Integrated Water Management Approach for Adaptation to Climate Change in Highly Water Stressed Basins

Water Resources Management - Adaptation aims at increasing the resilience of natural and human systems to current and future impacts of climate change. The main objective of the current research is...     

Image Segmentation via Iterative Fuzzy Clustering Based on Local Space-Frequency Multi-Feature Coherence Criteria

Fuzzy set theory has recently attracted much attention in the field of image classification, image understanding and image processing. One of the major topics in fuzzy image processing is the image classification problem. This paper presents a fast and accurate iterative fuzzy clustering (I.F.C.) method dynamically adapted to the classification process. This is used for high performance fuzzy segmentation which forms the basis for reliable image understanding. The proposed fuzzy segmentation scheme examines the image connectivity in the space and frequency domains. The detected fuzzy features are combined via a block synthesis and local correlation algorithmic procedure. Some results showing that the performance of the proposed I.F.C./clustering method is superior from that of the standard fuzzy c-means method are provided.     

Parallelization of a fuzzy control algorithm using quantum computation

Quantum computation is proposed for the parallelization of a fuzzy logic control (FLC) algorithm. Quantum computation speeds up the fuzzy inference since serial operations between matrices of large dimensionality are now replaced by a one-step quantum addition or a quantum subtraction. The unitarity properties of the algorithm prove that the FLC stands for a simulator of a quantum computing machine.     

Neural Structures Using the Eigenstates of a Quantum Harmonic Oscillator

The main result of the paper is the use of orthogonal Hermite polynomials as the basis functions of feedforward neural networks. The proposed neural networks have some interesting properties: (i) the basis functions are invariant under the Fourier transform, subject only to a change of scale, (ii) the basis functions are the eigenstates of the quantum harmonic oscillator, and stem from the solution of Schrödinger's diffusion equation. The proposed feed-forward neural networks demonstrate the particle-wave nature of information and can be used in nonparametric estimation. Possible applications of the proposed neural networks include function approximation, image processing and system modelling.     

Moment equations for stochastic distributed-parameter processes

Moment equations are derived for a general class of distributed-parameter dynamic processes corrupted by white in time distributed disturbances. An integrodifferential equation for the expected value of an arbitrary functional of the process is first derived simply by using the whiteness property of the stochastic disturbance. In this way there is no need to utilise the forward Kolmogorov's equation, which at present is not available for distributed-parameter processes. The moment equations are then derived as special cases of this functional integrodifferential equation.     

On the exact model matching controller design

The problem of designing a static state feedback controller which matches a given multivariable system to a desired ideal system (model) is treated. The system under control is assumed in state space form while the model is assumed in transfer matrix form. An algorithm is developed which separates the conditions which must be satisfied by the system under control and the model to be matched from the equations which must be solved to find the gains of the feedback controller. Two examples are included.