36,468 research outputs found
Hybrid Neural Networks for Frequency Estimation of Unevenly Sampled Data
In this paper we present a hybrid system composed by a neural network based
estimator system and genetic algorithms. It uses an unsupervised Hebbian
nonlinear neural algorithm to extract the principal components which, in turn,
are used by the MUSIC frequency estimator algorithm to extract the frequencies.
We generalize this method to avoid an interpolation preprocessing step and to
improve the performance by using a new stop criterion to avoid overfitting.
Furthermore, genetic algorithms are used to optimize the neural net weight
initialization. The experimental results are obtained comparing our methodology
with the others known in literature on a Cepheid star light curve.Comment: 5 pages, to appear in the proceedings of IJCNN 99, IEEE Press, 199
From Correlation to Causation: Estimation of Effective Connectivity from Continuous Brain Signals based on Zero-Lag Covariance
Knowing brain connectivity is of great importance both in basic research and
for clinical applications. We are proposing a method to infer directed
connectivity from zero-lag covariances of neuronal activity recorded at
multiple sites. This allows us to identify causal relations that are reflected
in neuronal population activity. To derive our strategy, we assume a generic
linear model of interacting continuous variables, the components of which
represent the activity of local neuronal populations. The suggested method for
inferring connectivity from recorded signals exploits the fact that the
covariance matrix derived from the observed activity contains information about
the existence, the direction and the sign of connections. Assuming a sparsely
coupled network, we disambiguate the underlying causal structure via
-minimization. In general, this method is suited to infer effective
connectivity from resting state data of various types. We show that our method
is applicable over a broad range of structural parameters regarding network
size and connection probability of the network. We also explored parameters
affecting its activity dynamics, like the eigenvalue spectrum. Also, based on
the simulation of suitable Ornstein-Uhlenbeck processes to model BOLD dynamics,
we show that with our method it is possible to estimate directed connectivity
from zero-lag covariances derived from such signals. In this study, we consider
measurement noise and unobserved nodes as additional confounding factors.
Furthermore, we investigate the amount of data required for a reliable
estimate. Additionally, we apply the proposed method on a fMRI dataset. The
resulting network exhibits a tendency for close-by areas being connected as
well as inter-hemispheric connections between corresponding areas. Also, we
found that a large fraction of identified connections were inhibitory.Comment: 18 pages, 10 figure
Optimal Exploitation of the Sentinel-2 Spectral Capabilities for Crop Leaf Area Index Mapping
The continuously increasing demand of accurate quantitative high quality information on land surface properties will be faced by a new generation of environmental Earth observation (EO) missions. One current example, associated with a high potential to contribute to those demands, is the multi-spectral ESA Sentinel-2 (S2) system. The present study focuses on the evaluation of spectral information content needed for crop leaf area index (LAI) mapping in view of the future sensors. Data from a field campaign were used to determine the optimal spectral sampling from available S2 bands applying inversion of a radiative transfer model (PROSAIL) with look-up table (LUT) and artificial neural network (ANN) approaches. Overall LAI estimation performance of the proposed LUT approach (LUTNâ
â) was comparable in terms of retrieval performances with a tested and approved ANN method. Employing seven- and eight-band combinations, the LUTNâ
â approach obtained LAI RMSE of 0.53 and normalized LAI RMSE of 0.12, which was comparable to the results of the ANN. However, the LUTN50 method showed a higher robustness and insensitivity to different band settings. Most frequently selected wavebands were located in near infrared and red edge spectral regions. In conclusion, our results emphasize the potential benefits of the Sentinel-2 mission for agricultural applications
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