Search Results for "use"

...This tool provides a calculator and generator for AWS VPC Terraform configurations. It allows you to quickly scaffold a secure VPC with multiple subnets. Quick Start To use this tool, run the generator: bash # Generate a VPC with 3 subnets python lab_runner.py vpc-gen --cidr 10.10.0.0/16 --subnets 3 --output my_vpc.tf

...This work was supported in part by SFI through CTVR. Please use the below citation when using this module: Arokkiam, Jerome A, Pedro Alvarez, Xiuchao Wu, Kenneth N Brown, Cormac J Sreenan, Marco Ruffini, Nicola Marchetti, Linda Doyle, and David Payne. “Design, Implementation, and Evaluation of an XG-PON Module for the Ns-3 Network Simulator.” SIMULATION 93, no. 5 (May 2017): 409–26. doi: 10.1177/0037549716682093

...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 comfortable confidence levels before real tests. This way, we develop simulation models where entities, named sensors, collect environment data, process data, communicate, move around the space and a lot more. JSensor is a sensor network simulator implemented at Computer Science Department of Federal University of Ouro Preto (DECOM-UFOP), Brazil. ...

...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 environment to build a virtual laboratory with GNS3 even without be familiar with GNU/Linux and configurations. So, if you are studying for a certification and/or if you want to build your own laboratory to simulate Cisco, Juniper devices and - in general - complex network topologies, you will find in D.N.S. a good friend :D The first public release has been published on Sunday, 9th of February 2014. ...

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.

The contiki OS running on the cooja wireless sensors network simulator makes use of the RPL protocol for its routing plan. Rplfuzzy project aims to use fuzzy logic for a routing that take care of several performance metrics as latency, energy, throughput and other.