Voice recognition is becoming an essential part of the Internet of Things (IoT), allowing us to control, monitor, and interact with devices hands-free. At its core, ESP32 speech to text works by capturing audio through a microphone, processing it, and converting it into text that devices can understand. This text is then used to trigger commands, just like how…
The "Smart AI-Powered Surveillance Camera" represents a powerful combination of the latest and most advanced AI and the power of IoT in a single strong and intelligent security system. It uses the Maixduino kit, which will join tectonic strengths of machine learning with network connectivity for a scalable and efficient solution for surveillance.
Impact Statement…
The Wearable Communication Assistant is designed as a portable, AI-driven solution that leverages machine learning models for real-time sign language recognition and speech-to-text translation. Our goal is to enable this functionality on a lightweight embedded device, ensuring accessibility and ease of use. The project uses a development board with a camera for…
According to WHO ( World Health Organization ) combined effects of ambient air pollution and household air pollution is associated with 7 million premature deaths annually.
Air pollution is a pressing global issue that poses a significant threat to human health and the environment. Many scientific studies have linked particle…
Face recognition is becoming increasingly popular these days. There are numerous project ideas available that we can implement using the face recognition concept, and we at CircuitDigest have already built many ESP32-CAM projects, including an attendance system and a door lock security system using ESP32-CAM. However, face recognition…
In this project, we have used the popular ESP32-CAM module to build an Image recognition system that can identify various vegetables. We have used the Edge Impulse platform to train our model and deployed it on the ESP32cam module, the results of the model are displayed on the OLED screen as output. In this tutorial we will understand the basics of edge computing and…
Manually sorting items is one of the most exhausting and time-consuming tasks. Manual sorting, whether of fruits or veggies or anything else, takes a lot of manpower and time. So, in this tutorial, we have tried to build a tomato sorting machine that could distinguish between red and green tomatoes. This Raspberry Pi-based Tomato Sorting system uses TensorFlow modules…
Edge Impulse Studio is a machine learning platform that enables developers to generate Machine Learning trained models in the cloud and deploy them on microcontrollers (e.g. Arduino and STM32), or single-board computers like the Raspberry Pi. Initially, Edge impulse didn’t support the Raspberry Pi but on April 2021 Edge Impulse announced their full support with Raspberry Pi…
In this tutorial, we are going to implement an Emotion Recognition System or a Facial Expression Recognition System on a Raspberry Pi 4. We are going to apply a pre-trained model to recognize the facial expression of a person from a real-time video stream. The “FER2013” dataset is used to train the model with the help of a VGG-like Convolutional Neural Network (CNN).
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Human pose estimation technology is becoming increasingly popular due to the recent growth and popularity of Virtual and Augmented Reality. In our previous tutorial, we used Media pipe Hand Tracking module to control Raspberry Pi media player using hand gestures. In this project, we are going to use the MediaPipe Pose estimation package to build a push up counter. We will be…
AI has been a buzzword this entire decade. People have been applying the concepts of AI in all possible fields – Agriculture, Medical Imaging, Autonomous Driving, Biometrics, encryption, Data Analysis, FinTech, and what not. However, because it is a rapidly emerging field there is a lot of changing and upcoming technologies. Frameworks get old within months and libraries…
Machine Learning and Artificial Intelligence are the trending topics in the industries now days and we can see their increasing involvement with the launch of every new electronics device. Almost every application of Computer science engineering is using Machine Learning for analyzing and predicting the future results. Already, there are many devices rolled in the market…
