Large cities in the Netherlands, like Rotterdam, have hundreds of playgrounds, but local governments have little information on how children and adults use them. Usage data can help create playgrounds that better fit with the residents' needs by identifying what elements of a playground are the most popular, and which do not get used. The AMS Institute and municipality of Rotterdam have asked us to develop a system to collect usage information of playgrounds without recording personally identifiable information. The system requirements are to record the locations of individual users and to estimate if a user is a child or an adult. It must do this without requiring an external power source and without a broadband internet connection. After considering different types of sensors (including computer vision, radio, sound, and mechanical acoustic signals), We decided to use a mmWave radar sensor due to its ability to provide accurate localization, easy installation, and low energy usage without recording identifiable information. We use a commercially available mmWave radar sensor that we configure to localize people in a 30m by 20m area when placed at the perimeter. We use the radar point cloud output from the device for classification by calculating statistics that we use as features for our classifier. We evaluate classifiers based on SVM, Random Forrest, fully connected and recurrent neural nets. We also analyze different methods for combining radar point clouds captured over time from the same person. We collected 100.000 radar point clouds of adults, children, and bicyclists at real playgrounds, split into a training and validation data set. We show that our SVM classifier achieves an accuracy of 79% on single radar frames, and 93% on the combined results of 10 second long sequences of our validation dataset. The classifier requires <1KB of memory and little processing power. Meaning it can execute on an embedded platform powered by a solar panel.

In order to measure the spectrum of radio emissions from galaxies and other deep space objects, a new superconducting spectrometer, working at very cold temperatures close to the absolute zero, is developed. An advanced cooling system called a cryostat is used to cool down the spectrometer. The cool down of the cryostat involves the control of multiple sensors and actuators connected to the cryostat to achieve a final temperature below 250 millikelvin. A software program is used for this purpose. As extra hardware components have been added to the cryostat, the existing program does no longer fulfill the requirements. For this reason a new software program, which can monitor temperatures of all components and start control processes, is developed. The developed program consists of a client server structure. The server handles the logic of the cryostat using several controllers. It can send data to a native client, which is the graphical user interface, or a REST API. The native client displays sensor readouts received from the server and allows full control of server, which means it can start the cool down process as well as manual control processes. The REST API allows the user to have full control over the server using a Python script to achieve measurements which cannot be done from the native client. The increased automation, improved control and ability to integrate with external Python scripts allow the user to focus on the essential parts of an experiment making the developed program an improvement over the previous program.