Formant estimation of speech signals using subspace-based spectral analysis

The objective of this paper is to propose a signal processing scheme that employs subspace-based spectral analysis for the purpose of formant estimation of speech signals. Specifically, the scheme is based on decimative spectral estimation that uses Eigenanalysis and SVD (Singular Value Decomposition). The underlying model assumes a decomposition of the processed signal into complex damped sinusoids. In the case of formant tracking, the algorithm is applied on a small amount of the autocorrelation coefficients of a speech frame. The proposed scheme is evaluated on both artificial and real speech utterances from the TIMIT database. For the first case, comparative results to standard methods are provided which indicate that the proposed methodology successfully estimates formant trajectories.

The interaction of riblets with wall-bounded turbulence

The purpose of this thesis is to give answer to the question: why do riblets stop working for a certain size? Riblets are small surface grooves aligned in the mean direction of an overlying turbulent flow, designed specifically to reduce the friction between the flow and the surface. They were inspired by biological surfaces, like the oriented denticles in the skin of fastswimming sharks, and were the focus of a significant amount of research in the late eighties and nineties. Although it was found that the drag reduction depends on the riblet size scaled in wall units, the physical mechanisms implicated have not been completely understood up to now. It has been explained how riblets of vanishing size interact with the turbulent flow, producing a change in the drag proportional to their size, but that is not the regime of practical interest. The optimum performance is achieved for larger sizes, once that linear behavior has broken down, but before riblets begin adopting the character of regular roughness and increasing drag. This regime, which is the most relevant from a technological perspective, was precisely the less understood, so we have focused on it. Our efforts have followed three basic directions. First, we have re-assessed the available experimental data, seeking to identify common characteristics in the optimum regime across the different existing riblet geometries. This study has led to the proposal of a new length scale, the square root of the groove crosssection, to substitute the traditional peak-to-peak spacing. Scaling the riblet dimension with this length, the size of breakdown of the linear behavior becomes roughly universal. This suggests that the onset of the breakdown is related to a certain, fixed value of the cross-section of the groove. Second, we have conducted a set of direct numerical simulations of the turbulent flow over riblets, for sizes spanning the full drag reduction range. We have thus been able to reproduce the gradual transition between the different regimes. The spectral analysis of the flows has proven particularly fruitful, since it has made possible to identify spanwise rollers immediately above the riblets, which begin to appear when the riblet size is close to the optimum. This is a quite surprising feature of the flow, not because of the uniqueness of the phenomenon, which had been reported before for other types of complex and porous surfaces, but because most previous studies had focused on the detail of the flow above each riblet as a unit. Our novel approach has provided the adequate tools to capture coherent structures with an extended spanwise support, which interact with the riblets not individually, but collectively. We have also proven that those spanwise structures are responsible for the increase in drag past the viscous breakdown. Finally, we have analyzed the stability of the flow with a simplified model that connects the appearance of rollers to a Kelvin–Helmholtz-like instability, as is the case also for the flow over plant canopies and porous surfaces. In spite of the model emulating the presence of riblets only in an averaged, general fashion, it succeeds to capture the essential attributes of the breakdown, and provides a theoretical justification for the scaling with the groove cross-section.

Analysis of defect-related localized emission processes in InGaN/GaN-based LEDs

This paper reports an extensive analysis of the defect-related localized emission processes occurring in InGaN/GaN-based light-emitting diodes (LEDs) at low reverse- and forward-bias conditions. The analysis is based on combined electrical characterization and spectrally and spatially resolved electroluminescence (EL) measurements. Results of this analysis show that: (i) under reverse bias, LEDs can emit a weak luminescence signal, which is directly proportional to the injected reverse current. Reverse-bias emission is localized in submicrometer-size spots; the intensity of the signal is strongly correlated to the threading dislocation (TD) density, since TDs are preferential paths for leakage current conduction. (ii) Under low forward-bias conditions, the intensity of the EL signal is not uniform over the device area. Spectrally resolved EL analysis of green LEDs identifies the presence of localized spots emitting at 600 nm (i.e., in the yellow spectral region), whose origin is ascribed to localized tunneling occurring between the quantum wells and the barrier layers of the diodes, with subsequent defect-assisted radiative recombination. The role of defects in determining yellow luminescence is confirmed by the high activation energy of the thermal quenching of yellow emission (Ea =0.64&eV). © 2012 IEEE.

Modelling and Fragility Analysis of Non-Ductile Reinforced Concrete Buildings in Low-to-Moderate Seismic Zones

Reinforced concrete buildings in low-to-moderate seismic zones are often designed only for gravity loads in accordance with the non-seismic detailing provisions. Deficient detailing of columns and beam-column joints can lead to unpredictable brittle failures even under moderate earthquakes. Therefore, a reliable estimate of structural response is required for the seismic evaluation of these structures. For this purpose, analytical models for both interior and exterior slab-beam-column subassemblages and for a 1/3 scale model frame were implemented into the nonlinear finite element platform OpenSees. Comparison between the analytical results and experimental data available in the literature is carried out using nonlinear pushover analyses and nonlinear time history analysis for the subassemblages and the model frame, respectively. Furthermore, the seismic fragility assessment of reinforced concrete buildings is performed on a set of non-ductile frames using nonlinear time history analyses. The fragility curves, which are developed for various damage states for the maximum interstory drift ratio are characterized in terms of peak ground acceleration and spectral acceleration using a suite of ground motions representative of the seismic hazard in the region.

On the relation of slow feature analysis and Laplacian eigenmaps

The past decade has seen a rise of interest in Laplacian eigenmaps (LEMs) for nonlinear dimensionality reduction. LEMs have been used in spectral clustering, in semisupervised learning, and for providing efficient state representations for reinforcement learning. Here, we show that LEMs are closely related to slow feature analysis (SFA), a biologically inspired, unsupervised learning algorithm originally designed for learning invariant visual representations. We show that SFA can be interpreted as a function approximation of LEMs, where the topological neighborhoods required for LEMs are implicitly defined by the temporal structure of the data. Based on this relation, we propose a generalization of SFA to arbitrary neighborhood relations and demonstrate its applicability for spectral clustering. Finally, we review previous work with the goal of providing a unifying view on SFA and LEMs. © 2011 Massachusetts Institute of Technology.

Randomized nonlinear component analysis

Copyright © (2014) by the International Machine Learning Society (IMLS) All rights reserved. Classical methods such as Principal Component Analysis (PCA) and Canonical Correlation Analysis (CCA) are ubiquitous in statistics. However, these techniques are only able to reveal linear re-lationships in data. Although nonlinear variants of PCA and CCA have been proposed, these are computationally prohibitive in the large scale. In a separate strand of recent research, randomized methods have been proposed to construct features that help reveal nonlinear patterns in data. For basic tasks such as regression or classification, random features exhibit little or no loss in performance, while achieving drastic savings in computational requirements. In this paper we leverage randomness to design scalable new variants of nonlinear PCA and CCA; our ideas extend to key multivariate analysis tools such as spectral clustering or LDA. We demonstrate our algorithms through experiments on real- world data, on which we compare against the state-of-the-art. A simple R implementation of the presented algorithms is provided.