In this paper, the extremely low frequency magnetic field produced by the tablet computers is explored. The measurement of the tablet computers’ magnetic field is performed by using a measuring geometry previously proposed for the laptop computers. The experiment is conducted on five Android tablet computers. The measured values of the magnetic field are compared to the widely accepted TCO safety standard. Then, the results are classified by the Self-Organizing Map method in order to create different levels of safety or danger concerning the magnetic field to which tablet computer users are exposed. Furthermore, a brief comparison of the obtained magnetic field levels with the ones from typical laptops is performed. At the end, a practical suggestion on how to avoid the high exposure to the low frequency magnetic field emitted by the tablet computers is given.
Signals provided by the ElectroEncephaloGraphy (EEG) are widely used in Brain-Computer Interface (BCI) applications. They can be further analyzed and used for thinking activity recognition. In this paper we proposed an algorithm that is able to recognize five mental tasks using 6 channel EEG data. The main idea is to separate the raw EEG signals into several frames and compute their spectrums. Next, a second-order derivative of Gaussian is applied to extract features and an optimum Gaussian kernel parameters grid search is performed with the help of cross-validation. The extracted features are further reduced by Principal Component Analysis. The processed data is utilized to train SVM classifier which is used for mental tasks recognition afterwards. The performance of the algorithm is estimated on publically available dataset. In terms of 5 folds cross-validation we obtained an average of 82.7% recognition rate (accuracy). Additional experiments were conducted using leave-one-out cross-validation where 67.2% correct classification was reported. Comparison to several state-of-the art methods reveals the advantages of the proposed algorithm.