## 1 Introduction
Uncooled IR detectors have become a popular choice for industrial applications due to their ability to detect gases of interest, whose strongest bands are found in the 1-5 micron range. Compared to other commercial IR sensors, uncooled IR detectors offer better response time, making them highly sensitive and able to provide both spatial and temporal information. However, their area of detection is often narrow, which can limit their effectiveness [24; 25]. In recent years, Internet of Things (IoT) sensors have played a critical role in primary flame detection. A camera-based system that utilizes multiband IR imagery and thermal analysis has been developed for wildfire detection. This system incorporates LoRa-based wireless communication for long-distance monitoring. Researchers have used the Dempster-Shafer theory and Linear Discriminant Analysis for data analysis and detection of false positives. Despite its effectiveness, the mathematical prediction model for this system is quite complicated and can be difficult to compute [26]. In recent years, fire detection technology has advanced rapidly, with the development of new systems that offer improved accuracy and speed in fire detection. One such system is a Low-Earth Orbit system that utilizes long- and shortwave IR sensors to detect and measure actively burning fires on the Earth's surface [27]. This system is particularly useful for monitoring large areas, such as forests, where traditional ground-based fire detection systems may not be sufficient. In addition to satellite-based systems, smart fire warning and alarm systems are also becoming more sophisticated. These systems typically use multiple sensors, such as flame detectors, humidity, heat, and smoke sensors, among others, to detect fires. To process the data from these sensors and generate a fire alert, Adaptive Neuro-Fuzzy Inference System is commonly employed [28; 29]. Despite the benefits of multi-sensor and IoT-based fire detection systems, these technologies also have some drawbacks. For example, they may suffer from slow response times, low sensitivity, high cost, and high system complexity, which can make them difficult to install and maintain. This paper introduces a new approach to fire detection and warning systems that offers a smart, compact, and cost-effective solution. The system incorporates a UV detector that operates in the range of 185-260 nm to detect the signal from the fire source, making it suitable for identifying the burning of different materials at various rates. To eliminate background noise, the signal processing circuits have been designed to cancel out any sporadic noise that may arise due to the background. The system also determines the direction of the fire source, allowing for more precise and efficient fire extinguishing. The alarm signal can be generated in a high-frequency pulse shape (100 Hz) and transmitted to a computer via Wi-Fi or sent to the user's smartphone. Experimental results demonstrate that the system can detect a fire source within a range of 50 m. Furthermore, an artificial intelligence (AI) system has been developed to prevent false alarms. The proposed system is well-suited for use in warehouses, seaports, and factories, and can be integrated into a monitoring network. The innovative approach presented in this paper offers a new, effective solution for fire detection and warning systems that can enhance safety and reduce property damage.