An Introduction to Convex Optimization Theory in Communication Signals Recognition

In this paper, convex optimization theory is introduced into the recognition of communication signals. The detailed content contains three parts. The first part gives a survey of basic concepts, main technology and recognition model of convex optimization theory. Special emphasis is placed on how to set up the new recognition model of communication signals with multisensor reports. The second part gives the solution method of the recognition model, which is called Logarithmic Penalty Barrier Function. The last part gives several numeric simulations, in contrast to D-S evidence inference method, this new method can also generate reasonable recognition results. Moreover, this new method can deal with the form of sensor reports which is more general than that allowed by the D-S evidence inference method, and it has much lower computation complexity than that of D-S evidence inference method. In addition, this new method has better recognition result, stronger anti-interference and robustness. Therefore, the convex optimization methods can be widely used in the recognition of communication signals.     

Automatic salient object segmentation using saliency map and color segmentation

A new method for automatic salient object segmentation is presented.Salient object segmentation is an important research area in the field of object recognition,image retrieval,image editing,scene reconstruction,and 2D/3D conversion.In this work,salient object segmentation is performed using saliency map and color segmentation.Edge,color and intensity feature are extracted from mean shift segmentation （MSS） image,and saliency map is created using these features.First average saliency per segment image is calculated using the color information from MSS image and generated saliency map.Then,second average saliency per segment image is calculated by applying same procedure for the first image to the thresholding,labeling,and hole-filling applied image.Thresholding,labeling and hole-filling are applied to the mean image of the generated two images to get the final salient object segmentation.The effectiveness of proposed method is proved by showing 80％,89％ and 80％ of precision,recall and F-measure values from the generated salient object segmentation image and ground truth image.     

Selective image enhancement method for low energy target information area

A selective subband enhancement method based on biorthogonal wavelet base is proposed. This novel image enhancement method is just for those images in which the energy of target information area is relatively lower. It includes two parts： one is enhancing the low frequency subband by wavelet decomposition and the other is building a new criterion based on entropy window to image evaluation. Experimental results show that this new scheme may result in a perfect image processing.     

Image Combination Analysis in SPECAN Algorithm of Spaceborne SAR

An analysis of image combination in SPECAN algorithm is delivered in time-frequency domain in de-tail and a new image combination method is proposed. For four multi-looks processing one sub-aperture data in every three sub-apertures is processed in this combination method. The continual sub-aperture processing in SPECAN algorithm is realized and the processing efficiency can be dramatically increased. A new parameter is also put forward to measure the processing efficient of SAR image processing. Finally, the raw data of RADARSAT are used to test the method and the result proves that this method is feasible to be used in SPECAN algorithm of spaceborne SAR and can improve processing efficiently. SPECAN algorithm with this method can be used in quick-look imaging.     

Novel Spatially Adaptive Image Denoising Algorithm Based on Covariance Estimation in Wavelet Domain

A new method for image denoising is proposed. By analyzing image‘s statistical properties in wavelet domain, it is shown that the natural image has a strong and spatial variable covariance structure relationship in local space of sub-band. A non-direct estimation method is suggested to make an adaptive estimate of spatial variable covariance by estimating the correlation coefficient and variance of subband image separately. It can be used to estimate adaptive filtering of subband image. The experiment shows that this method can improve the image‘s SNR, and has strong ability to preserve edges.     

Traffic sign recognition algorithm based on shape signature and dual-tree complex wavelet transform

A novel traffic sign recognition system is presented in this work. Firstly, the color segmentation and shape classifier based on signature feature of region are used to detect traffic signs in input video sequences. Secondly, traffic sign color-image is preprocessed with gray scaling, and normalized to 64×64 size. Then, image features could be obtained by four levels DT-CWT images. Thirdly, 2DICA and nearest neighbor classifier are united to recognize traffic signs. The whole recognition algorithm is implemented for classification of 50 categories of traffic signs and its recognition accuracy reaches 90%. Comparing image representation DT-CWT with the well-established image representation like template, Gabor, and 2DICA with feature selection techniques such as PCA, LPP, 2DPCA at the same time, the results show that combination method of DT-CWT and 2DICA is useful in traffic signs recognition. Experimental results indicate that the proposed algorithm is robust, effective and accurate.     

Adaptive key SURF feature extraction and application in unmanned vehicle dynamic object recognition

A new method based on adaptive Hessian matrix threshold of finding key SRUF( speeded up robust features) features is proposed and is applied to an unmanned vehicle for its dynamic object recognition and guided navigation. First,the object recognition algorithm based on SURF feature matching for unmanned vehicle guided navigation is introduced. Then,the standard local invariant feature extraction algorithm SRUF is analyzed,the Hessian Metrix is especially discussed,and a method of adaptive Hessian threshold is proposed which is based on correct matching point pairs threshold feedback under a close loop frame. At last,different dynamic object recognition experiments under different weather light conditions are discussed. The experimental result shows that the key SURF feature abstract algorithm and the dynamic object recognition method can be used for unmanned vehicle systems.     

Intelligent Information Processing in Imaging Fuzes

In order to study the problem of intelligent information processing in new types of imaging fuze,the method of extracting the invariance features of target images is adopted,and radial basis function neural network is used to recognize targets.Owing to its ability of parallel processing,its robustness and generalization,the method can realize the recognition of the conditions of missile-target encounters,and meet the requirements of re-al-time recognition in the imaging fuze.It is shown that based on artificial neural network target recognition and burst point control are feasible.     

灰度游程长度累加统计图象纹理分析方法

Based on gray level run length statistics,a new image texture analysis method that is, gray level run length sum statistical method is presented. First, a gray level run sum matrix is introduced. The matrix element h(i, k) is defined as     

基于区间值模糊集熵的图像阈值分割算法

提出了一种新的区间值模糊集的模糊熵,并将其用于图像的阈值分割.根据图像直方图确定模糊化因子,选择适当的主隶属函数实现图像的区间值模糊集表示.利用新的区间值模糊集的模糊熵研究了图像的阈值分割算法.仿真实验表明,新的基于区间值模糊集模糊熵的阈值分割算法在处理模糊图像及具有噪声图像方面均比经典图像阈值分割方法更有效.     

一种基于粗集与FCM结合的图像分割方法

传统的FCM 算法在做图像分割时,只是利用图像的每一个像素点的灰度值进行聚类,当图像受到噪声污染时,分割的准确性将大大降低,为此,提出了一种粗集与FCM算法相结合的图像分割方法,实验结果表明,该方法具有良好的分割性能.     

基于粗糙模糊集理论的卫星导航系统作战效能评估方法

针对卫星导航系统在信息化战争中的地位与作用,提出了一种基于粗糙集和模糊集理论的作战效能评估方法——粗糙模糊集理论,并对粗糙模糊集理论的算法进行了分析和设计,建立了卫星导航系统作战效能评估指标体系.最后结合实例,运用粗糙模糊集理论对卫星导航系统作战效能进行了评估,给出了效能评估的决策规则.     

一种基于粗集理论方法的水位模糊控制器设计

应用粗集理论中知识表达系统决策规则的简化方法对模糊控制规则进行约简来设计粗糙模糊控制器,通过约简简化控制系统,从而得到一个最简控制决策规则,使系统更易于控制。完成了水箱水位粗糙模糊控制器的设计,并分别进行了模糊控制和粗糙模糊控制仿真研究,研究结果表明该粗糙模糊控制能有效改善模糊控制的性能。     

Rough逻辑系统RSL与模糊逻辑系统Luk

基于rough集的偶序对<下近似,上近似)表示,通过改进基于rough集的逻辑系统(C)的方法引入新的rough蕴涵算子,研究了它的基本性质,并将其进一步拓广到一般正则双Stone代数中,证明了添加新蕴涵算子后的正则双Stone代数构成MV-代数.其次,以上述结果为背景,建立了一个基于rough蕴涵的逻辑形式系统RSL...     

Rough Set Based Fuzzy Neural Network for Pattern Classification

A rough set based fuzzy neural network algorithm is proposed to solve the problem of pattern recognition. The least square algorithm (LSA) is used in the learning process of fuzzy neural network to obtain the performance of global convergence. In addition, the numbers of rules and the initial weights and structure of fuzzy neural networks are difficult to determine. Here rough sets are introduced to decide the numbers of rules and original weights. Finally, experiment results show the algorithm may get better effect than the BP algorithm.     

粗糙集的模糊性

在研究粗糙集概念的基础上,结合模糊集的最近普通集和最远普通集两个方面,利用距离函数lp(A,B)、D(A,B)和粗糙隶属函数,得到了粗糙集的一种新的模糊度计算方法,证明了模糊度的一些重要性质。该方法在理论和应用中发挥着重要作用。     

模糊粗糙集的扩张定理

模糊粗糙集(FR)是基于知识的不确定性提出来的，由于其处理信息系统中知识的不完善、不确定问题较粗糙集和模糊集有其自身的优点，近年来模糊粗糙集理论越来越受到国际学术界的关注，文章所讨论了模糊粗糙变换的性质并用模糊粗糙变换的保并性质证明了模糊粗糙集的扩张定理。     

相似关系下的模糊粗糙集

经典的模糊粗糙集是建立在普通等价关系基础上的，提出了普通相似关系下的模糊粗糙集，讨论了一系列性质，得到了几个有意义的结果．最后研究了模糊粗糙集的粗相等。     

粗糙直觉模糊集的不确定性分析

基于粗糙直觉模糊集的基本概念和模糊熵的公理化定义,给出了直觉模糊集粗糙隶属函数的定义并讨论其基本性质,再利用粗糙隶属函数的直觉模糊熵,给出了粗糙直觉模糊集的不确定性度量,并讨论了度量的一些相关性质。