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Installation of ns3 in Ubuntu | Ubuntu 24.04 | ns-3.42

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Installing ns2 in Ubuntu 24.04 | Ubuntu | Network Simulator 2

Installation of NS2 in Ubuntu This post shows you how to install ns2 (ns-2.35) in Ubuntu 24.04. #ns2 #ubuntu #engineeringclinic NS-2.35 installation in Ubuntu 24.04  You can view the following video for complete instructions: Prerequisites ns-allinone-2.35 Ubuntu 24.04 Commands to be used: $ cat /etc/lsb-release $ sudo apt update $ sudo apt install build-essential autoconf automake libxmu-dev Requirements of ns2 gcc-4.8 g++-4.8 Were available only upto 18.04 which the codename is bionic $ sudo gedit /etc/apt/sources.list.d/ubuntu.sources make an entry in the above file as given in the video (see the picture below) Ubuntu Sources addition $ sudo apt update For any GPG error, include the following command $ sudo apt update $ sudo apt install gcc-4.8 g++-4.8 Download ns-2.35 from the website. http://sourceforge.net/projects/nsnam/files/allinone/ns-allinone-2.35/ns-allinone-2.35.tar.gz/download Since ns2.35 is too old (2011) and hence it will work on the gcc and g++ versions o

PyTorch Code for Simple Neural Networks for MNIST Dataset

PyTorch Introduction To Install PyTorch in Linux (Ubuntu), here is the step: $ sudo apt install python3-pip python3 python3-dev $ pip3 install torch torchvision torchaudio notebook MNIST Dataset (0 to 9 handwritten characters) as given below MNIST Dataset Given the dataset of MNIST, do the accuracy analysis of the dataset based on the following hyperparameters using Deep Learning with PyTorch 1. Number of epochs is 4,5,6 and 7 2. batch_size is 64 and 128 3. Number of Hidden layers is 1 and 2 4. Learning rate is 0.001, 0.002 and 0.003 Compute the accuracy in each case. Run the following code either using the Jupyter Notebook or Google Colab . To run the notebook, the command is  $ python3 -m notebook import torch import torch.nn as nn import torch.optim as optim from torch.utils.data import DataLoader, TensorDataset from torchvision import datasets, transforms from torch.autograd import Variable # Define a simple neural network model class SimpleNN(nn.Module): def __init__(self, input

VLAN implementation using NS2

VLAN implementation using NS2 HARDWARE / SOFTWARE REQUIRED: Network Simulator-2 Operating System – LINUX ( UBUNTU ) THEORY VLAN  is a custom network that is created from one or more local area networks. It enables a group of devices available in multiple networks to be combined into one logical network. The result becomes a virtual LAN that is administered like a physical LAN. The full form of VLAN is defined as Virtual Local Area Network. The below topology depicts a network having all hosts inside the same virtual LAN: Without VLANs, a broadcast sent from a host can easily reach all network devices. Each and every device will process broadcast received frames. It can increase the CPU overhead on each device and reduce the overall network security. In case if you place interfaces on both switches into separate VLANs, a broadcast from host A can reach only devices available inside the same VLAN. Hosts of VLANs will not even be aware that the communication took place. This is shown in t