Reza Zolfaghari

Large Deformation Diffeomorphic Metric Mapping and Fast-Multipole Boundary Element Method provide new insights for Binaural acoustics

This paper describes how Large Deformation Diffeomorphic Metric Mapping (LDDMM) can be coupled with a Fast Multipole (FM) Boundary Element Method (BEM) to investigate the relationship between morphological changes in the head, torso, and outer ears and their acoustic filtering (described by Head Related Transfer Functions, HRTFs). The LDDMM technique provides the ability to study and implement morphological changes in ear, head and torso shapes. The FM-BEM technique provides numerical simulations of the acoustic properties of an individuals head, torso, and outer ears. This paper describes the first application of LDDMM to the study of the relationship between a listeners morphology and a listeners HRTFs. To demonstrate some of the new capabilities provided by the coupling of these powerful tools, we morph the shape of a listeners ear, while keeping the torso and head shape essentially constant, and show changes in the acoustics. We validate the methodological framework by mapping the complete morphology of one listener to a target listener and obtaining the target listeners HRTFs. This work utilizes the data provided by the Sydney York Morphological and Acoustic Recordings of Ears (SYMARE) database.

Spectral analysis techniques for estimating power quality indices

This paper presents the theory, design and implementation of a real time virtual instrument to measure the power quality indices such as power factor, voltage harmonic distortion and current harmonic distortion as given by the IEEE standard. Windowing techniques offer an adjustable mechanism to detect low or high power harmonics as they have different leakage characteristics. Cross spectral density is a simple tool for finding phase angle differences among different voltage and current phases. The virtual instrument presented in this document integrates digital signal processing (DSP) methods such as windowing techniques, Welch power spectral density and cross-spectral density estimation to calculate voltage and current phasors. Simulation and experimental results under different waveforms are presented to show the accuracy and robustness of the virtual instrument. The real time experiment is carried out using a rectified AC waveform applied across an RL circuit.

A multiscale LDDMM template algorithm for studying ear shape variations

This paper describes a method to establish an average human ear shape across a population of ears by sequentially applying the Large Deformation Diffeomorphic Metric Mapping (LDDMM) framework at successively smaller physical scales. Determining such a population average ear shape, also referred to here as a template ear, is an essential step in studying the statistics of ear shapes because it allows the variations in ears to be studied relative to a common template shape. Our interest in the statistics of ear shapes stems from our desire to understand the relationship between ear morphology and the head-related impulse response (HRIR) filters that are essential for rendering 3D audio over headphones. The shape of the ear varies among listeners and is as individualized as a fingerprint. Because the acoustic filtering properties of the ears depend on their shape, the HRIR filters required for rendering 3D audio are also individualized. The contribution of this work is the demonstration of a sequential multiscale approach to creating a population template ear shape using the LDDMM framework. In particular we apply our sequential multiscale algorithm to a small population of synthetic ears in order to analyse its performance given a known reference ear shape.

Spectral analysis techniques with Kalman filtering for estimating power quality indices

This paper presents the theory, design and implementation of two real time virtual instruments to measure the power quality indices such as current harmonic distortion, voltage harmonic distortion and power factor as given by the IEEE standard. Different windowing techniques are explored in the estimation of the spectra. Cross spectral analysis is used for the measurement of phase angles in voltage and current signal. The virtual instrument integrates digital signal processing (DSP) methods such as windowing techniques and Cross-spectral density estimation with Welch spectral estimation to calculate voltage and current phasors. Moreover a tunable Kalman filter with a forgetting factor is applied to further enhance the results due to noise in the measurement. Simulations followed by a real time experiment using a rectified AC waveform applied across an RL circuit is followed to support the theoretical claims and further to show the robustness of the virtual instruments. Finally the two instruments are compared with each other in terms of performance and speed.

Using windowed ESPRIT spectral estimation for measuring power quality indices

This paper presents the theory, design and implementation of a virtual instrument based on a new method to measure the power quality indices such as current harmonic distortion, voltage harmonic distortion and power factor as given by the IEEE standard. A modern spectral estimation technique (ESPRIT) is used to estimate the power quality indices. Simulation results followed by experimental results of the real time virtual instrument is presented to show the advantages and accuracy of using this method and further to show conformance of this device to well known standards such as IEEE and IEC.

Generating a morphable model of ears

This paper describes the generation of a morphable model for external ear shapes. The aim for the morphable model is to characterize an ear shape using only a few parameters in order to assist the study of morphoacoustics. The model is derived from a statistical analysis of a population of 58 ears from the SYMARE database. It is based upon the framework of large deformation diffeomorphic metric mapping (LDDMM) and the vector space that is constructed over the space of initial momentums describing the diffeomorphic transformations. To develop a morphable model using the LDDMM framework, the initial momentums are analyzed using a kernel based principal component analysis. In this paper, we examine the ability of our morphable model to construct test ear shapes not included in the principal component analysis.

Kernel principal component analysis of the ear morphology

This paper describes features in the ear shape that change across a population of ears and explores the corresponding changes in ear acoustics. The statistical analysis conducted over the space of ear shapes uses a kernel principal component analysis (KPCA). Further, it utilizes the framework of large deformation diffeomorphic metric mapping and the vector space that is constructed over the space of initial momentums, which describes the diffeomorphic transformations from the reference template ear shape. The population of ear shapes examined by the KPCA are 124 left and right ear shapes from the SYMARE database that were rigidly aligned to the template (population average) ear. In the work presented here we show the morphological variations captured by the first two kernel principal components, and also show the acoustic transfer functions of the ears which are computed using fast multipole boundary element method simulations.