Fast and accurate methods of independent component analysis: A survey

summary:This paper presents a survey of recent successful algorithms for blind separation of determined instantaneous linear mixtures of independent sources such as natural speech or biomedical signals. These algorithms rely either on non-Gaussianity, nonstationarity, spectral diversity, or on a combination of them. Performance of the algorithms will be demonstrated on separation of a linear instantaneous mixture of audio signals (music, speech) and on artifact removal in electroencephalogram (EEG)

Detection of influential points by convex hull volume minimization

summary:A method of geometrical characterization of multidimensional data sets, including construction of the convex hull of the data and calculation of the volume of the convex hull, is described. This technique, together with the concept of minimum convex hull volume, can be used for detection of influential points or outliers in multiple linear regression. An approximation to the true concept is achieved by ordering the data into a linear sequence such that the volume of the convex hull of the first $n$ terms in the sequence grows as slowly as possible with $n$. The performance of the method is demonstrated on four well known data sets. The average computational complexity needed for the ordering is estimated by $O(N^{2+(p-1)/(p+1)})$ for large $N$, where $N$ is the number of observations and $p$ is the data dimension, i. e. the number of predictors plus 1

Design of mechatronic applications with a focus on safety function

Tato diplomová práce popisuje návrh testovacího robotického pracoviště. Teoretická část práce se věnuje mechatronice a teoretickému rozboru funkční bezpečnosti, která je kladena na strojní zařízení užívaná na území České republiky. V praktické části je práce zaměřena na možná řešení implementace robotického pracoviště do konkrétního prostředí, včetně analýzy a eliminace rizik za pomocí bezpečnostních opatření plynoucích z legislativy. Dále popisuje CAE software, který jsem využil při tvorbě návrhu. Jedná se zejména o software EPLAN Electric P8 pro tvorbu schémat, EPLAN Pro Panel pro návrh 3D rozvaděče, EPLAN Fluid pro návrh pneumatického schématu a Sistema pro ověření návrhu bezpečnosti. Následně práce sumarizuje ekonomické výhody nejen při použití těchto softwarů vzhledem k možnostem opakovatelnosti projektu. V závěru jsou popsány a zhodnoceny výsledky této diplomové práce.This diploma thesis describes the design of testing robotic workspace. The theoretical part describes mechatronics and theoretical risk analysis necessary for the design to be commissioned and operated in Czech Republic. In the practical part, the thesis deals with possible solution of real implementation of the new robotic workplace in a specific environment – involving all necessary engineering activities such as risk analysis, elimination of the risks in accordance to the current laws, norms and legislation. Next part describes the CAE software used for design development – EPLAN Electric P8 for electrical circuits diagrams, EPLAN Pro Panel for 3D electrical panel design, EPLAN Fluid for pneumatic circuits diagrams and Sistema for system Safety level verification. Following part targets the main economic benefits due to software unification and repeatability on subsequent projects. Final part of the thesis summarizes the outcomes and results.420 - Katedra elektrotechnikyvýborn

Low Complexity Damped Gauss-Newton Algorithms for CANDECOMP/PARAFAC

The damped Gauss-Newton (dGN) algorithm for CANDECOMP/PARAFAC (CP) decomposition can handle the challenges of collinearity of factors and different magnitudes of factors; nevertheless, for factorization of an $N$-D tensor of size $I_1\times I_N$ with rank $R$, the algorithm is computationally demanding due to construction of large approximate Hessian of size $(RT \times RT)$ and its inversion where $T = \sum_n I_n$. In this paper, we propose a fast implementation of the dGN algorithm which is based on novel expressions of the inverse approximate Hessian in block form. The new implementation has lower computational complexity, besides computation of the gradient (this part is common to both methods), requiring the inversion of a matrix of size $NR^2\times NR^2$, which is much smaller than the whole approximate Hessian, if $T \gg NR$. In addition, the implementation has lower memory requirements, because neither the Hessian nor its inverse never need to be stored in their entirety. A variant of the algorithm working with complex valued data is proposed as well. Complexity and performance of the proposed algorithm is compared with those of dGN and ALS with line search on examples of difficult benchmark tensors