Efficient time-adaptive Expectation Maximization (EM) algorithm for HMM tracking
In ULISSES we are developing data-driven calibration algorithms for autonomous CO2 sensor calibration. This has been done by developing an approach where a Hidden Markov model (HMM) is used to predict on a calibration coefficient to compensate for remaining dependencies of environmental impacts such as temperature on the sensor measurement output. The HMM model is learned from the past sensor data in an unsupervised way using the expectation maximization algorithm. For such an approach, convergence and sample complexity are critical factors.
In ULISSES we are developing networked data-driven calibration algorithms for autonomous CO2 sensor calibration. In a network each sensor has an own belief on the measurement. Sensors that have been recently calibrated using a reference might have a better calibrated sensor model than other sensors that have not been calibrated for long time and their model has become outdated.
Platinum diselenide (PtSe2): a novel 2D material with very promising properties for applications in electronics and sensing
In ULISSES we are actively researching its use for photodetectors. Its main strength is that, in contrast to many other materials, it can be synthesized at a relatively low temperature compatible to common CMOS processing, without strict substrate requirements...
The ULISSES partners organized a special session focussed on the application of graphene and 2D materials in integrated optical gas sensors in the European research projects ULISSES and AEOLUS at the Graphene 2022 conference in Aachen in July 2022.
What if you could have access to real-time air quality monitoring on your smart phone, mapping out where all the pollution hot spots are located? In cities, Air Quality (AQ), can change dramatically from one hour to the next and from one block to another. Everyone should have access to this data, right? We believe so!