Accelerated unsteady flow line integral convolution

Unsteady flow line integral convolution (UFLIC) is a texture synthesis technique for visualizing unsteady flows with high temporal-spatial coherence. Unfortunately, UFLIC requires considerable time to generate each frame due to the huge amount of pathline integration that is computed for particle value scattering. This paper presents accelerated UFLIC (AUFLIC) for near interactive (1 frame/second) visualization with 160,000 particles per frame. AUFLIC reuses pathlines in the value scattering process to reduce computationally expensive pathline integration. A flow-driven seeding strategy is employed to distribute seeds such that only a few of them need pathline integration while most seeds are placed along the pathlines advected at earlier times by other seeds upstream and, therefore, the known pathlines can be reused for fast value scattering. To maintain a dense scattering coverage to convey high temporal-spatial coherence while keeping the expense of pathline integration low, a dynamic seeding controller is designed to decide whether to advect, copy, or reuse a pathline. At a negligible memory cost, AUFLIC is 9 times faster than UFLIC with comparable image quality     

Parallel line integral convolution

Line integral convolution (LIC) is a powerful method for computing directional textures from vector data. LIC textures can be animated, yielding the effect of flowing motion. Both, static images and animation sequences are of great significance in scientific visualization. Although an efficient algorithm for computing static LIC textures is known, the generation of animation sequences still requires a considerable amount of computing time. In this paper we propose an algorithm for computing animation sequences on a massively parallel distributed memory computer. With this technique it becomes possible to utilise animated LIC for interactive vector field visualization. To take advantage of the strong temporal coherence between different frames, parallelization is performed in image space rather than in time. Image space coherence is exploited using a flexible update and communication scheme. In addition algorithmic improvements on LIC are proposed that can be applied to parallel and sequential algorithms as well.     

Dynamic line integral convolution for visualizing streamline evolution

The depiction of time-dependent vector fields is a central problem in scientific visualization. This article describes a technique for generating animations of such fields where the motion of the streamlines to be visualized is given by a second "motion" vector field. Each frame of our animation is a line integral convolution of the original vector field with a time-varying input texture. The texture is evolved according to the associated motion vector field via an automatically adjusted set of random particles. We demonstrate this technique with examples from electromagnetism.     

Output-sensitive 3D line integral convolution

We propose an output-sensitive visualization method for 3D line integral convolution (LIC) whose rendering speed is largely independent of the data set size and mostly governed by the complexity of the output on the image plane. Our approach of view-dependent visualization tightly links the LIC generation with the volume rendering of the LIC result in order to avoid the computation of unnecessary LIC points: early-ray termination and empty-space leaping techniques are used to skip the computation of the LIC integral in a lazy-evaluation approach; both ray casting and texture slicing can be used as volume-rendering techniques. The input noise is modeled in object space to allow for temporal coherence under object and camera motion. Different noise models are discussed, covering dense representations based on filtered white noise all the way to sparse representations similar to oriented LIC. Aliasing artifacts are avoided by frequency control over the 3D noise and by employing a 3D variant of MIPmapping. A range of illumination models is applied to the LIC streamlines: different codimension-2 lighting models and a novel gradient-based illumination model that relies on precomputed gradients and does not require any direct calculation of gradients after the LIC integral is evaluated. We discuss the issue of proper sampling of the LIC and volume-rendering integrals by employing a frequency-space analysis of the noise model and the precomputed gradients. Finally, we demonstrate that our visualization approach lends itself to a fast graphics processing unit (GPU) implementation that supports both steady and unsteady flow. Therefore, this 3D LIC method allows users to interactively explore 3D flow by means of high-quality, view-dependent, and adaptive LIC volume visualization. Applications to flow visualization in combination with feature extraction and focus-and-context visualization are described, a comparison to previous methods is provided, and a detailed performance analysis is included.     

Using line integral convolution for flow visualization: curvilineargrids, variable-speed animation, and unsteady flows

Line integral convolution (LIC), introduced by Cabral and Leedom (1993) is a powerful technique for imaging and animating vector fields. We extend the LIC technique in three ways. Firstly the existing algorithm is limited to vector fields over a regular Cartesian grid. We extend the algorithm and the animation techniques possible with it to vector fields over curvilinear surfaces, such as those found in computational fluid dynamics simulations. Secondly we introduce a technique to visualize vector magnitude as well as vector direction, i.e., variable-speed flow animation. Thirdly we show how to modify LIC to visualize unsteady (time dependent) flows. Our implementation utilizes texture-mapping hardware to run in real time, which allows our algorithms to be included in interactive applications     

A new convolution algorithm for loss probability analysis in multiservice networks

Performance analysis in multiservice loss systems generally focuses on accurate and efficient calculation methods for traffic loss probability. Convolution algorithm is one of the existing efficient numerical methods. Exact loss probabilities are obtainable from the convolution algorithm in systems where the bandwidth is fully shared by all traffic classes; but not available for systems with trunk reservation, i.e. part of the bandwidth is reserved for a special class of traffic. A proposal known as asymmetric convolution algorithm (ACA) has been made to overcome the deficiency of the convolution algorithm. It obtains an approximation of the channel occupancy distribution in multiservice systems with trunk reservation. However, the ACA approximation is only accurate with two traffic flows; increased approximation errors are observed for systems with three or more traffic flows.In this paper, we present a new Permutational Convolution Algorithm (PCA) for loss probability approximation in multiservice systems with trunk reservation. This method extends the application of the convolution algorithm and overcomes the problems of approximation accuracy in systems with a large number of traffic flows. It is verified that the loss probabilities obtained by PCA are very close to the exact solutions obtained by Markov chain models, and the accuracy outperforms the ACA approximation.     

Double-shifted Chebyshev series for convolution integral and integral equations

Double-shifted Chebyshev polynomials are developed in this study to approximate the solutions of the convolution integral, Volterra integral equation, and Fredholm integral equation. This method simplifies the computations of integral equations to the successive solutions of a linear algebraic equation in matrix form. In addition, the computational complexity can be reduced remarkably. Three examples are illustrated. It is seen that the proposed approach is straightforward and convenient, and converges faster in finding approximations than other existing orthogonal function methods.     

QoS-based cooperative algorithm for integral multi-commodity flow problem

This paper treats with integral multi-commodity flow through a network. To enhance the Quality of Service (QoS) for channels, it is necessary to minimize delay and congestion. Decreasing the end-to-end delay and consumption of bandwidth across channels are dependent and may be considered in very complex mathematical equations. To capture with this problem, a multi-commodity flow model is introduced whose targets are minimizing delay and congestion in one model. The flow through the network such as packets, also needs to get integral values. A model covering these concepts, is NP-hard while it is very important to find transmission strategies in real-time. For this aim, we extend a cooperative algorithm including traditional mathematical programming such as path enumeration and a meta-heuristic algorithm such as genetic algorithm. To find integral solution satisfying demands of nodes, we generalize a hybrid genetic algorithm to assign the integral commodities where they are needed. In this hybrid algorithm, we use feasible encoding and try to keep feasibility of chromosomes over iterations. By considering some random networks, we show that the proposed algorithm yields reasonable results in a few number of iterations. Also, because this algorithm can be applied in a wide range of objective functions in terms of delay and congestion, it is possible to find some routs for each commodity with high QoS. Due to these outcomes, the presented model and algorithm can be utilized in a variety of application in computer networks and transportation systems to decrease the congestion and increase the usage of channels.     

线积分卷积与双色调映射相结合的彩色素描模拟方法

目的 目前学者已经设计了很多模拟油画、水彩、水墨等风格的非真实感绘制方法,而能够生成彩色素描的算法还不是很多。针对这一课题,在前人工作的基础上,结合线积分卷积与双色调映射技术,改进了一种彩色素描模拟方法。方法 首先基于K-means聚类对彩色图像进行分割,通过计算色彩差异性为每个区域指定两种基本色,并利用双色调映射技术计算每种颜色的密度。而后利用线积分卷积分别生成两个基本色层的素描纹理,并将两层纹理相融合来生成彩色纹理。与此同时,利用霓虹变换生成素描轮廓线。最后,将轮廓与彩色纹理相融合来得到彩色素描效果。结果 实验结果表明,本文方法能够实现由彩色图像到彩铅画的自动、实时转化。结论 本文方法从轮廓和纹理两个角度模拟了真实的彩铅绘画过程。基于K-means聚类的分割方法得到的结果能够更好地反映彩色图像的颜色分布特性。通过色彩差异性计算指定基本色的策略提高了该环节的效率,满足了实时性要求。由于粉笔、蜡笔等绘画风格的调色与彩铅画类似,本文不同颜色层上下叠加的方式可以扩展到对其他介质绘画的模拟当中。     

一种新的矢量传感器线列阵波束形成算法

为了提高基于矢量线列阵的目标方位估计能力,将基于时域解析信号实现最小方差无畸变响应（ MVDR）的方法——TAMVDR算法引入到了矢量线列阵信号处理中,提出了实现矢量线列阵波束形成的VTAMVDR算法。理论分析了矢量线列阵VTAMVDR算法的原理,该算法通过Hilbert变换对时域宽带信号引入复权向量,不需要进行子带分割,且不需要对数据进行分块处理,获得稳定优化权向量估计所需要的数据长度远小于频域MVDR方法,数据长度合适时,单次快拍即可实现波束形成,大大降低了运算量。仿真和海上试验数据分析结果表明：VTMVDR算法相比于频域MVDR算法具有较好的性能,具有更高的分辨率和更窄的波束角,有更好的探测性能。     

A high-order fast method for computing convolution integral with smooth kernel

In this paper we report on a high-order fast method to numerically calculate convolution integral with smooth non-periodic kernel. This method is based on the Newton-Cotes quadrature rule for the integral approximation and an FFT method for discrete summation. The method can have an arbitrarily high-order accuracy in principle depending on the number of points used in the integral approximation and a computational cost of O(Nlog(N)), where N is the number of grid points. For a three-point Simpson rule approximation, the method has an accuracy of O(h⁴), where h is the size of the computational grid. Applications of the Simpson rule based algorithm to the calculation of a one-dimensional continuous Gauss transform and to the calculation of a two-dimensional electric field from a charged beam are also presented.     

A New Algorithm for Two-Dimensional Line Clipping via Geometric Transformation

Line segment clipping is a basic operation of the visualization process in computer graphics.So far there exist four computational models for clipping a line segment against a window,(1)the encoding,(2)the parametric,(3)the geometric transforma tion,and (4)the parallel cutting.This paper presents an algorithm that is based on the third method.By making use of symmetric properties of a window and transformation operations,both endpoints of a line segment are transformed,so that the basic cases are reduced into two that can be easily handled,thus the problems in NLN and AS where there are too many sub-procedure calls and basic cases that are difficult to deal with are tackled.Both analytical and experimental results from random input data show that the algorithm is better than other developed ones,in view of the speed and the number of operations.     

A robust genetic algorithm for scheduling realistic hybrid flexible flow line problems

This article addresses the problem of hybrid flexible flow line where some constraints are considered to alleviate the chasm between the real-world industries scheduling and the production scheduling theories. Sequence-dependent setup times, machine release date and time lags are three constraints deemed to project the circumstances commonly found in real-world industries. To tackle the complexity of the problem at hand, we propose an approach base on genetic algorithm (GA). However, the performance of most evolutionary algorithms is significantly impressed by the values determined for the miscellaneous parameters which these algorithms possess. Hence, response surface methodology is applied to set the parameters of GA and to estimate the proper values of GA parameters in continually intervals. Finally, problems of various sizes are utilized to test the performance of the proposed algorithm and to compare it with some existing heuristic in the literature such as SPT, LPT and NEH.     

基于新混沌与矩阵卷积运算的彩色图像加密算法

针对彩色图像加密过程中出现的强相关性和高冗余问题,提出基于云模型的Fibonacci混沌系统与矩阵卷积运算的彩色图像加密算法。首先对彩色图像的R、G、B分量拼接图像像素点坐标变换置乱;然后将混沌序列值作为卷积核的输入值与像素值进行矩阵卷积运算,实现像素值置换;再与云模型Fibonacci混沌序列及前相邻像素值进行正反双向2次异或操作生成加密图像。实验分析表明,加密后的图像直方图更加平滑,像素分布均匀,图像相邻像素相关性低,加密图像RGB分量平均水平、垂直和对角相关系数分别为-0.0010,0.0016和0.0031,能够抵抗差分攻击、明文攻击、噪声攻击和剪切攻击等攻击实验,提出的新加密算法具有加密安全性高、抗干扰性高、鲁棒性强等特点。     

A memetic algorithm for the flexible flow line scheduling problem with processor blocking

This paper introduces an efficient memetic algorithm (MA) combined with a novel local search engine, namely, nested variable neighbourhood search (NVNS), to solve the flexible flow line scheduling problem with processor blocking (FFLB) and without intermediate buffers. A flexible flow line consists of several processing stages in series, with or without intermediate buffers, with each stage having one or more identical parallel processors. The line produces a number of different products, and each product must be processed by at most one processor in each stage. To obtain an optimal solution for this type of complex, large-sized problem in reasonable computational time using traditional approaches and optimization tools is extremely difficult. Our proposed MA employs a new representation, operators, and local search method to solve the above-mentioned problem. The computational results obtained in experiments demonstrate the efficiency of the proposed MA, which is significantly superior to the classical genetic algorithm (CGA) under the same conditions when the population size is increased in the CGA.     

基于深度卷积特征光流的形变医学图像配准算法

光流法是一种基于光流场模型的重要而有效的形变配准算法。针对现有光流法所用特征质量不高使得配准结果不够准确的问题，将深度卷积神经网络特征和光流法相结合，提出了基于深度卷积特征光流（DCFOF）的形变医学图像配准算法。首先利用深度卷积神经网络稠密地提取图像中每个像素所在图像块的深度卷积特征，然后基于固定图像和浮动图像间的深度卷积特征差异求解光流场。通过提取图像的更为精确和鲁棒的深度学习特征，使求得的光流场更接近真实形变场，提升了配准精度。实验结果表明，所提算法能够更有效地解决形变医学图像配准问题，其配准精度优于Demons算法、尺度不变特征变换(SIFT) Flow算法以及医学图像专业配准软件Elastix。     

A genetic algorithm for integrating lot-sizing and sequencing in scheduling a capacitated flow line

In this paper we present a genetic algorithm for solving an important but difficult scheduling problem: that of integrating the lot-sizing and sequencing decisions in scheduling a flow line involving sequence dependent setup times, capacity constraints, limited buffer capacity between machines, and due dates. The problem is based on a real world manufacturing facility that is also described. Novel crossover and mutation operators are presented for both the lot-sizing and sequencing parts of the scheduling problem and the performance of the genetic algorithm is compared to a heuristic approach of integration previously shown to have been effective.