Essays about: "Graph sampling"
Showing result 1 - 5 of 18 essays containing the words Graph sampling.
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1. Animation Graph
University essay from Luleå tekniska universitet/Institutionen för system- och rymdteknikAbstract : This work introduces a data model, compiler and runtime interpreter to drive the logic of animation graphs in game engines. The primary purpose of an animation graph is to allow for animation logic to be evaluated in a data driven way, which enables game programmers and animators to work iteratively and in parallel. READ MORE
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2. Phase Unwrapping MRI Flow Measurements
University essay from Uppsala universitet/Avdelningen Vi3Abstract : Magnetic resonance images (MRI) are acquired by sampling the current of induced electromotiveforce (EMF). EMF is induced due to flux of the net magnetic field from coherent nuclear spins with intrinsic magnetic dipole moments. READ MORE
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3. An experimental analysis of Link Prediction methods over Microservices Knowledge Graphs
University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)Abstract : Graphs are a powerful way to represent data. They can be seen as a collection of objects (nodes) and the relationships between them (edges or links). The power of this structure has its intrinsic value in the relationship between data points that can even provide more information than the data properties. READ MORE
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4. Simulations of Point Processes on Directed Graphs
University essay from Uppsala universitet/Institutionen för informationsteknologiAbstract : Simulating a point process where the events correspond to vehicle collisions on a road networkcan be quite computationally heavy due to the large number of elements that are necessary toprovide a sufficient discretization of the network. This paper aims to present a computationallyefficient solution for simulating events of a point process. READ MORE
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5. Design and Implementation of Cellular Network Hotspot Forecast Using Graph Convolutional Neural Networks
University essay from Uppsala universitet/Institutionen för informationsteknologiAbstract : This project proposes a type of (Recurrent) Graph Neural Network (RGNN) to predict future hotspots in cellular network data. Current state-of-the-art algorithms process each antenna (celldata )’s in isolation, ignoring the performance of nearby cells and cell locations. READ MORE