Geometric subspace methods and time-delay embedding for EEG artifact removal and classification.

Generalized singular-value decomposition is used to separate multichannel electroencephalogram (EEG) into components found by optimizing a signal-to-noise quotient. These components are used to filter out artifacts. Short-time principal components analysis of time-delay embedded EEG is used to represent windowed EEG data to classify EEG according to which mental task is being performed. Examples are presented of the filtering of various artifacts and results are shown of classification of EEG from five mental tasks using committees of decision trees.     

Depolarization of broadband radiation in optical fibers with the twisted structure of birefringence

The depolarization of broadband linearly polarized radiation that propagates in optical fibers with the twisted structure of birefringence is investigated. The basic relationships for calculating the degree of radiation polarization at the fiber output at arbitrary fiber lengths and arbitrary azimuths of the input linear polarization are obtained. The depolarization length (on the order of several meters) at which the degree of polarization decreases from unity to a certain residual level is found. It is shown that the depolarization length depends on the width of the spectrum of a radiation source, the magnitude of the fiber’s built-in linear birefringence, and the twist pitch. For long fibers (of a length exceeding 50 m), the residual degree of polarization depends on the azimuth of the input linear polarization.     

Electromechanical Hardening of Nanostructured Alloy Layers Applied in Surfacing

Electromechanical hardening of applied surface layers of thermostable and wear-resistant iron and ferronickel alloys modified by ultrafine TiCN and WC particles is considered; the ferronickel alloy contains γ′-Ni₃Al phase. The dependence of the microhardness and the depth of the hardened layer on the current is established.     

Chaotic time series prediction with employment of ant colony optimization

In this study, the novel method to predict chaotic time series is proposed. The method employs the ant colony optimization paradigm to analyze topological structure of the attractor behind the given time series and to single out the typical sequences corresponding to the different part of the attractor. The typical sequences are used to predict the time series values. The method was applied to time series generated by the Lorenz system, the Mackey–Glass equation, and weather time series as well. The method is able to provide robust prognosis to the periods comparable with the horizon of prediction.     

Projection-based metal-artifact reduction for industrial 3D X-ray computed tomography

Multi-material components, which contain metal parts surrounded by plastic materials, are highly interesting for inspection using industrial 3D X-ray computed tomography (3DXCT). Examples of this application scenario are connectors or housings with metal inlays in the electronic or automotive industry. A major problem of this type of components is the presence of metal, which causes streaking artifacts and distorts the surrounding media in the reconstructed volume. Streaking artifacts and dark-band artifacts around metal components significantly influence the material characterization (especially for the plastic components). In specific cases these artifacts even prevent a further analysis. Due to the nature and the different characteristics of artifacts, the development of an efficient artifact-reduction technique in reconstruction-space is rather complicated. In this paper we present a projection-space pipeline for metal-artifacts reduction. The proposed technique first segments the metal in the spatial domain of the reconstructed volume in order to separate it from the other materials. Then metal parts are forward-projected on the set of projections in a way that metal-projection regions are treated as voids. Subsequently the voids, which are left by the removed metal, are interpolated in the 2D projections. Finally, the metal is inserted back into the reconstructed 3D volume during the fusion stage. We present a visual analysis tool, allowing for interactive parameter estimation of the metal segmentation. The results of the proposed artifact-reduction technique are demonstrated on a test part as well as on real world components. For these specimens we achieve a significant reduction of metal artifacts, allowing an enhanced material characterization.     

Toward Design of Novel Materials for Organic Electronics

Materials for organic electronics are presently used in prominent applications, such as displays in mobile devices, while being intensely researched for other purposes, such as organic photovoltaics, large‐area devices, and thin‐film transistors. Many of the challenges to improve and optimize these applications are material related and there is a nearly infinite chemical space that needs to be explored to identify the most suitable material candidates. Established experimental approaches struggle with the size and complexity of this chemical space. Herein, the development of simulation methods is addressed, with a particular emphasis on predictive multiscale protocols, to complement experimental research in the identification of novel materials and illustrate the potential of these methods with a few prominent recent applications. Finally, the potential of machine learning and methods based on artificial intelligence is discussed to further accelerate the search for new materials.