一种新型介质栅波导滤波特性的研究(英文)

提出了一种新型介质栅波导阻带滤波结构,并采用多模网络与严格模匹配相结合的方法对该阻带滤波特性进行了仔细分析,给出了主模TM模的Brillouin图及滤波结构的归一化中心频率、阻带的宽度和带内最大衰减等特性和结构参数的关系.与传统的介质栅滤波器相比,新滤波器结构不仅具有金属损耗小的优点,而且其阻带带宽和带内衰减都要比传统介质栅滤波器的大得多.文中所给出的计算结果对设计该新型带阻滤波结构有指导意义.

Random noise suppression for seismic data using a non-local Bayes algorithm

For random noise suppression of seismic data, we present a non-local Bayes （NL- Bayes） filtering algorithm. The NL-Bayes algorithm uses the Gaussian model instead of the weighted average of all similar patches in the NL-means algorithm to reduce the fuzzy of structural details, thereby improving the denoising performance. In the denoising process of seismic data, the size and the number of patches in the Gaussian model are adaptively calculated according to the standard deviation of noise. The NL-Bayes algorithm requires two iterations to complete seismic data denoising, but the second iteration makes use of denoised seismic data from the first iteration to calculate the better mean and covariance of the patch Gaussian model for improving the similarity of patches and achieving the purpose of denoising. Tests with synthetic and real data sets demonstrate that the NL-Bayes algorithm can effectively improve the SNR and preserve the fidelity of seismic data.

Non-Rigid Object Tracking by Anisotropic Kernel Mean Shift

Mean shift,an iterative procedure that shifts each data point to the average of data points in its neighborhood,has been applied to object tracker.However,the traditional mean shift tracker by isotropic kernel often loses the object with the changing object structure in video sequences,especially when the object structure varies fast.This paper proposes a non-rigid object tracker by anisotropic kernel mean shift in which the shape,scale,and orientation of the kernels adapt to the changing object structure.The experimental results show that the new tracker is self-adaptive and approximately twice faster than the traditional tracker,which ensures the robustness and real time of tracking.

A Stream Pattern Matching Method for Traffic Analysis

In order to identify any traces of suspicious activities for the networks security, Network Traffic Analysis has been the basis of network security and network management. With the continued emergence of new applications and encrypted traffic, the currently available approaches can not perform well for all kinds of network data. In this paper, we propose a novel stream pattern matching technique which is not only easily deployed but also includes the advantages of different methods. The main idea is： first, defining a formal description specification, by which any series of data stream can be unambiguously descrbed by a special stream pattern; then a tree representation is constructed by parsing the stream pattern; at last, a stream pattern engine is constructed with the Non-t-mite automata （S-CG-NFA） and Bit-parallel searching algorithms. Our stream pattern analysis system has been fully prototyped on C programming language and Xilinx Vn-tex2 FPGA. The experimental results show the method could provides a high level of recognition efficiency and accuracy.

Novel similarity measures for face representation based on local binary pattern

The successful face recognition based on local binary pattern(LBP)relies on the effective extraction of LBP features and the inferring of similarity between the extracted features.In this paper,we focus on the latter and propose two novel similarity measures for the local matching methods and the holistic matching methods respectively.One is Earth Mover's Distance with Hamming and Lp ground distance(EMD-HammingLp),which is a cross-bin dissimilarity measure for LBP histograms.The other is IMage Hamming Distance(IMHD),which is a dissimilarity measure for the whole LBP images.Experiments on FERET database show that the proposed two similarity measures outperform the state-of-the-art Chi-square similarity measure for extraction of LBP features.

Semantic-based dynamic positioning mechanism for problem solving in multi-agent systems

In multi-agent systems(MAS),finding agents which are able to service properly in an open and dynamic environment are the key issue in problem solving.However,it is difficult to find agent resources quickly and position agents accurately and complete the system integration by the keyword matching method,due to the lack of clear semantic information of the classical agent model.An semantic-based agent dynamic positioning mechanism was proposed to assist in the system dynamic integration.According to the semantic agent model and the description method,a two-stage process including the domain positioning stage and the service semantic matching positioning stage,was discussed.With this mechanism,proper agents that provide appropriate service to assign sub-tasks for task completion can be found quickly and accurately.Finally,the effectiveness of the positioning mechanism was validated through the in-depth performance analysis in the application of simulation experiments to the system dynamic integration.

A Novel Method for Multi-image Matching Synthesizing Image and Object-space Information

A novel method for multi-image matching by synthesizing image and object-space information is proposed. Firstly, four levels of image pyramids are generated according to the rule that the next pyramid level is generated from the previous level using the average gray values of the 3 by 3 pixels, and the first level of pyramid image is generated from the original image. The initial horizontal parallaxes between the reference image and each searching image are calculated at the highest level of the image pyramid. Secondly, corresponding image points are searched in each stereo image pair from the third level of image pyramid, and the matching results in all stereo pairs are integrated in the object space, by which the mismatched image points can be eliminated and more accurate spatial information can be obtained for the subsequent pyramid image matching. The matching method based on correlation coefficient with geometric constraints and global relaxation matching is introduced in the process of image matching. Finally, the feasibility of the method proposed in this paper is verified by the experiments using a set of digital frame aerial images with big overlap. Compared with the traditional image matching method with two images, the accuracy of the digital surface model (DSM) generated using the proposed method shows that the multiimage matching method can eliminate the mismatched points effectively and can improve the matching success rate significantly.