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Automated learning analytics is becoming an essential topic in the educational area, which needs effective systems to monitor the learning process and provides feedback to the teacher. Recent advances in visual sensors and computer vision methods enable automated monitoring of behavior and affective states of learners at different levels, from university to pre-school. The objective of this research was to build an automatic system that allowed the faculties to capture and make a summary of student behaviors in the classroom as a part of data acquisition for the decision making process. The system records the entire session and identifies when the students pay attention in the classroom, and then reports to the facilities. Our design and experiments show that our system is more flexible and more accurate than previously published work.

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Alloy nanocrystals (NCs) provide access and control of the parameters for nanoscale engineering because their physical and optical properties depend on size, shape, and composition. Here, alloy CdS x Se1−x NCs with different shapes were synthesised via a one-pot method using cadmium acetate, sulfur, and selenium as precursors in trioctylphosphine solution. The luminescence and shapes of NCs were characterised by fluorescence spectroscopy and transmission electron microscopy, respectively. It was found that the modification of precursor concentration resulted in NC shape variation, including branched NCs, long and short nanorods. Consequently, a series of alloy CdS x Se1−x NCs with different shape-based light emitting devices (LEDs) were fabricated and their operation characteristics were also compared. The obtained luminance and luminous efficiency showed that the control of NC shape is the key factor for the improvement of LED performance. We anticipate that this work will provide further insight into the design of CdSxSe1−x NCs-based LEDs with shape variation.

Two-dimensional Principal Component Analysis (2D PCA) is a global feature extraction method for Face recognition that works upon 2D matrices rather than 1D vectors. In every Face recognition system, different distance functions used in the classification stage can yield diverse recognition rates and one of the quests for the developers is to figure out which is the most preferable function. In this paper, we concentrate on the insights of distance metrics applied for 2D PCA. A new distance metric so called weighted p, in which an exponent p and eigenvalues are used, is also proposed. To evaluate the recognition performance of those functions, comparative experiments on the face database ORL are performed. The results show that the proposed function provides 2D PCA with higher recognition rates than existing rivals.