scholarly journals Attention-based Convolutional Neural Network for MRI Gibbs-ringing Artifact Suppression

2020 ◽  
pp. paper34-1-paper34-12
Author(s):  
Maksim Penkin ◽  
Andrey Krylov ◽  
Alexander Khvostikov
Keyword(s):  
Neural Network ◽  
Fourier Transform ◽  
Convolutional Neural Network ◽  
High Frequency ◽  
Gibbs Phenomenon ◽  
Linear Mapping ◽  
Full Spectrum ◽  
Non Linear ◽  
Ringing Artifact ◽  
Artifact Suppression

Gibbs-ringing artifact is a common artifact in MRI image processing. As MRI raw data is taken in a frequency domain, 2D in- verse discrete Fourier transform is applied to visualize data. Inability to take inverse Fourier transform of full spectrum (full k-space) leads to the insufficient sampling of the high frequency data and results in a well-known Gibbs phenomenon. It is worth to notice that truncation of high frequency information generates a significant blur, thus some techniques from other image restoration problems (for example, image deblur task) can be successfully used. We propose attention-based convolutional neural network for Gibbs-ringing reduction which is the extension of recently proposed GAS-CNN (Gibbs-ringing Artifact Suppression Convolutional Neural Network). Proposed method includes simplified non-linear mapping, amended by LRNN (Layer Recurrent Neural Network) refinement block with feature attention module, controlling the correlation between input and output tensors of the refinement unit. The research shows that the proposed post-processing refinement construction considerably simplifies the non-linear mapping.

scholarly journals Convolutional Neural Network Classification of Telematics Car Driving Data

Risks ◽  
2019 ◽  
Vol 7 (1) ◽  
pp. 6 ◽  
Author(s):  
Guangyuan Gao ◽  
Mario Wüthrich
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Time Series ◽  
Convolutional Neural Network ◽  
High Frequency ◽  
Location Data ◽  
Neural Network Classification ◽  
Feature Information ◽  
Car Driving

The aim of this project is to analyze high-frequency GPS location data (second per second) of individual car drivers (and trips). We extract feature information about speeds, acceleration, deceleration, and changes of direction from this high-frequency GPS location data. Time series of this feature information allow us to appropriately allocate individual car driving trips to selected drivers using convolutional neural networks.

scholarly journals Gradient-Guided Convolutional Neural Network for MRI Image Super-Resolution

2019 ◽  
Vol 9 (22) ◽  
pp. 4874 ◽  
Author(s):  
Xiaofeng Du ◽  
Yifan He
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
High Frequency ◽  
Super Resolution ◽  
Resonance Imaging ◽  
Reconstruction Accuracy ◽  
Main Challenge ◽  
Image Super Resolution ◽  
Magnetic Resonance Imaging Mri ◽  
Image Details

Super-resolution (SR) technology is essential for improving image quality in magnetic resonance imaging (MRI). The main challenge of MRI SR is to reconstruct high-frequency (HR) details from a low-resolution (LR) image. To address this challenge, we develop a gradient-guided convolutional neural network for improving the reconstruction accuracy of high-frequency image details from the LR image. A gradient prior is fully explored to supply the information of high-frequency details during the super-resolution process, thereby leading to a more accurate reconstructed image. Experimental results of image super-resolution on public MRI databases demonstrate that the gradient-guided convolutional neural network achieves better performance over the published state-of-art approaches.

scholarly journals A Monte Carlo Simulation Approach in Non-linear Structural Dynamics Using Convolutional Neural Networks

2021 ◽  
Vol 7 ◽  
Author(s):  
Franz Bamer ◽  
Denny Thaler ◽  
Marcus Stoffel ◽  
Bernd Markert
Keyword(s):  
Neural Network ◽  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Convolutional Neural Network ◽  
Ground Motion ◽  
Structural Response ◽  
Structural Failure ◽  
Non Linear ◽  
Time Histories ◽  
Sample Set

The evaluation of the structural response statistics constitutes one of the principal tasks in engineering. However, in the tail region near structural failure, engineering structures behave highly non-linear, making an analytic or closed form of the response statistics difficult or even impossible. Evaluating a series of computer experiments, the Monte Carlo method has been proven a useful tool to provide an unbiased estimate of the response statistics. Naturally, we want structural failure to happen very rarely. Unfortunately, this leads to a disproportionately high number of Monte Carlo samples to be evaluated to ensure an estimation with high confidence for small probabilities. Thus, in this paper, we present a new Monte Carlo simulation method enhanced by a convolutional neural network. The sample-set used for this Monte Carlo approach is provided by artificially generating site-dependent ground motion time histories using a non-linear Kanai-Tajimi filter. Compared to several state-of-the-art studies, the convolutional neural network learns to extract the relevant input features and the structural response behavior autonomously from the entire time histories instead of learning from a set of hand-chosen intensity inputs. Training the neural network based on a chosen input sample set develops a meta-model that is then used as a meta-model to predict the response of the total Monte Carlo sample set. This paper presents two convolutional neural network-enhanced strategies that allow for a practical design approach of ground motion excited structures. The first strategy enables for an accurate response prediction around the mean of the distribution. It is, therefore, useful regarding structural serviceability. The second strategy enables for an accurate prediction around the tail end of the distribution. It is, therefore, beneficial for the prediction of the probability of failure.

scholarly journals A Fast 4K Video Frame Interpolation Using a Hybrid Task-Based Convolutional Neural Network

Symmetry ◽  
2019 ◽  
Vol 11 (5) ◽  
pp. 619 ◽  
Author(s):  
Ha-Eun Ahn ◽  
Jinwoo Jeong ◽  
Je Woo Kim
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
High Frequency ◽  
State Of The Art ◽  
Visual Quality ◽  
Video Frame ◽  
Frame Interpolation ◽  
Algorithm Efficiency ◽  
Coarse To Fine

Visual quality and algorithm efficiency are two main interests in video frame interpolation. We propose a hybrid task-based convolutional neural network for fast and accurate frame interpolation of 4K videos. The proposed method synthesizes low-resolution frames, then reconstructs high-resolution frames in a coarse-to-fine fashion. We also propose edge loss, to preserve high-frequency information and make the synthesized frames look sharper. Experimental results show that the proposed method achieves state-of-the-art performance and performs 2.69x faster than the existing methods that are operable for 4K videos, while maintaining comparable visual and quantitative quality.

scholarly journals Identification of Non-linear Systems through Convolutional Neural Network

2019 ◽  
Vol 8 (3) ◽  
pp. 3429-3434
Keyword(s):  
Neural Network ◽  
Nonlinear Systems ◽  
Convolutional Neural Network ◽  
Control Systems ◽  
Linear Systems ◽  
Test Data ◽  
Training Data ◽  
Analysis And Design ◽  
Non Linear ◽  
Non Linear Systems

The theory of control systems deals with the analysis and design of interacting components of a system in a configuration that provides the desired behavior. This paper deals with the problem of the identification of non-linear systems through Convolutional Neural Network (CNN). We propose a structure of a CNN and perform simulations with test data using unsupervised learning for the identification of nonlinear systems. Also, MLP is used to compare the results when there is noise in the training data, which allows us to see that the proposed CNN has better performance and can be used for cases where the noise is present. The proposed CNN is validated with test data. Tests are carried out with Gas oven data, comparing the proposed structure of CNN with a MLP. When there is noise in the data, CNN has better performance than MLP.

scholarly journals Semisupervised definition of hippocampal ripples

2020 ◽  
Author(s):  
Yusuke Watanabe ◽  
Mami Okada ◽  
Yuji Ikegaya
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Local Field ◽  
High Frequency ◽  
Mixed Model ◽  
Slow Wave Sleep ◽  
Animal Movement ◽  
Local Field Potentials ◽  
Field Potentials ◽  
Weakly Supervised

AbstractHippocampal ripples are transient neuronal features observed in high-frequency oscillatory bands of local field potentials, and they occur primarily during periods of behavioral immobility and slow-wave sleep. Ripples have been defined based on mathematically engineered features, such as magnitudes, durations, and cycles per event. However, the “ripples” could vary from laboratory to laboratory because their definition is subject to human bias, including the arbitrary choice of parameters and thresholds. In addition, local field potentials are often influenced by myoelectric noise arising from animal movement, making it difficult to distinguish ripples from high-frequency noises. To overcome these problems, we extracted ripple candidates under few constraints and labeled them as binary or stochastic “true” or “false” ripples using Gaussian mixed model clustering and a deep convolutional neural network in a weakly supervised fashion. Our automatic method separated ripples and myoelectric noise and was able to detect ripples even when the animals were moving. Moreover, we confirmed that a convolutional neural network was able to detect ripples defined by our method. Leave-one-animal-out cross-validation estimated the area under the precision-recall curve for ripple detection to be 0.72. Finally, our model establishes an appropriate threshold for the ripple magnitude in the case of the conventional detection of ripples.

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