Showing papers by "University of Nicosia published in 1995"

A new technique for the classification and decomposition of EMG signals

Abstract: The shapes and firing rates of motor unit action potentials (MUAPs) in an electromyographic (EMG) signal provide an important source of information for the diagnosis of neuromuscular disorders. In order to extract this information from EMG signals recorded at force levels up to 20% of maximum voluntary contraction (MVC) it is required: (i) To identify the MUAPs composing the EMG signal, (ii) To classify MUAPs with similar shape and (iii) To decompose the superimposed MUAP waveforms into their constituent MUAPs. For the classification of MUAPs two different pattern recognition techniques are presented (i) An artificial neural network (ANN) technique based on unsupervised learning using the self-organizing feature maps (SOFM) algorithm and learning vector quantization (LVQ) and (ii) A statistical pattern recognition technique based on the euclidian distance. The success rate on real data for the ANN technique is about 96% and for the statistical one about 94%. For the decomposition of the superimposed waveforms the following technique is used: (i) Cross-correlation of each of the unique MUAP waveforms, obtained by the classification process with the superimposed waveforms in order to find the best matching point and (ii) A combination of euclidian distance and area measures in order to classify the components of the decomposed waveform. The success rate for the decomposition procedure is about 90%.

Training ANN using linear minimax techniques

Abstract: The purpose of this paper is to present a new method for training ANN. The method solves a sequence of linear minimax optimization problems and does not make any assumption of the network structure, but it builds up as the algorithm proceeds. The method does not create unnecessary regions of local minima and it guarantees the classification of the input feature space in a finite number of steps.

Essential features of a compiler target language for parallel machines

Abstract: Term Graph Rewriting Systems (TGRS) have been used extensively as an implementation vehicle for a number of, often divergent, programming paradigms ranging from the traditional functional programming ones to the (concurrent) logic programming ones and various amalgamations of them, to (concurrent) object-oriented ones. More recently, the relationship between TGRS and process calculi (such as the /spl pi/-calculus) as well as Linear Logic has also been explored. In this paper we describe our experience in using an intermediate Compiler Target Language (CTL) based on TGRS for mapping a variety of programming paradigms of the aforementioned types onto it, highlighting in the process some of the issues which we feel any such intermediate representation should address and which form effectively a minimum set of features every CTL should possess. >