Engineering Clinic

Step 1: What We Need: This will mainly work on Ubuntu 20.04 OS  Support till 2025 May. Name of the ROS: Noetic  For the complete installation step-by-step, you can watch the video given below Step 2: Commands Here are the commands to be used one after the other.  $ sudo sh -c 'echo "deb http://packages.ros.org/ros/ubuntu $(lsb_release -sc) main" > /etc/apt/sources.list.d/ros-latest.list' $ sudo apt install curl # if you haven't already installed curl $ curl -s https://raw.githubusercontent.com/ros/rosdistro/master/ros.asc | sudo apt-key add - The above commands add the ROS to the aptitude manager and we can now install ROS1 with simple commands $ sudo apt update $ sudo apt install build-essential autoconf automake libxmu-dev $ sudo apt install ros-noetic-desktop-full The above command needs 370MB of software to be downloaded. So the complete package of ROS installed.  We need some more packages to be installed that can create our own workspaces and manage our o

Installation of NS3 in Ubuntu  #/ns3 recently released ns-3.42, we are going to see how to install that in #ubuntu 24.04 OS Ubuntu OS also recently released in April 2024. Here are the requirements 1. Ubuntu 24.04 LTS 2. ns-allinone-3.42.tar.bz2 (Download the above software from https://www.nsnam.org/releases/ns-allinone-3.42.tar.bz2 and store it in the home folder (In my case it is /home/pradeepkumar/ folder) This #installation will work for ns-3.38, ns-3.39, ns-3.40, and ns-3.41 as well. Open a New Terminal The first command to do is (You can just copy paste the following in your terminal window) $ sudo apt update $ sudo apt install g++ python3 cmake ninja-build git gir1.2-goocanvas-2.0 python3-gi python3-gi-cairo python3-pygraphviz gir1.2-gtk-3.0 ipython3 tcpdump wireshark sqlite sqlite3 libsqlite3-dev qtbase5-dev qtchooser qt5-qmake qtbase5-dev-tools openmpi-bin openmpi-common openmpi-doc libopenmpi-dev doxygen graphviz imagemagick python3-sphinx dia imagemagick texlive dvipng lat

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 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 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