Synthetic image generation involves the creation of artificially generated images that are indistinguishable from real ones. This field is an answer to challenges in the world of data acquisition, where the need for data is outpacing the availability. In cooperation with Philips Medical Systems, the generation of synthetic X-ray images is studied. Using datasets derived from such images, equipment testing and physician training can be improved. Additionally, training data can be generated for machine learning purposes.

The generation of synthetic X-ray images has been an area of research since at least 1994. The images have traditionally been generated using ray-tracing techniques on CPUs or GPUs. While effective, these methods are computationally expensive and demand high memory bandwidths. More recently, machine learning techniques have been explored for X-ray image generation. These approaches are promising. However, they require large labelled datasets which are often unavailable and the quality of the results is difficult to predict.

The aim of this thesis is to investigate whether hardware acceleration using a field programmable gate array (FPGA) can solve the challenges other methods face. Specifically, it discusses an architecture that can handle the large amount of computations in parallel. The memory architecture required to handle the high bandwidth demands is also explained. The performance of the proposed architecture is studied to see whether it is a viable solution.

By simulating the traversal of rays through a voxelized model, an attenuation map was computed which can be used to determine X-ray intensities on a detector. The design separates computational tasks between a host machine and an FPGA, with an optimized High Bandwidth Memory architecture to maximize data throughput. Results demonstrated that the simulation produced realistic images with minimal error (2.26\% - 3.00\% deviation from CPU results), and performance is dependant on the detector resolution, achieving frame rates between 123 and 378 frames per second which are well above the goal of 60 frames per second. If more performance is required, upsampling can be used to speed up image generation by 33\% at an increased error of 0.6\% for an upsampling factor of two. These findings highlight the advantages of FPGA acceleration for deterministic, high-speed synthetic image generation without the need for large labelled datasets as required by machine learning algorithms.

This bachelor end project thesis is describing ways to use dry-electrode electroencephalography (EEG) measurements for authentication. There are five proposed methods based on the literature study, which in this report are called: ’frequency tagging’, ’pseudowords’, ’familiar music’, ’mental tasks’, and ’emotions’. First, all methods were tested, however, this proved too much work to perform each of them thoroughly and wellbounded, so two were selected for further investigation.

For the method involving frequency tagging, experiments were done and the obtained data was used to extract features. This analysis showed that tagging can be discovered in the EEG data, but re-induction of tagged words was hard to achieve, which would make it difficult to create an authentication system reliant on it. Analyzing results from experiments to test the pseudoword method was more promising. Some similarities between the features that were expected from papers and features from our results were found. These features were therefore used for classification, and resulted in small improvements in accuracy. This accuracy did however vary a lot between different data sets.