Search Results for "use"

2023-03-20: the latest version of the XG-PON model for ns-3.35 is in GitHub (https://github.com/ajerikko/xgpon4ns3-v3/releases/tag/v3.1). It contains major changes to example code. Please use it for new activities. 10-Gigabit-capable Passive Optical Network (XG-PON), is regarded as one of the key technologies for future Internet access networks. This project develops an XG-PON module for the NS-3 network simulator - a G987 (ITU-T) standards-compliant, configurable, and extensible module...

How can we analyze the water quality of a river? How can we monitor streets, cities, provinces of a country? How can we predict and prevent problems coming from earthquakes, snowstorms, fire spreads and all kinds of natural disasters? How can we test some new drugs without using animals? These and many other problems are initially solved by using simulators. It is extremely expensive, and sometimes dangerous, to test initial ideas in a real environment. We must use simulators to achieve...

D.N.S. - aka Debian Network Simulator - is a Virtual Machine prepared to run as a guest in VirtualBox to emulate complex networks. It is based on Debian Jessie and is built around the well known GNS3 Project. Thanks to D.N.S. you can emulate Cisco and Juniper devices, as well as different kind of servers, to build complex networks for testing purposes or to have an huge help to study for a certifications like CCNA, CCNP, JNCIA and so on. The goal of the project is to provide an easy to use...

This is a application for simula Neuronals Networks, now only support the Backpropagation Network. In the future maybe support ART, Adaline, Madaline, and other Neuronal Networks. Use gnuplot to draw the lms error.

Main source code is moved at https://github.com/jpahullo/planetsim. We recommend authors of contributions sections to move your code to github. Since then, contributions remain here for your use at will. PlanetSim is an object oriented simulation framework for overlay networks and services. This framework presents a layered and modular architecture with well defined hotspots documented using classical design patterns.

This is an implementation of the Granular Neural Network architecture defined by S. Dick, A. Tappenden, C. Badke, O. Olarewaju. It is provided for the use of the public, and the convenience of researchers who may wish to develop or use this new system.

SNNSraster is a utility for quick ANN analysis of raster GIS maps with the use of Stuttgart Neural Network Simulator trained network files. It was developed to read and write binary raster files. SNNSraster is a project of the Geography Laboratory of the University of Siena. The code was developed by Giancarlo Macchi Jánica between 2006 and 2007. SNNSraster's fundamental objective is to improve the ability to integrate the use of artificial neural networks in GIS environments.

ROOTSNNS is a set of C++ classes which allows one to use the Stuttgart Neural Network Simulator kernel (ansi-C) within the ROOT, a data analysis package. Multiple ANNs can be built, trained, and tested, while results and ANN performance can be saved.

The goal of the Narses project is to build a scalable, application-level network simulator that allows experimenters to use network models of varying degrees of accuracy and speed to efficiently simulate large distributed applications.