scholarly journals Neural-Network-Based Diagnosis Using 3-Dimensional Myocardial Architecture and Deformation: Demonstration for the Differentiation of Hypertrophic Cardiomyopathy

2020 ◽  
Vol 7 ◽  
Author(s):  
Alessandro Satriano ◽  
Yarmaghan Afzal ◽  
Muhammad Sarim Afzal ◽  
Ali Fatehi Hassanabad ◽  
Cody Wu ◽  
...  
Keyword(s):  
Neural Network ◽  
Hypertrophic Cardiomyopathy ◽  
3 Dimensional ◽  
Myocardial Architecture

Neural network and fuzzy logic techniques based collision avoidance for a mobile robot

Robotica ◽  
1997 ◽  
Vol 15 (6) ◽  
pp. 627-632 ◽  
Author(s):  
Minglu Zhang ◽  
Shangxian Peng ◽  
Qinghao Meng
Keyword(s):  
Neural Network ◽  
Fuzzy Logic ◽  
Mobile Robot ◽  
Fuzzy Controller ◽  
Perceptual Space ◽  
Space Partitioning ◽  
Reactive Navigation ◽  
3 Dimensional ◽  
Sensory Data ◽  
Cluttered Environment

This paper is concerned with a mobile robot reactive navigation in an unknown cluttered environment based on neural network and fuzzy logic. Reactive navigation is a mapping between sensory data and commands without planning. This article's task is to provide a steering command letting a mobile robot avoid a collision with obstacles. In this paper, the authors explain how to perform a currently perceptual space partitioning for a mobile robot by the use of an ART neural network, and then, how to build a 3-dimensional fuzzy controller for mobile robot reactive navigation. The results presented, whether experimented or simulation, show that our method is well adapted to this type of problem.

3-Dimensional regional and global strain abnormalities in hypertrophic cardiomyopathy

2019 ◽  
Vol 35 (10) ◽  
pp. 1913-1924 ◽  
Author(s):  
Alessandro Satriano ◽  
Bobak Heydari ◽  
Namrata Guron ◽  
Kate Fenwick ◽  
Matthew Cheung ◽  
...  
Keyword(s):  
Hypertrophic Cardiomyopathy ◽  
3 Dimensional ◽  
Global Strain

scholarly journals Self-Paced Convolutional Neural Network for PolSAR Images Classification

2019 ◽  
Vol 11 (4) ◽  
pp. 424 ◽  
Author(s):  
Changzhe Jiao ◽  
Xinlin Wang ◽  
Shuiping Gou ◽  
Wenshuai Chen ◽  
Debo Li ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
State Of The Art ◽  
Synthetic Aperture ◽  
Training Process ◽  
Polarimetric Synthetic Aperture Radar ◽  
Learning Mechanism ◽  
3 Dimensional ◽  
Spatial Features ◽  
Rgb Values

Fully polarimetric synthetic aperture radar (PolSAR) can transmit and receive electromagnetic energy on four polarization channels (HH, HV, VH, VV). The data acquired from four channels have both similarities and complementarities. Utilizing the information between the four channels can considerably improve the performance of PolSAR image classification. Convolutional neural network can be used to extract the channel-spatial features of PolSAR images. Self-paced learning has been demonstrated to be instrumental in enhancing the learning robustness of convolutional neural network. In this paper, a novel classification method for PolSAR images using self-paced convolutional neural network (SPCNN) is proposed. In our method, each pixel is denoted by a 3-dimensional tensor block formed by its scattering intensity values on four channels, Pauli’s RGB values and its neighborhood information. Then, we train SPCNN to extract the channel-spatial features and obtain the classification results. Inspired by self-paced learning, SPCNN learns the easier samples first and gradually involves more difficult samples into the training process. This learning mechanism can make network converge to better values. The proposed method achieved state-of-the-art performances on four real PolSAR dataset.

Alcohol Ablation in Hypertrophic Cardiomyopathy: Localization and Quantification of Target Septal Artery-dependent Myocardium With 3-Dimensional Echocardiography

2015 ◽  
Vol 68 (6) ◽  
pp. 531-533
Author(s):  
José Luis Moya-Mur ◽  
José Luis Mestre-Barcelo ◽  
Luisa Salido-Tahoces ◽  
Rocío Hinojar-Baydes ◽  
Rosana Hernández-Antolín ◽  
...  
Keyword(s):  
Hypertrophic Cardiomyopathy ◽  
Alcohol Ablation ◽  
3 Dimensional ◽  
Dimensional Echocardiography
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