It has become clear that repetitive sequences have played multiple roles in eukaryotic genome evolution including increasing genetic diversity through mutation, changes in gene expression and facilitating generation of novel genes. However, identification of repetitive elements can be difficult in the ab initio manner. Currently, some classical ab initio tools of finding repeats have already presented and compared. The completeness and accuracy of detecting repeats of them are little pool. To this end, we proposed a new ab initio repeat finding tool, named HashRepeatFinder, which is based on hash index and word counting. Furthermore, we assessed the performances of HashRepeatFinder with other two famous tools, such as RepeatScout and Repeatfinder, in human genome data hg19. The results indicated the following three conclusions: (1) The completeness of HashRepeatFinder is the best one among these three compared tools in almost all chromosomes, especially in chr9 (8 times of RepeatScout, 10 times of Repeatfinder); (2) in terms of detecting large repeats, HashRepeatFinder also performed best in all chromosomes, especially in chr3 (24 times of RepeatScout and 250 times of Repeatfinder) and chr19 (12 times of RepeatScout and 60 times of Repeatfinder); (3) in terms of accuracy, HashRepeatFinder can merge the abundant repeats with high accuracy.

In this paper, a novel method based on PCA with shape and intensity information is proposed for infrared forwardlooking airport recognition. Here, PCA is used to perform feature transformation and airport recognition. It maps an input image into a low-dimensional feature space in order to make the mapped features linearly separable. And the input image of conventional method only uses intensity information. The proposed method not only considers the intensity but also adopts shape-mask to emphasize the important object area information. The novel method is evaluated based on the sequence of infrared forward-looking airport images by using different airport recognition methods such as BP networking and SVM. The experiment's results have been compared based on percentage of correct classification, computation complexity and amount of training data, which show that this new method is superior to other recognition approach on computation complexity under almost the same recognition accuracy.

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Voltage divider biasing common emitter amplifier is one of the core contents in analog circuit curriculum, and almost all of traditional textbooks apply approximate calculation method to estimate all characteristic parameters. In calculating quiescent point, transistor base current is generally ignored to get the approximate base potential and emitter current, then other operating parameters, and AC small signal parameters can be acquired. The main purpose of this paper is to compare traditional and Thevenin equivalent methods and to get the difference of the two methods. A Formula is given to calculate the error of the traditional method. Example calculating reveals that the traditional method can generate an error about 10%, and even severe for small signal amplifier with higher quiescent point.

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