Fast animation of lightning using an adaptive mesh

We present a fast method for simulating, animating, and rendering lightning using adaptive grids. The "dielectric breakdown model" is an elegant algorithm for electrical pattern formation that we extend to enable animation of lightning. The simulation can be slow, particularly in 3D, because it involves solving a large Poisson problem. Losasso et al. recently proposed an octree data structure for simulating water and smoke, and we show that this discretization can be applied to the problem of lightning simulation as well. However, implementing the incomplete Cholesky conjugate gradient (ICCG) solver for this problem can be daunting, so we provide an extensive discussion of implementation issues. ICCG solvers can usually be accelerated using "Eisenstat's trick," but the trick cannot be directly applied to the adaptive case. Fortunately, we show that an "almost incomplete Cholesky" factorization can be computed so that Eisenstat's trick can still be used. We then present a fast rendering method based on convolution that is competitive with Monte Carlo ray tracing but orders of magnitude faster, and we also show how to further improve the visual results using jittering     

Automatic facial expression recognition using facial animation parameters and multistream HMMs

The performance of an automatic facial expression recognition system can be significantly improved by modeling the reliability of different streams of facial expression information utilizing multistream hidden Markov models (HMMs). In this paper, we present an automatic multistream HMM facial expression recognition system and analyze its performance. The proposed system utilizes facial animation parameters (FAPs), supported by the MPEG-4 standard, as features for facial expression classification. Specifically, the FAPs describing the movement of the outer-lip contours and eyebrows are used as observations. Experiments are first performed employing single-stream HMMs under several different scenarios, utilizing outer-lip and eyebrow FAPs individually and jointly. A multistream HMM approach is proposed for introducing facial expression and FAP group dependent stream reliability weights. The stream weights are determined based on the facial expression recognition results obtained when FAP streams are utilized individually. The proposed multistream HMM facial expression system, which utilizes stream reliability weights, achieves relative reduction of the facial expression recognition error of 44% compared to the single-stream HMM system.     

一种动态适应性软件体系结构模型理论研究

软件系统的演化需要具有灵活性、适应性的软件体系结构的支持。提出一种在组织结构上具有固有的动态性的软件体系结构理论模型——动态树型软件体系结构模型(DTSAM)。在DTSAM定义的软件体系结构组织结构下,软件系统的演化抽象成为一棵动态树的渐增和渐减。其中,所设计的渐增和渐减算法分别定义了动态树的增减过程;所设计的生长函数中适应性因子的调节,使得软件体系结构在组织结构层次上具有整体适应性和局部适应性。最后应用模型对例子系统仿真,其过程产生的结果也证实了这种适应性。     

人工心理模型驱动的人脸表情动画合成

提出了一种人脸表情动画合成方法,该方法以人工心理模型输出概率值作为权重向量,通过因素加权综合法,控制表情动画模型参数。实验结果表明,该方法实现了心理状态对表情的实时驱动,合成的人脸表情动画真实、自然。     

Highly efficient facial blendshape animation with analytical dynamic deformations

Multimedia Tools and Applications - Adding physics to facial blendshape animation is an active research topic. Existing physics-based approaches of facial blendshape animation are numerical, so...     

A fault tolerant NoC architecture using quad-spare mesh topology and dynamic reconfiguration

Network-on-Chip (NoC) is widely used as a communication scheme in modern many-core systems. To guarantee the reliability of communication, effective fault tolerant techniques are critical for an NoC. In this paper, a novel fault tolerant architecture employing redundant routers is proposed to maintain the functionality of a network in the presence of failures. This architecture consists of a mesh of 2 × 2 router blocks with a spare router placed in the center of each block. This spare router provides a viable alternative when a router fails in a block. The proposed fault-tolerant architecture is therefore referred to as a quad-spare mesh. The quad-spare mesh can be dynamically reconfigured by changing control signals without altering the underlying topology. This dynamic reconfiguration and its corresponding routing algorithm are demonstrated in detail. Since the topology after reconfiguration is consistent with the original error-free 2D mesh, the proposed design is transparent to operating systems and application software. Experimental results show that the proposed design achieves significant improvements on reliability compared with those reported in the literature. Comparing the error-free system with a single router failure case, the throughput only decreases by 5.19% and latency increases by 2.40%, with about 45.9% hardware redundancy.     

A novel approach for facial expression recognition using local binary pattern with adaptive window

Multimedia Tools and Applications - Facial Expression Recognition (FER) is an important area in human computer interaction. FER has different applications such as analysis of student behaviour in...     

A model for dynamic adaptive coscheduling

is paper proposes a dynamic adaptive coscheduling model DASIC to take advantage of excess available resources in a network of workstations(NOW). Besides coscheduling related subtasks dynamically,DASIC can scale up or down the process space depending upon the number of available processors on an NOW. Based on the dynamic idle processor group(IPG),DASIC employs thre modules:the coscheduling module,the scalabele scheduling module and the load balancing module,and uses six algorithms to achieve scalability.A simplified DASIC was also implemented,and experimental results are presented in this paper,which show that it can maximize system utilization,and achieve task parallelism as much as possible.     

A new MHD code with adaptive mesh refinement and parallelization for astrophysics

A new code, named MAP, is written in FORTRAN language for magnetohydrodynamics (MHD) simulations with the adaptive mesh refinement (AMR) and Message Passing Interface (MPI) parallelization. There are several optional numerical schemes for computing the MHD part, namely, modified Mac Cormack Scheme (MMC), Lax–Friedrichs scheme (LF), and weighted essentially non-oscillatory (WENO) scheme. All of them are second-order, two-step, component-wise schemes for hyperbolic conservative equations. The total variation diminishing (TVD) limiters and approximate Riemann solvers are also equipped. A high resolution can be achieved by the hierarchical block-structured AMR mesh. We use the extended generalized Lagrange multiplier (EGLM) MHD equations to reduce the non-divergence free error produced by the scheme in the magnetic induction equation. The numerical algorithms for the non-ideal terms, e.g., the resistivity and the thermal conduction, are also equipped in the code. The details of the AMR and MPI algorithms are described in the paper.     

动态自适应演进安全架构研究

自适应安全架构面对高级定向攻击具备智能与弹性安全防护能力,依托持续性地监测与回溯分析构建了自适应的未知攻击威胁预警体系,形成了集防御、监测、响应、预测为一体的安全防控流程闭环。基于自适应安全架构构建了适用于网络空间安全保密场景的动态防御体系,融合全息态势、智慧决策、动态防御等关键技术,提出了一种动态自适应演进的安全架构。     

Bilinear interpolation for facial expression and metamorphosis in real-time animation

This paper describes a new method for generating facial animation in which facial expression and shape can be changed simultaneously in real time. A 2D parameter space independent of facial shape is defined, on which facial expressions are superimposed so that the expressions can be applied to various facial shapes. A facial model is transformed by a bilinear interpolation, which enables a rapid change in facial expression with metamorphosis. The practical efficiency of this method has been demonstrated by a real-time animation system based on this method in live theater.     

A set-theoretic model reference adaptive control architecture with dead-zone effect

A new set-theoretic model reference adaptive control architecture with dead-zone effect is presented. The key feature of our approach utilizes a new generalized restricted potential function, where it not only provides a user-defined uncertain dynamical system performance but also has the capability to stop the adaptation when system errors are small (i.e., inside dead-zone) — a practice adopted in adaptive control applications. The stability of the proposed technique is analyzed through showing the boundedness of an energy function in all possible variations and its experimental validation is also given through an aerospace testbed.     

A new scheme for mesh generation and mesh refinement using graph theory

There are an extensive number of algorithms available from graph theory, some of which, for problems with geometric content, make graphs an attractive framework in which to model an object from its geometry to its discretization into a finite element mesh. This paper presents a new scheme for finite element mesh generation and mesh refinement using concepts from graph theory. This new technique, which is suitable for an interactive graphical environment, can also be used efficiently for fully automatic remeshing in association with self-adaptive schemes. Problems of mesh refinement around holes and local mesh refinement are treated. The suitability of the algorithms presented in this paper is demonstrated by some examples.     

A new error model for adaptive systems

A simple error model is described for both discrete and continuous time adaptive systems. The stability of the model for both bounded and unbounded inputs is analyzed for the discrete case and extended to continuous time models. It is shown thatDeltaphi(k)in I^{2}in the former case andphi(t)in L^{2}in the latter case due to the presence of a feedback signal and these in turn play an important role in the stability analysis of discrete and continuous time adaptive systems.     

A new method for adaptive model-based control of non-linear dynamic plants using a neuro-fuzzy-fractal approach

 We describe in this paper a new method for adaptive model-based control of non-linear dynamic plants using Neural Networks, Fuzzy Logic and Fractal Theory. The new neuro-fuzzy-fractal method combines Soft Computing (SC) techniques with the concept of the fractal dimension for the domain of Non-Linear Dynamic Plant Control. The new method for adaptive model-based control has been implemented as a computer program to show that our neuro-fuzzy-fractal approach is a good alternative for controlling non-linear dynamic plants. We illustrate in this paper our new methodology with the case of controlling biochemical reactors in the food industry. For this case, we use mathematical models for the simulation of bacteria growth for several types of food. The goal of constructing these models is to capture the dynamics of bacteria population in food, so as to have a way of controlling this dynamics for industrial purposes.     

Massively parallel adaptive mesh refinement and coarsening for dynamic fracture simulations

We use the graphical processing unit (GPU) to perform dynamic fracture simulation using adaptively refined and coarsened finite elements and the inter-element cohesive zone model. Due to the limited memory available on the GPU, we created a specialized data structure for efficient representation of the evolving mesh given. To achieve maximum efficiency, we perform finite element calculation on a nodal basis (i.e., by launching one thread per node and collecting contributions from neighboring elements) rather than by launching threads per element, which requires expensive graph coloring schemes to avoid concurrency issues. These developments made possible the parallel adaptive mesh refinement and coarsening schemes to systematically change the topology of the mesh. We investigate aspects of the parallel implementation through microbranching examples, which has been explored experimentally and numerically in the literature. First, we use a reduced-scale version of the experimental specimen to demonstrate the impact of variation in floating point operations on the final fracture pattern. Interestingly, the parallel approach adds some randomness into the finite element simulation on the structured mesh in a similar way as would be expected from a random mesh. Next, we take advantage of the speedup of the implementation over a similar serial implementation to simulate a specimen whose size matches that of the actual experiment. At this scale, we are able to make more direct comparisons to the original experiment and find excellent agreement with those results.     

A multilayer incremental neural network architecture for classification

A new multilayer incremental neural network (MINN) architecture and its performance in classification of biomedical images is discussed. The MINN consists of an input layer, two hidden layers and an output layer. The first stage between the input and first hidden layer consists of perceptrons. The number of perceptrons and their weights are determined by defining a fitness function which is maximized by the genetic algorithm (GA). The second stage involves feature vectors which are the codewords obtained automaticaly after learning the first stage. The last stage consists of OR gates which combine the nodes of the second hidden layer representing the same class. The comparative performance results of the MINN and the backpropagation (BP) network indicates that the MINN results in faster learning, much simpler network and equal or better classification performance.