A common type of integral to solve numerically in computational room acoustics and other applications is the diffraction integral. Various formulations are encountered but they are usually of the Fourier-type, which means an oscillating integrand which becomes increasingly expensive to compute for increasing frequencies. Classical asympotic solution methods, such as the stationary-phase method,...

In this paper, a Proportional-Derivative and Integral (PD-I) type Fuzzy-Neural Network Controller (FNNC) based on Sugeno fuzzy model is proposed for brushless DC drives to achieve satisfied performance under steady state and transient conditions. The proposed FNNC uses the speed error, change of error and the error integral as inputs. While the PD-FNNC is activated in transient states, the PI-F...

Given two families X and Y of integral polytopes with nice combinatorial and algebraic properties, a natural way to generate new class of polytopes is to take the intersection P = P1 ∩ P2, where P1 ∈ X, P2 ∈ Y . Two basic questions then arise: 1) when P is integral and 2) whether P inherits the “old type” from P1,P2 or has a “new type”, that is, whether P is unimodularly equivalent to some poly...

Professional scientists and engineers frequently analyze problems with heuristic techniques to get crude solutions quickly without worrying about the details of mathematical justification and firm error estimates. The method of steepest descent is one such technique that is widely used to evaluate functions represented by complex integrals. Routine application of the method however requires the...

In this paper, we introduce Path Integral Networks (PI-Net), a recurrent network representation of the Path Integral optimal control algorithm. The network includes both system dynamics and cost models, used for optimal control based planning. PI-Net is fully differentiable, learning both dynamics and cost models end-to-end by back-propagation and stochastic gradient descent. Because of this, P...

We match shapes, even under severe deformations, via a smooth re-parametrization of their integral invariant signatures. These robust signatures and correspondences are the foundation of a shape energy functional for variational image segmentation. Integral invariant shape templates do not require registration and allow for significant deformations of the contour, such as the articulation of th...

A connection between the maximum a posteriori (MAP) estimation and the variational formulation based on the minimization of a given variational integral subject to some noise constraints is established in this paper. A MAP estimator which uses a Markov or a maximum entropy random field model for the prior distribution can be viewed as a minimizer of a variational problem. Inspired by the maximu...

We show the convergence of an online stochastic gradient descent estimator to obtain drift parameter a continuous-time jump-diffusion process. The follows path in direction function find minimum, which our case determines estimate unknown parameter. decompose deviation from deterministic into four terms: weak solution non-local Poisson equation, Riemann integral, and covariation term. This deco...