Ashraf Badawi

Detecting cognitive profiles is critical to efficient adaptive learning systems that automatically adjust the content delivered depending on the learner’s cognitive states and skills. This study explores electroencephalography (EEG) and facial expressions as physiological monitoring tools to build models that detect two cognitive states, namely, engagement and instantaneous attention, and three cognitive skills, namely, focused attention, planning, and shifting. First, while wearing a 14-channel EEG Headset and being videotaped, data has been collected from 127 subjects taking two scientifically validated cognitive assessments. Second, labeling was performed based on the scores obtained from the used tools. Third, different shallow and deep models were experimented in the two modalities of EEG and facial expressions. Finally, the best performing models for the analyzed states are determined. According to the used performance measure, which is the f-beta score with beta = 2, the best obtained results for engagement, instantaneous attention, and focused attention are EEG-based models with 0.86, 0.82, and 0.63 scores, respectively. As for planning and shifting, the best performing models are facial expressions-based models with 0.78 and 0.81, respectively. The obtained results show that EEG and facial expressions contain important and different cues and features about the analyzed cognitive states, and hence, can be used to automatically and non-intrusively detect them.

The authors theoretically propose a design for electrically tuning a metasurface based on piezoelectric actuation. The design comprises 2D gap‐plasmon elements arranged periodically on a piezoelectric substrate which controls the period between them. The technique is numerically studied for a metasurface that anomalously reflects an incident p‐polarised wave by 30° at the optical communication wavelength, 1550 nm. The dynamic behaviour of the system is investigated for two main applications; (a) steering the reflection angle and (b) retrieving the reflection angle for an incident wave of a shifted wavelength. It is demonstrated that a reflected beam can cover an angle of ∼3° upon a strain of <3.3%. It is also shown that at the same strain value, the reflection angle is retrieved for a wavelength error of ±50 nm. These results suggest a potential to design large‐bandwidth, reconfigurable metasurfaces that are integrable with planar opto‐electromechanical devices.

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Detecting the cognitive profiles of learners is an important step towards personalized and adaptive learning. Electroencephalograms (EEG) have been used to detect the subject’s emotional and cognitive states. In this paper, an approach for detecting two cognitive skills, focused attention and working memory, using EEG signals is proposed. The proposed approach consists of the following main steps: first, subjects undergo a scientifically-validated cognitive assessment test that stimulates and measures their full cognitive profile while putting on a 14-channel wearable EEG headset. Second, the scores of focused attention and working memory are extracted and encoded for a classification problem. Third, the collected EEG data are analyzed and a total of 280 time- and frequency-domain features are extracted. Fourth, several classifiers were trained to correctly classify and predict three levels (low, average, and high) of the two cognitive skills. The classification accuracies that were obtained on 86 subjects were 84% and 81% for the focused attention and working memory, respectively. In comparison with similar approaches, the obtained results indicate the generalizability and suitability of the proposed approach for the detection of these two skills. Thus, the presented approach can be used as a step towards adaptive learning where real-time adaptation is to be done according to the predicted levels of the measured cognitive skills.

A three-dimensional quantum-mechanical method is used for modeling quantum tunneling and photon-assisted field emission processes in vertical and lateral vacuum nanotriodes. The electron transport through the devices is computed using a transfer-matrix technique. The possibility of using vacuum nanotriodes in current rectification is investigated at low voltages in a quasi-static and a high-frequency analyses. The obtained results show that a great enhancement in the rectification properties is achievable when a DC bias is applied between the gate and the collector. By comparing the results of the vertical and the lateral-type devices, it is shown that the former is superior in current rectification, while the latter provides higher values of current density. Furthermore, the high-frequency analysis shows that incident radiation results in photon-assisted current emission. It also demonstrates that the vacuum nanotriode can be much more efficient than the vacuum nanodiode in rectifying high-frequency signals.

We present a plasmonic absorber of Fabry-Perot-like resonator and demonstrate the tuning capability of its spectral response. The conventional resonator comprises two spaced layers of gold to act as a confinement for the light and resonant structure. The cavity thickness can be used efficiently as a tuning parameter for the absorber frequency response. The unloaded absorber shows high absorption greater than 90% of the incident power for oblique incident angles for both p- and s-polarized rays from 260-490[nm] (blue to ultraviolet). When the cavity is loaded with a dielectric layer, the absorbed power reduces by half or more at the same range. The device shows the potential to be used as an optical filter or switch, and its main advantage is simplicity in design, and fabrication. The device is analyzed using transmission line model (TLM), Transfer Matrix Method (TMM), and numerically verified by a commercial Full Wave Simulator COMSOL Multiphysics(FWS).