Engineering Clinic

Surface defect detection plays a critical role in modern manufacturing industries, especially in steel and metal production. Manual inspection is time-consuming, error-prone, and not scalable. In this post, I demonstrate how Deep Learning can be effectively used to detect surface defects using the NEU Surface Defect Dataset , along with a live Streamlit web application demo . I have also recorded a full live lecture , where I explain the dataset, training process, and real-time defect prediction using a Streamlit app. NEU Surface Defect Detection 📌 What This Project Covers Understanding the NEU Surface Defect Dataset Training a Deep Learning CNN model using PyTorch Running a Streamlit-based web application Uploading a test image to predict the type of surface defect End-to-end execution on Ubuntu 24.04 This project is highly useful for: Engineering students Research scholars Faculty members Industry professionals in manufacturing and quality inspecti...

Step-by-Step Guide: How to Install ns-3.46 on Ubuntu 24.04 LTS Welcome to Engineering Clinic! As Ubuntu continues to evolve with its 24.04 LTS release, network simulation tools must also keep pace. Today, we will walk through the complete installation process of ns-3.46 on the new Ubuntu 24.04 environment. Whether you are a student, researcher, or developer, setting up the environment correctly is the most critical step. This guide covers everything from installing dependencies to setting up the specific Python virtual environment required for this version. Check the video for complete instructions: Let’s get started. Step 1: Update and Install Dependencies Before we touch the network simulator, we need to prepare the Linux environment. Ubuntu 24.04 requires a specific set of libraries for C++, Python, and visualizer tools. First, update your repositories: $ sudo apt update Next, install the required dependencies. This is a comprehensive list ensuring that key features (like NetAnim...

CoAP using NodeJS | Californium CoAP Client This post shows the Constrained Application Protocol (CoAP) demonstration using Node.js Prerequisites: 1. Node.js Software 2. Windows 11 or Ubuntu , or Mac OS 3. A Coap Client (we are planning to use Californium, which is based on JavaFX ) 4. Oracle 11 JDK is needed 5. Java FX 17 version, which is to be unzipped in any folder. Step 1: The Source code is named coap.js Listen to the following video for an explanation of the source code: var coap = require('coap'); function randomInt(min, max) {     return (Math.floor(Math.random() * (max - min) + min)); } var portNumber = 5683; coap.createServer(function(req, res) {     console.info('CoAP device got a request from %s', req.url);     if (req.headers['Accept'] != ' application/json ') {         res.code = '4.06';         return res.end();     }     switch (req.url) {         case ...

✅ What You’ll Need: A USB drive with at least 8GB of storage ( Windows 11 installation files are ~5.4GB ) Windows 11 ISO image or installation DVD Administrator privileges on your current system 🔧 Step-by-Step Instructions: Step 1: Backup Your USB Drive Plug in your USB drive and back up any important data — this process will erase everything. Step 2: Improve USB Performance (Optional, but Recommended) Go to: Control Panel → Device Manager → Disk Drives → [Your USB Drive] → Properties → Policies Select “Better performance” for faster write speeds. (Pro Tip: With this, Windows 10 can install in under 15 minutes — even on machines with just 4GB RAM !) Step 3: Open Command Prompt as Administrator Go to: Start → Accessories → Right-click “ Command Prompt ” → Run as Administrator Step 4: Use DISKPART to Prepare the USB Drive Type the following commands one by one: DISKPART LIST DISK SELECT DISK # ← (Replace # with your USB disk number...

Random and Queuing Models using NS2 In this post, the readers may learn how to create random numbers for various distributions using different seeds Simulation of various queuing models like M/M/1 and M/D/1 13.1 Random Number Generation Random variables is an important concept in networks as the modelling of network traffic and other packet arrival times are mostly random models. Hence, there is a necessity to model such metrics in ns2 . NS2 supports various random models using different seed generations. Seeds are numbers that help generate random numbers. The seed number 0 indicates that the random number order changes every time the simulation is run. But other than 0, the order in which the random numbers are generated same. The following listing 13.1 shows the random number generation for various distributions. This listing will just tell you how to create random number generation for various distributions. For seeing the output, refer to listing 13.2 Listing 13.1 – Random Number ...