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Abstract-Adaptive filtering schemes exhibit a compromise between convergence speed and steady-state mean square error. Convex combination approaches that provide meaningful performance have been recently developed for system identification. The purpose of this work is to apply the convex combination strategy to multichannel active noise control systems, taking into account the secondary path between the adaptive filter output and the error sensor and the eventual unavailability of the disturbance signal, which depends on the filtering scheme considered. Even though this strategy involves a computational burden higher than the classic adaptive filters, it exhibits optimum performance in term of convergence speed and steady-state mean square error.

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Abstract-Multichannel active noise control (ANC) systems are commonly based on adaptive signal processing algorithms that require high computational capacity, which constrains their practical implementation. Graphics Processing Units (GPUs) are well known for their potential for highly parallel data processing. Therefore, GPUs seem to be a suitable platform for multichannel scenarios. However, efficient use of parallel computation in the adaptive filtering context is not straightforward due to the feedback loops. This paper compares two GPU implementations of a multichannel feedforward local ANC system working as a real-time prototype. Both GPU implementations are based on the filtered-x Least Mean Square algorithms; one is based on the conventional filtered-x scheme and the other is based on the modified filtered-x scheme. Details regarding the parallelization of the algorithms are given. Finally, experimental results are presented to compare the performance of both multichannel ANC GPU implementations. The results show the usefulness of many-core devices for developing versatile, scalable, and low-cost multichannel ANC systems.

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ElsevierPayri González, F.; Broatch Jacobi, JA.; Serrano Cruz, JR.; Piqueras Cabrera, P. (2011). Experimental theoretical methodology for determination of inertial pressure drop distribution and pore structure properties in wall-flow diesel particulate filters (DPFs AbstractWall-flow particulate filters have been placed as a standard technology for Diesel engines because of the increasing restrictions to soot emissions. The inclusion of this system within the exhaust line requires the development of computational tools to properly simulate its flow dynamics and acoustics behaviour. These aspects become the key to understand the influence on engine performance and driveability as a function of the filter placement. Since the pressure drop and the filtration process are strongly depending on the pore structure properties -permeability, porosity and pore size -a reliable definition of these characteristics is essential for model development. In this work a methodology is proposed to determine such properties based on the combination of the pressure drop rement in a steady flow test rig and two theoretical approaches. The later are a lumped model and a one-dimensional unsteady compressible flow model. The purpose is to simplify the integration of particulate filters into the global engine modelling and development processes avoiding the need to resort to specific and expensive characterisation tests. The proposed methodology was validated against measurements of the response of an uncoated DPF under different flow conditions as cold steady flow, impulsive flow and hot pulsating flow.