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In optical interferometers, fringe projection systems, and synthetic aperture radars, fringe patterns are common outcomes and usually degraded by unavoidable noises. The presence of noises makes the phase extraction and phase unwrapping challenging. Windowed Fourier transform (WFT) based algorithms have been proven to be effective for fringe pattern analysis to various applications. However, the WFT-based algorithms are computationally expensive, prohibiting them from real-time applications. In this paper, we propose a fast parallel WFT-based library using graphics processing units and computer unified device architecture. Real-time WFT-based algorithms are achieved with 4 frames per second in processing 256x256 fringe patterns. Up to 132x speedup is obtained for WFT-based algorithms using NVIDIA GTX295 graphics card than sequential C in quad-core 2.5GHz Intel(R)Xeon(R) CPU E5420.

We propose a projection algorithm for solving split feasibility problems involving paramonotone equilibria and convex optimization. The proposed algorithm can be considered as a combination of the projection ones for equilibrium and convex optimization problems. We apply the algorithm for finding an equilibrium point with minimal environmental cost for a model in electricity production. Numerical results for the model are reported.

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A B S T R A C TThe work proposed in this paper is a possible way of modelling some local observations at the surface of mild steel specimens submitted to uniaxial and multiaxial loads. It is clearly seen that local plasticity, controlled by local microstructural heterogeneities, plays a fundamental role in microcrack nucleation and damage orientation is closely related to the applied loading mode. The framework of irreversible thermodynamics with internal variables for time-independent, isothermal and small deformations has been used to build a critical plane damage model by assuming the existence of a link between mesoplasticity and mesodamage. Non-associated plasticity and damage rules allow the evolution of some plastic slip before any damage nucleation, as seen during the observations. A key feature of this proposal is the capacity to reflect nonlinear damage accumulation under variable amplitude loading.Keywords damage model; high cycle fatigue; meso-plasticity; multiaxial fatigue; sequence effect. N O M E N C L A T U R Ea = normal stress sensitivity coefficient for the damage threshold b = normal stress sensitivity coefficient governing the damage growth b = thermodynamic force relative to kinematic local hardening mechanisms c = kinematic hardening parameter d = damage effect variable d c = 'critical' value of the damage effect variable d f (τ , b,τ ) = plastic shear yield function at the mesoscopic scale g = isotropic hardening parameter h(F d , k; σ n ) = damage loading function k = conjugate force corresponding to the accumulated damage β k 0 = initial damage threshold m = unit vector defining the direction of the glide system n = unit vector defining the normal to the glide plane p = accumulated plastic strain q = damage kinetic parameter s = damage sensitivity parameter F d = thermodynamic force associated to the damage variable d G = elastic modulus H(F d , k; σ n ) = damage dissipation potential T = macroscopic resolved shear stress acting along a slip system β = accumulated damage variable ε p = mesoscopic plastic strain tensor γ e = elastic mesoscopic shear strain γ p = plastic mesoscopic shear strain μ = macroscopic shear modulus (Lame coefficient)

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The problem of finite-time H ∞ control for uncertain fractional-order neural networks is investigated in this paper. Using finite-time stability theory and the Lyapunov-like function method, we first derive a new condition for problem of finite-time stabilization of the considered fractional-order neural networks via linear matrix inequalities (LMIs). Then a new sufficient stabilization condition is proposed to ensure that the resulting closed-loop system is not only finite-time bounded but also satisfies finite-time H ∞ performance. Three examples with simulations have been given to demonstrate the validity and correctness of the proposed methods. Keywords Fractional order neural networks • Finite-time boundedness • H ∞ control problem • Linear matrix inequalities

PurposeThis paper aims to analyze the impacts of institutional quality on trade flows of NAFTA with a panel data set of 105 countries spanning the period 2006–2017.Design/methodology/approachWe applied the system generalized method of moment (GMM) estimator to investigate the impacts.FindingsThe results show that institutional quality is a positive and significant determinant of international trade flows of the NAFTA bloc and its trading partners. Our results also indicate that the impact of institutional quality depends on the level of economic development of NAFTA's trading partners. Specifically, the trade elasticity of institutional quality is the highest for NAFTA’s trade with middle-income countries and the lowest for NAFTA's trade with low-income countries. In the long run, the trade elasticity of institutional quality increased significantly, with the highest increase in the case of NAFTA's trade with medium-income countries and the lowest increase in the case of NAFTA's trade with low-income countries.Originality/valueThis study contributes to the existing literature in three different ways. First, we examine the differential impact of institutions on NAFTA's trade according to the level of economic development of NAFTA's trading partners. Second, we compare the differential trade elasticity of institutional quality in the long run. Finally, we support our findings through an improved research methodology by using the system GMM estimation. This method allows us to overcome the potential sample bias, omitted variable problems and endogeneity of explanatory variables.