Epilepsy is a chronic neurological disorder that occurs in the brain،
and affects approximately 2% of people around the world، where
epilepsy patients face a lot of difficulties in everyday life due to the
occurrence of seizures. Electroencephalog
ram (EEG) is used in
the automated detection of epileptic seizures، which has
Characteristics of non-linear and non-stationary. In this research،
we conducted automated detection of the seizures from the scalp
EEG signals using a Level 5 Discrete Wavelet Transforms DWT to
analyze the signal and extracting statistical features (maximum،
minimum، mean، average ، standard deviation، the ratio between
the mean values) and Categorizing using artificial neural networks
ANN for classification. The suggested detection method has
89.85% detection accuracy with 90.60% sensitivity ، and 89.1%
specificity.
A digital watermark is a signal that is embedded into digital data
(text, image, audio, video) in a manner that allows it to be extracted
later. This is done by embedding a pattern which contains the
author's data into the digital data.
In this r
esearch, we propose a comparison between three types of
transformations for embedding a watermark in the frequency
domain into digital images in an efficient and secure method that
allows the watermarking any type of digital images with good
perceptibility.
دراسة وتحليل أداء نظام التجميع بالتقسيم الترددي المتعامد باستخدام التحويل المويجي المتقطع مع السلاسل الطورية
