Robust and fast vowel recognition using optimum-path forest

The applications of Automatic Vowel Recognition (AVR), which is a sub-part of fundamental importance in most of the speech processing systems, vary from automatic interpretation of spoken language to biometrics. State-of-the-art systems for AVR are based on traditional machine learning models such as Artificial Neural Networks (ANNs) and Support Vector Machines (SVMs), however, such classifiers can not deal with efficiency and effectiveness at the same time, existing a gap to be explored when real-time processing is required. In this work, we present an algorithm for AVR based on the Optimum-Path Forest (OPF), which is an emergent pattern recognition technique recently introduced in literature. Adopting a supervised training procedure and using speech tags from two public datasets, we observed that OPF has outperformed ANNs, SVMs, plus other classifiers, in terms of training time and accuracy. ©2010 IEEE.

A Learning Function for Parameter Reduction in Spiking Neural Networks with Radial Basis Function

Spiking neural networks - networks that encode information in the timing of spikes - are arising as a new approach in the artificial neural networks paradigm, emergent from cognitive science. One of these new models is the pulsed neural network with radial basis function, a network able to store information in the axonal propagation delay of neurons. Learning algorithms have been proposed to this model looking for mapping input pulses into output pulses. Recently, a new method was proposed to encode constant data into a temporal sequence of spikes, stimulating deeper studies in order to establish abilities and frontiers of this new approach. However, a well known problem of this kind of network is the high number of free parameters - more that 15 - to be properly configured or tuned in order to allow network convergence. This work presents for the first time a new learning function for this network training that allow the automatic configuration of one of the key network parameters: the synaptic weight decreasing factor.

LEARNING A COMPLEX MOTOR SKILL FROM VIDEO and POINT-LIGHT DEMONSTRATIONS

The aim of this Study was to compare the learning process of a highly complex ballet skill following demonstrations of point light and video models 16 participants divided into point light and video groups (ns = 8) performed 160 trials of a pirouette equally distributed in blocks of 20 trials alternating periods of demonstration and practice with a retention test a day later Measures of head and trunk oscillation coordination d1 parity from the model and movement time difference showed similarities between video and point light groups ballet experts evaluations indicated superiority of performance in the video over the point light group Results are discussed in terms of the task requirements of dissociation between head and trunk rotations focusing on the hypothesis of sufficiency and higher relevance of information contained in biological motion models applied to learning of complex motor skills

Damage detection in a benchmark structure using AR-ARX models and statistical pattern recognition

Structural health monitoring (SHM) is related to the ability of monitoring the state and deciding the level of damage or deterioration within aerospace, civil and mechanical systems. In this sense, this paper deals with the application of a two-step auto-regressive and auto-regressive with exogenous inputs (AR-ARX) model for linear prediction of damage diagnosis in structural systems. This damage detection algorithm is based on the. monitoring of residual error as damage-sensitive indexes, obtained through vibration response measurements. In complex structures there are. many positions under observation and a large amount of data to be handed, making difficult the visualization of the signals. This paper also investigates data compression by using principal component analysis. In order to establish a threshold value, a fuzzy c-means clustering is taken to quantify the damage-sensitive index in an unsupervised learning mode. Tests are made in a benchmark problem, as proposed by IASC-ASCE with different damage patterns. The diagnosis that was obtained showed high correlation with the actual integrity state of the structure. Copyright © 2007 by ABCM.

Deep learning architecture for gesture recognition

Pós-graduação em Engenharia Mecânica - FEG

Learning HMMs for nucleotide sequences from amino acid alignments

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Teaching elliptical excision skills to novice medical students: a randomized controlled study comparing low- and high-fidelity bench models

Background: The search for alternative and effective forms of training simulation is needed due to ethical and medico-legal aspects involved in training surgical skills on living patients, human cadavers and living animals. Aims : To evaluate if the bench model fidelity interferes in the acquisition of elliptical excision skills by novice medical students. Materials and Methods: Forty novice medical students were randomly assigned to 5 practice conditions with instructor-directed elliptical excision skills' training (n = 8): didactic materials (control); organic bench model (low-fidelity); ethylene-vinyl acetate bench model (low-fidelity); chicken legs' skin bench model (high-fidelity); or pig foot skin bench model (high-fidelity). Pre- and post-tests were applied. Global rating scale, effect size, and self-perceived confidence based on Likert scale were used to evaluate all elliptical excision performances. Results : The analysis showed that after training, the students practicing on bench models had better performance based on Global rating scale (all P < 0.0000) and felt more confident to perform elliptical excision skills (all P < 0.0000) when compared to the control. There was no significant difference (all P > 0.05) between the groups that trained on bench models. The magnitude of the effect (basic cutaneous surgery skills' training) was considered large (>0.80) in all measurements. Conclusion : The acquisition of elliptical excision skills after instructor-directed training on low-fidelity bench models was similar to the training on high-fidelity bench models; and there was a more substantial increase in elliptical excision performances of students that trained on all simulators compared to the learning on didactic materials.