Earticle

Measurement of wound healing status is very important for monitoring and evaluation of progress in an individual patient. Thus wound classification is a vital step in the development of an automatic measurement system for wound healing assessment. In the existing system a RGB (Red, Green and Blue) histogram distributions of pixel values from wound colour images is being used in the new tissue classification protocol [1],[4] . This protocol has been carried out using the KNN classifier and results show that the in the proposed system the existing system protocol is integrated with another protocol i.e., fuzzy classification for maximum probability. Thus the maximum probable result is considered as final classification of the resultant wound type depending whether the wound categorizes under the acute or the chronic wound and denotes the healing period.

In this paper, we study many antenna beamforming algorithm research in cognitive radio two-way relay system. In this system, two primary users exchange information by second users, and the second users to configure multiple antennas, a user through the superposition signal sends it to the primary users, therefore in this article research the key problem is how to divide the primary user information of the signal power and power of user information signal; How to design the main users of beamforming matrix; How to design the beam forming matrix of users. This paper uses the maximum system capacity optimization guidelines, even is a convex optimization problem, this paper adopts positive semidefinite optimization and second-order cone optimization, closed solution is obtained. The simulation results show that this method due to previous beam forming algorithm.

Spatial relationships among image objects play an important role in countless domains of computer. But the research on the expression method of special directional relation ‘between’, ‘among’ and ‘surround’ is immature. In this paper, firstly the characteristics and defects of present basic directional relation models and special directional relation models are analyzed; secondly the basic idea and construction method of F-histogram are introduced; thirdly a new expression method to judge special directional relation ‘between’, ‘among’ and ‘surround’ based on F-histogram is described in detail; finally several calculation examples which can prove the correctness of the method are given.

Web queries clustering attract great concern recently. It is crucial for capturing frequently asked questions in question-answering system, most popular topics in search engine or automatic query expansion in information retrieval. The paper presents a new algorithm of web queries clustering using user click information in the query logs and applies it into query expansion. Different from previous work, in the new algorithm both word form and semantic information have been taken into considered. Experiments show that the new algorithm works effectively.

Noise estimation is an important part for noisy speech enhancement due to its momentous effect on the intelligibility and quality of the enhanced speech. In this paper, an effective noise estimation algorithm is presented by combining the minimum statistics estimation and Gaussian model assumption. In contrast to other methods, the proposed approach works in two steps. The noise power estimated by minimum statistics method in the first step suffers some bias which is removed by the second step of the proposed approach using Bayes theorem. As the noise estimation is refined in the second step, more accurate estimation can be obtained. The performance of the proposed approach is evaluated by objective and subjective tests under various noise environments and found to yield better results compared with conventional MS -based estimate.

Facial recognition (FR) is a challenging area of research due to difficulties with robust FR when the number of training samples is very small. The state-of-the-art sparse representation-based classification (SRC) shows very excellent FR performance. However, the recognition rate of SRC will drop dramatically when the number of training samples per class is very limited. To solve these issues, we propose a weighted multi-classifier optimization and sparse representation based (WMSRC) method for FR, which efficiently combines the local and global characteristics of face images. A face image is firstly divided into continuous but non-overlapped blocks by multi-resolution based blocking and each block is sparsely represented over the corresponding set of blocks of all training samples. The multi-scale SRC classifiers are then established and associated with different weights based on sub-block dictionary learning. According to the multiple voting results of the classifiers, the weights of multi-classifiers are optimized by a least-squares optimization equation with 2l-norm regularization. Finally, the classification results of all the blocks are combined by a weighted fusion criterion. Our experiments show that the WMSRC algorithm outperforms many existing block-based sparse representation classification algorithms, especially for FR when the available training samples per subject are very limited.

Support vector machine (SVM) is a kind of machine learning method, but the selection of parameters has important effects on the generalization ability of SVMs. In this study, the relation between the error penalty parameter C, kernel parameter σ and the generalization ability of SVMs is discussed. Parameter C adjusts the similarity among within-class members, while parameter σ adjusts the similarity between classes. Moreover, C and σ balances each other mutually within a certain range, which forms a fan-shaped optional parameter distribution region. The optimal parameter area should be located near the center of the sector where both C and σ are small. According to this, a method is suggested to first search a suitable area with coarse grids, and then determine the optimal parameter within the area with a fine bilinear grid. Experimental results show that the new parameter selection method can not only avoid local optima, and thus excluding the cases in which C and σ are big and unstable, but also can be extremely fast in searching process. Compared with other parameter selection methods, the performance of SVMs cannot be influenced, or even better in some cases.

The unit capacity of propulsion motor is greater than that of generator in all-electric ship electric system, in this high power load changes under the impact of random overload, the generator is extremely easy to have the fault causing system crashes. In this paper, a mathematical model of all-electric ship power system was established, and wavelet analysis was used to extract the feature of heavy load of power grid fault condition based on MATLAB. The simulation results prove that the simulating model of All Electric Ship Power System (AESPS) is reasonable, and by using the method wavelet analysis the feature information can be effectively extracted, which provides the basis for fault diagnosis.