A fast hybrid computation model for rectum deformation

It is a challenging task to realistically reproduce the complex deformation of soft bio-tissues in a surgical operation, especially when large deformations and movements exist. A hybrid model which we call the beads-on-string model is presented to handle the deformation and collision of the rectum in a virtual surgery simulation system. Specially tailored for this purpose, our model takes multiple layers to capture the dynamics of the rectum in an efficient manner. Inspired by the shape similarity between a rectum with regular bulges and a string of beads, we use a Cosserat rod model, coinciding with the centreline of the rectum, to calculate its deformation subject to external forces. We introduce rigid spheres, analogy to beads, moving along with the rod to approximate the shape of the rectum in handling collision. In addition, the beads (rigid spheres) provide a natural interlayer to map the deformation of the centreline to the associated mesh which presents detailed geometry of the rectum. Our approach is carefully crafted to achieve high computational efficiency and its multi-layer structure is designed to reproduce the physics of the deformation of the rectum.     

基于互不信任企业联盟的统一身份认证模型

现有的统一身份认证模型采用单个密码对用户身份进行验证,这种方式无法对企业的商业秘密进行有效地保护,并且认证效率低,因而不被大多数企业所接受.为了解决这个问题,在对现有模型存在的问题进行分析的基础上提出一种新的认证模型.该模型把用户的信息分成两部分--共享信息和私有信息,并采用一对密码--本地密码和共享密码分别对这两部分信息进行保护.用户可以利用共享密码在不同网站中漫游,实现统一身份认证;而网站通过本地密码来保护属于商业秘密的那部分信息.该模型能够保护商业秘密,又能够在一定程度上提高认证效率.     

A working memory model based on fast Hebbian learning

Recent models of the oculomotor delayed response task have been based on the assumption that working memory is stored as a persistent activity state (a 'bump' state). The delay activity is maintained by a finely tuned synaptic weight matrix producing a line attractor. Here we present an alternative hypothesis, that fast Hebbian synaptic plasticity is the mechanism underlying working memory. A computational model demonstrates a working memory function that is more resistant to distractors and network inhomogeneity compared to previous models, and that is also capable of storing multiple memories.     

A novel algorithm for fast computation of Zernike moments

Zernike moments (ZMs) have been successfully used in pattern recognition and image analysis due to their good properties of orthogonality and rotation invariance. However, their computation by a direct method is too expensive, which limits the application of ZMs. In this paper, we present a novel algorithm for fast computation of Zernike moments. By using the recursive property of Zernike polynomials, the inter-relationship of the Zernike moments can be established. As a result, the Zernike moment of order n with repetition m, Z_nm, can be expressed as a combination of Z_n−2,m and Z_n−4,m. Based on this relationship, the Zernike moment Z_nm, for n>m, can be deduced from Z_mm. To reduce the computational complexity, we adopt an algorithm known as systolic array for computing these latter moments. Using such a strategy, the multiplication number required in the moment calculation of Z_mm can be decreased significantly. Comparison with known methods shows that our algorithm is as accurate as the existing methods, but is more efficient.     

A fast analog front-end processor for digital imaging systems

With higher sensor resolutions available, the speed and dynamic range requirements for image processors in digital imaging systems are more demanding. A 12-bit, 50 Mpixels/s digital image acquisition system balances power end performance. The analog processor's total power dissipation is only 150 MW at full speed-an enviable quality for the portable market although the underlying technique requires special analog circuitry to handle fast gain changes, it achieves a far wider dynamic range     

Exploring correlation for fast skyline computation

Scaling skyline queries over high-dimensional datasets remains to be challenging due to the fact that most existing algorithms assume dimensional independence when establishing the worst-case complexity by discarding correlation distribution. In this paper, we present HashSkyline, a systematic and correlation-aware approach for scaling skyline queries over high-dimensional datasets with three novel features: First, it offers a fast hash-based method to prune non-skyline points by utilizing data correlation characteristics and speed up the overall skyline evaluation for correlated datasets. Second, we develop \(HashSkyline_{GPU}\), which can dramatically reduce the response time for anti-correlated and independent datasets by capitalizing on the parallel processing power of GPUs. Third, the HashSkyline approach uses the pivot cell-based mechanism combined with the correlation threshold to determine the correlation distribution characteristics for a given dataset, enabling adaptive configuration of HashSkyline for skyline query evaluation by auto-switching of \(HashSkyline_{CPU}\) and \(HashSkyline_{GPU}\). We evaluate the validity of HashSkyline using both synthetic datasets and real datasets. Our experiments show that HashSkyline consumes significantly less pre-processing cost and achieves significantly higher overall query performance, compared to existing state-of-the-art algorithms.     

A fast adaptive model reduction method based on Takenaka-Malmquist systems

An adaptive model reduction method is proposed for linear time-invariant systems based on the continuous-time rational orthogonal basis (Takenaka-Malmquist basis). The method is to find an adaptive approximation in the energy sense by selecting optimal points for the rational orthogonal basis. The stability of the reduced models holds, and the steady-state values of step responses are kept to be equal. Furthermore, the method automatically ensures the reduced system to be in the Hardy space H₂. The existence of the best approximation in the Hardy space H₂ by n Blaschke forms is proved in the proposed approach. The effectivity of this method is illustrated through three well-known examples.     

A multilayer self-organizing model for convex-hull computation

A self-organizing neural-network model is proposed for computation of the convex-hull of a given set of planar points. The network evolves in such a manner that it adapts itself to the hull-vertices of the convex-hull. The proposed network consists of three layers of processors. The bottom layer computes some angles which are passed to the middle layer. The middle layer is used for computation of the minimum angle (winner selection). These information are passed to the topmost layer as well as fed back to the bottom layer. The network in the topmost layer self-organizes by labeling the hull-processors in an orderly fashion so that the final convex-hull is obtained from the topmost layer. Time complexity of the proposed model is analyzed and is compared with existing models of similar nature     

A comparative analysis of algorithms for fast computation of Zernike moments

This paper details a comparative analysis on time taken by the present and proposed methods to compute the Zernike moments, Z_pq. The present method comprises of Direct, Belkasim's, Prata's, Kintner's and Coefficient methods. We propose a new technique, denoted as q-recursive method, specifically for fast computation of Zernike moments. It uses radial polynomials of fixed order p with a varying index q to compute Zernike moments. Fast computation is achieved because it uses polynomials of higher index q to derive the polynomials of lower index q and it does not use any factorial terms. Individual order of moments can be calculated independently without employing lower- or higher-order moments. This is especially useful in cases where only selected orders of Zernike moments are needed as pattern features. The performance of the present and proposed methods are experimentally analyzed by calculating Zernike moments of orders 0 to p and specific order p using binary and grayscale images. In both the cases, the q-recursive method takes the shortest time to compute Zernike moments.     

A fast procedure for manipulator inverse kinematics computation and singularities prevention

In this article, a fast procedure for numerical manipulator inverse kinematics computation and singularities prevention is presented. The approach is based upon solving a linear system and automatically calculating some parameters. These parameters are properly used in either one of two original schemes that are also proposed to induce robustness to the pseudoinverse. Furthermore, here it is also shown how to properly implement one of these schemes in conjunction with a recently developed approach for the singularities prevention of redundant manipulators. The resultant algorithms are tested on the simulation of a planar redundant manipulator. From the results obtained, it is observed that the proposed approach compares favorably with the approaches using a Gaussian elimination procedure and with pseudoinverse robustness based on a manipulability measure.     

A fast algorithm for integrating connected-component labeling and euler number computation

This paper proposes a fast algorithm for integrating connected-component labeling and Euler number computation. Based on graph theory, the Euler number of a binary image in the proposed algorithm is calculated by counting the occurrences of four patterns of the mask for processing foreground pixels in the first scan of a connected-component labeling process, where these four patterns can be found directly without any additional calculation; thus, connected-component labeling and Euler number computation can be integrated more efficiently. Moreover, when computing the Euler number, unlike other conventional algorithms, the proposed algorithm does not need to process background pixels. Experimental results demonstrate that the proposed algorithm is much more efficient than conventional algorithms either for calculating the Euler number alone or simultaneously calculating the Euler number and labeling connected components.     

A fast and scalable approach for IoT service selection based on a physical service model

Information Systems (ISs) have become one of the crucial tools for various organizations in managing and coordinating business processes. Now we are entering the era of the Internet of Things (IoT). IoT is a paradigm in which real-world physical things can be connected to the Internet and provide services through the computing devices attached. The IoT infrastructure is starting to be integrated with ISs thereby diminishing the boundaries between the physical world and the business IT systems. With the development of IoT technologies, the number of connected things and their available physical services are increasing rapidly. Thus, selecting an appropriate service that satisfies a user’s requirements from such services becomes a time-consuming challenge. To address this issue, we propose a Physical Service Model (PSM) as a common conceptual model to describe heterogeneous IoT physical services. PSM contains three core concepts (device, resource, and service) and specifies their relationships. Based on the proposed PSM, we define three types of Quality of Service (QoS) attributes and rate candidate services according to user requirements. To dynamically rate QoS values and select an appropriate physical service, we propose a Physical Service Selection (PSS) method that takes a user preference and an absolute dominance relationship among physical services into account. Finally, experiments are conducted to evaluate the performance of the proposed method.     

基于快速模型预测控制的超级电容城轨充电

超级电容作为一种新能源设备已经应用到了城轨列车中。常规时储能式城轨超级电容仅需在停站的30 s内充满电就能保证列车到下一站的正常运行。而这也要求充电设备提供快速上升无尖峰的大功率充电电流。考虑到充电需求及超级电容模型复杂性,将该充电问题构建为一个模型预测控制问题,通过一种改进的内点法对所构建的目标函数进行求解。相较于传统的内点法,仅选用一个经过试验验证的固定障碍参数进行求解。同时引入一种暖启动机制,将每一时刻的最终解作为下一时刻的初始解。两种机制的引入大大简化了目标函数的求解过程,实现了模型预测控制在大功率快速充电控制中的高效应用,控制效果达到预期。仿真及试验验证了所设计算法的有效性。     

The fast recursive computation of Tchebichef moment and its inverse transform based on Z-transform

The outputs of cascaded digital filters operating as accumulators are combined with a simplified Tchebichef polynomials to form Tchebichef moments (TMs). In this paper, we derive a simplified recurrence relationship to compute Tchebichef polynomials based on Z-transform properties. This paves the way for the implementation of second order digital filter to accelerate the computation of the Tchebichef polynomials. Then, some aspects of digital filter design for image reconstruction from TMs are addressed. The new proposed digital filter structure for reconstruction is based on the 2D convolution between the digital filter outputs used in the computation of the TMs and the impulse response of the proposed digital filter. They operate as difference operators and accordingly act on the transformed image moment sets to reconstruct the original image. Experimental results show that both the proposed algorithms to compute TMs and inverse Tchebichef moments (ITMs) perform better than existing methods in term of computation speed.     

A fast implementation algorithm of TV inpainting model based on operator splitting method

In this paper, we propose a fast algorithm to solve the well known total variation (TV) inpainting model. Classically, the Euler-Lagrange equation deduced from TV inpainting model is solved by the gradient descent method and discretized by an explicit scheme, which produces a slow inpainting process. Sometimes an implicit scheme is also used to tackle the problem. Although the implicit scheme is several times faster than the explicit one, it is still too slow in many practical applications. In this paper, we propose to use an operator splitting method by adding new variables in the Euler-Lagrange equation of TV inpainting model such that the equation is split into a few very simple subproblems. Then we solve these subproblems by an alternate iteration. Numerically, the proposed algorithm is very easy to implement. In the numerical experiments, we mainly compare our algorithm with the existing implicit TV inpainting algorithms. It is shown that our algorithm is about ten to twenty times faster than the implicit TV inpainting algorithms with similar inpainting quality. The comparison of our algorithm with harmonic inpainting algorithm also shows some advantages and disadvantages of the TV inpainting model.     

A variational model for normal computation of point clouds

In this paper we present a novel model for computing the oriented normal field on a point cloud. Differently from previous two-stage approaches, our method integrates the unoriented normal estimation and the consistent normal orientation into one variational framework. The normal field with consistent orientation is obtained by minimizing a combination of the Dirichlet energy and the coupled-orthogonality deviation, which controls the normals perpendicular to and continuously varying on the underlying shape. The variational model leads to solving an eigenvalue problem. If unoriented normal field is provided, the model can be modified for consistent normal orientation. We also present experiments which demonstrate that our estimates of oriented normal vectors are accurate for smooth point clouds, and robust in the presence of noise, and reliable for surfaces with sharp features, e.g., corners, ridges, close-by sheets and thin structures.     

A continuation/GMRES method for fast computation of nonlinear receding horizon control

In this paper, a fast numerical algorithm for nonlinear receding horizon control is proposed. The control input is updated by a differential equation to trace the solution of an associated state-dependent two-point boundary-value problem. A linear equation involved in the differential equation is solved by the generalized minimum residual method, one of the Krylov subspace methods, with Jacobians approximated by forward differences. The error in the entire algorithm is analyzed and is shown to be bounded under some conditions. The proposed algorithm is applied to a two-link arm whose dynamics is highly nonlinear. Simulation results show that the proposed algorithm is faster than the conventional algorithms.     

基于混合高斯模型的物体成分拟合方法

为了寻求代价更小、效率更高、适应性更强的图像原型表征方法,借鉴成分识别理论的观点,设计出一种更符合人类认知原理、更具有可理解性的物体拟合算法。利用二维高斯混合函数,用高斯成分来拟合物体的边缘图像,使得物体的表征由单一的像素表示转变为利用成分进行表征的方式。为了使得拟合结果更具有健壮性,在算法中还引入了分裂-归约机制来对拟合结果进行修正。实验结果表明,这种拟合手段能够很好地描述物体的特征成分,为图像进行后期的高级语义处理奠定了基础。