Quantum neural networks (QNN’s): inherently fuzzy feedforward neural networks

Abstract — This paper introduces quantum neural networks (QNN’s), a class of feedforward neural networks (FFNN’s) inherently capable of estimating the structure of a feature space in the form of fuzzy sets. The hidden units of these networks develop quantized representations of the sample information provided by the training data set in various graded levels of certainty. Unlike other approaches attempting to merge fuzzy logic and neural networks, QNN’s can be used in pattern classification problems without any restricting assumptions such as the availability of a priori knowledge or desired membership profile, convexity of classes, a limited number of classes, etc. Experimental results presented here show that QNN’s are capable of recognizing structures in data, a property that conventional FFNN’s with sigmoidal hidden units lack. Index Terms — Fuzzy classification, multilevel partitions, multilevel transfer functions, quantum neural networks, quantu

Automated extraction of temporal motor activity signals from video recordings of neonatal seizures based on adaptive block matching

Abstract-This paper presents a new method for tracking features in video. This method estimates the displacement of a feature between two successive frames by minimizing an error function defined in terms of the feature intensities at these frames. The minimization problem is made analytically tractable by approximating the error function using a second-order Taylor expansion. The displacement between two successive frames is computed in an iterative fashion using gradient descent. The improved reliability of the proposed method is illustrated by its application in the extraction of temporal motor activity signals from video recordings of neonatal seizures. successive frames is estimated by minimizing an error function defined in terms of the intensity functions at these frames. In the proposed procedure, the error function is approximated by using a second-order Taylor expansion for the intensity function at the next frame. The proposed feature tracking method is used to extract motor activity signals from video recordings of neonatal seizures. Motor activity signals can be extracted by projecting the location of selected anatomical sites to the horizontal and vertical axes. As the seizure progresses in time, these projections will produce temporal signals recording motor activity of the body parts of interest. Extraction of Temporal Motor Activity Signals From Video Recordings of Keywords-Feature tracking, motor activity signal, translation motion mode

JOURNAL OF APPLIED FUNCTIONAL ANALYSIS,VOL.1,NO1,9-32,2006,COPYRIGHT 2006 EUDOXUS PRESS LLC On the Capacity of Feed-forward Neural Networks for Fuzzy Classification

Abstract-This paper investigates the ability of feed-forward neural network (FFNN) classifiers trained with examples to generalize and estimate the structure of the feature space in the form of class membership information. A functional theory of FFNN classifiers is developed from formal definitions. The properties of discriminant functions learned by FFNN classifiers from sample data are also studied. These properties show that the ability of FFNNs to identify and quantify uncertainty in a feature space is sensitively dependent on the topology of the feature space and that FFNNs trained to classify overlapping classes of data tend to create sharp transitions between closely spaced feature vectors belonging to different classes. Key Words: discriminant function; feed-forward neural network; fuzzy classification; learning; membership profile; uncertainty

Competitive Neural Trees for Vector Quantization

This paper presents a self-organizing neural architecture for vector quantization, called Competitive Neural Tree (CNeT). At the node level, the CNeT employs unsupervised competitive learning. The CNeT performs hierarchical clustering of the feature vectors presented to it as examples, while its growth is controlled by a splitting criterion. Because of the tree structure, the prototype in the CNeT close to a given example can be determined by searching only a fraction of the tree. This paper introduces different search methods for the CNeT, which are utilized for training and recall. The efficiency of CNeTs is illustrated by their use in codebook design required for image compression of gray-scale images based on vector quantization

Detection of microcalcifications in digital mammograms using wavelets

Abstract—This paper presents an approach for detecting microcalcifications in digital mammograms employing wavelet-based subband image decomposition. The microcalcifications appear in small clusters of few pixels with relatively high intensity compared with their neighboring pixels. These image features can be preserved by a detection system that employs a suitable image transform which can localize the signal characteristics in the original and the transform domain. Given that the microcalcifications correspond to high-frequency components of the image spectrum, detection of microcalcifications is achieved by decomposing the mammograms into different frequency subbands, suppressing the low-frequency subband, and, finally, reconstructing the mammogram from the subbands containing only high frequencies. Preliminary experiments indicate that further studies are needed to investigate the potential of wavelet-based subband image decomposition as a tool for detecting microcalcifications in digital mammograms. Index Terms—Breast cancer screening, digital mammography, microcalcification detection, wavelet image decomposition. I

networks based on entropy-constrained

Power-conserving routing of ad hoc mobile wireles