Analytical solutions for sketch-based convolution surface modeling on the GPU

Convolution surfaces are attractive for modeling objects of complex evolving topology. This paper presents some novel analytical convolution solutions for planar polygon skeletons with both finite-support and infinite-support kernel functions. We convert the double integral over a planar polygon into a simple integral along the contour of the polygon based on Green’s theorem, which reduces the computational cost and allows for efficient parallel computation on the GPU. For finite support kernel functions, a skeleton clipping algorithm is presented to compute the valid skeletons. The analytical solutions are integrated into a prototype modeling system on the GPU (Graphics Processing Unit). Our modeling system supports point, polyline and planar polygon skeletons. Complex objects with arbitrary genus can be modeled easily in an interactive way. Resulting convolution surfaces with high quality are rendered with interactive ray casting.     

Performance analysis and optimization of MPI collective operations on multi-core clusters

Memory hierarchy on multi-core clusters has twofold characteristics: vertical memory hierarchy and horizontal memory hierarchy. This paper proposes new parallel computation model to unitedly abstract memory hierarchy on multi-core clusters in vertical and horizontal levels. Experimental results show that new model can predict communication costs for message passing on multi-core clusters more accurately than previous models, only incorporated vertical memory hierarchy. The new model provides the theoretical underpinning for the optimal design of MPI collective operations. Aimed at horizontal memory hierarchy, our methodology for optimizing collective operations on multi-core clusters focuses on hierarchical virtual topology and cache-aware intra-node communication, incorporated into existing collective algorithms in MPICH2. As a case study, multi-core aware broadcast algorithm has been implemented and evaluated. The results of performance evaluation show that the above methodology for optimizing collective operations on multi-core clusters is efficient.     

Neural network-based adaptive output feedback control for MIMO non-affine systems

An adaptive output feedback control scheme is proposed for a class of multi-input-multi-output (MIMO) non-affine nonlinear systems in which the output signal can track the reference signal. In the systems, the relative degree of the regulated output is assumed to be known. A state observer is constructed to estimate the unknown state in the systems. A neural network (NN) is introduced to compensate the modeling errors, and a robust control is also used to reduce the approximation error, which improves the capacity of resisting disturbance of the systems. The stability of the systems is rigidly proved through Lyapunov’s direct method. Simulation results demonstrate the effectiveness and feasibility of proposed scheme.     

A new optimizing parameter approach of LSSVM multiclass classification model

The parameter values of kernel function affect classification results to a certain extent. In the paper, a multiclass classification model based on improved least squares support vector machine (LSSVM) is presented. In the model, the non-sensitive loss function is replaced by quadratic loss function, and the inequality constraints are replaced by equality constraints. Consequently, quadratic programming problem is simplified as the problem of solving linear equation groups, and the SVM algorithm is realized by least squares method. When the LSSVM is used in multiclass classification, it is presented to choose parameter of kernel function on dynamic, which enhances preciseness rate of classification. The Fibonacci symmetry searching algorithm is simplified and improved. The changing rule of kernel function searching region and best shortening step is studied. The best multiclass classification results are obtained by means of synthesizing kernel function searching region and best shortening step. The simulation results show the validity of the model.     

Phoneme recognition using an adaptive supervised manifold learning algorithm

To effectively handle speech data lying on a nonlinear manifold embedded in a high-dimensional acoustic space, in this paper, an adaptive supervised manifold learning algorithm based on locally linear embedding (LLE) for nonlinear dimensionality reduction is proposed to extract the low-dimensional embedded data representations for phoneme recognition. The proposed method aims to make the interclass dissimilarity maximized, while the intraclass dissimilarity minimized in order to promote the discriminating power and generalization ability of the low-dimensional embedded data representations. The performance of the proposed method is compared with five well-known dimensionality reduction methods, i.e., principal component analysis, linear discriminant analysis, isometric mapping (Isomap), LLE as well as the original supervised LLE. Experimental results on three benchmarking speech databases, i.e., the Deterding database, the DARPA TIMIT database, and the ISOLET E-set database, demonstrate that the proposed method obtains promising performance on the phoneme recognition task, outperforming the other used methods.     

Hoeffding bound based evolutionary algorithm for symbolic regression

In symbolic regression area, it is difficult for evolutionary algorithms to construct a regression model when the number of sample points is very large. Much time will be spent in calculating the fitness of the individuals and in selecting the best individuals within the population. Hoeffding bound is a probability bound for sums of independent random variables. As a statistical result, it can be used to exactly decide how many samples are necessary for choosing i individuals from a population in evolutionary algorithms without calculating the fitness completely. This paper presents a Hoeffding bound based evolutionary algorithm (HEA) for regression or approximation problems when the number of the given learning samples is very large. In HEA, the original fitness function is used in every k generations to update the approximate fitness obtained by Hoeffding bound. The parameter 1?δ is the probability of correctly selecting i best individuals from population P, which can be tuned to avoid an unstable evolution process caused by a large discrepancy between the approximate model and the original fitness function. The major advantage of the proposed HEA algorithm is that it can guarantee that the solution discovered has performance matching what would be discovered with a traditional genetic programming (GP) selection operator with a determinate probability and the running time can be reduced largely. We examine the performance of the proposed algorithm with several regression problems and the results indicate that with the similar accuracy, the HEA algorithm can find the solution more efficiently than tradition EA. It is very useful for regression problems with large number of training samples.     

The effect of adding Ni and Ge microelements on the electromigration resistance of low-Ag based SnAgCu solder

Low-Ag-based Pb-free solder and its evaluation for electromigration (EM) reliability are research areas of active development. This paper studies the effect of adding Ni and Ge microelements on the EM behavior of low-Ag-based SnAgCu solder. Comparative measurements of the EM resistance of Sn1.0Ag0.5Cu (SAC) and Sn1.0Ag0.5Cu0.07Ni0.01Ge (SACNG) solders (in wt?%) were conducted. In these experiments, SACNG showed a higher EM resistance than SAC. It was concluded that adding Ni and Ge enhanced the EM resistance of low-Ag-based SnAgCu solders. This shows the same effect of Ni and Ge on the EM resistance as that which has been reported for Sn3.5Ag0.5Cu solder. An analysis of these observations suggests that grain boundary sliding plays a dominant role in stress relaxation and causes hillock growth. Based on this assumption, a possible mechanism was proposed to explain the effects of Ni and Ge on the EM resistance of SnAgCu solder.     

Rubber-assisted embossing of polymer thin films using molds with through-thickness microchannels

A flexible microfluidic chip is difficult to fabricate using the standard hot embossing technology. In this study, rubber-assisted embossing of polymer thin films using molds with through-thickness microchannels was investigated. The polymer film was thermoformed into the microchannels by rubber as a soft counter-tool. Different processing conditions, as well as material selections, affecting the thickness uniformity and replicated depth were examined. Results indicated that smoother surfaces on the embossed articles were created, and the thickness uniformity and the depth of the embossed channel were significantly affected by the embossing temperature, the embossing pressure, and the rubber hardness. The embossed film was sealed on one side with a layer of transparent adhesive film to form closed microchannels, and desired 3-D flow characteristics were obtained with this flexible microfluidic chip.     

Fabrication of PZT nano dot array and their ferroelectric properties

This paper demonstrates a promising and low-cost method for fabricating PZT nano-dot array by using sol–gel template method combined with laser interference lithography. ZrO₂ template with the pitch of 1?μm is firstly fabricated by sol–gel method and laser interference with a 325?nm He-Cd laser. Afterwards, PZT nano dot array with diameter of 500?nm and thickness of 20?nm is prepared on the ZrO₂ template. Atomic force microscope results show that the PZT nano dot array is uniform and highly ordered. Ferroelectric properties of PZT nano dot array are in situ measured through online-operation of TF analyzer and AFM. A ferroelectric hysteresis loop is obtained for the PZT nano dot.