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QUENAS (QUeued Event Network Automatic Simulator) is a NetworkSimulator that enables to create a network of nodes and simulate communications between then. Currently, the Hypercube protocol is implemented at network layer.
iSNS is an interactive neural networksimulator written in Java/Java3D. The program is intended to be used in lessons of Neural Networks. The program was developed by students as the software project at Charles University in Prague.
A networksimulator written in flash. Build up a topolog and send packets throught your network, see and inspect them as they travel, change the headers and observe such protocols as ARP and switch learning.
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The NS-Mapper ad-hoc scenario editor is improved and extended by adding more realistic strategies, such as random based node placement, movement and traffic to the ad-hoc simulation of the NetworkSimulator 2 (NS-2).
NS-2 Trace Statistics is a tool for easy generation of summary statistics from NetworkSimulator trace files, such as: total and network delay, packets generated, sent, received and dropped, run length histograms and MRU stack depth.
The goal of this project is to be an improvement of the original Network Animator (NAM) module provided as part of the NetworkSimulator 2 (NS2). This tool provides topology visualization, TCL script generation, and enhanced simulation animation.
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rsim is a simple discrete-event communications networksimulator initially developed to test routing protocols for undersea acoustic modem networks. If done well then rsim will eventually be a good replacement for "ns2".
Design and implementation of the Observation-based Cooperation Enforcement in Mobile Ad-hoc Networks (OCEAN) protocol, on top of the ns2 networksimulator, using Dynamic Source Routing (DSR).
DYMOUM is an implementation of the DYMO (Dynamic Manet On-demand) routing protocol both for Linux kernel and ns2 networksimulator, written in C and C++.
The aim of this project is to have a current implementation of the experimental VFER protocol (http://vfer.sf.net) within the ns-2 networksimulator. The design of VFER can then be tweaked using results from this simulation.
SNNSraster is a utility for quick ANN analysis of raster GIS maps with the use of Stuttgart Neural NetworkSimulator 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.
NS2 Linux is dedicated to improve the NetworkSimulator (NS-2) to match Linux performance. Planned contents include: an NS-2 module that runs Linux congestion control functions, tutorials on how to run NS-2 to match Linux performance, benchmark for TCP.
A modern and usable interface for the well known neuronal networksimulator SNNS. The GUI acts as a client to serveral servers embedding SNNS and supports team work aspects.
ROOTSNNS is a set of C++ classes which allows one to use the Stuttgart Neural NetworkSimulator 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.
This project deals with the implementation of a Fisheye State Routing (FSR) module for the networksimulator ns-2. It can be integrated into ns-2 to simulate mobile ad hoc networks using FSR for routing data packets.
WIPsim (Wireless IP Simulator) is a networksimulator for all layers of the OSI model. The current version is focussed on dealing with IPv6. The simulator was started to investigate wireless (data) networks and mobility.
niab - Network In A Box.
Create a virtual lab network inside one machine. A lab can include routers, firewalls, clients and servers connected by a network specified by you. [Linux NetworkSimulator, UML, user mode linux]
GNNS - GNNS Neural NetworkSimulator, is both a set of libraries and an interface for creating and learning neural networks. It is aimed to support as many network typs and learning algorithms as possible. GNNS is meant to support different platforms.