Strong-stability-preserving (SSP) time discretization methods (also known as total-variation-diminishing or TVD methods) are popular and effective algorithms for the simulation of partial differential equations having discontinuous or shock-like solutions. Optimal SSP Runge–Kutta (SSPRK)...

Power efficiency is one of the major considerations in the current hardware/software co-designs. This paper models hardware/software partitioning as an optimization problem with objective of minimizing power consumption under the constraints: hardware area A and execution time E. An efficient...

Railway interlocking systems are apparatuses that prevent conflicting movements of trains through an arrangement of tracks. A railway interlocking system takes into consideration the position of the switches (of the turnouts) and does not allow trains to be given clear signals unless the routes...

The length of the Spanish broad gauge network has decreased in the 1956–2006 period. When looking at different railway maps through this period, it seems that the network is offering fewer and fewer alternatives when a line is cut, that is, the network is becoming less and less flexible. The...

The aim of this paper is to present Cabri-graph, a system intended for students and researchers using graph theoric concepts. The objective of Cabri-graph is to alow any user to perform on his computer the same manipulations or computations on a graph that he can do on a sheet of paper, as...

In this paper, we present multi-almost periodicity of a general class of discrete-time neural networks derived from a well-known semi-discretization technique, that is, coexistence and exponential stability of 2N almost periodic sequence solutions of discrete-time neural networks subjected to...

Morphological transformations are efficient methods for shape analysis and representation. In this paper two morphological shape descriptors are described for object feature representation. Neural networks are then employed for object recognition and classification. Various coding schemes and...

This paper illustrates how internal model control of nonlinear processes can be achieved by recurrent neural networks, e.g. fully connected Hopfield networks. It is shown that using results developed by Kambhampati et al. (1995), that once a recurrent network model of a nonlinear system has been...