Wireless sensor networks (WSNs) and their applications have received considerable interest in the last few years. In WSNs, accurate path loss models should be considered to achieve a successful distribution of several nodes. In this work, two path loss models are proposed to evaluate the distance between two ZigBee WSNs. First, a path loss model based on conventional Log-Normal Shadowing Model (LNSM) is derived using the collected received signal strength indicator (RSSI) of the ZigBee in real time. Second, a new path loss model based on Particle Swarm Optimization (PSO) algorithm hybridized with Polynomial Equation (PE) is proposed. The PSO algorithm is used to select the optimum coefficients of PE. These coefficients can be utilized to optimize the distance estimation error based on the curve fitting. Therefore, the new path loss model called hybrid PE-PSO is innovated in this work. The hybrid PE-PSO model considerably improves the distance estimation accuracy compared with the LNSM. Results show that the hybrid PE-PSO achieves 85% improvement in distance error compared with the traditional LNSM. The mean absolute error of 0.77 m is obtained for distance estimation, which outperforms that by state of the arts.
Non-contact cardio signal monitoring is a requisite for premature infants, as adhesive sensors and electrodes such as that for an electrocardiogram can damage the epidermis. Many types of research on infants in the newborn intensive care unit (NICU) are still in their early stages with many challenges. Therefore, this study aims to use a digital camera as a non-contact photoplethysmography imaging method to measure and compare the heart rate from two experiments (without/with ultraviolet rays (UV)) inside the intensive care unit. The two experiments (without/with UV) rays yielded promising results in comparison with the reference measurements obtained from ten infants. The results reveal a robust correlation using the pearson correlation coefficients (PCC) of 0.99 (for without UV) and 0.96 (for with UV). Also, the spearman correlation coefficient (SCC) test of 0.97 and 0.95 for two experiments, respectively Bland–Altman analysis revealed a strong relationship between measured and reference readings from without and with UV. The experimental results obtained a low error rate for mean absolute error (MAE), and root mean squared error (RMSE). Therefore, this work presents a new aspect of non-contact monitoring, showing promising performance for upcoming medical applications.
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