This paper proposes the use of a similarity measure based on information theory called correntropy for the automatic classification of pathological voices. By using correntropy, it is possible to obtain descriptors that aggregate distinct spectral characteristics for healthy and pathological voices. Experiments using computational simulation demonstrate that such descriptors are very efficient in the characterization of vocal dysfunctions, leading to a success rate of 97% in the classification. With this new architecture, the classification process of vocal pathologies becomes much more simple and efficient.
We present an in-depth analysis of the fractal nature of 21 classical music pieces previously shown to have scale-free properties. The musical pieces are represented as networks where the nodes are musical notes and respective durations, and the edges are its chronological sequence. The node degree distribution of these networks is analyzed, looking for self-similarity. This analysis is done in the full network, in its fractal dimensions, and its skeletons. The assortativeness of the pieces is also studied as a fractal property. We show that two-thirds of these networks are scale-invariant, i.e. scale-free in some dimension or their skeleton. In particular, two pieces were given attention because of their exceptional tendency for fractality.
Resumo-Sistemas de rádio cognitivo devem ser capazes de perceber faixas de frequência desocupadas para transmissões oportunísticas, assim como detectar a presença de usuários primários quando estes ocupam seu espectro licenciado. Portanto, um elemento crucial para a operação desses sistemasé o sensoriamento espectral. Este trabalho apresenta uma arquitetura inteligente de sensoriamento, baseada no cálculo da função de densidade espectral cíclica do canal sensoriado e na utilização de um comitê de máquinas perceptron múltiplas camadas, para detectar sinais modulados na presença de ruído Gaussiano branco aditivo. A arquitetura proposta foi avaliada no sensoriamento de modulações QPSK e os resultados obtidos nos experimentos realizados comprovaram a eficiência desta arquitetura mesmo em cenários com baixa relação sinal-ruído.
This work presents a novel spectral sensing method for the detection of signals presenting nonlinear phase variation over time. The introduced method is based on the angle-time cyclostationarity theory, which applies transformations to the signal to be sensed in order to mitigate the effects of nonlinear phase variation. The architecture is employed for sensing binary phase shift keying (BPSK) signals, being also compared with time cyclostationarity. The obtained simulation results clearly demonstrate the efficiency of the proposed approach, while presenting improved performance in terms of the detection rate of primary users increased by about 8 dB.
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