A Framework for Real-Time Sensor Optimization and Decision Support using Synthetic Domain Adaptation

This paper introduces Syn-Optic, a novel hybrid framework designed to address the critical challenges of data scarcity, hardware variability, and decision uncertainty in autonomous diagnostic systems. The framework integrates three core components: a synthetic data generation module for creating annotated training datasets, a support vector regression-based optimization engine for real-time sensor parameter tuning, and a fusion architecture that combines learned perception with symbolic reasoning. We present extensive mathematical formulations for each component, including a novel loss function for synthetic-to-real domain adaptation and a constrained optimization routine for lens parameter adjustment. Comprehensive experiments demonstrate the framework's efficacy in two distinct domains: robotic-assisted medical imaging and autonomous vehicle perception. Results show that the Syn-Optic framework achieves a 37% reduction in mean reprojection error compared to baseline calibration methods, while the hybrid decision layer reduces safety-critical errors by over 99% compared to purely statistical models. The paper concludes that the integration of synthetic data, statistical optimization, and rule-based validation provides a robust pathway toward trustworthy and adaptive autonomous systems.