A novel drone-based system for accurate human temperature measurement and disease symptoms detection using thermography and AI.

The world continues to witness several waves of COVID-19 spread due to the emergence of new variants of the SARS-CoV-2 virus. Stopping the spread requires synergistic efforts that include the use of technologies such as unmanned aerial vehicles and machine learning. This paper presents a novel system for detecting disease symptoms from a distance using unmanned aerial vehicles equipped with thermal and visual image sensors. A hardware/software system that uses thermography to accurately calculate the skin temperature of targeted individuals using thermal cameras is developed. In addition, machine vision algorithms are developed to recognize human actions such as coughing and sneezing, which are paramount symptoms of respiratory infections. The proposed system is implemented and tested in outdoor environments. The results of experiments showed that the system can determine the skin temperature of multiple targeted individuals simultaneously with an error of less than 1 °C. The field experiments showed that the developed system is capable of simultaneously measuring the temperature of more than 10 individuals in less than 5 seconds. Just to give a perspective, it takes at least 3 seconds to measure one individual's temperature if this was done using traditional methods. Furthermore, the results showed that the system has accurately detected actions such as coughing and sneezing with almost 96% accuracy at a real-time performance of 28 frames/second.

Enhancement in ion adsorption rate and desalination efficiency in a capacitive deionization cell through improved electric field distribution using electrodes composed of activated carbon cloth coated with zinc oxide nanorods.

Electrodes composed of activated carbon cloth (ACC) coated with zinc oxide (ZnO) nanorods are compared with plain ACC electrodes, with respect to their desalination efficiency of a 17 mM NaCl solution at different applied potentials. Polarization of the ZnO nanorods increased the penetration depth and strength of the electric field between the electrodes, leading to an increase in the capacitance and charge efficiency at reduced input charge ratios. Uniform distribution of the electric field lines between two electrodes coated with ZnO nanorods led to faster ion adsorption rates, reduced the electrode saturation time, and increased the average desalination efficiency by ∼45% for all applied potentials. The electrodes were characterized for active surface area, capacitance from cyclic voltammetry, theoretical assessment of surface area utilization, and the magnitude of electric field force acting on an ion of unit charge for each potential.