Estimating functions for blind separation when sources have variance dependencies

A blind separation problem where the sources are not independent, but have variance dependencies is discussed. For this scenario Hyvärinen and Hurri (2004) proposed an algorithm which requires no assumption on distributions of sources and no parametric model of dependencies between components. In this paper, we extend the semiparametric approach of Amari and Cardoso (1997) to variance dependencies and study estimating functions for blind separation of such dependent sources. In particular, we show that many ICA algorithms are applicable to the variance-dependent model as well under mild conditions, although they should in principle not. Our results indicate that separation can be done based only on normalized sources which are adjusted to have stationary variances and is not affected by the dependent activity levels. We also study the asymptotic distribution of the quasi maximum likelihood method and the stability of the natural gradient learning in detail. Simulation results of artificial and realistic examples match well with our theoretical findings

Estimating Functions for Blind Separation When Sources Have Variance-Dependencies

The blind separation problem where the sources are not independent, but have variance-dependencies is discussed. Hyvärinen and Hurri[1] proposed an algorithm which requires no assumption on distributions of sources and no parametric model of dependencies between components. In this paper, we extend the semiparametric statistical approach of Amari and Cardoso[2] under variance-dependencies and study estimating functions for blind separation of such dependent sources. In particular, we show that many of ICA algorithms are applicable to the variance-dependent model as well. Our theoretical consequences were confirmed by artificial and realistic examples

Detection and analysis of human respiration using microwave Doppler radar

 Non-contact detection characteristic of Doppler radar provides an unobtrusive means of respiration detection and monitoring. This avoids additional preparations such as physical sensor attachment or special clothing. Furthermore, robustness of Doppler radar against environmental factors reduce environmental constraints and strengthens the possibility of employing Doppler radar as a practical biomedical devices in the future particularly in long term monitoring applications such as in sleep studies

Detection and analysis of human respiration using microwave Doppler radar

 Non-contact detection characteristic of Doppler radar provides an unobtrusive means of respiration detection and monitoring. This avoids additional preparations such as physical sensor attachment or special clothing. Furthermore, robustness of Doppler radar against environmental factors reduce environmental constraints and strengthens the possibility of employing Doppler radar as a practical biomedical devices in the future particularly in long term monitoring applications such as in sleep studies