We are operating one of the largest urban measurement networks for Greenhouse Gases (GHG) and Air Quality in the world. Most of it is automated to enable end-to-end data acquisition, communication, information retrieval, and visualization. We have a lot of topics for motivated students to work on and enhance these IoT systems throughout the whole pipeline. If you are interested in working with us get in contact with Patrick Aigner or Prof. Jia Chen and attach your latest CV, Transcript of Records (ToR), and the topics of interest. 

IDP Proposal #1: Visualization of IoT Sensor Data

In the Munich area, we are currently setting up multiple sensor networks, consisting of several devices each: 1) CO₂ - 20 sensor units, 2) Air quality (NO_X, O₃, CO, PM) - 50 sensor units, 3) CO₂ - 100 sensor units. Furthermore, one network is already in full operation since 2019, measuring the total columns of CO₂, CH₄, CO, H₂O and O₂: MUCCnet, consisting of 5 high-accuracy instruments. All networks generate time series data which needs to be visualized.

Until now, there are only some standalone visualizations like https://retrieval.esm.ei.tum.de/, https://airquality.ei.tum.de/nodes, and https://github.com/tum-esm/shareable-timeseries-visualization but no uniform solution.

This IDP should find a uniform solution for our data: 1. Requirements engineering for a uniform visualization tool. 2. Finding possible tools and architectures. 3. Implement a minimal example of the solution we picked.

If you are interested in this topic, get in contact with Patrick Aigner and attach your latest CV as well as your Transcript of Records (ToR).

IDP Proposal #2: Error Analysis of interferograms generated by our FTIR spectrometers

EM27/SUN spectrometers are Fourier-transformation infrared (FTIR) spectrometers that measure the spectrum of a light source through recording an interferogram. These spectra can be used to deduce the average mole fraction ("concentration") of several gases within the light path. In MUCCnet, we are operating five EM27/SUN spectrometers to determine the total columns of CO₂, CH₄, CO, H₂O and O₂ in and around Munich.

In order to convert the interferograms generated by the EM27/SUN instruments to averaged column concentrations, we use Proffast 2.2. On days with a high amount of direct sunlight, we have about 2500 to 3000 data points, but Proffast will throw exceptions for about 50% of them. Since these errors are scattered over the whole day, we still obtain continuous time series as outputs.

This IDP will analyze these errors: 1. Statistical analysis of the error over time/device/error code. 2. Search for possible explanations. 3. Try to find and indicator in the interferogram files whether Proffast will reject it or not. 4. Include this error-detection together with an existing error detection (https://github.com/tum-esm/detect-corrupt-ifgs) into a Python utility library by the chair (https://github.com/tum-esm/utils).

If you are interested in this topic, get in contact with Patrick Aigner and attach your latest CV as well as your Transcript of Records (ToR).