643 research outputs found
Overlearning in marginal distribution-based ICA: analysis and solutions
The present paper is written as a word of caution, with users of
independent component analysis (ICA) in mind, to overlearning
phenomena that are often observed.\\
We consider two types of overlearning, typical to high-order
statistics based ICA. These algorithms can be seen to maximise the
negentropy of the source estimates. The first kind of overlearning
results in the generation of spike-like signals, if there are not
enough samples in the data or there is a considerable amount of
noise present. It is argued that, if the data has power spectrum
characterised by curve, we face a more severe problem, which
cannot be solved inside the strict ICA model. This overlearning is
better characterised by bumps instead of spikes. Both overlearning
types are demonstrated in the case of artificial signals as well as
magnetoencephalograms (MEG). Several methods are suggested to
circumvent both types, either by making the estimation of the ICA
model more robust or by including further modelling of the data
Convolutive Blind Source Separation Methods
In this chapter, we provide an overview of existing algorithms for blind source separation of convolutive audio mixtures. We provide a taxonomy, wherein many of the existing algorithms can be organized, and we present published results from those algorithms that have been applied to real-world audio separation tasks
Sunyaev-Zel'dovich clusters reconstruction in multiband bolometer camera surveys
We present a new method for the reconstruction of Sunyaev-Zel'dovich (SZ)
galaxy clusters in future SZ-survey experiments using multiband bolometer
cameras such as Olimpo, APEX, or Planck. Our goal is to optimise SZ-Cluster
extraction from our observed noisy maps. We wish to emphasize that none of the
algorithms used in the detection chain is tuned on prior knowledge on the SZ
-Cluster signal, or other astrophysical sources (Optical Spectrum, Noise
Covariance Matrix, or covariance of SZ Cluster wavelet coefficients). First, a
blind separation of the different astrophysical components which contribute to
the observations is conducted using an Independent Component Analysis (ICA)
method. Then, a recent non linear filtering technique in the wavelet domain,
based on multiscale entropy and the False Discovery Rate (FDR) method, is used
to detect and reconstruct the galaxy clusters. Finally, we use the Source
Extractor software to identify the detected clusters. The proposed method was
applied on realistic simulations of observations. As for global detection
efficiency, this new method is impressive as it provides comparable results to
Pierpaoli et al. method being however a blind algorithm. Preprint with full
resolution figures is available at the URL:
w10-dapnia.saclay.cea.fr/Phocea/Vie_des_labos/Ast/ast_visu.php?id_ast=728Comment: Submitted to A&A. 32 Pages, text onl
Ecosystem Monitoring and Port Surveillance Systems
International audienceIn this project, we should build up a novel system able to perform a sustainable and long term monitoring coastal marine ecosystems and enhance port surveillance capability. The outcomes will be based on the analysis, classification and the fusion of a variety of heterogeneous data collected using different sensors (hydrophones, sonars, various camera types, etc). This manuscript introduces the identified approaches and the system structure. In addition, it focuses on developed techniques and concepts to deal with several problems related to our project. The new system will address the shortcomings of traditional approaches based on measuring environmental parameters which are expensive and fail to provide adequate large-scale monitoring. More efficient monitoring will also enable improved analysis of climate change, and provide knowledge informing the civil authority's economic relationship with its coastal marine ecosystems
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