Kalle Rutanen

Assessing Coupling Dynamics from an Ensemble of Time Series

Finding interdependency relations between time series provides valuable knowledge about the processes that generated the signals. Information theory sets a natural framework for important classes of statistical dependencies. However, a reliable estimation from information-theoretic functionals is hampered when the dependency to be assessed is brief or evolves in time. Here, we show that these limitations can be partly alleviated when we have access to an ensemble of independent repetitions of the time series. In particular, we gear a data-efficient estimator of probability densities to make use of the full structure of trial-based measures. By doing so, we can obtain time-resolved estimates for a family of entropy combinations (including mutual information, transfer entropy and their conditional counterparts), which are more accurate than the simple average of individual estimates over trials. We show with simulated and real data generated by coupled electronic circuits that the proposed approach allows one to recover the time-resolved dynamics of the coupling between different subsystems.

Blind Source Separation by Entropy Rate Minimization

An algorithm for the blind separation of mutually independent and/or temporally correlated sources is presented in this letter. The algorithm is closely related to the maximum likelihood approach based on entropy rate minimization but uses a simpler contrast function that can be accurately and efficiently estimated using nearest-neighbor distances. The advantages of the new algorithm are highlighted using simulations and real electroencephalographic data.

Quantitative Analysis of Dynamic Association in Live Biological Fluorescent Samples

Determining vesicle localization and association in live microscopy may be challenging due to non-simultaneous imaging of rapidly moving objects with two excitation channels. Besides errors due to movement of objects, imaging may also introduce shifting between the image channels, and traditional colocalization methods cannot handle such situations. Our approach to quantifying the association between tagged proteins is to use an object-based method where the exact match of object locations is not assumed. Point-pattern matching provides a measure of correspondence between two point-sets under various changes between the sets. Thus, it can be used for robust quantitative analysis of vesicle association between image channels. Results for a large set of synthetic images shows that the novel association method based on point-pattern matching demonstrates robust capability to detect association of closely located vesicles in live cell-microscopy where traditional colocalization methods fail to produce results. In addition, the method outperforms compared Iterated Closest Points registration method. Results for fixed and live experimental data shows the association method to perform comparably to traditional methods in colocalization studies for fixed cells and to perform favorably in association studies for live cells.

Object detection for dynamic adaptation of interconnections in inkjet printed electronics

A computer vision system for automatic detection of interconnection points in inkjet printed interconnected electronics modules during the manufacturing process is described. The location data can be used for calculating the amount of misalignments and the required corrections. The method uses a local filtering operation for finding candidate regions, from which the false matches are filtered out by classification using an artificial neural network. Experiments show that atypical false match rate is as low as 0.18%. The location data is later used for correcting the wiring that is inkjetted on top of the layout to connect the connectors correctly.

Least-Squares Transformations between Point-Sets

This paper derives formulas for least-squares transformations between point-sets in ℝ d . We consider affine transformations, with optional constraints for linearity, scaling, and orientation. We base the derivations hierarchically on reductions, and use trace manipulation to achieve short derivations. For the unconstrained problems, we provide a new formula which maximizes the orthogonality of the transform matrix.

Dynamic adaptation of interconnections in inkjet printed electronics

Printed electronics is a new technology for manufacturing miniature electronics modules. The basic principle is that the integrated circuits are molded into a background material such that the connectors are left visible on top. After the background substrate has hardened, the wiring is printed on top of the module using conductive ink. The technology allows flexible manufacturing of significantly smaller modules using wide range of new materials. Typically the components and their connection points are slightly displaced when the background material hardens. This paper proposes a method for adjusting the wiring to match the displaced components. Experiments show that correction reduces the number of false or missing connections significantly thus providing necessary yield improvement for the manufacturing process.

Minimal characterization of O-notation in algorithm analysis

Abstract Previously, we showed that linear dominance is the only definition of O-notation suitable for algorithm analysis [1] , [2] . Linear dominance was characterized by 8 primitive properties as a down-set of a non-trivial scale-invariant preorder which is preserved under certain modifications to algorithms and is consistent with pointwise partial order. In this paper, we provide a minimal characterization of O-notation, where there are no redundant properties. Such is given by excluding locality from primitive properties.