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Principal composite kernel feature analysis (PC-KFA) is presented to show kernel adaptations for nonlinear features of medical image data sets (MIDS) in computer-aided diagnosis (CAD). The proposed algorithm PC-KFA has extended the existing studies on kernel feature analysis (KFA), which extracts salient features from a sample of unclassified patterns by use of a kernel method. The principal composite process for PC-KFA herein has been applied to kernel principal component analysis [34] and to our previously developed accelerated kernel feature analysis [20]. Unlike other kernel-based feature selection algorithms, PC-KFA iteratively constructs a linear subspace of a high-dimensional feature space by maximizing a variance condition for the nonlinearly transformed samples, which we call data-dependent kernel approach. The resulting kernel subspace can be first chosen by principal component analysis, and then be processed for composite kernel subspace through the efficient combination representations used for further reconstruction and classification. Numerical experiments based on several MID feature spaces of cancer CAD data have shown that PC-KFA generates efficient and an effective feature representation, and has yielded a better classification performance for the proposed composite kernel subspace using a simple pattern classifier. [ABSTRACT FROM AUTHOR]

Among target tracking algorithms using Kalman filtering-like approaches, the standard assumptions are Gaussian process and measurement noise models. Based on these assumptions, the Kalman filter is widely used in single or multiple filter versions (e.g., in an interacting multiple model (1MM) estimator). The oversimplification resulting from the above assumptions can cause degradation in tracking performance. In this paper we explore the application of Kalman.Levy filter to handle maneuvering targets. This filter assumes a heavy-tailed noise distribution known as the Levy distribution. Due to the heavy-tailed nature of the assumed distribution, the Kalman-Levy filter is more effective in the presence of large errors that can occur, for example, due to the onset of acceleration or deceleration. However, for the same reason, the performance of the Kalman-Levy filter in the nonmaneuvering portion of track is worse than that of a Kalman filter. For this reason, an 1MM with one Kalman and one Kalman-Levy module is developed here. Also, the superiority of the 1MM with Kalman-Levy module over only Kalman-filter-based 1MM for realistic maneuvers is shown by simulation results. [ABSTRACT FROM AUTHOR]

A spectral interpretation using the frequency sensitivity of the Allan variance (Avar) and Thêo-Hybrid (ThêoH) is used to determine ƒα noise, or ‘power-law noise’. ThêoH has narrower chi-square confidence than Avar; consequently, ThêoH provides significantly better determination of ƒα noise types at long term. Furthermore, ThêoH has even narrower confidence than chi-square. Because the algorithms used to calculate these confidence intervals are computationally intensive, we have constructed an empirical formula that approximates confidence intervals as the percent error for ThêoH. [ABSTRACT FROM AUTHOR]

Explicit expressions are derived for the bias and the variance of the maximum likelihood (ML) estimator of the Gumbel distribution. [ABSTRACT FROM AUTHOR]

TheoBR is a high-confidence statistic that evaluates frequency stability at long τ values. However, for realworld data sets that contain thousands of points or more, the calculation of TheoBR can take hours, days, or even weeks on a typical PC. To make the calculation of TheoBR faster for these data sets, a method of averaging points together within the data set is developed. The error introduced by this technique is analyzed and compared with the exact value, and a correction formula is developed to minimize this error for FM noise types. Finally, the technique is applied to real data sets and determines stability at the longest τ values in seconds as opposed to weeks. [ABSTRACT FROM PUBLISHER]