MoRa · Physics × Vision
The inside is not
measurable. It is recoverable.
MoRa turns signals a machine already has into a live estimate of what is happening inside it, then acts locally on a chip that costs about $3.
This is a live simulation, not a video. Grey is measured; amber is the internal state MoRa estimates.
See the model behind the visual
The browser solves ∂²u/∂t² = c(x)²·∂²u/∂x² − γ(x)·∂u/∂t across 512 cells. The chirp, reflection and echo trace are computed in real time.
The primitive
One simple idea: use what a system already senses.
Machines reveal clues about their internal state through signals they already measure. MoRa turns those clues into a usable estimate, without adding another sensor.
The result is smaller, faster and practical enough to run directly in firmware.
- Boundary signalwhatever the machine already has
- Physics priorthe governing equation
- Hidden stateinferred, not measured
- Actionin firmware, on-device
The same four steps power both products. Only the physical system changes.
Proof · human airway
AneMoRa
A clip-on module for any inhaler that fires an acoustic chirp, listens to the echo, and reconstructs internal airway geometry and tissue state on-device.
A sensing modality, not a device. It turns an inhaler the patient already owns into an airway tracker.
What changes
Today’s smart inhalers know whether they were pressed. AneMoRa estimates what happened inside the airway during the dose.
Why it matters
Clinicians and pharma could see obstruction, mucus and drug deposition instead of relying on adherence data alone.
Designed to fit
It clips onto an inhaler the patient already owns, reads the response on-device and needs no cloud connection.
How AneMoRa worksOpen the technical model and novel read
From echo to airway state
An active chirp creates an echo. A compact, physics-informed model running on an ESP32 uses that echo to estimate the hidden state. No cloud round trip.
The novel read
c(x) estimates where the airway narrows. γ(x)estimates the tissue and mucus state at that point.
The γ read is the part other smart inhalers do not provide.
Validation and commercial pathOpen results, partners, revenue and regulatory detail
Channel, solved
Zenvision CDMO partnership confirmed (Dr. Siva Kumar). They file the patent, handle regulatory, and sell AneMoRa as an add-on through a 1,200+ account network.
Whitespace
Incumbents (Propeller, Adherium) do adherence. Acoustic-AI players do passive cough classification. Nobody does active, on-device airway-state estimation.
Acoustic reflectometry is validated academic physics, so the method is de-risked. The patent is filed narrow: clip-on coupler, edge inversion, γ-discrimination.
- Revenue
- pharma / CRO data licensing ₹15–40L per study → add-on device sales via Zenvision → inhaler-OEM licensing
- Regulatory
- CDSCO SaMD
Proof · sorption
NereMoRa
Smarter cycle control for water, humidity and thermal systems. NereMoRa estimates when the material is actually ready to switch, instead of relying on a fixed timer.
It works with the sensors and hardware operators already have, and runs directly in firmware.
Less guessing
The controller estimates how loaded the material is before the outlet sensor shows a late warning.
Better timing
Each cycle switches when the system needs it, reducing wasted water and energy caused by fixed schedules.
Fits existing machines
No new box. The software runs on a standard microcontroller across water harvesters, dry rooms and desiccant systems.
How NereMoRa worksOpen the sensors, physics model and control loop
- Boundary sensorsRH · temp · ΔP · flow
- Physics priorsorption kinetics
- Loading stateestimated
- Optimal cyclein firmware
Outlet humidity, temperature, pressure drop and flow remain the measured inputs. The loading state is estimated; the firmware uses it to decide when to switch the cycle.
Validation and commercial pathOpen partner, pilot and market context
Maithri Aquatech
securedMaker of the “Meghdoot” AWG and one of India’s leading atmospheric-water manufacturers. They are providing operating data for a validation case study, benchmarking our controller against their current fixed-cycle operation, with a pilot to follow.
Validation happens on real customer data, not a lab bench.
Why now
AI and data-centre water pressure is pulling real budget into sorption cooling. And physics-informed estimation now runs in milliseconds on sub-$5 chips.
Both halves of that sentence were false five years ago.
The platform
One core technology, proven in two very different systems.
A human airway and a water-harvesting material look unrelated. In both, MoRa turns existing boundary signals into a useful view of what is happening inside.
Two products and two industry partners show that the estimator—not a single device—is the reusable asset.
Compare the two systemsOpen the shared four-step architecture
AneMoRa · human airway
- Chirp echoone microphone
- Wave equationc(x) · γ(x)
- Airway stateinferred
- Read-outon ESP32
NereMoRa · sorption
- Outlet RHexisting sensors
- Sorption kineticsisotherm · rate
- Loading stateinferred
- Cycle switchin firmware
The ledger
Every claim, with the evidence that backs it.
Simulation and hardware results are reported separately, and nothing here is stated as proven beyond its evidence. The one open item is listed as an open item.
Open the evidence ledgerSimulation, bench, code and partner status—kept separate
Contact