Classiication of short duration acoustic signals can be very diicult due to the high degree of variability in the signatures. Input feature vectors, resulting from wavelets or short time Fourier analysis, are typically of high dimensionality, noisy, and contain incomplete information. In this paper, robust artiicial neu-ral networks (ANNs) are identiied that are less sensitive to noisy feature ...

Previous research has indicated the significance of accurate classification of fluorescence in situ hybridisation (FISH) signals for the detection of genetic abnormalities. Based on well-discriminating features and a trainable neural network (NN) classifier, a previous system enabled highly-accurate classification of valid signals and artefacts of two fluorophores. However, since this system em...

Obstructive sleep apnea (OSA) is the most common form of different types of sleep-related breathing disorders. It is characterized by repetitive cessations of respiratory flow during sleep, which occurs due to a collapse of the upper respiratory airway. OSA is majorly undiagnosed due to the inconvenient Polysomnography (PSG) testing procedure at sleep labs. This paper introduces an automated ap...

This paper deals with the problem of classiication of non-stationary signals using signatures which are essentially independent of the signal length. We develop the notion of a separable approximation to the Continuous Wavelet Transform (CW T) and use it to deene a power signature. We present a simple technique which uses the Singular Value Decomposition (SV D) to compute such an approximation,...

In this paper, we demonstrate the use of support vector (SV) techniques, for the binary classification of nonstationary sinusoidal signals with quadratic phase. We briefly describe the theory underpinning SV classification, and introduce the Cohen’s group time-frequency representation, which is used to process the non-stationary signals so as to define the classifier input space. We show that t...

We present a generalization of the corner classification approach to training feedforward neural networks that allows rapid learning of nonbinary data. These generalized networks, called fast classification (FC) networks, are compared against backpropagation and radial basis function networks and are shown to have excellent performance for prediction of time series and pattern recognition. FC n...

This article presents the development of a neural network cognitive model for the classification and detection of different frequency signals. The basic structure of the implemented neural network was inspired on the perception process that humans generally make in order to visually distinguish between high and low frequency signals. It is based on the dynamic neural network concept, with delay...

The contribution describes the design, optimization and verification of the off-line single-trial movement classification system. Four types of movements are used for the classification: the right index finger extension vs. flexion as well as the right shoulder (proximal) vs. right index finger (distal) movement. The classification system utilizes hidden information stored in the characteristic...

Pattern classification is an important task for many practical systems. Many classifier systems rely on similarity measures to classify unknown patterns. Signal patterns are an interesting class of patterns exhibited in many sensorbased systems. In this paper we present three fuzzy similarity measures that can be used for signal pattern classification. We use the three fuzzy similarity measures...

Continuous-state hidden Markov models (CS-HMMs) are developed as a tool for signal classification. Analogs of the Baum, Viterbi, and Baum–Welch algorithms are formulated for this class of models. The CS-HMM algorithms are then specialized to hidden Gauss–Markov models (HGMMs) with linear Gaussian state-transition and output densities. A new Gaussian refactorization lemma is used to show that th...