Hankel Singular Value Decomposition as a method of preprocessing the Magnetic Resonance Spectroscopy
The signal resulting from magnetic resonance spectroscopy is occupied by noises and irregularities so in the further analysis preprocessing techniques have to be introduced. The main idea of the paper is to develop a model of a signal as a sum of harmonics and to find its parameters. Such an approach is based on singular value decomposition applied to the data arranged in the Hankel matrix (HSVD) and can be used in each step of preprocessing techniques. For that purpose a method has was tested on real phantom data.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Hankel Singular Value Regularization for Highly Compressible State Space Models
Deep neural networks using state space models as layers are well suited for long-range sequence tasks but can be challenging to compress after training. We use that regularizing the sum of Hankel singular values of state…
Online Reduced-Order Data-Enabled Predictive Control
Data-enabled predictive control (DeePC) has garnered significant attention for its ability to achieve safe, data-driven optimal control without relying on explicit system models. Traditional DeePC methods use pre-collect…
Data Driven Optimal ControlA Fast Algorithm for Cosine Transform Based Tensor Singular Value Decomposition
Recently, there has been a lot of research into tensor singular value decomposition (t-SVD) by using discrete Fourier transform (DFT) matrix. The main aims of this paper are to propose and study tensor singular value dec…
Baseline wander and power line interference removal from ECG signals using eigenvalue decomposition
In this paper, a novel method is proposed for baseline wander (BW) and power line interference (PLI) removal from electrocardiogram (ECG) signals. The proposed methodology is based on the eigenvalue decomposition of th…
Low-Rank Approximation of Weighted Tree Automata
We describe a technique to minimize weighted tree automata (WTA), a powerful formalisms that subsumes probabilistic context-free grammars (PCFGs) and latent-variable PCFGs. Our method relies on a singular value decomposi…