Sökning: "deep space network"

Visar resultat 11 - 15 av 87 uppsatser innehållade orden deep space network.

  1. 11. Structure from Motion with a Neural Network

    Master-uppsats, Lunds universitet/Matematik LTH

    Författare :Jiarong Gong; [2023]
    Nyckelord :Technology and Engineering;

    Sammanfattning : This project delves into the 3D reconstruction of both single and multiple rigid motions, examining the potential of deep learning methods, such as that proposed by Moran et al., to supplant traditional geometry-based approaches. The project is structured into two main parts. LÄS MER

  3. 12. Monocular 3D Human Pose Estimation

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Robert Rey; [2023]
    Nyckelord :3D Human Pose Estimation; Monocular Images; Deep Learning; Artificial Neural Networks; 3D Människokroppspositionsuppskattning; Monokulära bilder; Djupinlärning; Konstgjorda neurala nätverk;

    Sammanfattning : The focus of this work is the task of 3D human pose estimation, more specifically by making use of key points located in single monocular images in order to estimate the location of human body joints in a 3D space. It was done in association with Tracab, a company based in Stockholm, who specialises in advanced sports tracking and analytics solutions. LÄS MER

  4. 13. Machine Learning-Based Instruction Scheduling for a DSP Architecture Compiler : Instruction Scheduling using Deep Reinforcement Learning and Graph Convolutional Networks

    Master-uppsats, KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Författare :Lucas Alava Peña; [2023]
    Nyckelord :Instruction Scheduling; Deep reinforcement Learning; Compilers; Graph Convolutional Networks; Schemaläggning av instruktioner; Deep Reinforcement Learning; kompilatorer; grafkonvolutionella nätverk;

    Sammanfattning : Instruction Scheduling is a back-end compiler optimisation technique that can provide significant performance gains. It refers to ordering instructions in a particular order to reduce latency for processors with instruction-level parallelism. LÄS MER

  5. 14. Reconstruction of Accelerated Cardiovascular MRI data

    Master-uppsats, Linköpings universitet/Statistik och maskininlärning

    Författare :Hussnain Khalid; [2023]
    Nyckelord :medical imaging; deep learning; CNN; Magnetic resonance imaging; MRI; Cardiac MRI; Cardiac; Cardiovascular; reconstruction; 4D flow MRI; Parallel Imaging; Compressed Sensing; FlowVN; Flow Variational Network; K-space; Reference images; sensitivity maps; Respiratory motion; undersampled images;

    Sammanfattning : Magnetic resonance imaging (MRI), is a noninvasive medical imaging testing techniquewhich is used to produce detailed images of internal structure of the human body, includingbones, muscles, organs, and blood vessels. MRI scanners use large magnets and radiowaves to create images of the body. LÄS MER

  7. 15. Deep learning for temporal super-resolution of 4D Flow MRI

    Master-uppsats, KTH/Matematik (Avd.)

    Författare :Pia Callmer; [2023]
    Nyckelord :Temporal super-resolution; 4D Flow MRI; CNN; Artificial Intelligence; MRI; 4D flow; Temporal superupplösning; 4D flöde MRI; CNN; artificiell intelligens; MRI; 4D-flöde;

    Sammanfattning : The accurate assessment of hemodynamics and its parameters play an important role when diagnosing cardiovascular diseases. In this context, 4D Flow Magnetic Resonance Imaging (4D Flow MRI) is a non-invasive measurement technique that facilitates hemodynamic parameter assessment as well as quantitative and qualitative analysis of three-directional flow over time. LÄS MER