Research highlights
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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.
Networked data-driven self-calibration routines
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calibrated sensor model than other sensors that have not been calibrated for long time and their model has become outdated.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...
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Graphene 2022
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.
Graphene Week 2022
During Graphene Week 2022 in Munich, AMO GmbH had a booth, where the ULISSES project was the main represented project.
Conference contributions
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"Theory and optimization of graphene photothermoelectric detectors"
"Everyone should have access to high-resolution and real-time air-quality data"
"Design and optimization of graphene photothermoelectric detectors"
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On Data-Driven Self-Calibration for IoT-Based Gas Concentration Monitoring Systems.
IEEE Internet of Things Journal 9, 13848–13861 (2022). doi: 10.1109/JIOT.2022.3144934. Archive:
Two-Dimensional Platinum Diselenide Waveguide-Integrated Infrared Photodetectors.
ACS Photonics acsphotonics.1c01517 (2022). doi: 10.1021/acsphotonics.1c01517. Archive: Zenodo
Wafer-level hermetically sealed silicon photonic MEMS.
Photonics Research 10, A14 (2022). doi: 10.1364/PRJ.441215. Archive: Infoscience
Project publications
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Publishable summary
A spotlight on the project advances.
A description of the project has been developed and is available for download, as a single page, printer-friendly PDF info sheet.
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CO2 sensors in smart phones — 27 January 2021
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!
Project video
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A video presentation of the ULISSES project has been produced by SCIPROM at the beginning of the project. A very special thanks to Staffan Ehde at Senseair for a beautiful voiceover.
Please fill this form to send a message to the project responsible.
Hans Martin
Project coordinator
Stationsgatan 12,
82471 Delsbo
Kirsten Leufgen
Project manager
Rue du Centre 70
CH-1025 St-Sulpice
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 825272 (ULISSES).