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Current high-end medical X-ray intervention devices provide a tremendous amount of high-definition images per second. Combined with the additional inputs from the numerous auxiliary devices, processing and compositing the data in real-time quickly becomes an arduous engineering challenge. Furthermore, the critical nature of this application domain allows no room for error, whereas the process has to be simultaneously flexible enough to permit the unhindered work of the corresponding medical professional. Such medical imaging applications presently rely on complete hardware implementations of their processing pipelines and compositor engines, as opposed to earlier adopted paradigms of hardware-accelerated solutions. Said engines are usually realised in FPGA platforms due to their massively parallel computational capabilities and exceptional connectivity. However, given the vast amount of input streams arriving at the compositor's pipeline, switching and routing to available processing resources has remained a persistent issue. Present commercial solutions prove to be either too costly, too slow or too inflexible for developers to consider. On the other hand, academic literature centred around the subject is severely lacking. This thesis proposes a unique hardware architecture solution to mitigate the aforementioned problem. The proposed architecture, developed in collaboration with Philips Healthcare and TU Delft, exhibits an average end-to-end latency of 100 ns and a throughput of 7.2 Gb/s. Additionally, these results were realised under marginal resource utilisation, proving that such a switch can fit in the FPGA device and, subsequently, be developed as an integrated part of the compositor engine. Finally, the limited amount of reserved memory resources allowed for near-linear scalability of the proposed design should an increased amount of I/O devices be added; as well as dynamic portability should migration to smaller devices be a concern., FitOptiVis, Electrical Engineer | Embedded Systems

Current high-end medical X-ray intervention devices provide a tremendous amount of high-definition images per second. Combined with the additional inputs from the numerous auxiliary devices, processing and compositing the data in real-time quickly becomes an arduous engineering challenge. Furthermore, the critical nature of this application domain allows no room for error, whereas the process has to be simultaneously flexible enough to permit the unhindered work of the corresponding medical professional. Such medical imaging applications presently rely on complete hardware implementations of their processing pipelines and compositor engines, as opposed to earlier adopted paradigms of hardware-accelerated solutions. Said engines are usually realised in FPGA platforms due to their massively parallel computational capabilities and exceptional connectivity. However, given the vast amount of input streams arriving at the compositor's pipeline, switching and routing to available processing resources has remained a persistent issue. Present commercial solutions prove to be either too costly, too slow or too inflexible for developers to consider. On the other hand, academic literature centred around the subject is severely lacking. This thesis proposes a unique hardware architecture solution to mitigate the aforementioned problem. The proposed architecture, developed in collaboration with Philips Healthcare and TU Delft, exhibits an average end-to-end latency of 100 ns and a throughput of 7.2 Gb/s. Additionally, these results were realised under marginal resource utilisation, proving that such a switch can fit in the FPGA device and, subsequently, be developed as an integrated part of the compositor engine. Finally, the limited amount of reserved memory resources allowed for near-linear scalability of the proposed design should an increased amount of I/O devices be added; as well as dynamic portability should migration to smaller devices be a concern., FitOptiVis, Electrical Engineering | Embedded Systems

During the last years, a well studied biological substrate, namely Physarum polycephalum, has been proven efficient on finding appropriate and efficient solutions in hard to solve complex mathematical problems. The plasmodium of P. polycephalum is a single-cell that serves as a prosperous bio-computational example. Consequently, it has been successfully utilized in the past to solve a variety of path problems in graphs and combinatorial problems. In this work, this interesting behaviour is mimicked by a robust unconventional computational model, drawing inspiration from the notion of Cellular and Learning Automata. Namely, we employ principles of Cellular Automata (CAs) enriched with learning capabilities to develop a robust computational model, able of modelling appropriately the aforementioned biological substrate and, thus, capturing its computational capabilities. CAs are very efficient in modelling biological systems and solving scientific problems, owing to their ability of incarnating essential properties of a system where global behaviour arises as an effect of simple components, interacting locally. The resulting computational tool, after combining CAs with learning capabilities, should be appropriate for modelling the behaviour of living organisms. Thus, the inherent abilities and computational characteristics of the proposed bio-inspired model are stressed towards the experimental verification of Physarum's ability to model Logic Gates, while trying to find minimal paths in properly configured mazes with food sources. The presented simulation results for various Logic Gates are found in good agreement, both qualitatively and quantitatively, with the corresponding experimental results, proving the efficacy of this unconventional bio-inspired model and providing useful insights for its enhanced usage in various computing applications.