The electroencephalography (EEG) signals are contaminated by ocular artifacts usually called as ElectroOculoGraphy(EOG) artifacts. This occurs due to an eye movement and repeatedly blinking eyes, it is a major barrier to overcome when analyzing ElectroEncephaloGram (EEG) data. In this paper, Generalized Eigen Value Decomposition (GEVD) algorithm based on Multichannel Wiener filter (MWF) was proposed. In the GEVD algorithm, the covariance matrix of the artifact is identified and substituted by low rank approximation. For both real and hybrid EEG data is demonstrated using this algorithm and also compared with other existing methods for removal of artifacts. This paper determines generic, robust and fast algorithm for artifact removal of various types of EEG signals. Signal to Error Ratio (SER) and Artifact to Residue Ratio (ARR) both are expressed in dBs. The better performance of artifact removal is expressed with high SER which measures clean EEG distortion and ARR measures the artifact estimation.
The characteristics of fruits are varying based on moisture content. The sulphur is fumigated to copra as preservative. As per WHO report the 65% children are suffered with asthmatic are sensitive and 75% of children are changes in behavior. It is difficult to identify sulphur added copra manually. In this proposed work drying process is used to identify the sulphur content of copra by comparative analysis with normal copra. The drying process leads to change in features like shape, colour and texture content of copra. The copra is dried in tray drier at 60°C for a regular interval of time. The image is capture at regular interval of time, GLCM features are extracted and compared between sulphur added and normal copra. The results are analyzed at different levels.
Educational organizations are gradually using multi prevention models to deal with tutorial requirements of pupils. The underpinning of those methods is that the execution of widespread practices premeditated to backing the tutorial requirements of the overwhelming widely held scholars. Investigators and experts are trying to valid measures to support teachers for effective classroom practices, which are sensitive to vary over time. Knowing the information about the classwork, students’ strength in a particular class, faculty details, passing the vital information to the students, alerting faculty with their classwork information, and automatic attendance system plays a key role in the smooth running of educational institutes or organizations. Particularly, in women colleges, passing the information about the ward to the parents and video surveillance is crucial. This paper aims at providing solutions for manual work to a great extent. The proposed system works using RaspberryPi 3 board with the display, face detection algorithm, face recognition using Local Binary Pattern method (LBP), GSM900, 4x4 Keyboard, mobile, and serial communication protocols. The new entrant to the campus will easily know about the college information and punctual working of the stakeholders in the campus. The unauthorized entry is detected using a webcam, and information is passed to the central unit for further action.
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