Earticle

Research on spectrum sensing and spectrum switching that are the key technologies of cognitive radio dynamic spectrum access is carried out in this paper. In order to reduce the required number of dualistic detection when in low spectrum occupancy rate and reduce the complexity of wideband spectrum sensing calculation, this paper presents a wideband spectrum sensing method based on group testing (GT) and constructs a two-step GT scheme based on polyphase filtering. In terms of spectrum switching, a method based on spectrum prediction is proposed for the determination of the switching time. Test results show that the main function of dynamic spectrum access is changed into reality. Both sides of the cognitive radio test platform could automatically find the idle channel link and establish links without common control channel, furthermore, when the current communication channel is reoccupied by primary users, the platform could switch to other idle channel automatically and quickly.

Wide range absorption is specially needed for different microtechnological applications along with microbolometers, photodetectors and coherent thermal emitters. Here we report, design of a wideband metamaterial absorber based on Titanium (Ti) and the analysis of its absorption and reflection characteristics. The structure of the absorber consists of two titanium layers spaced by a dielectric layer of refractive index 2. The simulation is done for analyzing its characteristics using finite difference time domain method (FDTD). The results show that the absorption occurs above 90 percent in the range 750 nm to 1200 nm while the reflection occurs below 10 percent. Overall absorption over the range (400 nm to 1500 nm) is found to be almost above 80 percent.

One-class support vector machine is an important and efficient classifier which is used in the situation that only one class of data is available, and the other is too expensive or difficult to collect. It uses vector as input data, and trains a linear or nonlinear decision function in vector space. However, there is reason to consider data as tensor. Tensor representation can make use of the structural information present in the data, which cannot be handled by the traditional vector based classifier. The significant benefit of using tensor as input is the reduction of the number of decision parameters, which can avoid the overfitting problems and especially suitable for small sample and large dimension cases. In this paper we have proposed a tensor based one-class classification algorithm named linear one-class support tensor machine. It aims to find a hyperplane in tensor space with maximal margin from the origin that contains almost all the data of the target class. We demonstrate the performance of the new tensor based classifier on several publicly available datasets in comparison with the standard linear one-class support vector machine. The experimental results indicate the validity and advantage of our tensor based classifier.

In order to improve the recognition accuracy of vision system on tomato picking robots, the paper proposed a method of feature extraction and recognition for ripe tomato based on illumination irrelevant images and support vector machine (SVM). In this method, we adopted vector median filter (VMF) to process the tomato images to eliminate noise and make the images more smooth firstly. To avoid the effects of natural environment illumination to the vision system, we processed tomato images and obtained the tomato illumination irrelevant images according to color constancy algorithm of the single pixel. Secondly, we segmented illumination irrelevant images using OSTU method, separated multiple objects by a watershed algorithm based on distance transform and got the target area with mathematical morphology. Also we extracted color, shape and textural features of the ripe tomatoes. Finally, we did experiments on recognizing tomatoes using support vector machine (SVM) with different kernel functions. At the same time, in order to obtain optimal model of SVM, we adopted cross validation and grid search method to optimize the model parameters. The experiment results show that illumination irrelevant processing not only can eliminate the influence of light intensity, but save a gray transferring step for further image segmentation. SVM with radial basis function is better than other kernel functions SVM and the tomato recognition accuracy is 95.7%. Through optimizing the parameter C and r of radial basis function, the tomato recognition accuracy reaches up to 96.9% with the increase of 1.2% when C and r is 4 and 16 respectively. This proves that it's feasible and effective to optimize SVM's parameters by cross validation and grid search method, which provide foundation for further research on vision system of tomato picking robots.

This paper proposes a hybrid technique to remove impulse noise from digital images. In this approach the filtering operation is based on 33 neighborhood of pixel under consideration. During filtering, the properties of neighborhood are considered to check whether it is highly corrupted with noise, medium or only itself act as impulse. Based upon these properties a new hybrid technique has been proposed to process the pixel which further uses different schemes. The experiments have been performed at various noise levels on standard images as well as on real images. The results have been evaluated on the basis of metrics like Signal to noise ratio (SNR), Edge preservation index ( EPI), Structure similarity index measure (SSIM), Multi scale structure similarity index measure (MS-SSIM) and Peak signal to noise ratio ( PSNR). From the results, it has been observed that proposed technique has worked efficiently by preserving the edges and fine lines. To demonstrate the effectiveness of proposed technique, the results have also been compared with other well accepted denoising techniques

With the rapid development of science and technology in the respect of electric power, the smartness standard of the power grid improves faster and faster. However, the power grid in the past has failed to keep pace with the needs of the current production. Therefore, it is very necessary to run the real-time, two-way, integrated and high speed communication system for the creation of the smart power grid. The article discusses the new requirements of the smart power grid on the electric power communication and the use of electric power communication in the smart power grid. It also elaborates the meanings of the electric power communication and the smart power grid, analyzing the operation condition of the smart power grid at home and the key performance of electric power communication in the smart power grid. Meanwhile, the paper studies fundamentally the real application of the electric power communication in the smart power grid. With the development of the power industry, the communication network is playing an increasingly vital role in the power system as an important part of modern power system.

With the continuous development of science and technology, computer technology and clustering technology of DM have become a part of the process of data analysis in this new era. How to use data mining technology to have better service for teaching evaluation field has been a research focus in the related research institutions at home and abroad. This paper has a research on application of ant colony clustering algorithm to Xinjiang ethnic groups’ evaluation system of Chinese teaching based on above. After briefly introducing the related theory and technology, this paper uses exploratory research method and survey method and designs evaluation system of Chinese teaching based on ant colony algorithm. Through the intensive study of a variety of data mining algorithm, this paper designs evaluation model of Chinese teaching using AHP. Then this paper gets data index through related survey and measures results after introducing ant colony clustering algorithm in evaluation system of Chinese teaching. The results show that application of ant colony clustering algorithm to Xinjiang ethnic groups’ evaluation system of Chinese teaching is more effective for teaching evaluation and improves the stability of the evaluation system and makes the system professional and provides effective reference experience to introduce ant colony clustering algorithm to other areas.