Advanced Node-RED

Learn to create a Node-RED node from a Node.js module, inspect html and js files, and implement a word-count function using msg.payload, then test it.

Create a JavaScript file for a Node-RED node, defining runtime behavior with module.exports and RED. Implement WordCountNode, use config from the editor, and listen for input messages.

Learn to create an HTML file that defines a node’s appearance in the editor, including the node definition, edit template, and help text, connected to the js file via registerType.

Package and publish the word count node for node-red by creating a proper package.json, organizing files, linking locally with npm, and publishing to the npm registry.

Create a name formatter node for Node-RED that formats first and last names into full, formal, or informal forms, validates inputs, and packages and publishes the node.

Build a rock, paper, scissors game in Node-RED on a Raspberry Pi using a camera and IBM Watson Visual Recognition to identify signs and log scores on a dashboard.

Learn to analyze images with IBM Watson Visual Recognition and Watson Studio, using deep learning for image classification and custom classifiers trained on labeled data to build an accurate model.

Train a visual model in IBM Watson Studio by creating a project, attaching COS cloud object storage, and uploading a training set to build a custom image classifier.

Create a node-red flow on a raspberry pi to capture an image from a usb camera and classify it with the watson visual recognition model.

make node-red play with you by routing image analysis results through change and switch nodes, handle empty classifications, then use random, join, and function nodes to play rock, paper, scissors.

Set up the node-red dashboard to track a rock paper scissors game, display signs and results, and store scores in sqlite with a history table.

Enhance a rock-paper-scissors game in node-red by adding a delete table query to reset data, an audio dashboard for instructions, and a dark-themed dashboard with sign images.

Explore the first feature of the office automation project: control lights with Alexa via AWS Lambda and a Node-RED flow on a Raspberry Pi, using JSON data.

Learn how Alexa works with skills, invocation names, and intents, and build simple automations like alarms using Alexa hosted Node.js or AWS Lambda.

Explore how AWS Lambda integrates with Alexa Skills Kit and AWS IoT to publish MQTT messages to Node-RED, including setup, testing, and essential libraries such as ASK-SDK and AWS-SDK.

Explore how Node-RED orchestrates data from AWS IoT using MQTT, configuring secure connections with certificates on port 8883, and shaping payloads with JSON, Lambda, and debug nodes.

Configure a Raspberry Pi with an externally powered 5V single-channel relay, wire GPIO 4 (pin 7), and control lights via Alexa and Lambda for a proof-of-concept office automation.

Automate leave approvals in node-red by building a Slack bot that checks two business days before leave, pulls earned leaves data from Quikchex, and uses Amazon Lex for intents.

Build a secure slack integration for node-red on raspberry pi with https and ngrok, creating a slack bot, configuring events, http in/out nodes, and Lex intents to process leave requests.

Add math functionality to the leave application bot in Node-RED, calculating deadlines from current and leave dates to enforce 48- or 96-hour windows.

Configure a Slack leave-application bot to reply to employee requests, using the postMessage method with token, channel, and text, and thread replies via thread_ts.

Develop a row checker notification by integrating Google Sheets API with Node, using a service account to monitor sheet updates and push alerts to Slack in the report_updates channel.

Build a security system with Azure face API and Node-RED for employee face recognition to control a door, plus a dashboard to enroll new staff.

Create an Azure Face API instance and understand how it works. Explore how face detection and face recognition extract facial geometry and compare it to a database of known images.

Test the Azure face API endpoints in Node-RED, configure the subscription key and West US region, and inspect face detect results and identify workflows with person groups.

Learn to use the face identify API to build a face recognition flow in node-red, including creating a person group, adding persons and faces, training the model, and performing recognition.

Complete a face recognition security project in node-red by implementing persisted context, configuring a multi-tab interface with admin, person group, and manage data sections, and linking dashboards with keypad security.

Capture handwritten documents as images, detect the text, and convert it to PDF, using Google Cloud Vision API for text detection and building a two-part Node-RED project.

Learn to perform text detection with Google Cloud Vision API using Node-RED, configuring API keys, buffers, and image inputs to extract text and prepare for PDF conversion.

Convert the identified text to pdf by saving it in a flow variable and triggering the pdf conversion with a button, using html-pdf or alternative APIs.

Add functionality to advanced node-red projects by triggering actions with Google Assistant via IFTTT, using webhooks, ngrok, delays, and a handwritten document to pdf converter.