Waves Tune Real-time Crack -

| Phase | Milestones | Estimated Effort | |-----------|----------------|----------------------| | 0 – Discovery | Clarify wave type, sensor hardware, target crack size. | 1 wk | | 1 – Prototype Core Loop | Build a sandbox (PC‑based) that:
• Generates arbitrary waveforms (MATLAB/Python).
• Simulates sensor response (FEM or recorded data).
• Runs a simple adaptive algorithm. | 3 wks | | 2 – Embedded Port | Port Wave Generator to target micro‑controller/DSP.
Integrate ADC driver and basic UI (Qt/Embedded). | 4 wks | | 3 – Detection Engine | Implement threshold method + a lightweight CNN (e.g., TinyML).
Collect a labeled dataset of cracked vs. intact specimens. | 5 wks | | 4 – Closed‑Loop Tuning | Combine adaptive

The feature you're referring to seems to be related to audio processing, specifically a real-time crack or distortion effect that can be applied to sound waves. Let's break down what this could entail and how it might work:

| Feature Name | Waves‑Tune Real‑Time Crack Detection | |------------------|------------------------------------------| | Goal | Dynamically adapt the characteristics of an excitation wave (frequency, amplitude, phase, waveform shape) in real time to maximize the sensitivity and reliability of crack detection in a target material or structure. | | Primary Users | • Field engineers & technicians (non‑technical)
• R&D scientists & analysts (technical)
• Maintenance managers (decision‑makers) | | Key Benefits | • Faster detection of micro‑cracks before they propagate.
• Reduced false‑positive/negative rates by auto‑tuning to material properties and environmental conditions.
• Live visual feedback → immediate action.
• Less manual trial‑and‑error → lower training overhead. | | Context | Typically used with:
• Ultrasonic/ acoustic emission transducers (solid media).
• Electromagnetic/ radar‑based wave probes (composites, pipelines).
• Seismic‑wave arrays for large structures (bridges, dams).
The system continuously streams sensor data, runs a lightweight inference engine, and updates the excitation waveform on‑the‑fly. | waves tune real-time crack

Feel free to tell me which parts match your vision and where you’d like more detail—or to let me know the exact domain (audio processing, structural‑health monitoring, ultrasound imaging, etc.) so I can tailor the spec further.

| Question | Impact | Possible Options | |--------------|------------|----------------------| | Domain of waves – ultrasonic, EM, acoustic, seismic? | Determines hardware spec, frequency ranges, safety constraints. | Choose target (e.g., 1–5 MHz ultrasound for metal plates). | | Adaptation Strategy – simple heuristic vs. ML? | Affects development effort, on‑device compute, data‑collection needs. | Start with SNR‑maximizing gradient, later add RL policy trained on labeled cracks. | | Number of sensors – single vs. multi‑array? | Influences firmware complexity, data bandwidth, UI layout. | Provide a modular sensor‑driver layer; start with single sensor for MVP. | | Deployment environment – handheld, drone‑mounted, fixed‑station? | Affects power budget, ruggedization, communication. | Define form factor early. | | Regulatory compliance – medical ultrasound vs. industrial NDT? | May impose strict limits on acoustic power, documentation. | Identify applicable standards now (e.g., ASME‑V, IEC 61345). | | Data privacy / Cloud – Do you need remote analytics? | Impacts API security, data‑retention policies. | Offer optional “cloud sync” toggle. | | Phase | Milestones | Estimated Effort |