Technology is playing an increasingly important role in education, partially thanks to emerging teaching methods such as hybrid education. Smart classrooms equipped with technology can make the lives of the educators and students easier, and aid in the switch to hybrid education. In this work we propose an IoT based smart classroom framework to create a base for smart classroom applications. Using this framework, multiple applications are manifested in the form of prototypes. One prototype introduces wireless sensor readout capabilities. The other prototype focuses on assisting the lecturer in a hybrid teaching setup. Using the prototype, the teacher is able to control whether online students are able to hear the students who are physically present and vice versa. The prototype also includes a way of indicating a question from an online student.

A common cardiac arrhythmia is atrial fibrillation, which is becoming more widespread worldwide. Currently there is some understanding about the mechanisms behind atrial fibrillation, however more insight into the conduction of the atrial tissue is desired. Therefore, invasive mapping studies have been performed where an array of electrodes is used to record the electrical activity on the heart’s surface during open-chest surgery. The moment in time when the tissue under an electrode depolarizes, called the local activation time can be used to reconstruct the propagation pattern of the signal that triggers the tissue to contract. In this thesis, the application of the cross-correlation for estimation of the local activation time of the atria is investigated. Specifically, the benefits of not only cross-correlating electrode pairs that are close, but also pairs that are far away are evaluated. A framework is constructed, based on a graph, that defines these higher order neighboring pairs of electrodes. This is compared to the golden standard of using the steepest deflection of an electrogram, as well as to other methods using the cross-correlation. Experiments are done on simulated electrograms where the true activation times are available, as well as on natural data recorded from patients. Finally some future research is proposed to investigate for which morphologies the proposed cross-correlation based methods may be most effective.