Abstract Solar energy has received great interest in recent years, for electric power generation. Furthermore, photovoltaic (PV) systems have been widely spread over the world because of the technological advances in this field. However, these PV systems need accurate monitoring and periodic follow-up in order to achieve and optimize their performance. The PV systems are influenced by various types of faults, ranging from temporary to permanent failures. A PV system failure poses a significant challenge in determining the type and location of faults to quickly and cost-effectively maintain the required performance of the system without disturbing its normal operation. Therefore, a suitable fault detection system should be enabled to minimize the damage caused by the faulty PV module and protect the PV system from various losses. In this work, different classifications of PV faults and fault detection techniques are presented. Specifically, thermography methods and their benefits in classifying and localizing different types of faults are addressed. In addition, an overview of recent techniques using different artificial intelligence tools with thermography methods is also presented.

Remote healthcare and telemedicine technology have witnessed a large and rapid development in the last decade with the large development of the Internet of Things (IoT) technology, where various types of medical sensors are aggregated for measuring medical parameters and transmitting them anywhere. Smart portable products can now be used to monitor different medical aspects to track human health. Also, they can be used in the prediagnosis of various diseases and in detecting abnormalities of organ functionality. In this article, we design and implement a multifunction and portable health monitoring system, which can help in daily medical inspections. The developed system monitors various medical aspects: heart rate (HR), blood oxygen saturation level (SpO2), body temperature, photoplethysmography (PPG) signal, electrocardiography (ECG) signal, room temperature, and room humidity. The obtained measurements are displayed on the built-in display or transmitted over Wi-Fi to either a mobile application, in the local mode, or to the cloud storage for remote monitoring. The developed system can be used to keep an eye on the people we need to care about, while keeping them in their normal daily life. The maximum error percentage of the proposed system is reported as 2.67%, 2.04%, and 1.58% for HR, SpO2, and body temperature, respectively, compared to commercial devices. In addition, statistical tests were performed and they showed a high level of agreement between the observed and the reference measurements. The results indicate the high accuracy and effectiveness of the proposed system to be used in daily medical applications.