Solving multiple objective quasi-convex goal programming problems by linear programming

This study presents a novel means of resolving multiple objective goal programming (GP) problems with quasi-convex linear penalty functions. The proposed method initially expresses a quasi-convex function by the maximum operator of two convex functions, then solves it via a linear programming technique. The proposed method does not contain any zero–one variables; nor does it require dividing the multi-objective quasi-convex GP problem into large sub-problems as in conventional methods. Some illustrative examples are provided.     

基于Haar小波的图像变换方法的研究

系统地介绍了基于快速滤波器组卷积算法的第一代小波和基于提升方案的第二代小波。通过对图像先行后列分离,结合惰性滤波器进行奇偶分割,由欧几里得算法求出预测算子和更新算子,实现了由此提升方案构造的Haar小波1级图像分解。实验结果表明提升方案构造的Haar小波实现起来相当容易,简化了很多复杂的计算过程,其本身的诸多特点更便于在实际中应用,且在变换后的效果上,与第一代基本无差异。     

Solving separable elliptic equations by the block stride reduction algorithm

A comparison of the Buneman version of the block cyclic reduction (BCR) algorithm and Stride Reduction (BSR) based on polynomial factorization for separable elliptic equations with Dirichlet boundary conditions is presented. This study was initiated by an interest in the parallel computing techniques that can be used to increase the computational efficiency of these model problems     

Separable reversible data hiding in encrypted image based on pixel value ordering and additive homomorphism

This work proposes a separable reversible data hiding scheme in encrypted images based on pixel value ordering (PVO). After the original image is encrypted using homomorphism encryption by the content owner, the data hider embeds the secret data in encrypted domain. The PVO strategy realizes hiding data in each block. Additive homomorphism guarantees the performance of PVO in encrypted domain is close to that in plain domain. Besides, the homomorphism encryption does not cause data expansion, and the payload can be further improved. With the watermarked encrypted image, if the receiver has only the data hiding key, he can extract the additional data. If the receiver has only the encryption key, he can obtain a decrypted image similar to the original one. If the receiver has both the data hiding key and the encryption key, he can extract the additional data without any error and recover the original image losslessly.     

A new algorithm for resolution of the quadratic programming problem with fuzzy relation inequality constraints

The minimization problem of a quadratic objective function with the max-product fuzzy relation inequality constraints is studied in this paper. In this problem, its objective function is not necessarily convex. Hence, its Hessian matrix is not necessarily positive semi-definite. Therefore, we cannot apply the modified simplex method to solve this problem, in a general case. In this paper, we firstly study the structure of its feasible domain. We then use some properties of n × n real symmetric indefinite matrices, Cholesky’s decomposition, and the least square technique, and convert the problem to a separable programming problem. Furthermore, a relation in terms of a closed form is presented to solve it. Finally, an algorithm is proposed to solve the original problem. An application example in the economic area is given to illustrate the problem. Of course, there are other application examples in the area of digital data service and reliability engineering.     

基于可分离Gabor滤波的纹理分析方法

提出一种基于可分离Gabor滤波的纹理分析新方法,该方法把传统的Gabor滤波器分解成为两个相互正交的低通和带通滤波器,以降低滤波运算量;并指出在正交分解条件不满足的情况下,通过旋转样本图像,解决Gabor滤波器在任意角度的正交分解问题.实验表明,新方法与传统方法相比,效率提高30%以上,并且准确率更高.     

A multi-view vision-based hand motion capturing system

Vision-based hand motion capturing approaches play a critical role in human computer interface owing to its non-invasiveness, cost effectiveness, and user friendliness. This work presents a multi-view vision-based method to capture hand motion. A 3-D hand model with structural and kinematical constraints is developed to ensure that the proposed hand model behaves similar to an ordinary human hand. Human hand motion in a high degree of freedom space is estimated by developing a separable state based particle filtering (SSBPF) method to track the finger motion. By integrating different features, including silhouette, Chamfer distance, and depth map in different view angles, the proposed motion tracking system can capture the hand motion parameter effectively and solve the self-occlusion problem of the finger motion. Experimental results indicate that the hand joint angle estimation generates an average error of 11°.     

Fast separable factorization and applications

In this paper we show that the separable decomposition of a univariate polynomial can be computed in softly optimal time, in terms of the number of arithmetic operations in the coefficient field. We also adapt the classical multi-modular strategy that speeds up the computations for many coefficient fields, and we analyze consequences of the new results to the squarefree and the irreducible factorizations. This work was partly supported by the French Research Agency via the Gecko project (gecko.inria.fr).     

Separability of scalar random multisine signals

Random multisines have successfully been used as input signals in many system identification experiments. In this paper, it is shown that scalar random multisine signals with a flat amplitude spectrum are separable of order one. The separability property means that certain conditional expectations are linear and it implies that random multisines can easily be used to obtain accurate estimates of the linear time-invariant part of a Hammerstein system. Furthermore, higher order separability is investigated.     

A connectionist approach to processing dimensional interaction

The difference between integral and separable interaction of dimensions is a classic problem in cognitive psychology (Garner 1970, American Psychologist, 25: 350-358, Shepard 1964, Journal of Mathematical Psychology, 1: 54-87) and remains an essential component of most current experimental and theoretical analyses of category learning (e.g. Ashby and Maddox 1994, Journal of Mathematical Psychology, 38: 423-466, Goldstone 1994, Journal of Experimental Psychology: General , 123: 178-200, Kruschke 1993, Connection Science, 5: 3-36, Melara et al. 1993, Journal of Experimental Psychology: Human Perception & Performance, 19: 1082-1104, Nosofsky 1992, Multidimensional Models of Perception and Cognition, Hillsdale NJ: Lawrence Erlbaum). So far the problem has been addressed through post hoc analysis in which empirical evidence of integral and separable processing is used to fit human data, showing how the impact of a pair of dimensions interacting in an integral or a separable manner enters into later learning processes. In this paper, we argue that a mechanistic connectionist explanation for variations in dimensional interactions can provide a new perspective through exploration of how similarities between stimuli are transformed from physical to psychological space when learning to identify, discriminate and categorize them. We substantiate this claim by demonstrating how even a standard backpropagation network combined with a simple image-processing Gabor filter component provides limited but clear potential to process monochromatic stimuli that are composed of integral pairs of dimensions differently from monochromatic stimuli that are composed of separable pairs of dimensions. Interestingly, the responses from Gabor filters are shown already to capture most ofthe dimensional interaction, which in turn can be operated upon by the neural network during a given learning task. In addition, we introduce a basic attention mechanism to back-propagation that gives it the ability to attend selectively to relevant dimensions and illustrate how this serves the model in solving a filtration versus condensation task (Kruschke 1993, Connection Science, 5: 3-36). The model may serve as a starting point in characterizing the general properties of the human perceptual system that causes some pairs of physical dimensions to be treated as integrally interacting and other pairs as separable. An improved understanding of these properties will aid studies in perceptual and category learning, selective attention effects and influences of higher cognitive processes on initial perceptual representations.