Got it 👍 You want to understand how a non-real-time signal can be used in a real-time application — and you mentioned “clear midline,” which I’ll interpret as needing a straightforward explanation (no fluff, clear and central idea).

✅ Core Idea (Midline)

A non-real-time signal can still be applied in real-time applications if you either:

Preprocess & store it → then use it as a reference/model in real-time.

Approximate or transform it into a real-time compatible form.

📌 Examples

Medical Signals

Non-real-time: An offline ECG recording.

Real-time application: Train an algorithm on the stored ECG to detect arrhythmias → then deploy it to monitor patients in real-time.

Audio/Video Processing

Non-real-time: Pre-recorded speech samples.

Real-time application: Use them to build a voice recognition model → then run live speech-to-text.

Control Systems

Non-real-time: Simulation data of a drone’s dynamics.

Real-time application: Implement the learned control law on an actual drone during flight.

Finance

Non-real-time: Historical stock price data.

Real-time application: Derive indicators, strategies, or machine-learning models from it → then apply them on streaming market data.

🔑 How It Works (Simple Flow)

Non-real-time signal → Process/Train/Extract patterns → Deploy model → Apply on live input

Would you like me to also sketch this as a visual diagram (flowchart) so it’s crystal clear?