Artificial neural network based autoregressive modeling technique with application in voice activity detection

A new method of estimating the coefficients of an autoregressive (AR) model using real-valued neural network (RVNN) technique is presented in this paper. The coefficients of the AR model are obtained from the synaptic weights and adaptive coefficients of the activation function of a two layer RVNN while the number of neurons in the hidden layer is estimated from over-constrained system of equations. The performance of the proposed technique has been evaluated using sinusoidal data and recorded speech so as to examine the spectral resolution and line splitting as well as its ability to detect voiced and unvoiced data section from a recorded speech. Results obtained show that the method can accurately resolve closely related frequencies without experiencing spectral line splitting as well as identify the voice and unvoiced segments in a recorded speech

Development of an intelligent scorpion detection technique using vibration analysis

A possible solution to address the problem of Scorpion stings is the capability of detecting its presence earlier before it stings. This paper presents efforts in Scorpion detection using substrate vibration modelling approach. An eight stage approach has been presented in this work. Using sinusoidal signal, signal representing Scorpion behaviour was firstly sampled and then amplified before transmitting to a nearby receiving module. The received signal undergoes filtering for noise removal before being modelled for coefficients determination. The computed coefficients were then clustered for analysis of behavioural determination. Results obtained in this work show that the proposed technique can be used for Scorpion detection

A new method of vascular point detection using artificial neural network

Vascular intersection is an important feature in retina fundus image (RFI). It can be used to monitor the progress of diabetes hence accurately determining vascular point is of utmost important. In this work a new method of vascular point detection using artificial neural network model has been proposed. The method uses a 5×5 window in order to detect the combination of bifurcation and crossover points in a retina fundus image. Simulated images have been used to train the artificial neural network and on convergence the network is used to test (RFI) from DRIVE database. Performance analysis of the system shows that ANN based technique achieves 100% accuracy on simulated images and minimum of 92% accuracy on RFI obtained from DRIVE database

A new method of vascular point detection using artificial neural network

Vascular intersection is an important feature in retina fundus image (RFI). It can be used to monitor the progress of diabetes hence accurately determining vascular point is of utmost important. In this work a new method of vascular point detection using artificial neural network model has been proposed. The method uses a 5x5 window in order to detect the combination of bifurcation and crossover points in a retina fundus image. Simulated images have been used to train the artificial neural network and on convergence the network is used to test (RFI) from DRIVE database. Performance analysis of the system shows that ANN based technique achieves 100% accuracy on simulated images and minimum of 92% accuracy on RFI obtained from DRIVE database

A real valued neural network based autoregressive energy detector for cognitive radio application

A real valued neural network (RVNN) based energy detector (ED) is proposed and analyzed for cognitive radio (CR) application. This was developed using a known two-layered RVNN model to estimate the model coefficients of an autoregressive (AR) system. By using appropriate modules and a well-designed detector, the power spectral density (PSD) of the AR system transfer function was estimated and subsequent receiver operating characteristic (ROC) curves of the detector generated and analyzed. A high detection performance with low false alarm rate was observed for varying signal to noise ratio (SNR), sample number, and model order conditions. The proposed RVNN based ED was then compared to the simple periodogram (SP), Welch periodogram (WP), multitaper (MT), Yule-Walker (YW), Burg (BG), and covariance (CV) based ED techniques. The proposed detector showed better performance than the SP, WP, and MT while providing better false alarm performance than the YW, BG, and CV. Data provided here support the effectiveness of the proposed RVNN based ED for CR application

Comparação entre escalogramas e bancos de filtros Wavelet utilizados na classificação de padrões epileptiformes

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia Elétrica, Florianópolis, 2016.A comprovação clínica do diagnóstico da epilepsia é realizada por um neurofisiologista que analisa registros de eletroencefalograma (EEG) do indivíduo com suspeita da doença, resultando em um processo demorado. Embora muitos estudos tenham proposto métodos ou sistemas de automatização da análise dos registros de EEG, ainda não existe um algoritmo ou sistema que realize este tipo de tarefa com o sucesso esperado no ambiente clínico. Uma solução adotada para aumentar o desempenho de tais classificadores é o processamento digital dos sinais de EEG. Dentre os métodos de processamento, a Transformada Wavelet tem apresentado resultados promissores. Em continuidade aos estudos desenvolvidos em uma linha de pesquisa do Instituto de Engenharia Biomédica (IEB-UFSC) da Universidade Federal de Santa Catarina (UFSC), este trabalho propõe-se a realizar uma comparação direta entre os dois métodos de aplicação da Transformada Wavelet: Banco de Filtros e Escalogramas. As funções Wavelet analisadas neste trabalho foram escolhidas de acordo com uma revisão da literatura. Uma base de dados com segmentos de sinais de EEG é processada com banco de filtros e escalogramas. A base de dados processada é aplicada à entrada de redes neurais artificiais para o reconhecimento de padrões eletrográficos característicos de pacientes com Epilepsia. Os resultados são analisados a partir de dois métodos: análise não paramétrica e análise da rede com maior eficiência obtida para cada função Wavelet. Utilizando análise não paramétrica, as funções Coif 4 e Db 4, utilizando Banco de Filtros, e Bior 3.1 e Coif 1, utilizando Escalogramas, apresentam bom desempenho. Por sua vez, fazendo análise com base em apenas uma única rede para cada função, a melhor configuração é utilizando a função Bior 3.1 com processamento por Banco de Filtros.Abstract : The clinical evidence of the diagnosis of epilepsy is performed by a neurophysiologist who analyzes the electroencephalogram (EEG) records of the individual with suspected disease, resulting in a time consuming process. Although many studies have proposed methods or automated systems for EEG record analysis, there is still no algorithm or system that performs this type of task with the expected success in the clinical setting. One solution adopted to increase the performance of such classifiers is the digital processing of the EEG signals. Among the processing methods, the Wavelet Transform has presented promising results. In continuity to the studies developed in a line of research of the Institute of Biomedical Engineering (IEB-UFSC) of the Federal University of Santa Catarina (UFSC), this work proposes to make a direct comparison between the two methods of application of the Wavelet Transform: Bank of Filters and Scalograms. The Wavelet functions analyzed in this work were chosen according to a literature review. A database with segments of EEG signals is processed with Wavelet Filter Banks and Scalograms. The processed database is applied to the input of artificial neural networks for the recognition of electrographic patterns characteristic of patients with Epilepsy. The results are analyzed using two methods: non-parametric analysis and the most efficient network obtained for each Wavelet function. Using non-parametric analysis, the Coif 4 and Db 4 functions, for Filter Banks, and the Bior 3.1 and Coif 1 functions, using Scalograms, perform well. In turn, doing the analysis based only a single network for each function, the best configuration is using the Bior 3.1 function and processing by Filter Bank

Artificial neural network based autoregressive modeling technique with application in voice activity detection

A new method of estimating the coefficients of an autoregressive (AR) model using real-valued neural network (RVNN) technique is presented in this paper. The coefficients of the AR model are obtained from the synaptic weights and adaptive coefficients of the activation function of a two layer RVNN while the number of neurons in the hidden layer is estimated from over-constrained system of equations. The performance of the proposed technique has been evaluated using sinusoidal data and recorded speech so as to examine the spectral resolution and line splitting as well as its ability to detect voiced and unvoiced data section from a recorded speech. Results obtained show that the method can accurately resolve closely related frequencies without experiencing spectral line splitting as well as identify the voice and unvoiced segments in a recorded speech