面向噪声图像的自适应高容量隐写算法

面向雷达、遥感图像等含噪图像,提出了一种利用噪声自适应嵌入的位平面加密算法。首先对不同的噪声图像利用遗传算法自适应找到最佳嵌入阈值,在噪声值较高处使用一种无须密钥的加密算法将两位秘密信息分别交叉嵌入到第一、三位平面中。然后利用BBE(binary-block embedding)算法将标记位数据压缩到第二位平面。最后在第二位平面冗余处用同样的加密算法将剩余秘密信息分别嵌入到第二、四位平面中完成信息隐藏。实验分析表明在满足高容量嵌入的条件下,与其他自适应位平面隐写算法相比有良好的隐蔽性以及较强的鲁棒性,同时能抵抗目前较好的隐写分析算法,保证了数据传送的安全性。     

A fuzzy algorithm for color quantization of images

In this paper, we review a number of techniques for fuzzy color quantization. We show that the fuzzy membership paradigm is particularly suited to color quantization, where color cluster boundaries are not well defined. We propose a new fuzzy color quantization technique which incorporates a term for partition index. This algorithm produces better results than fuzzy C-means at a reduced computational cost. We test the results of the fuzzy algorithms using quality metrics which model the perception of the human visual system and illustrate that substantial quality improvements are achieved.     

基于直觉模糊散度的噪声图像边缘检测

针对传统图像边缘检测算法抑制噪声能力差的问题,提出一种基于直觉模糊集（Intuitionistic Fuzzy Set,IFS）的边缘检测算法。该算法设定了一个表示平坦区域的模板图像,并在图像窗口内构造了一种同时考虑了图像梯度和图像窗口的方差信息的隶属度函数,然后通过计算图像窗口与模板图像之间的模糊直觉散度（Intuitionistic Fuzzy Divergence,IFD）对边缘进行定位和输出。实验结果表明,对于被高斯噪声或均匀噪声严重污染的图像,该算法能够得到较好的检测结果。     

Edge detection algorithm based on ICA-domain shrinkage in noisy images

We propose a robust edge detection method based on ICA-domain shrinkage （in- dependent component analysis）. It is known that most basis functions extracted from natural images by ICA are sparse and similar to localized and oriented receptive fields, and in the proposed edge detection method, a target image is first transformed by ICA basis functions and then the edges are detected or recon- structed with sparse components. Furthermore, by applying a shrinkage algorithm to filter out the components of noise in ICA-domain, we can readily obtain the sparse components of the original image, resulting in a kind of robust edge detec- tion even for a noisy image with a very low SN ratio. The efficiency of the proposed method is demonstrated by experiments with some natural images.     

Cost‐effective queue schemes for reducing head‐of‐line blocking in fat‐trees

The fat‐tree is one of the most common topologies among the interconnection networks of the systems currently used for high‐performance parallel computing. Among other advantages, fat‐trees allow the use of simple but very efficient routing schemes. One of them is a deterministic routing algorithm that has been recently proposed, offering a similar (or better) performance than adaptive routing while reducing complexity and guaranteeing in‐order packet delivery. However, as other deterministic routing proposals, this deterministic routing algorithm cannot react when high traffic loads or hot‐spot traffic scenarios produce severe contention for the use of network resources, leading to the appearance of Head‐of‐Line (HoL) blocking, which spoils the network performance. In that sense, we describe in this paper two simple, cost‐effective strategies for dealing with the HoL‐blocking problem that may appear in fat‐trees with the aforementioned deterministic routing algorithm. From the results presented in the paper, we conclude that, in the mentioned environment, these proposals considerably reduce HoL‐blocking without significantly increasing switch complexity and the required silicon area. Copyright © 2011 John Wiley & Sons, Ltd.     

A fuzzy self‐organizing map algorithm for biological pattern recognition

Data clustering is a key task for various processes including sequence analysis and pattern recognition. This paper studies a clustering algorithm that aimed to increase accuracy and sensitivity when working with biological data such as DNA sequences. The new algorithm is a modified version of fuzzy C‐means (FCM) and is based on the well‐known self‐organizing map (SOM). In order to show the performance of the algorithm, seven different data sets are processed. The experimental results demonstrate that the proposed algorithm has the potential to outperform SOM and FCM in terms of clustering and classification accuracy abilities. Additionally, a brief comparison is made the proposed algorithm with some previously studied ‘FCM‐SOM’ hybrid algorithms from the literature.     

Effects of line length,orientation angle,and drawing direction on the straight‐line drawing performance of elderly Taiwanese adults during iPad use

The present study investigated the effects of drawing direction (top‐down or bottom‐up) and orientation angle (0°, 60°, or 300°) on the stability of straight‐line drawing with varied line length (5.7, 11.4, or 17.1 cm) of elderly Taiwanese adults. These three independent variables were within‐subject factors. In total, 38 elderly adults participated. The straight‐line drawing task was performed on an iPad using an electronic pen. Straight‐line drawing performance was analyzed using three‐way repeated analysis of variance measures. The results indicated that the error rate was significantly higher in the top‐down condition than in the bottom‐up condition and was higher for the 5.7‐cm line length than for the 11.4 or 17.1 cm lengths. In addition, the average drawing speed in the top‐down condition was significantly greater than that in the bottom‐up condition and was significantly greater at 17.1 and 11.4 cm than at 5.7 cm. The orientation angle also significantly affected the average drawing speed, which demonstrated that the 60° condition yielded a significantly greater speed than the 0° or 300° conditions. Our findings have implications for product design and computer–human interface design for seniors. Further studies are needed to determine whether these findings can be generalized to more complex motor movements and cognitive abilities.     

A meta-heuristic algorithm for the fuzzy assembly line balancing type-E problem

Consideration is given to a single-model assembly line balancing problem with fuzzy task processing times. The problem referred to herein as f-SALBP-E consists of finding a combination of the number of workstations and the cycle time as well as a respective line balance such that the efficiency of the line is maximized. f-SALBP-E is an extension of the classical SALBP-E under fuzziness. First, a formulation of the problem is given with the tasks processing times presented by triangular fuzzy membership functions. Then, since the problem is known to be NP-hard, a meta-heuristic based on a Genetic Algorithm (GA) is developed for its solution. The performance of the proposed solution approach is studied and discussed over multiple benchmarks test problems taken from the open literature. The results demonstrate very satisfactory performance for the developed approach in terms of both solution time and quality.     

新的模糊矢量中值滤波算法

依据模糊综合评判原理提出一种保持彩色图像细节的模糊矢量中值滤波算法。该算法设置一个可在保持图像细节与去噪能力上进行权衡的参数,并基于中值矢量在模长、辐角等方面的特性,计算矢量处于“中间”状态的隶属度来确定中值矢量。该隶属度为图像的细节保护提供分析基础。与同类矢量滤波算法相比较,该算法在运行速度上有明显优势,具有保护图像细节性好、去噪能力强等优点。     

A new fuzzy c-means method with total variation regularization for segmentation of images with noisy and incomplete data

The objective function of the original (fuzzy) c-mean method is modified by a regularizing functional in the form of total variation (TV) with regard to gradient sparsity, and a regularization parameter is used to balance clustering and smoothing. An alternating direction method of multipliers in conjunction with the fast discrete cosine transform is used to solve the TV-regularized optimization problem. The new algorithm is tested on both synthetic and real data, and is demonstrated to be effective and robust in treating images with noise and missing data (incomplete data).     

A multidegree‐of‐freedom manipulator for curtain‐wall installation

Recently, the trend in architectural forms has been towards larger and taller buildings. Building materials are, therefore, also becoming larger and heavier. Typical construction machineries are not adequate for handling such materials, and most construction works are still managed by a human operator. Construction processes are, therefore, fraught with a number of problems, including frequent accidents, high construction cost, and heterogeneous construction quality depending on the experience of the workers. Automation has been introduced at various sites to address these construction problems. In this paper, the process of a curtain‐wall installation in a skyscraper is analyzed, and the prototype of a construction robot for curtain‐wall installation (CRCWI) is proposed. Use of the proposed CRCWI can reduce the need for manpower and the construction period and cost, and can assure safety in the curtain‐wall construction site. The performance of the proposed CRCWI was verified with a real application test in a skyscraper construction site. © 2006 Wiley Periodicals, Inc.     

改进的模糊C-均值算法在医学图像分割中的应用

针对随机选取聚类中心易使得迭代过程陷入局部最优解的缺点,提出了一种混合优化蚁群和动态模糊C-均值的图像分割方法,该方法利用蚁群算法较强处理局部极值的能力,并能动态确定聚类中心和数目.针对传统的分阶段结合遗传算法和蚁群算法的策略存在收敛速度慢,聚类精度差的问题,提出在整个优化过程综合遗传算法和蚁群算法,并在蚁群算法中引入拥挤度函数,利用遗传算法的快速性、全局收敛性提高了蚁群算法的收敛速度,同时利用蚁群算法的并行性和正反馈性提高了聚类的精确度.最后将该算法应用到医学图像分割,对比实验表明,混合算法具有很强的模糊边缘和微细边缘分割能力.     

A quality‐of‐service‐based framework for creating distributed heterogeneous software components

Component‐based software development offers a promising solution for taming the complexity found in today's distributed applications. Today's and future distributed software systems will certainly require combining heterogeneous software components that are geographically dispersed. For the successful deployment of such a software system, it is necessary that its realization, based on assembling heterogeneous components, not only meets the functional requirements, but also satisfies the non‐functional criteria such as the desired quality of service (QoS). In this paper, a framework based on the notions of a meta‐component model, a generative domain model and QoS parameters is described. A formal specification based on two‐level grammar is used to represent these notions in a tightly integrated way so that QoS becomes a part of the generative domain model. A simple case study is described in the context of this framework. Copyright © 2002 John Wiley & Sons, Ltd.     

An improved sum‐of‐squares based approach to fuzzy tracking control design of nonlinear systems

In this paper, using a more general Lyapunov function, less conservative sum‐of‐squares (SOS) stability conditions for polynomial‐fuzzy‐model‐based tracking control systems are derived. In tracking control problems the objective is to drive the system states of a nonlinear plant to follow the system states of a given reference model. A state feedback polynomial fuzzy controller is employed to achieve this goal. The tracking control design is formulated as an SOS optimization problem. Here, unlike previous SOS‐based tracking control approaches, a full‐state‐dependent Lyapunov matrix is used, which reduces the conservatism of the stability criteria. Furthermore, the SOS conditions are derived to guarantee the system stability subject to a given H_∞ performance. The proposed method is applied to the pitch‐axis autopilot design problem of a high‐agile tail‐controlled pursuit and another numerical example to demonstrate the effectiveness and benefits of the proposed method.     

基于直线段检测器的多阈值图像表格框线提取算法

针对传统表格结构识别算法中,前期图像预处理工作量大、复杂表格结构识别率低、高分辨率和高复杂度表格时间开销过于大的问题,提出先对图像表格结构利用直线段检测器进行框线检测,再利用双阈值直线判断规则,对本应属于同一直线的多条线段进行合并细化,最后对横纵线交点处缺失或过长线段,采用表格结构整体框线对齐的快速识别算法.实验结果表...     

A nonlinear laplace operator as edge detector in noisy images

An edge detection scheme is developed robust enough to perform well over a wide range of signal-to-noise ratios. It is based upon the detection of zero crossings in the output image of a nonlinear Laplace filter. Specific characterizations of the nonlinear Laplacian are its adaptive orientation to the direction of the gradient and its inherent masks which permit the development of approximately circular (isotropic) filters. We have investigated the relation between the locally optimal filter parameters, smoothing size, and filter size, and the SNR of the image to be processed. A quantitative evaluation shows that our edge detector performs at least as well—and in most cases much better—than edge detectors. At very low signal-to-noise ratios, our edge detector is superior to all others tested.     

A modified fuzzy C-means image segmentation algorithm for use with uneven illumination patterns

A novel fuzzy C-mean (FCM) algorithm is proposed for use when active or structured light patterns are projected onto a scene. The underlying inhomogeneous illumination intensity due to the point source nature of the projection, surface orientation and curvature has been estimated and its effect on the object segmentation minimized. Firstly, we modified the recursive FCM algorithm to include biased illumination field estimation. New clustering center and fuzzy clustering functions resulted based on the intensity and average intensity of a pixel neighborhood based object function. Finally, a dilation operator was used on the initial segmented image for further refinement. Experimental results showed the proposed method was effective for segmenting images illuminated by patterns containing underlying biased intensity fields. A higher accuracy was obtained than for traditional FCM and thresholding techniques.     

A local-spectral fuzzy segmentation for MSG multispectral images

In this article, a segmentation approach for cloud detection in Meteosat Second Generation (MSG) multispectral images is proposed. The proposed algorithm uses recursive segmentation that dynamically reduces the number of classes. This algorithm consists of two steps. First, an initial segmentation of the image is obtained using local fuzzy clustering. The clustering algorithm is formulated by modifying the similarity measure of the standard fuzzy c-means (FCM) algorithm. The new similarity function includes the spectral information as well as the homogeneity and spatial clustering information of each considered pixel. In the second step, a hierarchical region-merging process is used to reduce the number of image clusters. At each iteration, the segmentation algorithm proceeds with a new partition until the final result of the segmentation is obtained. The proposed method has been tested using synthetic and MSG images. It yields a compact and coherent segmentation map, with a satisfactory reproduction of the image contours. Moreover, the different types of clouds are well detected and separated with appropriate accuracy.     

A fast algorithm for parallel model combination for noisy speech recognition

Based on the log-normal assumption, parallel model combination (PMC) provides an effective method to adapt the cepstral means and variances of speech models for noisy speech recognition. In addition, the log-add method has been derived to adapt the mean by ignoring the cepstral variance during the process of PMC. This method is efficient for speech recognition in a high signal-to-noise ratio (SNR) environment. In this paper, a new interpretation of the log-add method is proposed. This leads to a modified scheme for performing the adaptation procedure in PMC. This modified method is shown to be efficient in improving recognition accuracy in low SNR. Based on this modified PMC method, we derive a direct adaptation procedure for the variance of speech models in the cepstral domain. The proposed method is a fast algorithm because the computation for the transformation of the covariance matrix is no longer required. Three recognition tasks are conducted to evaluate the proposed method. Experimental results show that the proposed technique not only requires lower computational cost but it also outperforms the original PMC technique in noisy environments.     

A generalized version space learning algorithm for noisy anduncertain data

This paper generalizes the learning strategy of version space to manage noisy and uncertain training data. A new learning algorithm is proposed that consists of two main phases: searching and pruning. The searching phase generates and collects possible candidates into a large set; the pruning then prunes this set according to various criteria to find a maximally consistent version space. When the training instances cannot completely be classified, the proposed learning algorithm can make a trade-off between including positive training instances and excluding negative ones according to the requirements of different application domains. Furthermore, suitable pruning parameters are chosen according to a given time limit, so the algorithm can also make a trade-off between time complexity and accuracy. The proposed learning algorithm is then a flexible and efficient induction method that makes the version space learning strategy more practical