## 6 Results
### One-Dimensional CNN for EIT Data Analysis
After compression, the FLOPs are significantly reduced by more than 99% in both cases. Specifically, the FLOPs drop to 30.720 K and 15.360 K for, respectively, 40 BVs and 20 BVs models demonstrating the remarkable computational efficiency of the compressed models. The lower FLOPs for the 20 BVs configuration further reinforce its suitability for edge devices with limited computing resources. The near-linear relationship between input dimensionality reduction and FLOPs reduction is particularly noteworthy. When the input dimensionality was halved from 40 to 20 BVs, the FLOPs correspondingly decreased by approximately 50%. This shows an almost linear computational scaling, indicating that the reduction in input dimensionality directly translates into a proportional reduction in FLOPs and thus computational requirements, which suggests an inherent scalability in the model's architectural design, where computational efficiency is maintained even as the model complexity is reduced. In fact, the idea was to have an identical KD approach for both cases. Varying the input dimensions while using the same student model with the same hyperparameters during the KD approach during the compression pipeline was effective in our case, which is why we had the same variation for FLOPs, allowing significant computational efficiency gains while staying close to the original performance levels. The reduction in FLOPs demonstrates the dramatic improvement in computational efficiency, making the compressed models highly suitable for real-time edge applications. This is particularly important for resource-constrained devices where power efficiency and processing speed are critical. The loss analysis shows the trade-offs associated with compression and input dimensionality reduction. The initial models achieve a minimal loss of 0.01 for both 40 BVs and 20 BVs, indicating excellent convergence during training. However, after compression, the loss increases significantly for both configurations, reaching 0.31 (40 BVs) and 0.36 (20 BVs). The slightly higher loss for the compressed 20 BVs model reflects the challenges of combining input reduction and compression, which may affect the model's ability to fully capture the complexity of the data. Nevertheless, the loss values remain within acceptable limits for practical applications, suggesting that the trade-off in representational power is justified by the significant reductions in size and computational requirements. These results highlight the need for the careful calibration of compression techniques to minimise the impact on loss while maintaining efficiency. The F1 score, defined as the harmonic mean of precision and recall [41], serves as a comprehensive measure of a model's performance by balancing its ability to correctly identify positive instances (precision) and capture all relevant instances (recall). The F1 score results highlight the strong predictive performance of the initial models, with values of 0.9269 for the 40 BVs model and 0.9438 for the 20 BVs model. After compression, the 40 BVs model shows a significant improvement, with the F1 score increasing to 0.9907. This increase indicates that the compression technique not only reduces the model size, but also improves generalisation by reducing overfitting, especially in scenarios with larger input dimensions. For the 20 BVs model, the F1 score remains nearly unchanged at 0.9431 after compression, demonstrating the stability of the compression process in preserving critical predictive capabilities. These results underscore the effectiveness of compression in maintaining or even improving performance metrics such as the F1 score, ensuring the reliability of these optimised models for use on edge devices. In conclusion, this analysis highlights the importance of combining input dimensionality reduction and model compression to optimise CNN models for edge computing applications.