The current COVID-19 pandemic shows the necessity of personal protective equipment and face masks. In the project, a filter module with an in-situ ultraviolet-sterilization technique is designed that can serve as a new kind of smart personal protective equipment (SPPE). This technique is not used in wearable devices as of yet. The project thus aims to take the next step into the future of facemasks. The complete SPPE design is split into three submodules. In this thesis, the Sensing and Control submodule is designed. The Sensing and Control submodule is divided into three parts as well. In the first part of the design, a negative feedback control loop is developed. A photodiode transimpedance amplifier circuit provides the feedback, and the controller is programmed on a microcontroller. The control parameters are derived from a model in Simulink. In the second part of the design, the temperature and relative humidity are measured to transform the control loop’s reference value into a reference function. In the third part of the design, an estimation of the filter state of health is made by measuring the pressure drop over the filter material. Additionally, the airflow in the SPPE is calculated using equations from fluid mechanics to set the maximum allowable pressure drop. At the end of the thesis, the Sensing and Control submodule allows the SPPE to measure environmental conditions, control the ultraviolet intensity accordingly, and indicate if the filter requires replacement. The design is finalized with printed circuit board designs and an algorithm. With the design of the Sensing and Control submodule, the next step is taken towards the future of face masks.

Drones that perform complex autonomous movements require a perfect estimate of their current position. However, internal measurement unit (IMU) errors introduce drift in this estimate, leading to significant discrepancies between the predicted and actual location. Various solutions have been proposed to calibrate the IMU, including methods involving cameras and humans in the loop. This thesis suggests implementing a previously developed technique that involves projecting a precise static light polarisation grid into a room. Although this pattern is invisible to the human eye it can be observed using a polariser and colour sensor combination. A drone equipped with such a sensor setup can recalibrate for IMU drift by utilising the perceived polarisation patterns as optical landmarks. The system design is further developed by exploring the potential of visible light communication (VLC) as an alternative to traditional radio frequency (RF) links for drone control. By leveraging the existing infrastructure used for the projection of the polarisation grid, a VLC link is integrated into the system. With the addition this work strives to fuse polarisation-based localisation and VLC, setting the first steps in creating a fully visible light-based drone platform. To validate the system, a prototype is created that achieves real-time simultaneous localisation and communication on an embedded drone. This is accomplished through machine learning based classification, a drone motion model, an optimised polarisation pattern enabling fast localisation and a noise-resistant VLC link. Experiments show a median 2D tracking error of 10cm using only light-based methods and a VLC link range of up to 2.5 meters under various conditions.