Drowsiness detection is crucial for ensuring the safety of individuals engaged in high-risk activities. Numerous studies have explored drowsiness detection techniques based on EEG signals, but these have typically been validated on computers, which limits their portability. In this paper, we introduce the design and implementation of a drowsiness detection technique utilizing EEG signals, executed on a Zynq7020 System on Chip (SoC) as part of a Pynq-Z2 module. This approach is more suitable for portable applications. We have implemented the Discrete Wavelet Transform (DWT) and feature extraction functions as intellectual property (IP) cores, while other functions run on the ARM processor of the Zynq7020.

Smart sensors are increasingly used in agriculture to monitor environmental conditions and support data-driven decision-making. However, traditional sensor implementations face critical challenges related to power consumption, especially in remote farms—such as pitaya plantations—where access to electricity and ongoing maintenance is limited. This paper presents a smart energy management system for agricultural sensor nodes integrating a machine learning model for adaptive sampling and a batching strategy to optimize energy usage. A lightweight Stochastic Gradient Descent (SGD) regressor trained on temperature dynamics runs on-device to predict the sampling interval ($T_s$). In parallel, the node adjusts the number of buffered samples as the battery state of charge ($SOC$) decreases, reducing Long Range (LoRa) transmissions. Field experiments show that the proposed approach reduces energy consumption by 77.8% compared with fixed-interval sampling, while maintaining good temperature fidelity with Mean Absolute Error (MAE) of 0.537 °C for temperature reconstruction.

This work proposes a low energy forwarding routing algorithm for Wireless Sensor Networks (WSN). The proposed algorithm is based on the Minimum Cost Forwarding Algorithm (MCFA) and utilizes a cost function in order to determine the best transmission routes with the lowest energy consumption. The proposed cost function is calculated considering the link quality between nodes and the transmission cost of the neighboring nodes. The link quality is estimated in a bidirectional way, which means it considers reception quality and transmission quality, and is power aware. For the reception quality WMEWMA is used, and for the transmission quality the power of the transceiver and the number of transmission attempts are considered. The performance of the proposed algorithm is tested in three scenarios and compared with the performance in the same scenarios of MCFA as routing algorithm with WMEWMA as cost function. For the purpose of testing, physical nodes were designed and built using ATmega328P microprocessor and nRF24L01 transceiver.

Temperature and humidity chambers are often employed for stress testing materials. In the case of cultural heritage materials, it is also crucial to incorporate light condition in these evaluations. Consequently, controlling these parameters is essential to effectively simulate accelerated environmental conditions. This letter outlines the design and implementation of an embedded system that manages temperature, humidity, and light levels. Built on an ARM Cortex-M3 System on Chip, the system integrates various temperature and humidity sensors and actuators, along with a light controller. It also features an embedded user interface and facilitates communication with an external PC. The validity of our proposal is demonstrated through the implementation of a proportional-integer control mechanism for the regulation of temperature and relative humidity.