A first attempt to incorporate Fuzzy Cognitive Maps (FCMs), in pattern classification applications is performed in this paper. Fuzzy Cognitive Maps, as an illustrative causative representation of modeling and manipulation of complex systems, can be used to model the behavior of any system. By transforming a pattern classification problem into a problem of discovering the way the sets of patterns interact with each other and with the classes that they belong to, we could describe the problem in terms of Fuzzy Cognitive Maps. More precisely, some FCM architectures are introduced and studied with respect to their pattern recognition abilities. An efficient novel hybrid classifier is proposed as an alternative classification structure, which exploits both neural networks and FCMs to ensure improved classification capabilities. Appropriate experiments with four well-known benchmark classification problems and a typical computer vision application establish the usefulness of the Fuzzy Cognitive Maps, in a pattern recognition research field. Moreover, the present paper introduces the use of more flexible FCMs by incorporating nodes with adaptively adjusted activation functions. This advanced feature gives more degrees of freedom in the FCM structure to learn and store knowledge, as needed in pattern recognition tasks.

The goal of this paper is to present the development of a new reconstruction methodology for restoring magnetic resonance images (MRI) from reduced scans in k-space. The proposed approach considers the combined use of regularized neural network models and Bayesian restoration, in the problem of MRI image extraction from sparsely sampled k-space, following several different sampling schemes, including spiral and radial. Effective solutions to this problem are indispensable especially when dealing with MRI of dynamic phenomena since then, rapid sampling in k-space is required. The goal is such a case is to make measurement time smaller by reducing scanning trajectories as much as possible. In this way, however, underdetermined equations are introduced and poor image reconstruction follows. It is suggested here that significant improvements could be achieved concerning quality of the extracted image, by judiciously applying regularization based neural networks and Bayesian estimation methods to the k-space data. More specifically, it is demonstrated that regularization based neural network techniques could construct efficient priors and introduce them in the procedure of Bayesian reconstruction. These regularized ANN priors are independent of specific image properties and probability distributions. They are based on training supervised multilayer perceptron (MLP) regularized neural filters to estimate the missing samples of complex k-space and thus, to improve k-space information capacity. Such a neural filter based prior is integrated to the maximum likelihood procedure involved in the Bayesian reconstruction. It is found that the proposed methodology leads to enhanced image extraction results favorably compared to the ones obtained by the traditional Bayesian MRI reconstruction approach as well as by the pure MLP based reconstruction approach.

This paper concerns a novel application of neural networks to magnetic resonance imaging (MRI) by considering regularized neural network models for the problem of image reconstruction from sparsely sampled k-space. Effective solutions to this problem are indispensable especially when dealing with MRI of dynamic phenomena since then, rapid sampling in k-space is required. The goal in such a case is to reduce the measurement time by omitting as many scanning trajectories as possible. This approach, however, entails underdetermined equations and leads to poor image reconstruction. It is proposed here that significant improvements could be achieved concerning image reconstruction if a procedure, based on neural network function approximation methodology and involving regularization techniques, for estimating the missing samples of complex k-space were introduced. To this end, the viability of involving neural network algorithms with/without regularization for such a problem is considered and it is found that their image reconstruction results are very favorably compared to the ones obtained by the trivial zero-filled k-space approach or traditional more sophisticated interpolation approaches. Moreover, it is found that regularized multilayer perceptrons outperform the ones not involving regularization during their training.

Real-time sophisticated robot control schemes require the online evaluation of the inverse robot dynamics model, a computationally intensive task. Research efforts to create efficient implementations of inverse models belong to the domain of computational robot dynamics. The current trends in this domain postulate that major improvements in computational efficiency can be achieved only in the context of customizing the dynamics formulations for specific robots and organizing the numerical computation. In this context, the objective of this paper is to introduce a novel SIMD parallel architecture based upon the distributed arithmetic technique for the efficient implementation of the general-purpose inverse robot dynamic problem. The approach is embedded in the Lagrangian formulation of robot dynamics with all equations expanded symbolically in their scalar form. In the proposed architecture any inherent parallelism of the computations is exploited to partition the inverse dynamics problem into discrete subtasks. The efficiency of the procedure is illustrated via the positioning system of the Puma robot.

This paper refers to the fast implementation of the forward kinematic equations of position of robotics manipulators, using a distributed arithmetic-based pipeline architecture. The building blocks of this pipeline architecture are the distributed arithmetic-based circuits that implement the matrix-vector multiplications involved in the calculation of the forward kinematics of position. The matrix-vector multiplications are implemented in the distributed arithmetic technique by using auxiliary binary functions, which are stored in look-up tables. The digit-serial configuration of the proposed implementation is described. The serial and the parallel configurations may result as special extreme cases of the digit-serial configuration.

This work presents a new approach and an algorithm for binary image representation, which is applied for the fast and efficient computation of moments on binary images. This binary image representation scheme is called image block representation, since it represents the image as a set of nonoverlapping rectangular areas. The main purpose of the image block representation process is to provide an efficient binary image representation rather than the compression of the image. The block represented binary image is well suited for fast implementation of various processing and analysis algorithms in a digital computing machine. The two-dimensional (2-D) statistical moments of the image may be used for image processing and analysis applications. A number of powerful shape analysis methods based on statistical moments have been presented, but they suffer from the drawback of high computational cost. The real-time computation of moments in block represented images is achieved by exploiting the rectangular structure of the blocks.

A simple approach for the fast shape discrimination using one-dimensional moments is presented. For each shape, the authors determine the centroid and calculate the distances from the centroid to the boundary, taking into account the holes in the shape, if any exist. The four one-dimensional moments of these latter distances, which uniquely characterize any two-dimensional shape, are calculated. The proposed approach is simple, involves only simple calculations, and is invariant under rotation, translation, scale, and starting point.>