Earticle

Application of a Tunable-Q Wavelet Transform based technique is proposed in this paper for the extraction of Fundamental frequency component in Power Quality Disturbances. The TQWT filters are designed to extract the fundamental frequency component from the complete voltage (or) current signal. This is achieved by tuning the Q-factor and redundancy of the wavelet by primarily investigating the presence of interharmonics near the fundamental frequency. To test the effectiveness of the proposed scheme, the system is verified with various Power Quality Disturbances as per IEEE standards encountered in power system are considered here.

Crack inspection is a critical processing step in an AXI (automatic X-ray inspection) system. There are challenges for crack inspection since images are usually with serious noise, non-uniform brightness, etc. This paper proposes a novel line filter that can segment various widths of crack regions in those inhomogeneity brightness and noisy X-ray images. First, the input image is smoothed by the Gaussian functions with different scales, resulting in an image pyramid. Second, the adjacent images in the pyramid are subtracted to generate a Difference of Gaussian (DoG) pyramid and the extreme points from the subtracting result are selected as the output of the method. An edge suppression function is then defined to remove the edge response of the filter. Finally, the output of the proposed filter is defined as the combination of the DoG extreme result and the suppression function. The proposed method was applied to an AXI system and it performed better than the other two exited approaches. Experimental results showed that the proposed filter is robust to the image noise, crack sizes and brightness.

Range image segmentation is one of the most common problem in the field of computer vision. In view of the defect about traditional range image segmentation methods, this paper introduces a new range image segmentation method based on snake active contour model (SCAM). First, this paper expresses the snake active contour model in the form of parameters, and illustrates the contour line data points inside and outside of the movement and pseudo code description of the motion of a point algorithm; then constructs energy function, pushing the Euler equation and discretization; finally gets the results by Cholesky decomposition. Numerical experiment results show that the method is accurate and efficient, of good effect and the segmentation results are consistent with human subjective visual perception.

In this paper we proposed a multi band antenna in which fed is provided by SMA Coaxial Probe. This antenna is contain circular patch and Microstrip line. Using Microstrip line and providing different feed the antenna provide different frequency band. These are 1.2-1.4GHz, 1.6-1.7GHz and 2.1 to 10.7GHz. In this paper we design and simulated antenna use for different application. Here we use FR4 substrate for the design of antenna. The simulation of our proposed design is done with the help of full wave electromagnetic software which is based on FIT.

In allusion to the losses of image detail and texture structure information during image de-noising process, an image de-noising algorithm based on non-related dictionary learning is proposed in this paper. Firstly, this algorithm is adopted to obtain self-adaption redundant dictionary for the noisy image through the dictionary learning algorithm; secondly, HOG features and gray-level statistical features of each atom in the dictionary are extracted to form the feature set, and meanwhile the feature set of the atoms is adopted to divide the atoms into two types (non-noisy atoms and noisy atoms); finally, the non-noisy atoms are adopted to recover the image, thus to realize the de-nosing purpose. The experiment result shows: the proposed algorithm does not need to know the prior information of the noise and PSNR performance thereof is better than that of existing algorithms, and meanwhile the proposed algorithm can well keep the image detail and texture structure information, thus to improve visual effect.

With the development of information technology, in particular, to improve the video and image processing technology, intelligent video surveillance system collected data can be used to extract the relevant information on the vehicle for life and related investigations provided for convenience. Since the acquisition by the light, angle, and the impact of weather, so that the video data is difficult to retrieve, for this problem, this paper presents a background model fusion method can effectively eliminate the adverse weather light and other factors. This algorithm can quickly and efficiently and with high accuracy to detect vehicle information.