Muhammad Alhammami

Facial-based recognition systems are commonly used for building access control, with the accuracy and computing requirements still being improved. On the other hand, QR codes are gaining rising attention as an input interface to many embedded applications. This paper proposes an embedded access control system that customises both previous techniques to be implemented on the CPU of a low-cost Raspberry Pi 4 computer. The achieved system works smoothly with a frame rate of 8.27 FPS, increasing the accessing control's robustness compared to a system based on face recognition only. It also offers the ability to control the access of unknown faces. In tandem with integration, this strengthens security measures, improves user experience, and outperforms conventional access control approaches, creating an attractive offer for many businesses.

This paper outlines the development of a Hardware Development Kit (HDK) for a remote training platform on FPGA Devices designed to provide university students pursuing degrees in electronic and informatics engineering (at the bachelor’s, master’s, and PhD levels) with the tools they need to learn and develop systems related to artificial intelligence, digital signal processing, image processing, digital systems, integrated and embedded systems using an FPGA chip. Through the internet or a local network, students can engage with the platform to conduct engineering experiments. The HDK is equipped with a Raspberry Pi, a screen, a camera, and LEDs to facilitate the transfer of experiment results and to aid in assessing the validity of lab experiment execution. The Raspberry Pi plays a crucial role in the HDK, providing control and monitoring, remote access, data processing, user interface, and integration with other possible systems.

Action recognition is gaining more importance and attention due to its wide applications. But the need of implementing it in real time is essential. This paper suggests a hardware/software co-design which accelerates the whole recognizing function. In this setup we are using Microsoft © Kinect device, Intel © Atom system and FPGA based SoC device from Altera © . The initial experiments show that it is possible to recognize actions in real time using the proposed setup by processing the skeleton data. This promising result would encourage designers to take the step forward of using mixed CPU and FPGA based SoC platform to increase the performance of image and video processing operations especially in recognition systems.