The design, micro-fabrication, and characterization of a resistance temperature detector (RTD) based micro sensor for minimally invasive breathing analysis and monitoring is presented. Experimental results demonstrate that the change in air temperature while inhaling and exhaling can be transduced into a time varying electrical signal, which is subsequently used to determine the breathing frequency (respiratory rate). The RTD is placed into a Wheatstone bridge to simultaneously reduce the sensor’s output noise and improve overall system accuracy. The proposed design could potentially aid health care providers in the determination of respiratory rates, which is of critical importance during the current COVID-19 pandemic.

This paper presents the concept and design of a system that embeds piezoelectric sensors to measure the voltage of a mechanical load applied to it. COMSOL Multiphysics, a finite element simulation tool, was used to design the system and analyze the data to find a possible fingerprint of voltage changes. The sensors’ voltage readings were affected by the load applied to the surface of the structure with different magnitudes and speeds. The analyzed data show the effect of position and mass on the voltage readings and indicates the possibility of speed prediction. The obtained dataset results validated the concept of the proposed system, where the collected data can serve as a digital data pipeline model for future research on different artificial intelligence (AI) or machine learning (ML) modeling applications. From the obtained data, a reasonable view shows that voltage reading matrices can be utilized for the detection of vehicle speed, location, and mass if used as training data for machine learning modeling, which can benefit the Internet of Things (IoT) technology.

Accurate detection of salt in water is crucial in many applications. Numerous techniques, using direct and indirect methods, have been employed to design seawater sensors. Among the indirect sensing methods, optical sensors are known to be the most accurate, easy to implement, and suitable for application where the chemical properties of the solution to be tested should stay unchanged. This research presents a novel method for real-time label-free biochemical detection of salty water combining various optics concepts with a machine learning system. COMSOL Multiphysics has been employed to design and simulate the proposed sensor. The designed device uses a laser light emitted from the top of a water container, with a sensing part located on the bottom surface. The laser light initially propagates in the air portion, then refracts when it comes into contact with the air-water interface. Different parameters, including the laser beam wavelength $\lambda$ and its incident angles${\theta }_i$, the temperature, and the air-water levels are employed to generate a set of data and the multilayer perceptron classifier (MLP) to model prediction. The obtained results validated the concept of the proposed sensor using machine learning. The sensor’s prediction precision under various temperature conditions is R² = 0.844, the equivalent of an MSE of 0.155.

Photovoltaic (PV) systems are key renewable energy sources due to their ease of implementation, scalability, and global solar availability. Enhancing their lifespan and performance is vital for wider adoption. Identifying degradation root causes is essential for improving PV design and maintenance, thus extending lifespan. This paper proposes a hybrid fault diagnosis method combining a bond graph-based PV cell model with empirical degradation models to simulate faults, and a deep learning approach for root-cause detection. The experimentally validated model simulates degradation effects on measurable variables (voltage, current, ambient, and cell temperatures). The resulting dataset trains an Optimized Feed-Forward Neural Network (OFFNN), achieving 75.43% accuracy in multi-class classification, which effectively identifies degradation processes.