18035243
Level-Set-Based Deformation Methods for Adaptive Grids
A new method for generating adaptive moving grids is formulated based on physical quantities. Level set functions are used to construct the adaptive grids, which are solutions of the standard level set evolution equation with the Cartesian coordinates as initial values. The intersection points of the level sets of the evolving functions form a new grid at each time. The velocity vector in the evolution equation is chosen according to a monitor function and is equal to the node velocity. A uniform grid is then deformed to a moving grid with desired cell volume distribution at each time. The method achieves precise control over the Jacobian determinant of the grid mapping as the traditional deformation method does. The new method is consistent with the level set approach to dynamic moving interface problems.
9209043
An Adaptive Grid Method and Its Application to Steady Euler Flow Calculations
An adaptive remeshing procedure based on a cell volume deformation method is presented. Starting with an initial grid, this method offers direct cell volume control through the specification of the transformation Jacobian. Grid points are moved with appropriate grid velocities so that the specified cell volume distribution can be achieved at the end of the grid movement without adding or removing grid points. The grid velocities are determined by solving a scalar Poisson equation. This method is applied to solving the compressible Euler equations. Computational test cases of transonic flow over an airfoil are presented and demonstrate the desired control of grid sizes across shock waves.
26250215
Wideband Signal DOA Estimation Based on Modified Quantum Genetic Algorithm
A new wideband signal DOA estimation algorithm based on modified quantum genetic algorithm (MQGA) is proposed in the presence of the errors and the mutual coupling between array elements. In the algorithm, the narrowband signal subspace fitting method is generalized to wideband signal DOA finding according to the character of space spectrum of wideband signal, and so the rule function is constructed. Then, the solutions is encoded onto chromosomes as a string of binary sequence, the variable quantum rotation angle is defined according to the distribution of optimization solutions. Finally, we use the MQGA algorithm to solve the nonlinear global azimuths optimization problem, and get optimization azimuths by fitness values. The computer simulation results illustrated that the new algorithm have good estimation performance.
62620121
Learning color and locality cues for moving object detection and segmentation
This paper presents an algorithm for automatically detecting and segmenting a moving object from a monocular video. Detecting and segmenting a moving object from a video with limited object motion is challenging. Since existing automatic algorithms rely on motion to detect the moving object, they cannot work well when the object motion is sparse and insufficient. In this paper, we present an unsupervised algorithm to learn object color and locality cues from the sparse motion information. We first detect key frames with reliable motion cues and then estimate moving sub-objects based on these motion cues using a Markov Random Field (MRF) framework. From these sub-objects, we learn an appearance model as a color Gaussian Mixture Model. To avoid the false classification of background pixels with similar color to the moving objects, the locations of these sub-objects are propagated to neighboring frames as locality cues. Finally, robust moving object segmentation is achieved by combining these learned color and locality cues with motion cues in a MRF framework. Experiments on videos with a variety of object and camera motion demonstrate the effectiveness of this algorithm.
7524507
Video retargeting: automating pan and scan
When a video is displayed on a smaller display than originally intended, some of the information in the video is necessarily lost. In this paper, we introduce Video Retargeting that adapts video to better suit the target display, minimizing the important information lost. We define a framework that measures the preservation of the source material, and methods for estimating the important information in the video. Video retargeting crops each frame and scales it to fit the target display. An optimization process minimizes information loss by balancing the loss of detail due to scaling with the loss of content and composition due to cropping. The cropping window can be moved during a shot to introduce virtual pans and cuts, subject to constraints that ensure cinematic plausibility. We demonstrate results of adapting a variety of source videos to small display sizes.
6983597
A Comparative Study of Parallel Prefix Adders in FPGA Implementation of EAC
Several regular parallel trees have been proposed over the years to optimize logic depth, area, fan-out and in- terconnect count for logic circuits. In this paper, we propose a comparative study of different parallel prefix trees used in the design of a new end-around carry (EAC) adder targeting FPGA technology. This new adder is based on the fast 128-bit binary floating-point EAC adder which has been implemented in the IBM POWER6 microprocessor's fused multiply-add unit. The parallel prefix tree implemented on the IBM's EAC adder is a Kogge-Stone tree which has been chosen for its high performance and its low power consumption. Our comparative study highlights the main performance differences among fourteen different architecture configurations when targeting an FPGA EAC adder design. We focus on the area requirements and the critical path delay of these designs. Our experimental results show that there is one architecture configuration with the lower area requirement and the higher performance.
15250571
Frequent Itemset Mining Based on Heuristic Two Level Counting
Recently, many enchanced Apriori algorithms have been proposed to efficiently generate all frequent itemsets from datasets in data mining field. Although efficient techniques were presented, those algorithms are either time-consuming or memory-consuming. To address the issue further, a new algorithm, which introduced a novel support counting method, heuristic two level counting, is proposed. HTLC method adopts an improved itemset generating technology in the generation process of low level itemsets, which promotes the production of low level frequent itemsets or candidate itemsets. It also applies a heuristic traversal technology which speeds up one pass over datasets and support counting technology which largely reduces the number of passes over datasets to the generation of high level frequent itemsets. Finally, the experimental results show that it outperforms existing Apriori-like algorithms in mostly datasets