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A brain computer interface (BCI) is a system that aims to control devices by analyzing brain signals patterns. In this work, a convenient time-frequency representation (TFR) for visualizing ERD/ERS phenomenon (Event related synchronization and desynchronization) based on Hilbert transform and spatial patterns is addressed, and a wavelet based feature extraction method for motor imagery tasks is presented...
Many brain disorders are diagnosed by analysing the EEG signals. EEG refers to the recording of the brain's spontaneous electrical activity over a short period of time. In this paper an efficient approach for detecting the presence of epileptic seizures in EEG signals is presented. Epilepsy is a disease due to temporary alternation in brain functions due to abnormal electrical activity of a group...
Discrete wavelet transform has both good qualities in time domain and frequency domain which is an ideal tool in analyzing unsteady signals. Discrete cosine transform is one of the approaches used in image compressing which is also used to extract features. This paper proposes a combined feature extraction method which is based on DWT and DCT for face recognition. First the original face image is...
Railway sleepers are a key engineering element of all railways. Lack of much sophistication in monitoring railway sleepers makes it a key problem within the rail transportation domain. Current day condition monitoring applications involving wooden railway sleepers are mostly carried out through visual inspection and if necessary some impact acoustic examination is carried out. Decision making is largely...
As the rapid development of Content-Based Image Retrieval, the Semantic understanding of image becomes the focus. However, the complexity and diversity of textures present great challenges to the traditional content analysis of images. Nowadays texture classification based on mathematical parameters is becoming very popular, but failed to break through the semantic obstacle between visual features...
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