International Medias Data Services (Canada)

The current state of the art in organic light-emitting diodes (OLEDs) depends on the use of glass as a substrate and hence, suffer from mechanical brittleness and lack of flexibility. In this paper, a fully flexible OLED fabricated on the highly flexible substrate, namely polyethylene terephthalate (PET), using a solution-based process is proposed. In this regard, the organic materials, i.e., PEDOT:PSS and F8BT, as the hole transport layer (HTL) and emission layer (EML), respectively, are deposited by the spin-coating technique on the PET substrate coated with indium tin oxide (ITO). The experimental results show that the flexible OLED prototype has a similar emission spectrum to that of OLED fabricated on conventional ITO-coated glass. Finally, a feasible approach is recommended to further enhance the performance of the proposed flexible OLED in terms of operating voltage and ohmic losses without any impact on its flexibility.

The use of Higher-Order Singular Value Decomposition (HOSVD) and other tensor decomposition methods are popular in the face recognition domain, yet a direct application to handwritten character recognition has not shown promising results so far. Character recognition is commonly performed in two steps: feature extraction and classification. In this paper, we propose a feature extraction algorithm based on HOSVD which is then combined with standard statistical classification. The algorithm constructs a tensor from the training data and applies HOSVD in order to obtain a feature extractor matrix for arbitrary character images. We evaluate the proposed handwriting features in combination with SVM classification for character recognition on the CEDAR benchmark data set. The results indicate that our proposed approach significantly outperforms the standard HOSVD classification method.