Face recognition is required in various applications, and major progress has been witnessed in this area. Many face recognition algorithms have been proposed thus far; however, achieving high recognition accuracy and low execution time remains a challenge. In this work, a new scheme for face recognition is presented using hybrid orthogonal polynomials to extract features. The embedded image kernel technique is used to decrease the complexity of feature extraction, then a support vector machine is adopted to classify these features. Moreover, a fast-overlapping block processing algorithm for feature extraction is used to reduce the computation time. Extensive evaluation of the proposed method was carried out on two different face ima

Solid dispersion (SD) formulation has attracted much attention due to its potential in enhancing dissolution performances of poorly soluble active pharmaceutical ingredients (API). Recently, a review on dissolution performances of SDs classifies the improvement into 3 categories, where 82 % of the studies showed improved bioavailability, 8 % showed reduced bioavailability and 10 % revealed similar bioavailability as compared to pure APIs. This indicates the inconsistent degrees of dissolution improvement of poorly soluble APIs in SD. Although a few factors related to the choice of carriers have been suggested to contribute to the dissolution improvement, however, the underlying factor determining the discrepancy in the degree of dissolution