With the exponential growth in transactions and exchanges carried out via the Internet, the risks of the falsification and distortion of information are multiplying, encouraged by widespread access to the virtual world. In this context, digital image watermarking has emerged as an essential solution for protecting digital content by enhancing its durability and resistance to manipulation. However, no current digital watermarking technology offers complete protection against all forms of attack, with each method often limited to specific applications. This field has recently benefited from the integration of deep learning techniques, which have brought significant advances in information security. This article explores the implementation of digital watermarking in embedded systems, addressing the challenges posed by resource constraints such as memory, computing power, and energy consumption. We propose optimization techniques, including frequency domain methods and the use of lightweight deep learning models, to enhance the robustness and resilience of embedded systems. The experimental results validate the effectiveness of these approaches for enhanced image protection, opening new prospects for the development of information security technologies adapted to embedded environments.

Newborns cry intensely, and most parents struggle to understand the reason behind their crying, as the baby cannot verbally express their needs. This makes it challenging for parents to know if their child has a need or a health issue. An embedded solution based on a Raspberry Pi is presented to address this problem. The module analyzes audio techniques to capture, analyze, classify, and remotely monitor a baby’s cries. These techniques rely on prosodic and cepstral features, such as MFCC coefficients. They can differentiate the reason behind a baby’s cry, such as hunger, stomach pain, or discomfort. A machine learning model was trained to anticipate the reason based on audio features. The embedded system includes a microphone to capture real-time cries and a display screen to show the anticipated reason. In addition, the system sends the collected data to a web server for storage, enabling remote monitoring and more detailed data analysis. A cell phone application has also been developed to notify parents in real time of why their baby is crying. This application enables parents to adapt quickly and efficiently to their infant’s needs, even when they are not around.