TY - JOUR A1 - Goessel, Michael A1 - Chakrabarty, Krishnendu A1 - Ocheretnij, V. A1 - Leininger, Andreas T1 - A signature analysis technique for the identification of failing vectors with application to Scan-BIST N2 - We present a new technique for uniquely identifying a single failing vector in an interval of test vectors. This technique is applicable to combinational circuits and for scan-BIST in sequential circuits with multiple scan chains. The proposed method relies on the linearity properties of the MISR and on the use of two test sequences, which are both applied to the circuit under test. The second test sequence is derived from the first in a straightforward manner and the same test pattern source is used for both test sequences. If an interval contains only a single failing vector, the algebraic analysis is guaranteed to identify it. We also show analytically that if an interval contains two failing vectors, the probability that this case is interpreted as one failing vector is very low. We present experimental results for the ISCAS benchmark circuits to demonstrate the use of the proposed method for identifying failing test vectors Y1 - 2004 SN - 0923-8174 ER - TY - JOUR A1 - Müller, Klaus-Robert A1 - Vigario, R. A1 - Meinecke, Frank C. A1 - Ziehe, Andreas T1 - Blind source separation techniques for decomposing event-related brain signals N2 - Recently blind source separation (BSS) methods have been highly successful when applied to biomedical data. This paper reviews the concept of BSS and demonstrates its usefulness in the context of event-related MEG measurements. In a first experiment we apply BSS to artifact identification of raw MEG data and discuss how the quality of the resulting independent component projections can be evaluated. The second part of our study considers averaged data of event-related magnetic fields. Here, it is particularly important to monitor and thus avoid possible overfitting due to limited sample size. A stability assessment of the BSS decomposition allows to solve this task and an additional grouping of the BSS components reveals interesting structure, that could ultimately be used for gaining a better physiological modeling of the data Y1 - 2004 SN - 0218-1274 ER - TY - JOUR A1 - Sugiyama, Masashi A1 - Kawanabe, Motoaki A1 - Müller, Klaus-Robert T1 - Trading variance reduction with unbiasedness : the regularized subspace information criterion for robust model selection in kernel regression N2 - A well-known result by Stein (1956) shows that in particular situations, biased estimators can yield better parameter estimates than their generally preferred unbiased counterparts. This letter follows the same spirit, as we will stabilize the unbiased generalization error estimates by regularization and finally obtain more robust model selection criteria for learning. We trade a small bias against a larger variance reduction, which has the beneficial effect of being more precise on a single training set. We focus on the subspace information criterion (SIC), which is an unbiased estimator of the expected generalization error measured by the reproducing kernel Hilbert space norm. SIC can be applied to the kernel regression, and it was shown in earlier experiments that a small regularization of SIC has a stabilization effect. However, it remained open how to appropriately determine the degree of regularization in SIC. In this article, we derive an unbiased estimator of the expected squared error, between SIC and the expected generalization error and propose determining the degree of regularization of SIC such that the estimator of the expected squared error is minimized. Computer simulations with artificial and real data sets illustrate that the proposed method works effectively for improving the precision of SIC, especially in the high-noise-level cases. We furthermore compare the proposed method to the original SIC, the cross-validation, and an empirical Bayesian method in ridge parameter selection, with good results Y1 - 2004 SN - 0899-7667 ER - TY - JOUR A1 - Ziehe, Andreas A1 - Kawanabe, Motoaki A1 - Harmeling, Stefan T1 - Blind separation of post-nonlinear mixtures using linearizing transformations and temporal decorrelation N2 - We propose two methods that reduce the post-nonlinear blind source separation problem (PNL-BSS) to a linear BSS problem. The first method is based on the concept of maximal correlation: we apply the alternating conditional expectation (ACE) algorithm-a powerful technique from nonparametric statistics-to approximately invert the componentwise nonlinear functions. The second method is a Gaussianizing transformation, which is motivated by the fact that linearly mixed signals before nonlinear transformation are approximately Gaussian distributed. This heuristic, but simple and efficient procedure works as good as the ACE method. Using the framework provided by ACE, convergence can be proven. The optimal transformations obtained by ACE coincide with the sought-after inverse functions of the nonlinearitics. After equalizing the nonlinearities, temporal decorrelation separation (TDSEP) allows us to recover the source signals. Numerical simulations testing "ACE-TD" and "Gauss-TD" on realistic examples are performed with excellent results Y1 - 2004 SN - 1532-4435 ER - TY - JOUR A1 - Scheffler, Thomas A1 - Schnor, Bettina T1 - Securing Next generation Mobile Networks Y1 - 2004 SN - 0-86341-388-9 ER - TY - JOUR A1 - Friedrich, Sven A1 - Krahmer, Sebastian A1 - Schneidenbach, Lars A1 - Schnor, Bettina T1 - Loaded : Server Load Balancing for IPv6 Y1 - 2004 ER - TY - JOUR A1 - Vandenhouten, Ralf A1 - Behrens, Thomas A1 - Schnor, Bettina T1 - Entwicklung eines Gatewaysystems für telematikbasiertes Gerätemonitoring Y1 - 2004 SN - 0949-8214 ER -