AIMC Topic: Pattern Recognition, Automated
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An autonomous robot inspired by insect neurophysiology pursues moving features in natural environments.
Journal of neural engineering
Aug 1, 2017
OBJECTIVE: Many computer vision and robotic applications require the implementation of robust and efficient target-tracking algorithms on a moving platform. However, deployment of a real-time system is challenging, even with the computational power o...
Repeatability of grasp recognition for robotic hand prosthesis control based on sEMG data.
IEEE ... International Conference on Rehabilitation Robotics : [proceedings]
Jul 1, 2017
Control methods based on sEMG obtained promising results for hand prosthetics. Control system robustness is still often inadequate and does not allow the amputees to perform a large number of movements useful for everyday life. Only few studies analy...
Application of support vector machines in detecting hand grasp gestures using a commercially off the shelf wireless myoelectric armband.
IEEE ... International Conference on Rehabilitation Robotics : [proceedings]
Jul 1, 2017
The propose of this study was to assess the feasibility of using support vector machines in analysing myoelectric signals acquired using an off the shelf device, the Myo armband from Thalmic Lab, when performing hand grasp gestures. Participants (n =...
Pavement type and wear condition classification from tire cavity acoustic measurements with artificial neural networks.
The Journal of the Acoustical Society of America
Jun 1, 2017
Tire road noise is the major contributor to traffic noise, which leads to general annoyance, speech interference, and sleep disturbances. Standardized methods to measure tire road noise are expensive, sophisticated to use, and they cannot be applied ...
Convolutional neural network-based automatic classification of midsagittal tongue gestural targets using B-mode ultrasound images.
The Journal of the Acoustical Society of America
Jun 1, 2017
Tongue gestural target classification is of great interest to researchers in the speech production field. Recently, deep convolutional neural networks (CNN) have shown superiority to standard feature extraction techniques in a variety of domains. In ...
Auditory feature representation using convolutional restricted Boltzmann machine and Teager energy operator for speech recognition.
The Journal of the Acoustical Society of America
Jun 1, 2017
In this letter, authors propose an auditory feature representation technique with the filterbank learned using an annealing dropout convolutional restricted Boltzmann machine (ConvRBM) and noise-robust energy estimation using the Teager energy operat...
Improving CCTA-based lesions' hemodynamic significance assessment by accounting for partial volume modeling in automatic coronary lumen segmentation.
Medical physics
Mar 1, 2017
PURPOSE: The goal of this study was to assess the potential added benefit of accounting for partial volume effects (PVE) in an automatic coronary lumen segmentation algorithm that is used to determine the hemodynamic significance of a coronary artery...
Using deep learning to segment breast and fibroglandular tissue in MRI volumes.
Medical physics
Feb 1, 2017
PURPOSE: Automated segmentation of breast and fibroglandular tissue (FGT) is required for various computer-aided applications of breast MRI. Traditional image analysis and computer vision techniques, such atlas, template matching, or, edge and surfac...
Deep Learning for Magnetic Resonance Fingerprinting: A New Approach for Predicting Quantitative Parameter Values from Time Series.
Studies in health technology and informatics
Jan 1, 2017
The purpose of this work is to evaluate methods from deep learning for application to Magnetic Resonance Fingerprinting (MRF). MRF is a recently proposed measurement technique for generating quantitative parameter maps. In MRF a non-steady state sign...
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