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Sensing & Control

Oneirix Labs undertakes complex sensing & control projects with our cutting-edge know-how of electronics, algorithms, applied physics and mathematics

We have solid experience creating solutions to sense and control diverse phenomena, including movement, speed, heat, force, pressure, velocity, flow, light and more.

Typical large operations receive raw data streams from tens of thousands of sensors. One must surmount several challenges to turn these raw streams into high-quality data:

Noise in individual sensors, rendering data imperfect in unpredictable ways
Huge volume of data being generated, at a high rate
Sensor fusion, or how to consistently and coherently merge data from many sensors
Hidden variable discovery, i.e. recovering indirect or inferred information from sensed data

High quality data, once obtained, is used for decisions. Long term decisions fall in the domain of optimization. Decisions to be taken almost instantaneously fall under the purview of “control”. Taking and actuating optimal decisions every instant is challenging, requiring careful engineering and advanced mathematical algorithms.


The field of sensing and control evolves rapidly, making it hard to devise optimal solutions. We help clients find the right mix of techniques from the kaleidoscope of options available. And we invent new ones when nothing suitable exists.

Our careful electronic circuit and amplifier design ensures freedom from circuit and electrical noise. We deeply understand calibration methods, how best to interface with various kinds of transducers, and how to build the appropriate amplifier chain.

Our advanced signal processing algorithms improve data quality and create inferences. Further, our control algorithms use these results, and other techniques, for accurate control.

We have used our applied physics expertise to give hitherto unknown sensing/actuation abilities to devices. This includes developing new sensor actuation physics, and developing new theory and simulations for such new sensors.

And last, but not least, here are some of the applied math techniques we have used to dissolve many obstacles in sensing and control:

To solve sensing problems:

Traditional signal processing, dominated by transform techniques such as Fourier and wavelet transforms
Optimal and statistical signal processing based on the theories of optimization, and stochastic processes
Adaptive signal processing, which can reoptimize algorithms on the fly
Nonlinear signal processing which can go beyond various linearity assumptions made by traditional theories
Newer methods called “topological signal processing” which can go beyond assumptions of vector spaces made by traditional theories
Inverse methods, inverse boundary value problems, and probabilistic inverse methods for hidden variable recovery

To solve control problems:

System identification theory
Nonlinear dynamics and nonlinear control theory
Boundary control of partial differential equations
Feedback and adaptive control


Multi-axial Material Testing Machine

No machine exists that can accurately measure parameters of mechanical behavior of non-linear materials. So we decided to build one. Though the machine is novel because of a host of algorithms running inside, the sensing & control problem itself was quite tricky: the machine has 13 actuators, 40 sensors, and a vast distributed sensing & control system.

Read more about the machine and its applications

Single Fiber Endoscopy

New theoretical advancements can create new sensing techniques. In this project, we used physics, mathematics and algorithms to design an endoscope that can crawl through micron-sized spaces.

Learn more

Medical Imaging

Medical imaging is the art of sensing the inside of a human body from the outside. This field is filled with advanced physics and signal processing algorithms that deal with large amounts of noisy data collected by highly accurate sensors. We have done a lot of work in the field of medical imaging and “inverse methods”.

Sample our work on medical imaging