This thesis addresses the challenge of generating long-duration, high-quality videos using the XL30 FEG/SFEG/SIRION Scanning Electron Microscope (SEM), a task traditionally hindered by slow acquisition speeds, sample drift, and the absence of automated frame scheduling. As a result, researchers lack practical tools to observe nanoscale dynamics over extended experiments. The goal of the project for this subsystem was to develop an image-processing pipeline capable of stabilising SEM recordings and enabling automated video creation from sequential still images. To achieve this, software was developed to process frames acquired at user-defined intervals, correct drift, and assemble images into a cohesive video. Drift was mitigated through a dual-layer strategy: small displacements were compensated using purely software based frame stabilisation techniques, while larger misalignments were addressed by calculating beam shift vectors to support mechanical correction in collaboration with the SEM Control subsystem. The drift between two images was calculated using a phase correlation based algorithm. The resulting prototype successfully stabilised long image sequences and produced smooth, high-definition SEM videos, even in the presence of significant sample drift. These outcomes demonstrate the feasibility of automated SEM video generation and provide a modular framework that can be extended with improved robustness for challenging imaging conditions.

As part of making a power grid simulator, a hardware controller is needed. This thesis specifically describes the design of such a controller. The objective is to design it in such a way that it is self-explanatory and interactive. Therefore, the following user controls and feedback are made possible. A small solar panel makes it possible to provide input for a solar generator in the simulation. A slider gives input for a wind farm in the simulation. This is visualized by adding a small physical turbine to the hardware, going faster or slower based on the input.
A small knob to go back or forward in the simulation, if pressed, pauses or resumes the time.
A RGB LED indicates the situation on the grid. Based on the loading of transmission lines, the color of the LED changes. These user-control actions and feedback from the simulation are processed on an Arduino Mega Rev3 development board. This uses an ATMega2560 microcontroller. To interact with the simulation, bidirectional serial communication is needed between the Arduino and the computer running the simulation with the Godot game engine. These components are implemented on a PCB to finalize the design.

This report explains the processes behind the design, construction and testing of a high-voltage power grid simulator and visualizer. For this, a system based on data found in a paper was created. This paper describes an aggregated grid for the Dutch high-voltage power grid in 2018. The main use case of this system would be for educative purposes, where interested students and potential students can come, play and learn at the same time while making interactive decisions and seeing how their actions are influencing the system as a whole. The subsystem in this report is tasked with the integration of the data from the forecasting & scenario module and the inputs from the hardware module, furthermore it calculates the power flow using a Gauss-Seidel algorithm and visualizes its results alongside the forecasting in Godot