Helsinki University of Technology

About: Helsinki University of Technology is a based out in . It is known for research contribution in the topics: Artificial neural network & Finite element method. The organization has 8962 authors who have published 20136 publications receiving 723787 citations. The organization is also known as: TKK & Teknillinen korkeakoulu.

Statistical inversion and Monte Carlo sampling methods in electrical impedance tomography

Abstract: This paper discusses the electrical impedance tomography (EIT) problem: electric currents are injected into a body with unknown electromagnetic properties through a set of contact electrodes. The corresponding voltages that are needed to maintain these currents are measured. The objective is to estimate the unknown resistivity, or more generally the impedivity distribution of the body based on this information. The most commonly used method to tackle this problem in practice is to use gradient-based local linearizations. We give a proof for the differentiability of the electrode boundary data with respect to the resistivity distribution and the contact impedances. Due to the ill-posedness of the problem, regularization has to be employed. In this paper, we consider the EIT problem in the framework of Bayesian statistics, where the inverse problem is recast into a form of statistical inference. The problem is to estimate the posterior distribution of the unknown parameters conditioned on measurement data. From the posterior density, various estimates for the resistivity distribution can be calculated as well as a posteriori uncertainties. The search of the maximum a posteriori estimate is typically an optimization problem, while the conditional expectation is computed by integrating the variable with respect to the posterior probability distribution. In practice, especially when the dimension of the parameter space is large, this integration must be done by Monte Carlo methods such as the Markov chain Monte Carlo (MCMC) integration. These methods can also be used for calculation of a posteriori uncertainties for the estimators. In this paper, we concentrate on MCMC integration methods. In particular, we demonstrate by numerical examples the statistical approach when the prior densities are non-differentiable, such as the prior penalizing the total variation or the L1 norm of the resistivity.

BW media—media with negative parameters, capable of supporting backward waves

Abstract: Both isotropic and uniaxially anisotropic media capable of supporting backward waves are reviewed. Such an effect recently has been of great interest, and certain man-made composite media have been introduced under the names “media with negative refraction factor” or “left-handed materials,” effective in a certain band of microwaves. Neither of these names appears to be well founded, and “backward-wave medium” (or BW medium) is suggested instead. It is shown that, at an interface of a regular and a BW medium, Snell's law does not imply a negative refraction factor. However, the refraction is anomalous in the sense that the transmitted plane wave is a backward wave with a Poynting vector and a wave vector pointing in opposite lateral directions. The significance of the Zenneck wave and guided modes in a cylindrical guide made of BW medium is discussed. Finally, the BW property is extended to uniaxially anisotropic media, and its occurrence is studied for different value combinations of its medium parameters. It is shown that a lateral backward wave can arise when a plane wave is transmitted through an interface when one of the four parameters of the uniaxial medium is negative. © 2001 John Wiley & Sons, Inc. Microwave Opt Technol Lett 31: 129–133, 2001.

Industrial microwave sensors

Abstract: Surveys the different types of microwave sensors and reviews the latest developments reported by European institutes and companies. Attention is given to resonator, transmission, reflection, radar, and radiometer sensors, and to active imaging. >

Switchable Static and Dynamic Self-Assembly of Magnetic Droplets on Superhydrophobic Surfaces

Abstract: Self-assembly is a process in which interacting bodies are autonomously driven into ordered structures. Static structures such as crystals often form through simple energy minimization, whereas dynamic ones require continuous energy input to grow and sustain. Dynamic systems are ubiquitous in nature and biology but have proven challenging to understand and engineer. Here, we bridge the gap from static to dynamic self-assembly by introducing a model system based on ferrofluid droplets on superhydrophobic surfaces. The droplets self-assemble under a static external magnetic field into simple patterns that can be switched to complicated dynamic dissipative structures by applying a time-varying magnetic field. The transition between the static and dynamic patterns involves kinetic trapping and shows complexity that can be directly visualized.

The 2005 PASCAL visual object classes challenge

Abstract: The PASCAL Visual Object Classes Challenge ran from February to March 2005. The goal of the challenge was to recognize objects from a number of visual object classes in realistic scenes (i.e. not pre-segmented objects). Four object classes were selected: motorbikes, bicycles, cars and people. Twelve teams entered the challenge. In this chapter we provide details of the datasets, algorithms used by the teams, evaluation criteria, and results achieved.