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Biometric authentication is recently used for verification someone’s identity according to their physiological and behavioural characteristics. The most popular biometric techniques are fingerprints, facial and voices recognition. However, these techniques have disadvantage in which it can be easily to be imitated and mimicked by hackers to access a device or a system. Therefore, this study proposed electroencephalogram (EEG) as a biometric technique to encounter this problem. The wavelet packet decomposition is explored in this study for feature extraction method. The wavelet packet decomposition feature is represented in the average wavelet, root mean squared (RMS) wavelet and power wavelet were selected as features to extract a meaningful information from the original EEG signal based on the visual representation. These features were applied to classify between familiar and unfamiliar image responses (visual representation) and to recognize 13 subjects by using Support Vector Machine (SVM), k-Nearest Neighbor (KNN) and Random Forest (RF). The analysis of the classification between familiar and unfamiliar images responses obtained that gamma frequency (30 – 45 Hz) achieved the highest correct recognition rate (CRR) and KNN obtained the accuracy of 92.8% was obtained with KNN in the classification between familiar and unfamiliar image responses. Using the gamma frequency band, the classification between the EEG responses of the 13 subjects was evaluated using the percentage of false acceptance rate (FAR) and false rejection rate (FRR). From the overall view, the value of FAR is lower than FRR. These values were used in authentication system as threshold for security level. As the result of classification between the subjects, SVM performed better compared as KNN and RF in which the error rate for acceptance of unauthorized person and rejection of authorized person were the lowest.

Stress is one of the factors that affect human health in many aspects. It is considered as one of the culprits in increasing the risk of getting sick that could probably lead to critical physical or mental illnesses. Stress can be experienced everywhere and in different circumstances. Hence, stress should be controlled and managed by monitoring its progress or regress. Physiological information can be used to determine stress levels. One of these is the Galvanic Skin Response (GSR) that utilizes skin conductance which is known to be directly involved in the emotional behavioural regulation in humans. In this study, a method on how to determine stress when a person is engaged in mobile communication is proposed. An Android application was developed that is capable of determining the stress level of a person while doing SMS composition. GSR data were utilized and the performance of the proposed method was found of no significant difference with a commercially available device. Factors like phone size and period of texting was investigated and were found out that these only contribute an extremely low level of stress. The developed App could be used to determine stress levels especially if emotional conversations are considered.

Visible light communication (VLC) is a type of data communications which uses the visible light spectrum in the 350-800nm wavelength range. Light signals are converted into electrical pulses to indicate a specific information which in this case, diving instructions. In this study, VLC is used in an underwater communication system for recreational diving activities in order to reinforce the conventional hand signaling protocols. Wearable LED-based transmitter and phototransistor-based receiver were used. The hand-held transmitter was used to emit different light pulses corresponding to 16 commands in which 13 are standard scuba diving hand signals. The goggle receiver process and translates these pulses into an audio signal which can be heard by the diver through waterproof earphones. The VLC system developed was able to achieve an average signal reception accuracy of at least 97.0% on a series of tests conducted underwater with a maximum transmitter-to-receiver distance of 5m using white LEDs.

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