This diploma thesis describes and implements the design of a graph neural network usedfor 2D segmentation of neural structure. The first chapter of the thesis briefly introduces the problem of segmentation. In this chapter, segmentation techniques are divided according to the principles of the methods they use. Each type of technique contains the essence of this category as well as a description of one representative. The second chapter of the diploma thesis explains graph neural networks (GNN for short). Here, the thesis divides graph neural networks in general and describes recurrent graph neural networks(RGNN for short) and graph autoencoders, that can be used for image segmentation, in more detail. The specific image segmentation solution is based on the message passing method in RGNN, which can replace convolution masks in convolutional neural networks.RGNN also provides a simpler multilayer perceptron topology. The second type of graph neural networks characterised in the thesis are graph autoencoders, which use various methods for better encoding of graph vertices into Euclidean space. The last part ofthe diploma thesis deals with the analysis of the problem, the proposal of its specific solution and the evaluation of results. The purpose of the practical part of the work was the implementation of GNN for image data segmentation. The advantage of using neural networks is the ability to solve different types of segmentation by changing training data. RGNN with messaging passing and node2vec were used as implementation GNNf or segmentation problem. RGNN training was performed on graphics cards provided bythe school and Google Colaboratory. Learning RGNN using node2vec was very memory intensive and therefore it was necessary to train on a processor with an operating memory larger than 12GB. As part of the RGNN optimization, learning was tested using various loss functions, changing topology and learning parameters. A tree structure method was developed to use node2vec to improve segmentation, but the results did not confirman improvement for a small number of iterations. The best outcomes of the practical implementation were evaluated by comparing the tested data with the convolutional neural network U-Net. It is possible to state comparable results to the U-Net network, but further testing is needed to compare these neural networks. The result of the thesisis the use of RGNN as a modern solution to the problem of image segmentation and providing a foundation for further research.

A home automation system usually contains a set of tools that users use to control devices in their homes, often remotely. These devices often include but are not limited to light switches, thermostats, thermometers, window blinds, and climate controls. The potential for these kinds of systems is huge because of the sheer number of devices that could be controlled and managed with minimal and inexpensive extra hardware. Many of the appliances in a normal home could benefit from being connected to a system that allows the owner to manage and control the devices in their home. Thus the number of potential devices is orders of magnitude larger than the number of homes connected to the system. There are several systems on the market that provide systems to monitor and control a home environment, however these systems only support specific in system devices. This uncovers a problem where a homeowner only has the opportunity to use specific products that fit into these systems. By introducing an open platform for the public that are not bound to any system we can allow more devices to be integrated in the home and contribute to further development of smarter homes. The goal with this project was to provide a scalable open platform with the possibility of asynchronous updating. This has been done by implementing multiple logical parts to both provide a web interface for the user and to allow us to handle communication and storage of data. All these parts are linked together to form a system of servers that handles all background operations. This thesis discusses and presents implementations of all of these servers, how they are implemented, communicate with each other, provide secure connections and how they can scale with increasing usage. In this process we also discuss and present techniques that were used, how to use them and their benefits, to help us reach our goal. / ”Home automation” syftar till ett system som låter användaren kontrollera och styra olika apparater i hemmet, ofta sker detta utifrån. Dessa apparater inkluderar, men är inte begränsade till ljusbrytare, termostater, termometrar, persienner eller klimatanläggningar. Potentialen för ett sådant system är enormt då antalet apparater som skulle kunna övervakas med endast minimal och billig extra hårdvara är stort. Många av dessa apparater kan dra nytta av att vara ansluten till ett system som gör det möjligt för ägaren att hantera och styra enheter i deras hem. Antalet apparater är därför mångdubbelt fler än antalet hem som är kopplade till systemet. Det finns flera system på marknaden som ger användaren ett sätt att övervaka och styra en hemmiljö, men dessa system är ofta låsta och stödjer bara specifika enheter. Genom att införa en öppen plattform för allmänheten som inte är bunden till något system, kan vi tillåta att fler enheter kan integreras i hemmet och bidra till ytterligare utveckling av smartare hem. Målet med detta projekt var att skapa en skalbar öppen plattform med möjlighet till asynkron uppdatering. Detta har gjorts genom att implementera flera logiska delar för att förse användaren med ett webbgränssnitt och för att tillåta oss hantera kommunikation och lagring av data. Alla dessa delar är sammanlänkade för att bilda ett system av servrar som hanterar alla bakgrundsprocesser. Denna avhandling diskuterar och presenterar implementeringar av alla dessa servrar, hur de genomförs, kommunicera med varandra, ger säkra anslutningar och hur de kan skala med ökad användning. I denna process diskuterar och presenterar vi de tekniker som använts, hur man använder dem och deras fördelar.

As manufacturers introduce new machines with more cores, more NUMA-like architectures, and more tightly integrated heterogeneous processors, the traditional abstraction of a monolithic OS running on a SMP system is encountering new challenges.  One proposed path forward is the multikernel operating system.  Previous efforts have shown promising results both in scalability and in support for heterogeneity.  However, one effort\'s source code is not freely available (FOS), and the other effort is not self-hosting and does not support a majority of existing applications (Barrelfish). In this thesis, we present Popcorn, a Linux-based multikernel operating system.  While Popcorn was a group effort, the boot layer code and the memory partitioning code are the author\'s work, and we present them in detail here.  To our knowledge, we are the first to support multiple instances of the Linux kernel on a 64-bit x86 machine and to support more than 4 kernels running simultaneously. We demonstrate that existing subsystems within Linux can be leveraged to meet the design goals of a multikernel OS.  Taking this approach, we developed a fast inter-kernel network driver and messaging layer.  We demonstrate that the network driver can share a 1 Gbit/s link without degraded performance and that in combination with guest kernels, it meets or exceeds the performance of SMP Linux with an event-based web server.  We evaluate the messaging layer with microbenchmarks and conclude that it performs well given the limitations of current x86-64 hardware.  Finally, we use the messaging layer to provide live process migration between cores. / Master of Science

Ray tracing is widely used technique for realistic rendering of computer scenes. Its major drawback is time needed to compute the image, therefore it's usually parallelized. This thesis describes parallelization and ray tracing in general. It explains the possibility of how can be ray tracing parallelized as well as it defines the problems which may occur during the process. The result is parallel rendering application which uses selected ray tracing software and measurement of how successful this application is.

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No / Grid computing is a computational concept based on an infrastructure that integrates and collaborates the use of high end computers, networks, databases and scientific instruments owned and managed by several organisations. It involves large amounts of data and computing which require secure and reliable resource sharing across organisational domains. Despite its high computing performance orientation, communication delays between grid computing nodes is a big hurdle due to geographical separation in a realistic grid computing environment. Communication schemes such as broadcasting, multicasting and routing should, therefore, take communication delay into consideration. Such communication schemes in a grid computing environment pose a great challenge due to the arbitrary nature of its topology. In this context, a heuristic algorithm for multicast communication is proposed for grid computing networks with finite capacity and bursty background traffic. The scheme facilitates inter-node communication for grid computing networks and it is applicable to a single-port mode of message passing communication. The scheme utilises a queue-by-queue decomposition algorithm for arbitrary open queueing network models, based on the principle of maximum entropy, in conjunction with an information theoretic decomposition criterion and graph theoretic concepts. Evidence based on empirical studies indicates the suitability of the scheme for achieving an optimal multicast communication cost, subject to system decomposition constraints.