基于微分平滑滤波的特征点跟踪

光流跟踪作为一种重要的二维运动估计技术,在运动目标检测和跟踪中有着重要的作用.L-K光流跟踪算法是一种利用Newton-Raphson梯度下降法进行图像匹配的算法,所以离散图像的偏导数的计算显得尤为重要.由于数字图像离散的方格结构导致在计算各阶偏导数时产生较大的误差.利用微分平滑滤波的方法先利用一个连续的基础函数模拟离...     

Estimation of distribution algorithm for permutation flow shops with total flowtime minimization

In this paper, an Estimation of Distribution Algorithm (EDA) is proposed for permutation flow shops to minimize total flowtime. Longest Common Subsequence (LCS) is incorporated into the probability distribution model to mine good “genes”. Different from common EDAs, each offspring individual is produced from a seed, which is selected from the population by the roulette method. The LCS between the seed individual and the best solution found so far is regarded as good “genes”, which are inherited by offspring with a probability less than 100% to guarantee the population diversity. An effective Variable Neighborhood Search (VNS) is integrated into the proposed EDA to further improve the performance. Experimental results show that the inheritance of good “genes” obtained by LCS can improve the performance of the proposed EDA. The proposed hybrid EDA outperforms other existing algorithms for the considered problem in the literature. Furthermore, the proposed hybrid EDA improved 42 out of 90 current best solutions for Taillard benchmark instances.     

Solving single-product economic lot-sizing problem with non-increasing setup cost,constant capacity and convex inventory cost in O(N log N) time

This paper considers an economic lot sizing model with constant capacity, non-increasing setup cost, and convex inventory cost function. Algorithms with computational time of O(N×TD_N)have been developed for solving the model, where N is the number of planning periods and TD_N is the total demand. This study partially characterizes the optimal planning structure of the model. A new efficient algorithm with computational time of O(N log N) has also been developed based on the partial optimal structure. Moreover, computational study demonstrates that the new algorithm is efficient.     

Efficient and short certificateless signatures secure against realistic adversaries

The notion of certificateless cryptography is aimed to eliminate the use of certificates in traditional public key cryptography and also to solve the key-escrow problem in identity-based cryptography. Many kinds of security models have been designed for certificateless cryptography and many new schemes have been introduced based on the correspondence of the security models. In generally speaking, a stronger security model can ensure a certificateless cryptosystem with a higher security level, but a realistic model can lead to a more efficient scheme. In this paper, we focus on the efficiency of a certificateless signature (CLS) scheme and introduce an efficient CLS scheme with short signature size. On one hand, the security of the scheme is based on a realistic model. In this model, an adversary is not allowed to get any valid signature under false public keys. On the other hand, our scheme is as efficient as BLS short signature scheme in both communication and computation and, therefore, turns out to be more efficient than other CLS schemes proposed so far. We provide a rigorous security proof of our scheme in the random oracle model. The security of our scheme is based on the k-CAA hard problem and a new discovered hard problem, namely the modified k-CAA problem. Our scheme can be applied to systems where signatures are typed in by human or systems with low-bandwidth channels and/or low-computation power.     

Stability analysis for discrete delayed Markovian jumping neural networks with partly unknown transition probabilities

This paper addresses the analysis problem of asymptotic stability for a class of uncertain neural networks with Markovian jumping parameters and time delays. The considered transition probabilities are assumed to be partially unknown. The parameter uncertainties are considered to be norm-bounded. A sufficient condition for the stability of the addressed neural networks is derived, which is expressed in terms of a set of linear matrix inequalities. A numerical example is given to verify the effectiveness of the developed results.     

Rule extraction from artificial neural networks to discover causes of quality defects in fabric production

In this paper, a novel classification rule extraction algorithm which has been recently proposed by authors is employed to determine the causes of quality defects in a fabric production facility in terms of predetermined parameters like machine type, warp type etc. The proposed rule extraction algorithm works on the trained artificial neural networks in order to discover the hidden information which is available in the form of connection weights in them. The proposed algorithm is mainly based on a swarm intelligence metaheuristic which is known as Touring Ant Colony Optimization (TACO). The algorithm has a hierarchical structure with two levels. In the first level, a multilayer perceptron type neural network is trained and its weights are extracted. After obtaining the weights, in the second level, the TACO-based algorithm is applied to extract classification rules. The main purpose of the present work is to determine and analyze the most effective parameters on the quality defects in fabric production. The parameters and their levels which give the best quality results are tried to be discovered and evaluated by making use of the proposed algorithm. It is also aimed to compare the accuracy of proposed algorithm with several other rule-based algorithms in order to present its competitiveness.     

Design and fabrication of a freeform microlens array for uniform beam shaping

Freeform optics has become a practical solution to solving number of problems in modern optical design. In this paper, we proposed a fabrication method using the combination of ultraprecision diamond machining and microinjection molding to achieve high volume and low cost freeform microlens manufacturing. The freeform microlens array discussed in this research is capable of redistributing a collimated light into a pre determined, in this case, a uniform pattern. The optical design, slow tool servo diamond machining, microinjection molding process and optical measurement were discussed. The simple optical design provided a platform for freeform microlens calculation. Slow tool servo diamond broaching was selected to fabricate the mold insert. After the mold insert was fabricated, microinjection molding machine was utilized to replicate the optical geometry into plastic substrates. The freeform microlens array that was fabricated in this research could achieve light re-distribution at the target with approximately 80% uniformity. The research conducted in this paper can be readily implemented in optical industry.     

Use of center of gravity with the common vector approach in isolated word recognition

In this paper, the subspace based classifier, common vector approach (CVA), with the center of gravity (COG) method is used for isolated word recognition. Since the CVA classifier is sensitive to shifts through the time axis, endpoint detection becomes extremely important for the recognition of isolated words. The COG method eliminates the need for endpoint detection. The effects of the COG method and a classical endpoint detection algorithm on the recognition rates of isolated words are investigated. The experimental results show that the COG method yields slightly higher recognition rates than the endpoint detection method in the TI-digit database when CVA is used.     

An automatic diabetes diagnosis system based on LDA-Wavelet Support Vector Machine Classifier

In this paper, an automatic diagnosis system for diabetes on Linear Discriminant Analysis (LDA) and Morlet Wavelet Support Vector Machine Classifier: LDA–MWSVM is introduced. The structure of this automatic system based on LDA-MWSVM for the diagnosis of diabetes is composed of three stages: The feature extraction and feature reduction stage by using the Linear Discriminant Analysis (LDA) method and the classification stage by using Morlet Wavelet Support Vector Machine (MWSVM) classifier stage. The Linear Discriminant Analysis (LDA) is used to separate features variables between healthy and patient (diabetes) data in the first stage. The healthy and patient (diabetes) features obtained in the first stage are given to inputs of the MWSVM classifier in the second stage. Finally, in the third stage, the correct diagnosis performance of this automatic system based on LDA–MWSVM for the diagnosis of diabetes is calculated by using sensitivity and specificity analysis, classification accuracy, and confusion matrix, respectively. The classification accuracy of this system was obtained at about 89.74%.