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Fourier Analysis

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Prediction of Self-Interacting Proteins from Protein Sequence Information Based on Random Projection Model and Fast Fourier Transform.

International journal of molecular sciences
It is significant for biological cells to predict self-interacting proteins (SIPs) in the field of bioinformatics. SIPs mean that two or more identical proteins can interact with each other by one gene expression. This plays a major role in the evolu...

Classification of needle-EMG resting potentials by machine learning.

Muscle & nerve
INTRODUCTION: The diagnostic importance of audio signal characteristics in needle electromyography (EMG) is well established. Given the recent advent of audio-sound identification by artificial intelligence, we hypothesized that the extraction of cha...

Predicting chemo-brain in breast cancer survivors using multiple MRI features and machine-learning.

Magnetic resonance in medicine
PURPOSE: Breast cancer (BC) is the most common cancer in women worldwide. There exist various advanced chemotherapy drugs for BC; however, chemotherapy drugs may result in brain damage during treatment. When a patient's brain is changed in response t...

Real-time cardiovascular MR with spatio-temporal artifact suppression using deep learning-proof of concept in congenital heart disease.

Magnetic resonance in medicine
PURPOSE: Real-time assessment of ventricular volumes requires high acceleration factors. Residual convolutional neural networks (CNN) have shown potential for removing artifacts caused by data undersampling. In this study, we investigated the ability...

A machine-learning framework for automatic reference-free quality assessment in MRI.

Magnetic resonance imaging
Magnetic resonance (MR) imaging offers a wide variety of imaging techniques. A large amount of data is created per examination which needs to be checked for sufficient quality in order to derive a meaningful diagnosis. This is a manual process and th...

Deep learning for undersampled MRI reconstruction.

Physics in medicine and biology
This paper presents a deep learning method for faster magnetic resonance imaging (MRI) by reducing k-space data with sub-Nyquist sampling strategies and provides a rationale for why the proposed approach works well. Uniform subsampling is used in the...

Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram.

Neural networks : the official journal of the International Neural Network Society
Seizure prediction has attracted growing attention as one of the most challenging predictive data analysis efforts to improve the life of patients with drug-resistant epilepsy and tonic seizures. Many outstanding studies have reported great results i...

Deep D-Bar: Real-Time Electrical Impedance Tomography Imaging With Deep Neural Networks.

IEEE transactions on medical imaging
The mathematical problem for electrical impedance tomography (EIT) is a highly nonlinear ill-posed inverse problem requiring carefully designed reconstruction procedures to ensure reliable image generation. D-bar methods are based on a rigorous mathe...

Learning Support Correlation Filters for Visual Tracking.

IEEE transactions on pattern analysis and machine intelligence
For visual tracking methods based on kernel support vector machines (SVMs), data sampling is usually adopted to reduce the computational cost in training. In addition, budgeting of support vectors is required for computational efficiency. Instead of ...

Quaternion-Based Texture Analysis of Multiband Satellite Images: Application to the Estimation of Aboveground Biomass in the East Region of Cameroon.

Acta biotheoretica
Within the last decade, several approaches using quaternion numbers to handle and model multiband images in a holistic manner were introduced. The quaternion Fourier transform can be efficiently used to model texture in multidimensional data such as ...