This paper proposes a hybrid multi-step-ahead forecasting model based on two stages to improve pelagic fish-catch time-series modeling. In the first stage, the Fourier power spectrum is used to analyze variations within a time series at multiple periodicities, while the stationary wavelet transform is used to extract a high frequency (HF) component of annual periodicity and a low frequency (LF)...

In this paper, we investigate the possibility of using visual and audio visual stimulus for detecting the human emotion by measuring electroencephalogram (EEG). Visual and audiovisual stimulus based protocols is designed to acquire the EEG signals over five healthy subjects using 63 biosensors. We propose the analysis of EEG signals using discrete wavelet transform and classification using neur...

The main goal of the present work is to decrease the computational burden for optimum design of steel frames with frequency constraints using a new type of neural networks called Wavelet Neural Network. It is contested to train a suitable neural network for frequency approximation work as the analysis program. The combination of wavelet theory and Neural Networks (NN) has lead to the developmen...

0957-4174/$ see front matter 2013 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.eswa.2013.12.027 ⇑ Corresponding author. Tel.: +91 416 2202364; fax: +91 416 2243092. E-mail addresses: [email protected] (M. Monica Subashini), sksahoo@ vit.ac.in (S.K. Sahoo). 1 Tel.: +91 416 2202467; fax: +91 416 2243092. 2 Abbreviations used: PCNN, pulse coupled neural networks; ICM, i...

This paper presents the experimental pilot study to investigate the effects of pulsed electromagnetic field (PEMF) at extremely low frequency (ELF) in response to photoplethysmographic (PPG), electrocardiographic (ECG), electroencephalographic (EEG) activity. The assessment of wavelet transform (WT) as a feature extraction method was used in representing the electrophysiological signals. Consid...

In this paper, a wavelet-based neural network (WNN) classifier for recognizing EEG signals is implemented and tested under three sets EEG signals (healthy subjects, patients with epilepsy and patients with epileptic syndrome during the seizure). First, the Discrete Wavelet Transform (DWT) with the Multi-Resolution Analysis (MRA) is applied to decompose EEG signal at resolution levels of the com...

Nowadays Epileptic disorder is a most challenge aspects in brain activation. Electroencephalograph (EEG) is one of the popular procedures to understand the human brain condition. The activation of brain will be changed due to the symptoms of neurological disorder. We have been proposed a procedure to find epilepsy disorder, using discrete wavelet transform and neural network classifier. The EEG...

Motion control of mobile robots is a typical nonlinear tracking control issue and has been discussed with different control schemes such as PID, GPC, sliding mode, predictive control etc[1]-[3]. Intelligent control techniques, based on neural networks and fuzzy logic, have also been developed for path tracking control of mobile robots[4][5]. While conventional neural networks have good ability ...

We introduce a two-layer wavelet scattering network, for object classification. This scattering transform computes a spatial wavelet transform on the first layer and a new joint wavelet transform along spatial, angular and scale variables in the second layer. Numerical experiments demonstrate that this two layer convolution network, which involves no learning and no max pooling, performs effici...