Stereo matching cost computation based on nonsubsampled contourlet transform

A new matching cost computation method based on nonsubsampled contourlet transform (NSCT) for stereo image matching is proposed in this paper. Firstly, stereo image is decomposed into high frequency sub-band images at different scales and along different directions by NSCT. Secondly, by utilizing coefficients in high frequency domain and grayscales in RGB color space, the computation model of weighted matching cost between two pixels is designed based on the gestalt laws. Lastly, two types of experiments are carried out with standard stereopairs in the Middlebury benchmark. One of the experiments is to confirm optimum values of NSCT scale and direction parameters, and the other is to compare proposed matching cost with nine known matching costs. Experimental results show that the optimum values of scale and direction parameters are respectively 2 and 3, and the matching accuracy of the proposed matching cost is twice higher than that of traditional NCC cost.     

Design and Implementation of a Flexible Manufacturing Control System Using Neural Network

Design and implementation of a sequential controller based on the concept of artificial neural networks for a flexible manufacturing system are presented. The recurrent neural network (RNN) type is used for such a purpose. Contrary to the programmable controller, an RNN-based sequential controller is based on a definite mathematical model rather than depending on experience and trial and error techniques. The proposed controller is also more flexible because it is not limited by the restrictions of the finite state automata theory. Adequate guidelines of how to construct an RNN-based sequential controller are presented. These guidelines are applied to different case studies. The proposed controller is tested by simulations and real-time experiments. These tests prove the successfulness of the proposed controller performances. Theoretical as well as experimental results are presented and discussed indicating that the proposed design procedure using Elman's RNN can be effective in designing a sequential controller for event-based type manufacturing systems. In addition, the simulation results assure the effectiveness of the proposed controller to outperform the effect of noisy inputs.     

一种新的加权最小二乘测距定位方法

三站二维定位问题是最小定位问题，一般只利用几何关系就能获得定位估计，但由于未利用距离测量的统计信息，定位精度较差。该文先将点到点的距离测量转化为点到线的距离估计，再在此基础上推导出一种新的加权几休定位方法，其中加权的确定利用了距离测量的统计信息。仿真实验表明，在测距误差较小时新方法具有更高的定位精度。     

Numerical simulation of typical contact situations of brake friction materials

In the paper, a model typical for contact situations of automotive brakes is established based on the method of movable cellular automata. The processes taking place at local contacts in an automotive brake system are analysed. Based on microscopic and micro-analytical observations, the following contact situations were simulated: (i) a couple of ferritic steel against pearlitic steel, both covered by an oxide layer mixed with graphite nanoparticles and (ii) the same situation but without oxide layers. The results of calculated mean coefficients of friction of the oxide-on-oxide contact correspond well to expected values for a real braking system, whereas steel-on-steel contact are twice as high. This allows one to make some conclusions; for example, oxide formation will take place more quickly than friction layer elimination, and finally this is responsible for the stabilisation of the coefficient of friction.     

Solving the trade-off between fairness and throughput: Token bucket and leaky bucket-based weighted fair queueing schedulers

In this article, we present two efficient weighted fair queueing (WFQ) scheduling algorithms leaned on the well-known token bucket and leaky bucket shaping/policing algorithms. The performance of the presented algorithms is compared to those of the state-of-the-art WFQ approximations such as weighted round robin (WRR) and the recently proposed bin sort fair queueing (BSFQ). Our simulation results show that the proposed algorithms provide a better fairness at a lower implementation complexity while simultaneously achieving a comparable network utilization.     

Learning Automata from Ordered Examples

Connectionist learning models have had considerable empirical success, but it is hard to characterize exactly what they learn. The learning of finite-state languages (FSL) from example strings is a domain which has been extensively studied and might provide an opportunity to help understand connectionist learning. A major problem is that traditional FSL learning assumes the storage of all examples and thus violates connectionist principles. This paper presents a provably correct algorithm for inferring any minimum-state deterministic finite-state automata (FSA) from a complete ordered sample using limited total storage and without storing example strings. The algorithm is an iterative strategy that uses at each stage a current encoding of the data considered so far, and one single sample string. One of the crucial advantages of our algorithm is that the total amount of space used in the course of learning for encoding any finite prefix of the sample is polynomial in the size of the inferred minimum state deterministic FSA. The algorithm is also relatively efficient in time and has been implemented. More importantly, there is a connectionist version of the algorithm that preserves these properties. The connectionist version requires much more structure than the usual models and has been implemented using the Rochester Connectionist Simulator. We also show that no machine with finite working storage can iteratively identify the FSL from arbitrary presentations.     

Coexisting static and flowing regions in a centrifuging granular heap

We report an unexpectedly rich variety of new flow patterns on a granular heap that is centrifuged so as to simulate a reduction in gravity. These surface patterns exhibit coexisting static and flowing regions that depend strongly on centrifugal stress, but surprisingly not on mass flow rate. A discrete cellular automata model reproduces some of the patterning features and indicates that subsurface jamming may precipitate the formation of localized frozen patterns on the surface. This model provides insights into the mechanics of granular flows under controlled stress environment and jammed-to-flowing transitions in granular media.