Procedural Synthesis using Vortex Particle Method for Fluid Simulation

We propose a fast and effective technique to improve sub‐grid visual details of the grid based fluid simulation. Our method procedurally synthesizes the flow fields coming from the incompressible Navier‐Stokes solver and the vorticity fields generated by vortex particle method for sub‐grid turbulence. We are able to efficiently animate smoke which is highly turbulent and swirling with small scale details. Since this technique does not solve the linear system in high‐resolution grids, it can perform fluid simulation more rapidly. We can easily estimate the influence of turbulent and swirling effect to the fluid flow.     

The Relationship Between R&D Investment and Firm Profitability Under a Three-Stage Sigmoid Curve Model: Evidence From an Emerging Economy

The relationship between investment in research and development (R&D) and firm performance has been the subject of numerous academic investigations, but the findings of these investigations have varied greatly, with research revealing a number of different patterns in the R&D–performance relationship. This inconsistency may be partly attributable to the failure of the commonly used linear modeling method to capture the full dynamics of the R&D–performance relationship. Based on the sigmoid (S) curve paradigm, as well as on other economic foundations, this study proposes the use of a three-stage S-curve model to help reconcile the disparities in the literature. This S-curve model shows that the relationship between R&D intensity and firm profitability is nonlinear, with the slope negative at low levels (stage 1), positive at medium levels (stage 2), and negative again at high levels of R&D investment (stage 3). Empirical evidence from a sample of 377 publicly listed Taiwanese high-tech manufacturing firms and 179 nonhigh-tech firms, examined during the period between 2000 and 2007, confirmed our proposed model. This study not only establishes a relationship pattern that differs from that shown in past studies, but also has important managerial implications for R&D managers and important policy implications for governments.      

Detection of OFDM Signals in Fast-Varying Channels With Low-Density Pilot Symbols

In fast-varying channels, an orthogonal frequency-division multiplexing system needs to insert denser pilot symbols among transmitted symbols in tracking the variation of a channel. However, using denser pilot symbols reduces transmission throughput. In this paper, we propose a pseudopilot algorithm for data detection in fast-varying channels without increasing the pilot density. Our algorithm is based on a regressional model-based least-squares-fitting approach. Within a block of received symbols, we select some data symbols and regard them as pseudopilot symbols. The receiver considers all the possible patterns of the pseudopilots and associates each of them with a data sequence and a corresponding metric. The associated data sequence, whose metric is minimum, is selected as the detected data sequence. Our algorithm is not based on a decision-directed or decision-feedback architecture because the pseudopilots do not come from any detected symbols. The proposed algorithm needs to search all the possible patterns of the pseudopilots, and the complexity may increase with the number of pseudopilots and constellation size. To reduce the number of search, we further propose two modified approaches. The simulation results show that the performance of the proposed algorithms could approach a bit-error probability lower bound that is obtained by letting the receiver know the true values of the pseudopilots. Compared with the linear interpolation method, the proposed algorithm shows obvious improvement in fast-varying channels. The proposed modified approaches could also effectively reduce the number of search while maintaining the performance. We also give the complexity analysis of the proposed algorithm and an approach to determine the degree of the regression polynomial.