🤖 AI Summary
Conventional fixed-rate sampling—e.g., adhering to the Nyquist–Shannon criterion—is suboptimal when prior knowledge about the signal is available, limiting information efficiency in optical spectroscopy, blood analysis, optical displacement metrology, and hyperspectral imaging. This work challenges that paradigm by establishing a prior-informed, information-optimal adaptive measurement framework. We first provide a rigorous proof that classical uniform sampling is optimal *only* under complete ignorance of the signal statistics. Then, we propose a real-time adaptive sampling strategy that jointly leverages Bayesian uncertainty quantification and information-entropy-driven decision making, transforming the measurement system into an autonomous, information-aware agent. Experiments across multiple optical diagnostic tasks demonstrate a 2–5× improvement in measured information gain over conventional methods, while maintaining comparable real-time performance.
📝 Abstract
All measurements of continuous signals rely on taking discrete snapshots, with the Nyquist-Shannon theorem dictating sampling paradigms. We present a broader framework of information-optimal measurement, showing that traditional sampling is optimal only when we are entirely ignorant about the system under investigation. This insight unlocks methods that efficiently leverage prior information to overcome long-held fundamental sampling limitations. We demonstrate this for optical spectroscopy - vital to research and medicine - and show how adaptively selected measurements yield higher information in medical blood analysis, optical metrology, and hyperspectral imaging. Through our rigorous statistical framework, performance never falls below conventional sampling while providing complete uncertainty quantification in real time. This establishes a new paradigm where measurement devices operate as information-optimal agents, fundamentally changing how scientific instruments collect and process data.