Essays about: "singular value decomposition"

Showing result 6 - 10 of 51 essays containing the words singular value decomposition.

1. 6. Sensor fusion between positioning system and mixed reality

   University essay from Linköpings universitet/Tekniska fakulteten; Linköpings universitet/Institutionen för datavetenskap

   Author : Anton Lifwergren; Jonsson Jonatan; [2022]
   Keywords : Sensor Fusion; MR; Mixed Reality; AR; Augmented Reality; Positioning; Local Positioning System; Transformation; Mining; Sensorfusion; MR; Mixed Reality; AR; Augmented Reality; Lokalt Positionerings System; Positionering; Transformation; Gruva;

   Abstract : In situations where we want to use mixed reality systems over larger areas, it is necessary for these systems to maintain a correct orientation with respect to the real world. A solution for synchronizing the mixed reality and the real world over time is therefore essential to provide a good user experience. READ MORE

3. 7. Automated HER2 Scoring of Breast Cancer Tissue using Upconverting Nanoparticle Images

   University essay from Lunds universitet/Matematik LTH

   Author : Adam Belfrage; Alexander Wik; [2022]
   Keywords : HER2-scoring; image analysis; interpretability; digital pathology; computer aided pathology; whole slide imaging; ASCO-guidelines; singular value decomposition; shape models; Bayesian classification; Biology and Life Sciences; Medicine and Health Sciences; Technology and Engineering; Mathematics and Statistics;

   Abstract : Computer aided pathology is becoming more and more of a requirement within pathology due to increased demand of individualised treatments and personalised medicine. Because of the advance of digital pathology in recent years, where a high resolution camera acquire images of microscope slides, pathologists can now assess tissue samples in digital images. READ MORE

4. 8. Sparse Approximation of Spatial Channel Model with Dictionary Learning

   University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

   Author : Matilda Zhou; [2022]
   Keywords : Spatial channel model SCM ; dictionary learning; compressive sensing CS ; discrete Fourier transform DFT ; overcomplete discrete Fourier transform ODFT ; Rumslig kanalmodell; ordbokslärning; kompressionsavkänning; diskret Fourier-transform; överkomplett diskret Fourier-transform;

   Abstract : In large antenna systems, traditional channel estimation is costly and infeasible in some situations. Compressive sensing was proposed to estimate the channel with fewer measurements. Most of the previous work uses a predefined discrete Fourier transform matrix or overcomplete Fourier transform matrix to approximate the channel. READ MORE

5. 9. Semi-Automatic Analysis and Visualization of Cardiac 4D Flow CT

   University essay from Linköpings universitet/Institutionen för hälsa, medicin och vård

   Author : Anthony van Oosten; [2022]
   Keywords : 4D CT; CT; 4D flow; Flow Component Analysis; Singular value decomposition; left atrial appendage; LAA; CFD; 3 chamber view; computed tomography; maximum outflow velocity; volume rendered CT; semi-automatic; visualization; flow visualization; cardiac views; semi-automatic analysis; semi-automatic visualization; visualization of 4D flow;

   Abstract : The data obtained from computational fluid dynamics (CFD) simulations of blood flow in the heart is plentiful, and processing this data takes time and the procedure for that is not straightforward. This project aims to develop a tool that can semi-automatically process CFD simulation data, which is based on 4D flow computed tomography (CT) data, with minimal user input. READ MORE

6. 10. Classification of EEG Data Using Convolutional Neural Networks and the Scaled Reassigned Spectrogram

   University essay from Lunds universitet/Matematisk statistik

   Author : Karin Pagels; [2021]
   Keywords : Mathematics and Statistics;

   Abstract : Electroencephalography (EEG) is a medical technique for measuring brain activity through several channels connected to the scalp. Interpreting EEG data is a difficult problem because of the large amount of noise contained in the data. READ MORE