Area One · Signal Processing
Deterministic algorithms for next-generation wireless infrastructure and sensing, with roots in compressed sensing.
One of two areas built on a shared mathematical foundation.
What we do
Most real-world signals are sparse in some basis. Our work uses that property to build algorithms that focus on the structure that matters and skip the rest.
Sparse beamspace processing for massive arrays. Steering and nulling that scale with array size without scaling latency.
Wireless infrastructure
Channel estimation and precoding under realistic bandwidth and energy budgets, with predictable runtime.
Wireless infrastructure
Real-time identification of sparse frequency components in wideband captures, with reproducible behavior suitable for certification.
RF intelligence
Vibration analysis, bearing fault detection, and condition monitoring with the same mathematical core.
Industrial & predictive maintenance
ECG, EEG, and HRV analysis suitable for high-reliability medical wearables and continuous monitoring.
Medical & wearables
Target detection, clutter reduction, and range estimation with sparse priors instead of brute-force search.
Defense & automotive sensing
The technical approach
Compressed sensing showed that signals with sparse representations can be reconstructed from far fewer measurements than classical sampling theory requires. Our work takes that lineage into production-grade, deterministic algorithms.
The same signal, rendered two ways. The dense panel shows every frequency bin. The sparse panel keeps only the components that carry information.
Why deterministic
Reproducibility is the property that makes everything downstream tractable. When a function returns the same answer for the same inputs across runs and machines, you can test it the way you test other software. You can audit it. You can ship it into regulated domains without writing a custom validation framework first.
Most signal processing libraries treat reproducibility as a nice-to-have. We treat it as the starting point. Our algorithms are deterministic by construction, with bounded worst-case runtime and memory known ahead of deployment.
Audit-ready
Traceable behavior for regulated domains.
Certifiable
Validated timing and resource profiles.
The other area
The properties that make sparse methods useful for signals also apply to neural networks. That's the second area of our work.
Research collaborations, licensing, or technical consultations.
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