KWTA networks and their applications

Winner-Take-All (WTA) or K-Winner-Take-All (KWTA) networks have been frequently used as the basic building blocks of complex neural networks. This paper introduces a new selection rule for network connections that implements stable KWTA networks. To widen the applications of WTA networks, a new class of WTA networks is proposed, and their efficient design methods are presented. We demonstrate the properties of the generalized class of WTA networks, through three application examples.     

Three-dimensional rotated digital filters: Design,stability, and applications

Three-dimensional (3-D) signal processing offers many advantages over two-dimensional (2-D) processing, because it preserves 3-D correlations. In this paper the design and the stability of 3-D rotated filters are considered. These filters are designed by rotating a one-dimensional (1-D) digital filter in 3-D space. The rotated filters are valuable in the design of various 3-D filters which possess prescribed spectral specifications. An efficient algorithm for the design of 3-D lowpass (LP) digital filters, with approximately spherically symmetric magnitude responses, is introduced. To achieve the desirable spectral characteristics, a number of 3-D rotated filters is cascaded. The stability of the spherically symmetric filters designed is considered, and stable realizations are proposed. The relation between the cut-off isopotential sphere of the 3-D filter and the cut-off frequency of the 1-D filter employed in the design, is derived. Finally, configurations that result in highpass (HP) and bandpass (BP) filters are proposed. Examples of LP, HP, and BP filters, designed on the basis of the method proposed, are presented.This research was supported by the Public Benefit Foundation, Alexander S. Onassis.     

Exemplifying the theory of evidence in remote sensing image classification

This article introduces the mathematical theory of evidence in classifying remote sensing images. Its main intent is to introduce the less familiar concepts of belief functions in image classification. The belief function can be considered as a generalisation of the classical Bayes probability function that includes, however, a way to assess the strength of evidence. To illustrate the theory of evidence, seven examples are given.     

Artificial neural networks for discriminating pathologic from normal peripheral vascular tissue

The identification of the state of human peripheral vascular tissue by using artificial neural networks is discussed in this paper. Two different laser emission lines (He-Cd, Ar+) are used to excite the chromophores of tissue samples. The fluorescence spectrum obtained, is passed through a nonlinear filter based on a high-order (HO) neural network neural network (NN) [HONN] whose weights are updated by stable learning laws, to perform feature extraction. The values of the feature vector reveal information regarding the tissue state. Then a classical multilayer perceptron is employed to serve as a classifier of the feature vector, giving 100% successful results for the specific data set considered. Our method achieves not only the discrimination between normal and pathologic human tissue, but also the successful discrimination between the different types of pathologic tissue (fibrous, calcified). Furthermore, the small time needed to acquire and analyze the fluorescence spectra together with the high rates of success, proves our method very attractive for real-time applications.     

Classification of washing machines vibration signals using discretewavelet analysis for feature extraction

Wavelets provide a powerful tool for nonstationary signal analysis. In vibration monitoring, the occurrence of occasional transient disturbances makes the recorded signal nonstationary, especially during the start-up of an engine. Through the wavelet analysis, transients can be decomposed into a series of wavelet components, each of which is a time-domain signal that covers a specific octave frequency band. Disturbances of small extent (duration) are amplified relative to the rest of the signal when projected to similar size wavelet bases and, thus, they can be easily detected in the corresponding frequency band. This paper presents a new method for extracting features in the wavelet domain and uses them for classification of washing machines vibration transient signals. The discrete wavelet transform (DWT), in conjunction with statistical digital signal processing techniques, is used for feature extraction. The Karhunen Loeve transform (KLT) is used for feature reduction and decorrelation of the feature vectors. The Euclidean, Mahalanobis, and Bayesian distance classifiers, the learning vector quantization (LVQ) classifier, and the fuzzy gradient classifier are used for classification of the resulting feature space. Classification results are illustrated and compared for the rising part of vibration velocity signals of a variety of real washing machines with various defects     

A transmitter IC for TETRA systems based on a Cartesian feedback loop linearization technique

A fully integrated linearized transmitter for the next generation TETRA systems for PMR and public safety applications in a CMOS-based SiGe technology is described. The presented single-chip transmitter employs a Cartesian feedback loop technique in order to improve the linearity of the externally connected power amplifier. The transmitter is usable in a frequency range from 300 MHz up to 800 MHz, providing a linearity improvement of more than 40 dB.     

Design of a new restoration algorithm based on the constrained mean-square-error criterion

In this paper the joint optimization of different criteria is addressed, as a powerful means of incorporatinga priori information in linear image-restoration algorithms. Theconstrained mean-square-error (CMSE) approach is introduced, which enables the incorporation of both spatial and spectral information regarding the peculiarities of the problem. Depending on the nature of the spatial information, this approach can be interpreted as either a regularized or an adaptive scheme. As a regularized scheme, it offers an alternative to conventional approaches, in which the ringing artifacts are efficiently suppressed by means of the regularizing operator. As an adaptive scheme, the CMSE approach offers the flexibility of applying either linear Wiener filtering or inverse filtering, depending on the local signal activity. Optimal techniques for the selection of the regularization parameter in both nonadaptive and adaptive cases are introduced. The capabilities of the CMSE approach as a regularized scheme and an adaptive scheme, are demonstrated through restoration examples.     

An automated approach for analyzing D-periods in collagen fibril images

This paper considers an approach for analyzing fibrillar collagen structures based on fundamental concepts of pattern recognition. It focuses on the quantitative comparison between collagen structural data (electron-optical data) and chemical data. Theoretical models in the form of sequence-generated histograms are used as reference for extracting and analyzing the structural unit in images from collagen fibrils. In this respect, collagen provides a valuable model system for studying the chemical basis of ultrastructure, as well as detecting the alterations in collagen fibril structure produced by a disorder. Application examples are presented and the results are compared with biochemical studies.     

In-depth analysis and evaluation of diffusive glioma models

Glioma is one of the most aggressive types of brain tumor. Several mathematical models have been developed during the past two decades, toward simulating the mechanisms that govern the development of glioma. The most common models use the diffusion-reaction equation (DRE) for simulating the spatiotemporal variation of tumor cell concentration. Nevertheless, despite the applications presented, there has been little work on studying the details of the mathematical solution and implementation of the 3-D diffusion model and presenting a qualitative analysis of the algorithmic results. This paper presents a complete mathematical framework on the solution of the DRE using different numerical schemes. This framework takes into account all characteristics of the latest models, such as brain tissue heterogeneity, anisotropic tumor cell migration, chemotherapy, and resection modeling. The different numerical schemes presented have been evaluated based upon the degree to which the DRE exact solution is approximated. Experiments have been conducted both on real datasets and a test case for which there is a known algebraic expression of the solution. Thus, it is possible to calculate the accuracy of the different models.     

Convergence properties of Gauss-Newton iterative algorithms in nonlinear image restoration

Nonlinear image restoration is a complicated problem that is receiving increasing attention. Since every image formation system involves a built-in nonlinearity, nonlinear image restoration finds applications in a wide variety of research areas. Iterative algorithms have been well established in the corresponding linear restoration problem. In this paper, a generalized analysis regarding the convergence properties of nonlinear iterative algorithms is introduced. Moreover, the applications of the iterative Gauss-Newton (GN) algorithm in nonlinear image restoration are considered. The convergence properties of a general class of nonlinear iterative algorithms are rigorously studied through the Global Convergence Theorem (GCT). The derivation of the convergence properties is based on the eigen-analysis, rather than on the norm analysis. This approach offers a global picture of the evolution and the convergence properties of an iterative algorithm. Moreover, the generalized convergence-analysis introduced may be interpreted as a link towards the integration of minimization and projection algorithms. The iterative GN algorithm for the solution of the least-squares optimization problem is introduced. The computational complexity of this algorithm is enormous, making its implementation very difficult in practical applications. Structural modifications are introduced, which drastically reduce the computational complexity while preserving the convergence rate of the GN algorithm. With the structural modifications, the GN algorithm becomes particularly useful in nonlinear optimization problems. The convergence properties of the algorithms introduced are readily derived, on the basis of the generalized analysis through the GCT. The application of these algorithms on practical problems, is demonstrated through several examples.