In this paper, a new acquisition protocol is adopted for identifying individuals from electroencephalogram signals based on eye blinking waveforms. For this purpose, a database of 10 subjects is collected using Neurosky Mindwave headset. Then, the eye blinking signal is extracted from brain wave recordings and used for the identification task. The feature extraction stage includes fitting the extracted eye blinks to auto-regressive model. Two algorithms are implemented for auto-regressive modeling namely; Levinson-Durbin and Burg algorithms. Then, discriminant analysis is adopted for classification scheme. Linear and quadratic discriminant functions are tested and compared in this paper. Using Burg algorithm with linear discriminant analysis, the proposed system can identify subjects with best accuracy of 99.8%. The obtained results in this paper confirm that eye blinking waveform carries discriminant information and is therefore appropriate as a basis for person identification methods.

In the past decade, biomedical instrumentations have witnessed major developments and now it is very easy to measure human biomedical electrical signals. One of these signals is the brain waves, known as electroencephalogram (EEG) signals, which became very easy to be measured using portable devices and dry electrodes. This opens the way for the use of brain waves in different applications rather than the biomedical diagnosis. One of the most recent non-medical applications for brain waves is the biometric authentication. Brain waves have some advantages which are not present in the commonly used identifiers, such as face and fingerprints, making them robust to spoof attacks. However, brain waves still face many challenges with reference to permanence and uniqueness. In this study, the authors discuss the employment of brain signals for human recognition tasks and focus on the challenges facing these signals towards the deployment of a practical biometric system. This study, also, provides a comprehensive review of the proposed approaches developed in EEG-based biometric authentication systems.

In the past decade, biomedical instrumentations have witnessed major developments and now it is very easy to measure human biomedical electrical signals. One of these signals is the brain waves, known as electroencephalogram (EEG) signals, which became very easy to be measured using portable devices and dry electrodes. This opens the way for the use of brain waves in different applications rather than the biomedical diagnosis. One of the most recent non-medical applications for brain waves is the biometric authentication. Brain waves have some advantages which are not present in the commonly used identifiers, such as face and fingerprints, making them robust to spoof attacks. However, brain waves still face many challenges with reference to permanence and uniqueness. In this study, the authors discuss the employment of brain signals for human recognition tasks and focus on the challenges facing these signals towards the deployment of a practical biometric system. This study, also, provides a comprehensive review of the proposed approaches developed in EEG-based biometric authentication systems.

In the past decade, biomedical instrumentations have witnessed major developments and now it is very easy to measure human biomedical electrical signals. One of these signals is the brain waves, known as electroencephalogram (EEG) signals, which became very easy to be measured using portable devices and dry electrodes. This opens the way for the use of brain waves in different applications rather than the biomedical diagnosis. One of the most recent non-medical applications for brain waves is the biometric authentication. Brain waves have some advantages which are not present in the commonly used identifiers, such as face and fingerprints, making them robust to spoof attacks. However, brain waves still face many challenges with reference to permanence and uniqueness. In this study, the authors discuss the employment of brain signals for human recognition tasks and focus on the challenges facing these signals towards the deployment of a practical biometric system. This study, also, provides a comprehensive review of the proposed approaches developed in EEG-based biometric authentication systems.

NULL

In this paper, the strengthening of two-way slabs using CFRP sheets is evaluated experimentally. The reinforcement ratio equal to 1.29% was chosen to serve the purpose of demarcating the punching shear failure mode. Results show that the punching capacity of two-way slabs can increase to 40% over that of the reference specimen. However, since bridge deck slabs directly sustain repeated moving wheel loads, they are one of the most bridge elements susceptible to fatigue failure. Therefore, this research is designed to

Nine one-way concrete slabs reinforced with carbon-FRP bars were constructed and tested to failure under two-point loading. The effect of reinforcement ratios, bar diameters, and various concrete compressive strengths were investigated to determine the concrete's contribution to shear strength. Slab structural behavior in terms of crack patterns, modes of failure, and ultimate capacities were examined. All slabs ultimately failed in shear that caused rupture and complete separation of both parts of the slab. One of the tested

The paper describes a comprehensive experimental program involving 4 large- scale wall specimens, one reinforced with steel bars (as reference) and three totally reinforced with GFRP bars. Reinforcements were detailed to represent single medium-rise shear wall in regions of low to moderate seismic risk. All walls failed due to concrete compression failure, reaching their flexural capacities with no strength degradation, controlling shear, sliding shear, and anchorage failures. The results show recoverable

ABSTRACT Experimental analysis of reinforced shear-wall with glass-fiber reinforced polymer (GFRP) showed its applicability in resisting lateral loads with no strength degradation, acceptable deformation capacity and energy dissipation as illustrated in an earlier article by the authors. The observed performance of GFRP-reinforced shear walls strongly suggested the necessity of proposing design procedure for such lateral resisting member. To propose design guidelines, however, determination of the elastic