysis, image processing, voxel analysis, time series management and improved graphical user interface (http://trac.osgeo.org/grass/wiki/Grass7/NewFeatures). The core system offers a new Python API and large file support for massive data analysis. Many modules have been undergone major optimization also in terms of speed. The presentation will highlight the advantages for users to migrate to the upcoming GRASS GIS 7 release.
Vom Laptop zum Großrechner: Neues in GRASS GIS 7Markus Neteler
GRASS GIS 7 bietet neue Module zur Vektornetzwerk-, Voxelanalyse, Zeitreihenspeicherung und -management, dazu ein Animationstool für Raster-und Vektorkartenzeitreihen, ein graphisches Bildklassifikationtool, "Map Swiper" zum interaktiven Kartenvergleich nebst verbesserter massiver Datenanalyse.
GRASS GIS (Geographic Ressourcen Analysis Support System) blickt mit nun 30 Jahren auf die längste Entwicklungsgeschichte in der FOSSGIS Community zurück. Die stark ansteigende Nachfrage nach robusten und modernen freien Analysewerkzeugen, v.a. im Hinblick auf die heutzutage enormen räumlichen Datenmengen führte 2008 zum Beginn der GRASS GIS 7 Entwicklung. In Bezug auf GRASS GIS 6.4 wurden inzwischen mehr als 10.000 Verbesserungen vorgenommen.
Die Entwicklercommunity hat eine Reihe von neuen Modulen für Vektornetzwerkanalyse, Bildverarbeitung, Voxelanalyse, Zeitreihenspeicherung (Raster, Vektor, Voxel) und eine verbesserte grafische Benutzeroberfläche integriert (http://trac.osgeo.org/grass/wiki/Grass7/NewFeatures). GRASS GIS 7 bietet eine neue Python Schnittstelle, die auf einfache Weise ermöglicht, neue Anwendungen zu erstellen, die leistungsfähig und effizient sind. In der Benutzeroberfläche gibt es nun ein neues Werkzeug für die Animation von Raster-und Vektorkartenzeitreihen, einen verbesserten Georektifier, ein neues Werkzeug zur überwachten Bildklassifikation, einen "map swiper" zum interaktiven Vergleich zweier Karten (z.B. für Katastrophen) und ein visuelles Zeitreihenmanagement.
Darüber hinaus wurde insbesondere die topologische Vektorbibliothek in Bezug auf die Unterstützung von großen Dateien verbessert. Des weiteren gibt es eine Reihe von neuen Analysefunktionen und auch im Raster-/Bildbereich die Unterstützung für massive Datenanalyse. Auch werden nun Projektionen andere Planeten unterstützt. Viele Module wurden in Bezug auf Geschwindigkeit signifikant optimiert. Der Vortrag illustriert die interessantesten Neuerungen und zeigt, wie Benutzer auf einfache Weise auf die kommende GRASS GIS 7 Version migrieren können. Testversionen stehen für alle üblichen Betriebssysteme zur Verfügung (http://grass.osgeo.org/download/software/).
GRASS GIS 7 capabilities: a graphical overviewMarkus Neteler
The Geographic Resources Analysis Support System (http://grass.osgeo.org/), commonly referred to as GRASS GIS, is an Open Source Geographic Information System providing powerful raster, vector and geospatial processing capabilities in a single integrated software suite. GRASS GIS includes tools for spatial modeling, visualization of raster and vector data, management and analysis of geospatial data, and the processing of satellite and aerial imagery. It also provides the capability to produce sophisticated presentation graphics and hardcopy maps. GRASS GIS has been translated into about twenty languages and supports a huge array of data formats. It can be used either as a stand-alone application or as backend for other software packages such as QGIS and R geostatistics. It is distributed freely under the terms of the GNU General Public License (GPL). GRASS GIS is a founding member of the Open Source Geospatial Foundation (OSGeo).
After that many years of development of mobile applications for working in the field, HydroloGIS recently release the new app to support all kind of outdoor activities, SMASH – Smart Mobile Application for Surveyor’s Happiness. SMASH (https://www.geopaparazzi.org/smash/index.html). SMASH is a user-friendly, modern, faster to develop and cross platform app for the eyes of IOS, Android, but also Macos and Linux users. The main functionalities are the possibility to collect georeferenced pictures, notes and GPS tracks logs.
It opens directly on the map view where it is possible to navigate local or online raster and vector data in different formats: Geopackage, shapefile, tiff+png+jpg with wtf, MBTiles, Mapsforge and GPX, all with a custom CRS and the support for standard SLD style definition. Online supported formats are WMS and TMS and there is a nice service catalog and wizard for adding most common WMS/TMS. Geopackage and PostGIS layers also have editing support.
SMASH simplifies the data collection and the export of the survey into easy processable data files (GPX, PDF) or through a direct synchronization with the GSS (Geopaparazzi Survey Server). The Survey Server has been redesigned with the same technology used by SMASH and has now the ability to visualize data in the same look and feel as the mobile app.
Notes serverside-versioning has been introduced to enhance synchronization of data by teams. A redmine plugin is being developed by community members to create a geo-ticketing system.
This presentation gives an insight about the state of the art of the SMASH focusing on the possible applications and customization.
The latest version of Android, Nougat (v7.x), was released in late 2016, and offers access to raw GNSS measurements. Google demonstrated functionality to obtain Pseudoranges, Dopplers and Carrier Phase from a phone or tablet last September at the Institute of Navigation’s conference.
This seminar will explain the current API, hardware limitations and possible future advantages of this exciting new development. We will discuss data collection, raw measurements and demo processing example in Matlab and Python.
ePOM - Fundamentals of Research Software Development - Code Version ControlGiuseppe Masetti
E-learning Python for Ocean Mapping (ePOM) project.
Complementary slides to the "Code Version Control" module (part of the Fundamentals of Research Software Development training).
More details at https://www.hydroffice.org/epom
This document discusses Android's new GNSS API in version 24+, which provides raw GNSS data and enables more accurate positioning than previous APIs. It notes that while Google has released MATLAB code to work with this raw data, a Python library does not yet exist. The goal is to create an open-source Python library that abstracts calculations and can be used across various domains like data science, development, and research to improve positioning in areas like urban environments.
This document provides an overview and agenda for a workshop on the RINA simulator (RINASim). It discusses the requirements, installation instructions, file navigation structure, documentation, and contact information for the simulator. It also outlines the key components in RINASim's design including application processes, IPC resource managers, and DIF allocators. Finally, it indicates a live demonstration of a simple relay example will be shown.
- A prototype was created using sensors from a Grove IoT kit to monitor temperature and door status of a refrigerated truck trailer. This demonstrated an IoT solution for connected transportation.
- The prototype was then scaled up to an industrial-grade solution using an Intel IoT gateway, industrial sensors, and Intel System Studio software. This new solution could be deployed as a commercial product.
- The prototype solution involved dividing the work into user interface development, application logic, and sensor integration to create a proof of concept that monitored temperature and alerts when thresholds were exceeded or doors opened unexpectedly.
IRJET- IOT Dune Buggy –Control it from AnywhereIRJET Journal
This document describes an IOT dune buggy robot that can be controlled over the internet from anywhere in the world. The robot uses an Arduino microcontroller along with a WiFi module to connect to cloud services and receive commands sent through a mobile app. The objective is to reduce human efforts by allowing remote control and surveillance through the robot. The robot's architecture connects the Arduino, WiFi module and sensors to the cloud where commands are sent from an Android app to control the robot's movement and stream data from its sensors.
IRJET- IOT Dune Buggy –Control it from AnywhereIRJET Journal
This document describes an IOT dune buggy robot that can be controlled over the internet from anywhere in the world. The robot uses an Arduino microcontroller connected to a WiFi module to receive commands through a cloud service and move accordingly. An Android app is designed to allow the user to control the robot's movements and view its surroundings through a camera. The objectives are to reduce human efforts and time by allowing remote surveillance and control of the robot. Future applications could include using the robot for border surveillance or bomb disposal tasks in the military.
A talk about the OSGeo Live project; covering 43 projects that are available in a live DVD format (for you to run without installing). The project is much improved with OGC documentation and a description of many of the projects. New this year (thanks to some sponsorship) is quickstarts for several of the projects.
MLOps implemented - how we combine the cloud & open-source to boost data scie...GetInData
Check out more about this presentation here: https://www.youtube.com/watch?v=nSsssYHiylQ&t=17s
Presentation from the performance given by our team during the NSML Summit.
Authors: Krzysztof Zarzycki, Marek Wiewiórka
Linkedin: https://www.linkedin.com/in/kzarzycki/
https://www.linkedin.com/in/marekwiewiorka/
___
Getindata is a company founded in 2014 by ex-Spotify data engineers. From day one our focus has been on Big Data projects. We bring together a group of best and most experienced experts in Poland, working with cloud and open-source Big Data technologies to help companies build scalable data architectures and implement advanced analytics over large data sets.
Our experts have vast production experience in implementing Big Data projects for Polish as well as foreign companies including i.a. Spotify, Play, Truecaller, Kcell, Acast, Allegro, ING, Agora, Synerise, StepStone, iZettle and many others from the pharmaceutical, media, finance and FMCG industries.
https://getindata.com
Merging Realities
Using the Web to Bring the Internet of Things to High End Augmented Reality
FullStack Conference
#FullStackCon, London UK <2019-07-10>
Philippe Coval
Fabien Benetou
URL: http://purl.org/aframe-webthing
WAM: An embedded web runtime history for LG webOS and Automotive Grade LinuxIgalia
WAM is a web runtime manager that was created for webOS and has been used in LG smart TVs and other products since 2013. It is based on Chromium and manages the launching and lifecycle of web applications. Some benefits of WAM include its support for web standards, efficient use of resources, security features, and developer tools. While originally dependent on Qt, it no longer requires Qt. After starting at HP/Palm, WAM transitioned to LG and also became part of the Automotive Grade Linux project. It has proven stable over 10 years while adapting to changes in dependencies and products.
In recent years there has been increased interest in systems with a free license. In the field of 3D design a powerful system with a free license is the Blender. With its small volume, not any serious high demands on hardware systems, combined with many advantages and innovations Blender 3D graphics system
is gaining more followers. This trend bodes fast nonlinear positive progression, which gives a reason the use of the system open source Blender in design practice to be considered appropriate and correct.
Javascript as a target language - GWT kickoff - part1/2JooinK
This document summarizes a presentation about Google Web Toolkit (GWT) given by Alberto Mancini and Francesca Tosi. It discusses what GWT is, provides statistics on its usage and popularity, explores why developers use GWT and its benefits, and gives examples of using GWT with computer vision libraries to enable augmented reality applications in the browser.
A significant proportion of developments in the Internet of Things (IoT) is driven by non-technical innovators and ambitious hobbyists. Node-RED targets this audience and offers a widely used rapid prototyping platform for IoT data plumbing on the basis of JavaScript. Data platforms for the IoT provide storage facilities and value in the form of visualisation & analytics to business and end users alike. This report details how Node-RED connects to 11 different platforms and what additional services these provide.
FogLAMP is an open source platform for IoT that simplifies collecting, processing, and distributing IoT data. It uses a modular microservices architecture to collect sensor data, store it, process it, and forward it to historians, enterprise systems, and cloud services. FogLAMP is written in C/C++ and Python and can run on various operating systems and hardware, from small microcontrollers to larger servers. It aims to provide a standardized way to manage IoT data and address challenges like security, lack of skills, and upfront costs through its open source approach.
https://social.samsunginter.net/web/statuses/101091908485239453# #Cdl2018 : #WebThing using #WebThingIotJs on #TizenRT on #ARTIK05x connected to @MozillaIot featuring @The_Jst #JerryScript + #IotJs , video to be published by @CapitoleDuLibre
webthing-iotjs-tizenrt-cdl2018-20181117rzr
Philippe Coval from Samsung Open Source Group gave a presentation on using JavaScript for IoT applications. He discussed how JavaScript is well-suited for IoT due to its widespread use among web developers. He demonstrated how to set up the IoT.js runtime environment and write simple JavaScript programs to access sensors, communicate over HTTP, and interface with IoT platforms and standards. He also proposed challenges for a hackathon around connecting vehicles to nearby devices using web technologies.
GRASS GIS 7 capabilities: a graphical overviewMarkus Neteler
The Geographic Resources Analysis Support System (http://grass.osgeo.org/), commonly referred to as GRASS GIS, is an Open Source Geographic Information System providing powerful raster, vector and geospatial processing capabilities in a single integrated software suite. GRASS GIS includes tools for spatial modeling, visualization of raster and vector data, management and analysis of geospatial data, and the processing of satellite and aerial imagery. It also provides the capability to produce sophisticated presentation graphics and hardcopy maps. GRASS GIS has been translated into about twenty languages and supports a huge array of data formats. It can be used either as a stand-alone application or as backend for other software packages such as QGIS and R geostatistics. It is distributed freely under the terms of the GNU General Public License (GPL). GRASS GIS is a founding member of the Open Source Geospatial Foundation (OSGeo).
After that many years of development of mobile applications for working in the field, HydroloGIS recently release the new app to support all kind of outdoor activities, SMASH – Smart Mobile Application for Surveyor’s Happiness. SMASH (https://www.geopaparazzi.org/smash/index.html). SMASH is a user-friendly, modern, faster to develop and cross platform app for the eyes of IOS, Android, but also Macos and Linux users. The main functionalities are the possibility to collect georeferenced pictures, notes and GPS tracks logs.
It opens directly on the map view where it is possible to navigate local or online raster and vector data in different formats: Geopackage, shapefile, tiff+png+jpg with wtf, MBTiles, Mapsforge and GPX, all with a custom CRS and the support for standard SLD style definition. Online supported formats are WMS and TMS and there is a nice service catalog and wizard for adding most common WMS/TMS. Geopackage and PostGIS layers also have editing support.
SMASH simplifies the data collection and the export of the survey into easy processable data files (GPX, PDF) or through a direct synchronization with the GSS (Geopaparazzi Survey Server). The Survey Server has been redesigned with the same technology used by SMASH and has now the ability to visualize data in the same look and feel as the mobile app.
Notes serverside-versioning has been introduced to enhance synchronization of data by teams. A redmine plugin is being developed by community members to create a geo-ticketing system.
This presentation gives an insight about the state of the art of the SMASH focusing on the possible applications and customization.
The latest version of Android, Nougat (v7.x), was released in late 2016, and offers access to raw GNSS measurements. Google demonstrated functionality to obtain Pseudoranges, Dopplers and Carrier Phase from a phone or tablet last September at the Institute of Navigation’s conference.
This seminar will explain the current API, hardware limitations and possible future advantages of this exciting new development. We will discuss data collection, raw measurements and demo processing example in Matlab and Python.
ePOM - Fundamentals of Research Software Development - Code Version ControlGiuseppe Masetti
E-learning Python for Ocean Mapping (ePOM) project.
Complementary slides to the "Code Version Control" module (part of the Fundamentals of Research Software Development training).
More details at https://www.hydroffice.org/epom
This document discusses Android's new GNSS API in version 24+, which provides raw GNSS data and enables more accurate positioning than previous APIs. It notes that while Google has released MATLAB code to work with this raw data, a Python library does not yet exist. The goal is to create an open-source Python library that abstracts calculations and can be used across various domains like data science, development, and research to improve positioning in areas like urban environments.
This document provides an overview and agenda for a workshop on the RINA simulator (RINASim). It discusses the requirements, installation instructions, file navigation structure, documentation, and contact information for the simulator. It also outlines the key components in RINASim's design including application processes, IPC resource managers, and DIF allocators. Finally, it indicates a live demonstration of a simple relay example will be shown.
- A prototype was created using sensors from a Grove IoT kit to monitor temperature and door status of a refrigerated truck trailer. This demonstrated an IoT solution for connected transportation.
- The prototype was then scaled up to an industrial-grade solution using an Intel IoT gateway, industrial sensors, and Intel System Studio software. This new solution could be deployed as a commercial product.
- The prototype solution involved dividing the work into user interface development, application logic, and sensor integration to create a proof of concept that monitored temperature and alerts when thresholds were exceeded or doors opened unexpectedly.
IRJET- IOT Dune Buggy –Control it from AnywhereIRJET Journal
This document describes an IOT dune buggy robot that can be controlled over the internet from anywhere in the world. The robot uses an Arduino microcontroller along with a WiFi module to connect to cloud services and receive commands sent through a mobile app. The objective is to reduce human efforts by allowing remote control and surveillance through the robot. The robot's architecture connects the Arduino, WiFi module and sensors to the cloud where commands are sent from an Android app to control the robot's movement and stream data from its sensors.
IRJET- IOT Dune Buggy –Control it from AnywhereIRJET Journal
This document describes an IOT dune buggy robot that can be controlled over the internet from anywhere in the world. The robot uses an Arduino microcontroller connected to a WiFi module to receive commands through a cloud service and move accordingly. An Android app is designed to allow the user to control the robot's movements and view its surroundings through a camera. The objectives are to reduce human efforts and time by allowing remote surveillance and control of the robot. Future applications could include using the robot for border surveillance or bomb disposal tasks in the military.
A talk about the OSGeo Live project; covering 43 projects that are available in a live DVD format (for you to run without installing). The project is much improved with OGC documentation and a description of many of the projects. New this year (thanks to some sponsorship) is quickstarts for several of the projects.
MLOps implemented - how we combine the cloud & open-source to boost data scie...GetInData
Check out more about this presentation here: https://www.youtube.com/watch?v=nSsssYHiylQ&t=17s
Presentation from the performance given by our team during the NSML Summit.
Authors: Krzysztof Zarzycki, Marek Wiewiórka
Linkedin: https://www.linkedin.com/in/kzarzycki/
https://www.linkedin.com/in/marekwiewiorka/
___
Getindata is a company founded in 2014 by ex-Spotify data engineers. From day one our focus has been on Big Data projects. We bring together a group of best and most experienced experts in Poland, working with cloud and open-source Big Data technologies to help companies build scalable data architectures and implement advanced analytics over large data sets.
Our experts have vast production experience in implementing Big Data projects for Polish as well as foreign companies including i.a. Spotify, Play, Truecaller, Kcell, Acast, Allegro, ING, Agora, Synerise, StepStone, iZettle and many others from the pharmaceutical, media, finance and FMCG industries.
https://getindata.com
Merging Realities
Using the Web to Bring the Internet of Things to High End Augmented Reality
FullStack Conference
#FullStackCon, London UK <2019-07-10>
Philippe Coval
Fabien Benetou
URL: http://purl.org/aframe-webthing
WAM: An embedded web runtime history for LG webOS and Automotive Grade LinuxIgalia
WAM is a web runtime manager that was created for webOS and has been used in LG smart TVs and other products since 2013. It is based on Chromium and manages the launching and lifecycle of web applications. Some benefits of WAM include its support for web standards, efficient use of resources, security features, and developer tools. While originally dependent on Qt, it no longer requires Qt. After starting at HP/Palm, WAM transitioned to LG and also became part of the Automotive Grade Linux project. It has proven stable over 10 years while adapting to changes in dependencies and products.
In recent years there has been increased interest in systems with a free license. In the field of 3D design a powerful system with a free license is the Blender. With its small volume, not any serious high demands on hardware systems, combined with many advantages and innovations Blender 3D graphics system
is gaining more followers. This trend bodes fast nonlinear positive progression, which gives a reason the use of the system open source Blender in design practice to be considered appropriate and correct.
Javascript as a target language - GWT kickoff - part1/2JooinK
This document summarizes a presentation about Google Web Toolkit (GWT) given by Alberto Mancini and Francesca Tosi. It discusses what GWT is, provides statistics on its usage and popularity, explores why developers use GWT and its benefits, and gives examples of using GWT with computer vision libraries to enable augmented reality applications in the browser.
A significant proportion of developments in the Internet of Things (IoT) is driven by non-technical innovators and ambitious hobbyists. Node-RED targets this audience and offers a widely used rapid prototyping platform for IoT data plumbing on the basis of JavaScript. Data platforms for the IoT provide storage facilities and value in the form of visualisation & analytics to business and end users alike. This report details how Node-RED connects to 11 different platforms and what additional services these provide.
FogLAMP is an open source platform for IoT that simplifies collecting, processing, and distributing IoT data. It uses a modular microservices architecture to collect sensor data, store it, process it, and forward it to historians, enterprise systems, and cloud services. FogLAMP is written in C/C++ and Python and can run on various operating systems and hardware, from small microcontrollers to larger servers. It aims to provide a standardized way to manage IoT data and address challenges like security, lack of skills, and upfront costs through its open source approach.
https://social.samsunginter.net/web/statuses/101091908485239453# #Cdl2018 : #WebThing using #WebThingIotJs on #TizenRT on #ARTIK05x connected to @MozillaIot featuring @The_Jst #JerryScript + #IotJs , video to be published by @CapitoleDuLibre
webthing-iotjs-tizenrt-cdl2018-20181117rzr
Philippe Coval from Samsung Open Source Group gave a presentation on using JavaScript for IoT applications. He discussed how JavaScript is well-suited for IoT due to its widespread use among web developers. He demonstrated how to set up the IoT.js runtime environment and write simple JavaScript programs to access sensors, communicate over HTTP, and interface with IoT platforms and standards. He also proposed challenges for a hackathon around connecting vehicles to nearby devices using web technologies.
It's Not Continuous Delivery If You Can't Deploy Right NowKen Mugrage
This document contains notes from a presentation on continuous delivery. It discusses the importance of continuous integration and deployment practices, including deploying incomplete work using techniques like feature toggles. It also covers managing risk through practices like canary releases and dark launching. Continuous delivery is defined as the ability to deploy any changes, from features to bug fixes, to production or users quickly and sustainably. Continuous integration is described as a prerequisite, with notes on ensuring code is integrated at least daily and that broken builds are fixed quickly.
Summit 16: NetIDE: Integrating and Orchestrating SDN ControllersOPNFV
NetIDE is a EU-funded project that is known to the OpenDaylight community, because we have contributed a component to the Beryllium release. However, the full NetIDE ecosystem is much more. It is an extended SDN controller framework that allows users to cherry-pick the best of breed both for the network facing controller and the SDN framework for applications. In addition it provides an application composition engine that allows network operators to introduce software development concepts like code reusability in their production cycle. In this talk, I will introduce the whole Network Engine as well as the NetIDE Eclipse plugin that allows us to create SDN applications, test them and run them on the NetIDE engine and reflect on why we contributed what we contributed to OpenDaylight.
Build "Privacy by design" Webthings
With IoT.js on TizenRT and more
#MozFest, Privacy and Security track
Ravensbourne University, London UK <2018-10-27>
This document summarizes the new features in FME 2020.0, including workbench improvements, new connectors and formats, enhanced Revit support, an automations writer, computer vision capabilities via PicterraConnector, and the introduction of FME augmented reality. Key highlights are improved performance, scalability, and support for spatial and enterprise data. The next webinar will focus on automating workflows with FME 2020.0.
Tracking emerging diseases from space: Geoinformatics for human healthMarkus Neteler
European and other countries are at increasing risk for new or re-emerging vector-borne diseases. Among the top ten vector-borne diseases with greatest potential to affect European citizens are Dengue fever, Chikungunya, Hantavirus, and Crimean-Congo hemorrhagic fever. Despite the risk of disease transmission, many vectors like the Asian tiger mosquito or ticks are also a nuisance in daily life. The examination of disease vector spread and a better understanding of spatio-temporal patterns in disease transmission and diffusion is greatly facilitated by Geoinformatics. New methods including the use of high resolution time series from space in spatial models enable us to predict species invasion and survival, and to assess potential health risks. Geoinformatics is able to address the increasing challenge for human and veterinary public health not only in Europe, but across the globe, assisting decision makers and public health authorities to develop surveillance plans and vector control.
Deriving environmental indicators from massive spatial time series using open...Markus Neteler
Geospatial Analytics Forum at North Carolina State University, 4 Sept 2014 - http://geospatial.ncsu.edu/about/geoforum/
See also: http://opensource.com/education/14/9/back-school-grass-gis
This document discusses the history and capabilities of GRASS GIS, an open source geospatial software suite. GRASS was first developed in 1984 and continues to be improved through contributions from its development team and user community. It provides tools for geospatial data management, spatial modeling, map production, spatial analysis, and visualization. GRASS can process large LIDAR datasets, connect to external data sources, perform 3D modeling and visualization, and is increasingly integrated with web-based tools and programming interfaces like Python.
From a niche to a global user community: Open Source GIS and OSGeoMarkus Neteler
OGRS 2009: International Opensource Geospatial Research Symposium
www.ogrs2009.org
From a niche to a global user community: Open Source GIS and OSGeo
Markus Neteler
IASMA Research and Innovation Centre
Fondazione Edmund Mach
Environment and Natural Resources Area
GIS and Remote Sensing Unit, Trento, Italy
Web: http://gis.fem-environment.eu/
Email: markus.neteler . iasma.it
Geographical Information Systems (GIS) have evolved from a highly specialized niche to a technology that affects nearly every aspect of our lives, from finding driving directions to managing natural disasters. The masses have discovered geospatial data and technologies through the availability of popular globes; wiki-fied street mapping which was started by a few individuals has grown to weekly mapping parties around the globe. Today almost everybody can create customized maps or overlay GIS data. Current GIS technology covers viewing maps and images on the web, simple and complex spatial analysis, modeling and simulations.
In our presentation we'll present highlights of the last 20 years of Open Source GIS developments. Many projects are born as initiative of individuals when the lack of available software for a specific application is solved by own development and the result is then made available to the public on the Internet for further collaborative development. In the early 80's, the first Open Source GIS (MOSS and GRASS GIS) reached production status followed by the PROJ4 library project, a first crucial library for many Open Source GIS applications. In 1995 the UMN MapServer project was started to implement OGC standard. The second cross-project library GDAL/OGR was born in 1998. While these projects became mature, new applications were started with partially extraordinary success (OpenEV, OSSIM, MapBuilder, PostGIS, Geoserver, Quantum GIS, uDIG, MapGuide Open Source, MapBender, gvSIG, Geonetwork and OpenLayers).
The wealth of available but partially unconnected projects suggested to establish an umbrella foundation to foster source code and knowledge sharing. Hence, in February 2006, the Open Source Geospatial Foundation (OSGeo, www.osgeo.org) has been created to support and promote worldwide use and collaborative development of Open Source geospatial technologies and data. The foundation supports outreach and advocacy activities to promote Open Source concepts. It also builds shared infrastructure for improved cross-project collaboration. OSGeo has been a stimulating force for cooperative developments of sister projects, leveraging each other efforts by developing shared architecture components and expanding interoperability.
To become an OSGeo member, the software project needs to undergo a rigorous review of its source code, development structure and community health. In these community-developed projects a whole “ecosystem” of users, translators, developers, and provides quick support and tested solutions, both for beginners and professionals.
In our opinion, Open Source GIS is an appropriate choice for scientific computing as it is developed in a peer review process. We will show some case studies for GRASS GIS usage in research which illustrates its academic roots especially in environmental applications. This covers analysis of spatio-temporal data sets such as multi-temporal Lidar and remote sensing data including processing of large amounts of geospatial data on a cluster.
GRASS and OSGeo: a framework for archeologyMarkus Neteler
Use of GIS and geospatial data in archeology. Contribution to:
Quarto Workshop Italiano "Open Source, Free Software e Open Format nei processi di ricerca archeologica", Roma, 27 e 28 aprile 2009. Sede centrale del Consiglio Nazionale delle Ricerche (CNR)
http://www.archeo-foss.org/
Abstract:
With the widespread availability of desktop GIS, archaeologists have gained the tools to comprehensively analyze the important spatial component of their data. Initial archaeological use of GIS was (and still is in many instances) for making maps of archaeological sites. Rather quickly GIS became used for predictive modeling of site locations. More recently, viewshed analysis has seen increasing use, in efforts to understand prehistoric perceptions of the landscape.
In the last years, Open Source GIS software evolved to a powerful set of software products which support both scientific as well as common GIS users. In particular, the integration of GIS with image processing capabilities, geospatial data analysis, database management system and Web mapping software enables archaeologists to perform their tasks in a completely free environment. Since 2006, the Open Source Geospatial Foundation (OSGeo) operates as umbrella foundation for Web Mapping, Desktop GIS Applications, Geospatial Libraries, Metadata Catalog as well as the Public Geospatial Data project and the Education and Curriculum project.
In our presentation, we focus on GRASS GIS (http://grass.osgeo.org/) for spatial data analysis and visualization. GRASS is the largest Open Source GIS program currently available. The new version GRASS 6.4.0 is interoperable as it supports all common vector and raster GIS formats. Its capabilities cover raster and volume spatial analysis and modeling, time-series and landscape analysis, image processing, and visualization of 2D and 3D (voxel) raster data. Vector data can be digitized, extracted, extruded to 3D, and vector networks analyzed. Vector data are handled topologically. Vector attributes are stored in internal or externally connected databases. All general GIS tasks like map reprojection, georeferencing, and transformations are available for raster and vector data. The data storage concept of GRASS permits for single as well as multi-user access set up via network file system.
GRASS 6.4.0, the new stable release after more than one year of development and testing, brings a number of exciting enhancements to the GIS. Besides the hundreds of new module features, supported data formats, and language translations. The 6.4.0 release also runs in MS-Windows, a new installer is provided. A new graphical user interface with integrated location wizard and new vector digitizer is also included.
The presentation concludes with a series of applications relevant to archaeology including image processing, Lidar data analysis, fast viewshed analysis and more.
The need of Interoperability in Office and GIS formatsMarkus Neteler
Free GIS and Interoperability: The need of Interoperability in Office and GIS formats
GIS Open Source, interoperabilità e cultura del dato nei SIAT della Pubblica Amministrazione
[GIS Open Source, interoperability and the 'culture of data' in the spatial data warehouses of the Public Administration]
Free GIS Software meets zoonotic diseases: From raw data to ecological indica...Markus Neteler
The document discusses using satellite data from MODIS sensors to develop indicators for monitoring zoonotic diseases. Some key points:
1) Land surface temperature (LST) and vegetation index data from MODIS sensors can be used to develop indicators like frost periods and growing degree days linked to disease transmission.
2) LST data correlates well with meteorological station data and can be used to reconstruct continuous temperature time series.
3) Derived indicators like LST, snow cover maps, and enhanced vegetation index can be input into machine learning models to create risk models for tick-borne diseases like TBE.
Orca: Nocode Graphical Editor for Container OrchestrationPedro J. Molina
Tool demo on CEDI/SISTEDES/JISBD2024 at A Coruña, Spain. 2024.06.18
"Orca: Nocode Graphical Editor for Container Orchestration"
by Pedro J. Molina PhD. from Metadev
Stork Product Overview: An AI-Powered Autonomous Delivery FleetVince Scalabrino
Imagine a world where instead of blue and brown trucks dropping parcels on our porches, a buzzing drove of drones delivered our goods. Now imagine those drones are controlled by 3 purpose-built AI designed to ensure all packages were delivered as quickly and as economically as possible That's what Stork is all about.
Penify - Let AI do the Documentation, you write the Code.KrishnaveniMohan1
Penify automates the software documentation process for Git repositories. Every time a code modification is merged into "main", Penify uses a Large Language Model to generate documentation for the updated code. This automation covers multiple documentation layers, including InCode Documentation, API Documentation, Architectural Documentation, and PR documentation, each designed to improve different aspects of the development process. By taking over the entire documentation process, Penify tackles the common problem of documentation becoming outdated as the code evolves.
https://www.penify.dev/
Ensuring Efficiency and Speed with Practical Solutions for Clinical OperationsOnePlan Solutions
Clinical operations professionals encounter unique challenges. Balancing regulatory requirements, tight timelines, and the need for cross-functional collaboration can create significant internal pressures. Our upcoming webinar will introduce key strategies and tools to streamline and enhance clinical development processes, helping you overcome these challenges.
Streamlining End-to-End Testing Automation with Azure DevOps Build & Release Pipelines
Automating end-to-end (e2e) test for Android and iOS native apps, and web apps, within Azure build and release pipelines, poses several challenges. This session dives into the key challenges and the repeatable solutions implemented across multiple teams at a leading Indian telecom disruptor, renowned for its affordable 4G/5G services, digital platforms, and broadband connectivity.
Challenge #1. Ensuring Test Environment Consistency: Establishing a standardized test execution environment across hundreds of Azure DevOps agents is crucial for achieving dependable testing results. This uniformity must seamlessly span from Build pipelines to various stages of the Release pipeline.
Challenge #2. Coordinated Test Execution Across Environments: Executing distinct subsets of tests using the same automation framework across diverse environments, such as the build pipeline and specific stages of the Release Pipeline, demands flexible and cohesive approaches.
Challenge #3. Testing on Linux-based Azure DevOps Agents: Conducting tests, particularly for web and native apps, on Azure DevOps Linux agents lacking browser or device connectivity presents specific challenges in attaining thorough testing coverage.
This session delves into how these challenges were addressed through:
1. Automate the setup of essential dependencies to ensure a consistent testing environment.
2. Create standardized templates for executing API tests, API workflow tests, and end-to-end tests in the Build pipeline, streamlining the testing process.
3. Implement task groups in Release pipeline stages to facilitate the execution of tests, ensuring consistency and efficiency across deployment phases.
4. Deploy browsers within Docker containers for web application testing, enhancing portability and scalability of testing environments.
5. Leverage diverse device farms dedicated to Android, iOS, and browser testing to cover a wide range of platforms and devices.
6. Integrate AI technology, such as Applitools Visual AI and Ultrafast Grid, to automate test execution and validation, improving accuracy and efficiency.
7. Utilize AI/ML-powered central test automation reporting server through platforms like reportportal.io, providing consolidated and real-time insights into test performance and issues.
These solutions not only facilitate comprehensive testing across platforms but also promote the principles of shift-left testing, enabling early feedback, implementing quality gates, and ensuring repeatability. By adopting these techniques, teams can effectively automate and execute tests, accelerating software delivery while upholding high-quality standards across Android, iOS, and web applications.
What is Continuous Testing in DevOps - A Definitive Guide.pdfkalichargn70th171
Once an overlooked aspect, continuous testing has become indispensable for enterprises striving to accelerate application delivery and reduce business impacts. According to a Statista report, 31.3% of global enterprises have embraced continuous integration and deployment within their DevOps, signaling a pervasive trend toward hastening release cycles.
Nashik's top web development company, Upturn India Technologies, crafts innovative digital solutions for your success. Partner with us and achieve your goals
The Role of DevOps in Digital Transformation.pdfmohitd6
DevOps plays a crucial role in driving digital transformation by fostering a collaborative culture between development and operations teams. This approach enhances the speed and efficiency of software delivery, ensuring quicker deployment of new features and updates. DevOps practices like continuous integration and continuous delivery (CI/CD) streamline workflows, reduce manual errors, and increase the overall reliability of software systems. By leveraging automation and monitoring tools, organizations can improve system stability, enhance customer experiences, and maintain a competitive edge. Ultimately, DevOps is pivotal in enabling businesses to innovate rapidly, respond to market changes, and achieve their digital transformation goals.
Building API data products on top of your real-time data infrastructureconfluent
This talk and live demonstration will examine how Confluent and Gravitee.io integrate to unlock value from streaming data through API products.
You will learn how data owners and API providers can document, secure data products on top of Confluent brokers, including schema validation, topic routing and message filtering.
You will also see how data and API consumers can discover and subscribe to products in a developer portal, as well as how they can integrate with Confluent topics through protocols like REST, Websockets, Server-sent Events and Webhooks.
Whether you want to monetize your real-time data, enable new integrations with partners, or provide self-service access to topics through various protocols, this webinar is for you!
Baha Majid WCA4Z IBM Z Customer Council Boston June 2024.pdfBaha Majid
IBM watsonx Code Assistant for Z, our latest Generative AI-assisted mainframe application modernization solution. Mainframe (IBM Z) application modernization is a topic that every mainframe client is addressing to various degrees today, driven largely from digital transformation. With generative AI comes the opportunity to reimagine the mainframe application modernization experience. Infusing generative AI will enable speed and trust, help de-risk, and lower total costs associated with heavy-lifting application modernization initiatives. This document provides an overview of the IBM watsonx Code Assistant for Z which uses the power of generative AI to make it easier for developers to selectively modernize COBOL business services while maintaining mainframe qualities of service.