Verhaert has a extensive portfolio of technologies to accelerate innovation. Within each technology domain there are different IP building blocks that can be used for different solutions.
- Artificial Intelligence
- Sensor design
Optical Coherence Tomography
Our Optical Coherence Tomography technology has been customized so it can scan larger arteries to measure blood flow, lumen and other clinical parameters. In addition, this OCT scans those parameters in 3 dimensions.
Ex-vivo blood vessel measurement
We built ex-vivo blood vessel measurement device based on Optical Coherence Tomography, so you can look deeper into the skin and even below. This allows measuring blood flow rates in 3D with lumen sizes.
Differential Interference Contrast (DIC) is a well-known optical microscopy technique to improve contrast unstained transparent samples. Our design IP ranges from visual to IR images with space optimization in the same optical column.
Critical environment evaluation
This critical environment evaluation solution simulates both digital control domain and physical simulation domains at the same time. As a result critical environments, for example of building setups, can be evaluated more quickly.
Laser scanning confocal microscope
A custom laser scanning confocal microscope was developed and built for ESA to investigate cell behavior in orbit. In addition, we had to make sure it could withstand the extreme space conditions.
PPG signal processor
Verhaert owns IPs to process PPG signals on several levels into relevant clinical and lifestyle information. Our IP covers optical improvements through polarization, multispectral signal correlation and anomaly rejection due to movements and artifacts.
Magnetic / impedance sensor
When compared to other impedance sensors, the IP of Verhaert is situated on the principle of mutual coupling. These sensors provide an enhanced way of measuring distance that eliminates the drawbacks from classic impedance measurements.
US 3D localization sensor
This low cost sensor for aquatic environments measures the 3D distance between 2 sensor nodes, delivering distance and 2 angles (horizontal & vertical). It avoids having complex triangulation processing and uses a ‘hello’ pulse from both sides.
Fire propagation prediction
For fire prediction, propagation within a phase and evolution from one to another is much more important than detecting the stage of the fire. An SVM with several classes can determine the speed of propagation from one state to another.