Platform Technology
One sensing class. Multiple critical applications.
TECHNOLOGY
A New Class of Mechanical Sensing
Altwave develops a new class of mechanical sensing systems that encode physical information into engineered acoustic signatures — operating without electronics, power, or wireless infrastructure in the environments that demand the most.
SENSOR PLATFORM — A
Electronics-Free Flowmeter
Mechanical aeroacoustic structures generate information-rich sound signatures directly from fluid flow — no electronics, no power source, no failure modes from corrosion or pressure.
APPLICATIONS
- Multi-line chemical injection monitoring
- Drill-bit flow sensing
- Pipeline monitoring
- Multiphase flow systems
- Process water monitoring
- Petrochemical flow sensing
SENSOR PLATFORM — B
Mechanical Rotation & Position Encoder
A mechanical encoder generating acoustic signatures corresponding to shaft position and motion — built for environments where electronic encoders fail or cannot be deployed.
APPLICATIONS
- Harsh-environment robotics
- Industrial machinery
- Valve position sensing
- Underground mining machinery
- Remote rotating equipment monitoring
SENSOR PLATFORM — C
Structural & Pressure Monitoring
APPLICATIONS
- Tank/reservoir pressure monitoring
- Infrastructure settlement sensing
- Dam/foundation monitoring
- Long-term remote monitoring
DIFFERENTIATOR
Signal Interpretation Layer
Altwave sensors do not generate random noise — they produce structured, physics-encoded acoustic signatures. This fundamental difference reshapes every layer of the signal processing stack.
Engineered Signals
Physical parameters are encoded into predictable harmonic structures — not extracted from noise. Every signature carries deterministic, interpretable information.
Better Conditioning
Structured signals require less filtering, less amplification, and fewer pre-processing stages — reducing system complexity and improving reliability at the edge.
Lower Data Requirements
Because signals are information-dense by design, full measurement fidelity is achievable with far less raw data — enabling lightweight transmission and local inference.
Physics-Grounded AI
Machine learning models trained on structured acoustic signatures operate from a known physical basis — improving interpretability, reducing training data needs, and increasing robustness in deployment.