Internal Noise

Internal noise, inherent in various data and system types, significantly impacts the performance and reliability of many computational models and physical systems. Current research focuses on mitigating the effects of this noise across diverse applications, including medical image analysis (using multi-modal fusion techniques like Modal-Domain Attention), analog neural networks (investigating noise propagation in echo state networks), and infrared target detection (employing generative models to overcome target-level insensitivity). Understanding and addressing internal noise is crucial for improving the accuracy, robustness, and reliability of these systems, leading to advancements in fields ranging from healthcare diagnostics to physical system modeling.