Decomposition of large engineering system models is desirable since increased model size reduces reliability and speed of numerical solution algorithms. The article presents a methodology for optimal model-based decomposition (OMBD) of design problems, whether or not initially cast as optimization problems. The overall model is represented by a hypergraph and is optimally partitioned into weakl...

Computing the k dominant eigenvalues and eigenvectors of massive graphs is a key operation in numerous machine learning applications; however, popular solvers suffer from slow convergence, especially when k is reasonably large. In this paper, we propose and analyze a novel multi-scale spectral decomposition method (MSEIGS), which first clusters the graph into smaller clusters whose spectral dec...

This paper presents a voice conversion technique based on bilinear models and introduces the concept of contextual modeling. The bilinear approach reformulates the spectral envelope representation from line spectral frequencies feature to a two-factor parameterization corresponding to speaker identity and phonetic information, the so-called style and content factors. This decomposition offers a...

Let Ω be an open, simply connected, and bounded region in R, d ≥ 2, and assume its boundary ∂Ω is smooth. Consider solving an elliptic partial differential equation Lu = f over Ω with zero Dirichlet boundary values. The problem is converted to an equivalent elliptic problem over the unit ball B, and then a spectral method is given that uses a special polynomial basis. With sufficiently smooth p...

Single energy computed tomography (CT) scanners use measurements of densities to detect explosives in luggage. It is desirable to apply dual energy techniques to these CT scanners to obtain atomic number measurements to reduce false alarm rates. However, the direct application of existing dual energy techniques has practical problems, such as, approximation errors and lack of boundary constrain...

Given a positive definite matrix M and an integer Nm ≥ 1, Oja’s subspace algorithm will provide convergent estimates of the first Nm eigenvalues of M along with the corresponding eigenvectors. It is a common approach to principal component analysis. This paper introduces a normalized stochastic-approximation implementation of Oja’s subspace algorithm, as well as new applications to the spectral...

This paper presents a new wide-band speech coding system based on a fast wavelet packet transform algorithm as well as a formulation of temporal and spectral psychoacoustic models of masking. The proposed FFT-like overlapped block orthogonal transform allows us to approximate the auditory critical band decomposition in an e cient manner, which is a major advantage over previous approaches that ...

In this paper, we propose a speech enhancement method based on spectral magnitude estimation. We modify the noise estimation from the minimum statistics method and combine with a maximum a posterior (MAP) decomposition, using the Rice-conditional probability and a non-Gaussian statistic model of the speech. We derive two versions of magnitude decomposition and magnitude-phase decomposition and ...

This paper addresses the problem of tracking the covariance matrix eigenstructure, based on SVD (Singular Value Decomposition) updating, of a time-varying data matrix formed from received vectors. This problem occurs frequently in signal processing applications such as adaptive beamforming, direction nding, spectral estimation, etc. As this problem needs to be solved in real time, it is natural...

General Comment: This study pursues to outline the evaluation methods applied during the three phases of the AQMEII activity in view of building an evaluation strategy. Annual simulations of the WRF-CMAQ and IFSChimere are evaluated, as well as runs with perturbed emissions or boundary conditions or ozone deposition. The analysis focuses on ozone simulations over selected European and North Ame...