In this paper, we propose a noncircular-parallel factor (NC-PARAFAC) algorithm for two-dimensional direction of arrival (DOA) estimation of noncircular signals for acoustic vector-sensor array. The proposed algorithm enhances the angle estimation performance via utilizing the noncircularity of the signals, and it can be suitable for arbitrary array subjected to unknown locations and achieve aut...

The baseband Volterra–Parafac model is a useful tool to represent a nonlinear communication channel with a parametric complexity reduced with respect to the full Volterra model. In this paper we include additional symmetry properties of real power amplifier kernels in the equivalent baseband Volterra-Parafac approach in order to gain a further reduction in the number of parameters. To illustrat...

Nonnegative tensor factorization is an extension of nonnegative matrix factorization(NMF) to a multilinear case, where nonnegative constraints are imposed on the PARAFAC/Tucker model. In this paper, to identify speaker from a noisy environment, we propose a new method based on PARAFAC model called constrained Nonnegative Tensor Factorization (cNTF). Speech signal is encoded as a general higher ...

The ability to accurately quantitate trace pesticides and polycyclic aromatic hydrocarbons (PAHs) by excitation–emission matrix (EEM) fluorescence spectroscopy, when coupled with parallel factor analysis (PARAFAC) deconvolution of the EEM spectra, is demonstrated and discussed. Two EEM fluorometers were investigated. One fluorometer, using a cuvette cell sample holder, realized limits of detect...

Parallel factor analysis (PARAFAC, called also CP model)) is a tensor (multiway array) factorization method which allows to find hidden factors (component matrices) from a multidimensional data. Most of the existing algorithms for the PARAFAC, especially the alternating least squares (ALS) algorithm need to compute Khatri-Rao products of tall factors and multiplication of large-scale matrices a...

In MIMO communications systems, the data has a natural multidimensional structure composed of time, frequency and space dimensions. Recently, multidimensional techniques that take into account the data multidimensional structure have been proposed for model order selection, parameter estimation and prewhitening. These multidimensional techniques require an array with a PARAFAC structure. Howeve...

Retention mechanisms involved in supercritical fluid chromatography (SFC) are influenced by interdependent parameters (temperature, pressure, chemistry of the mobile phase, and nature of the stationary phase), a complexity which makes the selection of a proper stationary phase for a given separation a challenging step. For the first time in SFC studies, Parallel Factor Analysis (PARAFAC) was em...

Low dimensional and speaker-independent linear vocal tract parametrizations can be obtained using the 3-mode PARAFAC factor analysis procedure first introduced by Harshman et al. (1977). The following study used PARAFAC to investigate the stress distinction in German vowel production. Tongue movements of six German speakers were recorded by means of EMMA. The speech material consisted of the 15...

The PARAFAC method of factor analysis was used to investigate patterns of tongue shaping in a corpus of 15 German vowels spoken in 3 consonant contexts by 7 speakers at 2 speech rates, using data from electromagnetic articulography. A two-factor model was extracted, giving a succinct, speaker-independent characterization of the German vowel space and of some important coarticulatory effects on ...

—In this research, we address the problem of TwoDimensional (2D) angle estimation for monostatic MultipleInput Multiple-Output (MIMO) radar. An expanded PARAFAC model is proposed to make full use of the Vandermonde-like structure of the data model, which expands the received data through unitary transformation and links the problem of 2D angle estimation to the PARAFAC model. Unlike the tradit...