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We investigate the detection of epileptic seizure onset based on electroencephalography (EEG) signal in short-time sessions (less than one second) from various samples from epilepsy and healthy people. Wavelet transform methods are applied to extract the features embedded in the high-dimensional epileptic EEG signal. It is found that results of wavelet transform play significant roles in dimensional...
Iris location is an essential module in iris recognition. Traditional iris location methods involve a large range of search, which is computation wasting and sensitive to noise. And these methods adopt circular template to locate the pupillary boundary. It may not accurately describe the pupillary actual boundary and bring the error for the following feature extraction and recognition. To address...
The particles in urinary microscopic images are hard to classify because of noisy background and strong variability of objects in shape and texture. In order to overcome these difficulties, firstly, a new method of texture feature extraction using the distance mapping based on a set of local grayvalue invariants is introduced and the feature is robust to the shift and rotation. Secondly, we reduce...
In this work, we proposed a method for a binary classification in an EEG-based brain computer interface (BCI) with wavelet packet transform and neural networks. For feature extraction, we introduced a new method which combined the slow cortical potentials (SCPs) and the specific energy from the time-frequency domain in beta-band via the wavelet packet transform. A 3-layer perceptron established by...
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