Noriaki Suetake

Maximum entropy ICA constrained by individual entropy maximization employing self-organizing maps

We propose new method of the independent component analysis (ICA), which doesnt require to know the classes of the distribution of the sources beforehand in contrast with conventional methods and achieves separation of sources with higher precision than the conventional methods. The proposed method employs the self-organizing maps (SOM) to the Bell-Sejnowskis method (1995) for the purpose of making use of the ability of SOM to approximate the probability density. SOM grasps the probability density of the input signals by nature. It can also track the changes of the probability density of the input signal adaptively. In this paper, the effectiveness and validity of the proposed method are verified by applying it to the separation of linearly mixed sounds and linearly mixed pictures by the computer simulations.

Neo fuzzy neuron filter aiming at reduction of a Gaussian-impulsive noise

We propose novel FIR-OS hybrid type filter employing the neo fuzzy neuron, and frameworks of a linear FIR filter and an order statistic (OS) filter, aiming at elimination of a Gaussian noise and an impulsive noise at the same time, and high restoration of the signal, simultaneously. The proposed filter is synthesized by learning method which guarantees optimal design caused by employing the neo fuzzy neuron (NFN) model. In this paper, the effectiveness and validity of the proposed filter are verified by applying it to the filtering of the noisy images.