We evaluated the bone healing effect of grafting with synthetic β-tricalcium phosphate (β-TCP; Cerasorb®), bovine-derived hydroxyapatite (HA; Bio-Oss®), and a mixture of β-TCP and HA in rats. Each material was grafted in prepared 8-mm frontal bone defects in 15 rats. The control group underwent surgery without any grafting materials and was examined after 4 weeks, whereas the experimental groups received grafting materials and were examined after 1, 2, and 4 weeks. After implantation, the rats were sacrificed for histomorphometric studies using light microscopy, and the data were analyzed using analysis of variance. Considerable inflammation and fibrosis were observed after 1 and 2 weeks in all experimental groups, whereas the inflammation was reduced and fibrosis was stabilized after 4 weeks. New bone formation was observed at the defect margin. Statistically, there was no difference in new bone formation among the three experimental groups. In conclusion, there was no difference in new bone formation using Bio-Oss®, Cerasorb®, and a mixture of Bio-Oss® and Cerasorb®.
In this paper, we suggests how to segment the face when there is the man under complex environment, extracts the features from segmented the image and proposes a effective recognition system using the discrete wavelet transform (DWT). This algorithm is proposed by following processes. First, two gray-level images is captured with 256 level of the size of 256X 256 in constant illumination. We use a Gaussian filter to remove noise of input image and get a differential image between background and input image. Second, a mask is made from erosion and dilation process after binary of the differential image. Third, facial image is divided by projecting the mask into input image. Most characteristic information of human face is in eyebrow, eyes, nose and mouth. In the reason, the facial characteristic are detected after selecting local area including this area. Forth, detecting the characteristic of segmented face image, edge is detected with Sobel operator. Then, eye area and the center of face are searched by using horizontal and vertical components of edge. Characteristic area consists of eyes, a nose, a mouth, eye brows and cheeks, is detected by searching the edge of the image. Finally, characteristic vectors are extracted from performing DWT of this characteristic area and are normalized it between +1 and -1. Normalized vectors is used with input vector of neural network. Simulation results show recognition rate of 100 % about learned image and 92% about test image.
A common concern of neural network models has been the problem of relating the function of complex systems of neurons to what is known of individual neurons, their interconnections and offsetsIn this paper, we propose a new model of neural networks that can control and produce the offset patterns of the input layer, the hidden layer, and the output layer neurons. It consists of the input layer for the signal patterns, the hidden layer for the offset patterns production, and memory part between the hidden layer and the output layer. The output of neurons is calculated by the offsets control parameter R01.The input layer calculates the input patterns to be learned so that the proposed neural network can control and produce the offset patterns, and sends the results to the next layer. The hidden layer produces the offset patterns after receiving the pattern information from the input layer, and it sends the output information of the hidden layer to the memory part. The memory part stores the learned output patterns of the hidden layer after comparing it with the input pattern, and sends the stored information to the output layer after the entire learning.Simulation results show that the proposed neural network can produce the offset patterns and it can be efficiently applied in the logic circuit design and pattern classification.
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