## 2 Materials and Methods
### Hybrid Sensor Design and Edge AI Detection  
The fire detection subsystem is centered on a hybrid sensing module that integrates photoelectric smoke sensing with thermistor-based heat detection, enhanced by an onboard edge AI processor. This dual-sensor approach addresses the limitations of conventional single-mode detectors, which often yield high false alarm rates or delayed responses when relying solely on smoke opacity or temperature thresholds. By fusing data from both sensing modalities, the system achieves greater sensitivity to early-stage combustion signatures while maintaining robustness against environmental noise such as steam, dust, or transient heat sources. Recent advances in hybrid fire detection have demonstrated the advantages of integrating multiple sensor types--such as smoke, heat, and gas sensors--through AI-based data fusion. In particular, the combination of photoelectric and thermal sensing can be further \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Task** & **Notable Model(s)** & **Key Improvement/Result** & **Citation** \\ \hline Object Detection & Geometric Prior DETR & +5\% mAP over deformable DETR & [34] \\ Semantic Segmentation & RS-Lambda, GLOTS, CG-Swin & SOTA accuracy, efficient on large images & [31; 33] \\ Change Detection & AMTNet, DMATNet & Outperforms previous SOTA on 4 datasets & [33; 35] \\ Super-Resolution & TTST & Higher PSNR, lower computation & [36] \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Representative transformer-based models in remote sensing. enhanced by incorporating machine learning algorithms (e.g., support vector machines, neural networks) that discriminate between real fire events and environmental interference with high accuracy [36; 37]. Edge AI processors embedded within the detection units allow real-time data processing and classification using deep learning models, such as CNNs and YOLO variants, enabling rapid response even under complex conditions like fog, dust, or dynamic lighting [38; 39]. These hybrid systems also support spatiotemporal feature fusion--integrating both the spatial signatures of flame or smoke and the temporal evolution of sensor readings--to further reduce false positives while maintaining swift detection capabilities [40; 41]. As a result, the proposed subsystem achieves early and reliable fire detection performance suitable for deployment in smart buildings and urban infrastructure, where environmental variability and safety-critical demands require both precision and resilience. The smoke detection component utilizes an optical scattering method, where infrared light emitted into a sensing chamber is dispersed by airborne particles generated during pyrolysis or open flame combustion. The heat detection unit monitors rapid temperature rise using a digital thermistor calibrated to detect both fixed threshold exceedance and rate-of-rise patterns. These signals are continuously sampled at 0.2-s intervals and pre-processed through an analog-to-digital converter (ADC) integrated into the microcontroller. At the core of the module is a lightweight convolutional neural network (CNN) model designed for efficient fire event classification using time-series sensor data. CNNs are well-regarded for their ability to automatically extract complex spatiotemporal features, making them highly suitable for fire detection tasks in real-time and resource-constrained environments. The architecture consists of three convolutional layers, each followed by max-pooling and dropout regularization, culminating in a fully connected softmax output layer for multi-class classification. This design reflects trends in recent studies that prioritize architectural efficiency without compromising accuracy [42; 43]. The input feature vector is constructed from a rolling time window of optical density and temperature values, along with their first-order derivatives. This enables the model to capture both static and dynamic combustion characteristics.