Results 1 - 10 of 45

Estimation of Parameters and Eigenmodes of Multivariate Autoregressive Models

by Arnold Neumaier, Tapio Schneider , 2001
"... Dynamical characteristics of a complex system can often be inferred from analyses of a stochastic time series model fitted to observations of the system. Oscillations in geophysical systems, for example, are sometimes characterized by principal oscillation patterns, eigenmodes of estimated autoregre ..."
Abstract - Cited by 103 (2 self) - Add to MetaCart
Dynamical characteristics of a complex system can often be inferred from analyses of a stochastic time series model fitted to observations of the system. Oscillations in geophysical systems, for example, are sometimes characterized by principal oscillation patterns, eigenmodes of estimated autoregressive (AR) models of first order. This paper describes the estimation of eigenmodes of AR models of arbitrary order. AR processes of any order can be decomposed into eigenmodes with characteristic oscillation periods, damping times, and excitations. Estimated eigenmodes and confidence intervals for the eigenmodes and their oscillation periods and damping times can be computed from estimated model parameters. As a computationally efficient method of estimating the parameters of AR models from high-dimensional data, a stepwise least squares algorithm is proposed. This algorithm computes model coefficients and evaluates criteria for the selection of the model order stepwise for AR models of successively decreasing order. Numerical simulations indicate that, with the least squares algorithm, the AR model coefficients and the eigenmodes derived from the coefficients are estimated reliably and that the approximate 95% confidence intervals for the coefficients and eigenmodes are rough approximations of the confidence intervals inferred from the simulations.

Directed cortical information flow during human object recognition: analyzing induced EEG gammaband responses in brain’s source space

by Alois Schlögl, Nelson Trujillo-barreto, Matthias M. Müller, Thomas Gruber - PLoS ONE , 2007
"... The increase of induced gamma-band responses (iGBRs; oscillations.30 Hz) elicited by familiar (meaningful) objects is well established in electroencephalogram (EEG) research. This frequency-specific change at distinct locations is thought to indicate the dynamic formation of local neuronal assemblie ..."
Abstract - Cited by 20 (1 self) - Add to MetaCart
The increase of induced gamma-band responses (iGBRs; oscillations.30 Hz) elicited by familiar (meaningful) objects is well established in electroencephalogram (EEG) research. This frequency-specific change at distinct locations is thought to indicate the dynamic formation of local neuronal assemblies during the activation of cortical object representations. As analytically power increase is just a property of a single location, phase-synchrony was introduced to investigate the formation of largescale networks between spatially distant brain sites. However, classical phase-synchrony reveals symmetric, pair-wise correlations and is not suited to uncover the directionality of interactions. Here, we investigated the neural mechanism of visual object processing by means of directional coupling analysis going beyond recording sites, but rather assessing the directionality of oscillatory interactions between brain areas directly. This study is the first to identify the directionality of oscillatory brain interactions in source space during human object recognition and suggests that familiar, but not unfamiliar, objects engage widespread reciprocal information flow. Directionality of cortical information-flow was calculated based upon an established Granger-Causality coupling-measure (partial-directed coherence; PDC) using autoregressive modeling. To enable comparison with previous coupling studies lacking directional information, phase-locking analysis was applied, using wavelet-based signal decompositions. Both, autoregressive modeling and wavelet analysis, revealed an augmentation of iGBRs
(Show Context)

Improving the Quality of IMU-Derived Doppler Estimates for Ultra-Tight

by Ravindra Babu, Jinling Wang - GPS/INS Integration. GNSS 2004 , 2004
"... ABSTRACT: GPS and INS, with their complementary benefits, can be integrated in 3 different ways: loose, tight and ultra-tight configurations. In the ultra-tight architecture, the Doppler derived from the navigation Kalman filter, which integrates the I (in-phase), Q (quadrature-phase) measurements f ..."
Abstract - Cited by 16 (7 self) - Add to MetaCart
ABSTRACT: GPS and INS, with their complementary benefits, can be integrated in 3 different ways: loose, tight and ultra-tight configurations. In the ultra-tight architecture, the Doppler derived from the navigation Kalman filter, which integrates the I (in-phase), Q (quadrature-phase) measurements from the GPS receiver with Position, Velocity, and Attitude from the INS, drives the receiver’s carrier NCO (Numerically Controlled Oscillator). This results in a significant reduction of the carrier loop bandwidth, providing accurate measurements, improving anti-jam properties and cycle-slip detection. The key to the successful implementation of this system depends on how well the Doppler is estimated. The inertial sensor errors, deterministic and stochastic, affect the accuracy of the IMU-derived Doppler estimates. The

Independent subspace analysis on innovations

by Barnabás Póczos, Bálint Takács, András Lőrincz - in: Proceedings of ECML , 2005
"... Abstract. Independent subspace analysis (ISA) that deals with multi-dimensional independent sources, is a generalization of independent com-ponent analysis (ICA). However, all known ISA algorithms may become ineffective when the sources possess temporal structure. The innovation process instead of t ..."
Abstract - Cited by 13 (6 self) - Add to MetaCart
Abstract. Independent subspace analysis (ISA) that deals with multi-dimensional independent sources, is a generalization of independent com-ponent analysis (ICA). However, all known ISA algorithms may become ineffective when the sources possess temporal structure. The innovation process instead of the original mixtures has been proposed to solve ICA problems with temporal dependencies. Here we show that this strategy can be applied to ISA as well. We demonstrate the idea on a mixture of 3D processes and also on a mixture of facial pictures used as two-dimensional deterministic sources. ISA on innovations was able to find the original subspaces, while plain ISA was not. 1
(Show Context)

Low-Variance Multitaper MFCC Features: a Case Study in Robust Speaker Verification

by Tomi Kinnunen, Rahim Saeidi, Filip Sedlák, Kong Aik Lee, Johan Sandberg, Maria Hansson-Sandsten, Haizhou Li , 2012
"... In speech and audio applications, short-term signal spectrum is often represented using mel-frequency cepstral coefficients (MFCCs) computed from a windowed discrete Fourier transform (DFT). Windowing reduces spectral leakage but variance of the spectrum estimate remains high. An elegant extension ..."
Abstract - Cited by 13 (3 self) - Add to MetaCart
In speech and audio applications, short-term signal spectrum is often represented using mel-frequency cepstral coefficients (MFCCs) computed from a windowed discrete Fourier transform (DFT). Windowing reduces spectral leakage but variance of the spectrum estimate remains high. An elegant extension to windowed DFT is the so-called multitaper method which uses multiple time-domain windows (tapers) with frequencydomain averaging. Multitapers have received little attention in speech processing even though they produce low-variance features. In this paper, we propose the multitaper method for MFCC extraction with a practical focus. We provide, firstly, detailed statistical analysis of MFCC bias and variance using autoregressive process simulations on the TIMIT corpus. For speaker verification experiments on the NIST 2002 and 2008 SRE corpora, we consider three Gaussian mixture model based classifiers with universal background model (GMM-UBM), support vector machine (GMM-SVM) and joint factor analysis (GMM-JFA). Multitapers improve MinDCF over the baseline windowed DFT by relative 20.4 % (GMM-SVM) and 13.7 % (GMM-JFA) on the interview-interview condition in NIST 2008. The GMM-JFA system further reduces MinDCF by 18.7 % on the telephone data. With these improvements and generally noncritical parameter selection, multitaper MFCCs are a viable candidate for replacing the conventional MFCCs.
(Show Context)

Inductive Process Modeling

by Pat Langley
"... Abstract. In this paper, we pose a novel research problem for machine learning that involves constructing a process model from continuous data. We claim that casting learned knowledge in terms of processes with associated equations is desirable for scientific and engineering domains, where such nota ..."
Abstract - Cited by 11 (3 self) - Add to MetaCart
Abstract. In this paper, we pose a novel research problem for machine learning that involves constructing a process model from continuous data. We claim that casting learned knowledge in terms of processes with associated equations is desirable for scientific and engineering domains, where such notations are commonly used. We also argue that existing induction methods are not well suited to this task, although some techniques hold partial solutions. In response, we describe an approach to learning process models from time-series data and illustrate its behavior in three domains. In closing, we describe open issues in process model induction and encourage other researchers to tackle this important problem.
(Show Context)

1 Classification of Energy Consumption in Buildings with Outlier Detection

by Xiaoli Li, Chris P. Bowers, Thorsten Schnier
"... Abstract—In this paper, we propose an intelligent data analysis method for modelling and prediction of daily electricity consumption in buildings. The objective is to enable a building management system to be used for forecasting and detection of abnormal energy use. First, an outlier detection meth ..."
Abstract - Cited by 10 (1 self) - Add to MetaCart
Abstract—In this paper, we propose an intelligent data analysis method for modelling and prediction of daily electricity consumption in buildings. The objective is to enable a building management system to be used for forecasting and detection of abnormal energy use. First, an outlier detection method is proposed to identify abnormally high or low energy use in building. Then a canonical variate analysis is employed to describe latent variables of daily electricity consumption profiles, which can be used to group the data sets into different clusters. Finally, a simple classifier is used to predict the daily electricity consumption profiles. A case study, based on a mixed use environment, was studied. The results demonstrate the method proposed in this paper can be used in conjunction with a building management system to identify abnormal utility consumption and notify building operators in real time.
(Show Context)

Fronto-parietal connection asymmetry regulates working memory distractibility

by Fredrik Edin , Torkel Klingberg , Tommy Stödberg , Jesper Tegnér - Journal of Integrative Neuroscience , 2007
"... Recent functional magnetic resonance imaging studies demonstrate that increased taskrelated neural activity in parietal and frontal cortex during development and training is positively correlated with improved visuospatial working memory (vsWM) performance. Yet, the analysis of the corresponding un ..."
Abstract - Cited by 4 (0 self) - Add to MetaCart
Recent functional magnetic resonance imaging studies demonstrate that increased taskrelated neural activity in parietal and frontal cortex during development and training is positively correlated with improved visuospatial working memory (vsWM) performance. Yet, the analysis of the corresponding underlying functional reorganization of the frontoparietal network has received little attention. Here, we perform an integrative experimental 567 568 Edin et al. and computational analysis to determine the effective balance between the superior frontal sulcus (SFS) and intraparietal sulcus (IPS) and their putative role(s) in protecting against distracters. To this end, we performed electroencephalographic (EEG) recordings during a vsWM task. We utilized a biophysically based computational cortical network model to analyze the effects of different neural changes in the underlying cortical networks on the directed transfer function (DTF) and spiking activity. Combining a DTF analysis of our EEG data with the DTF analysis of the computational model, a directed strong SFS → IPS network was revealed. Such a configuration offers protection against distracters, whereas the opposite is true for strong IPS → SFS connections. Our results therefore suggest that the previously demonstrated improvement of vsWM performance during development could be due to a shift in the control of the effective balance between the SFS-IPS networks.
(Show Context)

Temporal Aggregation Bias and Inference of Causal Regulatory Networks

by Stephen Bay, Lonnie Chrisman, Andrew Pohorille, Jeff Shrager
"... this paper, we show that temporal aggregation can bias algorithms for causal inference and lead them to discover spurious relations that would not be found if the signal were sampled at a faster rate. We discuss the effects of temporal aggregation on inference, the problems it creates, and potential ..."
Abstract - Cited by 3 (0 self) - Add to MetaCart
this paper, we show that temporal aggregation can bias algorithms for causal inference and lead them to discover spurious relations that would not be found if the signal were sampled at a faster rate. We discuss the effects of temporal aggregation on inference, the problems it creates, and potential directions for solutions

Discovering Ecosystem Models from Time-Series Data

by Dileep George, Kazumi Saito, Pat Langley, Stephen Bay - Proceedings of the Sixth International Conference on Discovery Science , 2003
"... Ecosystem models are used to interpret and predict the interactions of different species among themselves and with their environment. In this paper, we address the task of... ..."
Abstract - Cited by 3 (3 self) - Add to MetaCart
Ecosystem models are used to interpret and predict the interactions of different species among themselves and with their environment. In this paper, we address the task of...