Modelling and analysis of an open-loop induction motor drive incorporating the effect of inverter dead-time

The objective of this paper is to study the influence of inverter dead-time on steady as well as dynamic operation of an open-loop induction motor drive fed from a voltage source inverter (VSI). Towards this goal, this paper presents a systematic derivation of a dynamic model for an inverter-fed induction motor, incorporating the effect of inverter dead-time, in the synchronously revolving dq reference frame. Simulation results based on this dynamic model bring out the impact of inverter dead-time on both the transient response and steady-state operation of the motor drive. For the purpose of steady-state analysis, the dynamic model of the motor drive is used to derive a steady-state model, which is found to be non-linear. The steady-state model shows that the impact of dead-time can be seen as an additional resistance in the stator circuit, whose value depends on the stator current. Towards precise evaluation of this dead-time equivalent resistance, an analytical expression is proposed for the same in terms of inverter dead-time, switching frequency, modulation index and load impedance. The notion of dead-time equivalent resistance is shown to simplify the solution of the non-linear steady-state model. The analytically evaluated steady-state solutions are validated through numerical simulations and experiments.

FAST AND ACCURATE BILATERAL FILTERING USING GAUSS-POLYNOMIAL DECOMPOSITION

The bilateral filter is a versatile non-linear filter that has found diverse applications in image processing, computer vision, computer graphics, and computational photography. A common form of the filter is the Gaussian bilateral filter in which both the spatial and range kernels are Gaussian. A direct implementation of this filter requires O(sigma(2)) operations per pixel, where sigma is the standard deviation of the spatial Gaussian. In this paper, we propose an accurate approximation algorithm that can cut down the computational complexity to O(1) per pixel for any arbitrary sigma (constant-time implementation). This is based on the observation that the range kernel operates via the translations of a fixed Gaussian over the range space, and that these translated Gaussians can be accurately approximated using the so-called Gauss-polynomials. The overall algorithm emerging from this approximation involves a series of spatial Gaussian filtering, which can be efficiently implemented (in parallel) using separability and recursion. We present some preliminary results to demonstrate that the proposed algorithm compares favorably with some of the existing fast algorithms in terms of speed and accuracy.