Commonsense knowledge representation and reasoning with fuzzy neural networks

This paper highlights the theory of common-sense knowledge in terms of representation and reasoning. A connectionist model is proposed for common-sense knowledge representation and reasoning. A generic fuzzy neuron is employed as a basic element for the connectionist model. The representation and reasoning ability of the model is described through examples

Face image matching using fractal dimension

A new method is presented in this paper for calculating the correspondence between two face images on a pixel by pixel basis. The concept of fractal dimension is used to develop the proposed non-parametric area-based image matching method which achieves a higher proportion of matched pixels for face images than some well-known methods

Adaptive integral sliding mode control for active vibration absorber design

A new tuning method for active vibration absorber design is presented in this paper. A robust, adaptive control scheme based on a variable structure with an adaptive discontinuity surface is designed and simulated. Robust synthesis of an adaptive discontinuity surface based on an augmented state-space is discussed. The proposed tuning scheme has three superior features compared with the existing counterparts in that: (i) it is completely insensitive to changes in the stiffness and damping of the absorber, (ii) it is capable of suppressing cyclic vibrations over a wide range of frequencies, (iii) its real-time operation requires only one adjustable gain

Example-based shape from shading: 3D heads form 2D face images

Images taken of human faces vary in perspective depending on the view point of the observer. Few of the face recognition methods reported in the literature are capable of recognising faces under varying poses. To improve the ability of face recognition systems to recognise faces under varying poses, a novel method is proposed, that takes a face image of arbitrary pose and synthesises a standard frontal pose image of the same face

The influence of pseudo conductivities on the EEG forward computation for human head modelling

In brain imaging, the accuracy involved in calculating scalp potentials due to cerebral electric sources depends on the realism of the head model. Existing methods assume homogeneous conductivity throughout each component tissue. This assumption introduces inaccuracies in computing the potentials. This paper proposes a new approach based on the use of pseudo-conductivity values in place of the uniform conductivity values assigned to tissues. Simulation results reveal that the conductivity values have a significant effect on the computed potentials, thereby invalidating the uniform conductivity assumption

Multiresolution eigenface-components

This paper presents a face recognition system that imitates the multi-resolution processing technique employed by the human visual system. In the proposed system, a different degree of importance is assigned to each part of a face image, and each region of the face image is processed with a different resolution. This proposed system reduces the computational complexity of the eigenface method, and achieves higher compression ratios and higher recognition rates in comparison with the eigenface method. Experimental results are presented and discussed

A pseudo-conductivity inhomogeneous head model for computation of EEG

Human head models for the forward computation of EEG using FEM require a large set of elements to represent the head geometry accurately. Anatomically, the electrical property of each element is different, even though they may represent the same type of tissue (white matter, grey matter, etc.). Since it is impossible to obtain the electrical properties of the cranial tissues for every element in the head model, most algorithms which claim to deal with inhomogeneity can, in reality, only implement the computation for the homogeneous case. This paper presents a new numerical approach which can more precisely model the head by using a set of pseudo conductivity values for the computation of the inhomogeneous case. This set of values is extrapolated from the limited amount of real conductivity values available in the literature. Simulation studies, based on both this proposed approach and the homogeneous approach which utilises mean-valued conductivities, are performed. The studies reveal that the computation results for the potential distribution on the surface of the scalp, obtained using both approaches, are significantly different