Fraunhofer-Gesellschaft Zur Foerderung Der Angewandten Forschung E.v.

Technische Unversität Berlin

Technische Universitat Braunschweig

Technische Universitaet Berlin

Technische Universitaet Beriin

Klaus-Robert Mueller

Alexander Binder

Thomas Wiegand

Detlev Marpe

Heiko Schwarz

Santiago De Luxán Hernández

Wojciech Samek

Meinhard Schilling

Simeon Sauer

Alexander Von Luehmann

Frederick Klauschen

Motoaki Kawanabe

Konrad Rieck

Pavel Laskov

Patrick Duessel

Benjamin Blankertz

Gabriel Curio

Franziska Horn

Mihail Bogojeski

Nataliya Yakut

Sebastian Lapuschkin

Grégoire Montavon

The coding efficiency of predictive picture codecs using transform-based residual coding is enhanced by allowing the codec to switch between a set of transforms for the sake of the transform-based residual coding. It turned out that even when using explicit signaling from encoder to decoder in order to signal the actual transform to be used out of the set of transforms from encoder to decoder, an increase in coding efficiency may result. Alternatively, the switching between the set of transforms may be performed without explicit signaling at all, or using a combination of explicit signaling and some sort of prediction of the switching.

Image data compression using switching between a set of transforms

The task of relevance score assignment to a set of items onto which an artificial neural network is applied is obtained by redistributing an initial relevance score derived from the network output, onto the set of items by reversely propagating the initial relevance score through the artificial neural network so as to obtain a relevance score for each item. In particular, this reverse propagation is applicable to a broader set of artificial neural networks and/or at lower computational efforts by performing same in a manner so that for each neuron, preliminarily redistributed relevance scores of a set of downstream neighbor neurons of the respective neuron are distributed on a set of upstream neighbor neurons of the respective neuron according to a distribution function.

Relevance score assignment for artificial neural networks

By accurately predicting industrial aging processes (IAP), such as the slow deactivation of a catalyst in a chemical plant, it is possible to schedule maintenance events further in advance, thereby ensuring a cost-efficient and reliable operation of the plant. So far, these degradation processes were usually described by mechanistic models or simple empirical prediction models. In order to accurately predict IAP, data-driven models are proposed, comparing some traditional stateless models (linear and kernel ridge regression, as well as feed-forward neural networks) to more complex stateful recurrent neural networks (echo state networks and long short-term memory networks). Additionally, variations of the stateful models are discussed. In particular, stateful models using mechanistical pre-knowledge about the degradation dynamics (hybrid models). Stateful models and their variations may be more suitable for generating near perfect predictions when they are trained on a large enough dataset, while hybrid models may be more suitable for generalizing better given smaller datasets with changing conditions.

Forecasting industrial aging processes with machine learning methods

Disclosed is a biosignal acquisition device for the acquisition, in particular the concurrent or simultaneous acquisition, of optical and electrical biosignals. The optical and electrical biosignals are both received by an analog front end device for biosignals, with an opto-electric converter for converting the optical biosignals into electrical signals. Also disclosed are a system of a plurality of biosignal acquisition devices and a biosignal acquisition method.

Biosignal acquisition device and system, method for acquisition of biosignals

Method for the automatic analysis of an image of a biological sample with respect to a pathological relevance, wherein a)local features of the image are aggregated to a global feature of the image using a bag of visual word approach, b) step a) is repeated at least two times using different methods resulting in at least two bag of word feature datasets, c) computation of at least two similarity measures using the bag of word features obtained from a training image dataset and bag of word features from the image, d) the image training dataset comprising a set of visual words, classifier parameters, including kernel weights and bag of word features from the training images, e) the computation of the at least two similarity measures is subject to an adaptive computation of kernel normalization parameters and/or kernel width parameters, f) for each image one score is computed depending on the classifier parameters and kernel weights and the at least two similarity measures, the at least one score being a measure of the certainty of one pathological category compared to the image training dataset, g) for each pixel of the image a pixel-wise score is computed using the classifier parameters, the kernel weights, the at least two similarity measures, the bag of word features of the image, all the local features used in the computation of the bag of word features of the image and the pixels used in the computations of the local features, h) the pixel-wise score is stored as a heatmap dataset linking the pixels of the image to the pixel-wise scores.

Method and system for the automatic analysis of an image of a biological sample

The invention is concerned with a method and an apparatus for automatic comparison of at least two data sequences characterized in&#x2014;an evaluation of a local relationship between any pair of subsequences in two or more sequences; &#x2014;an evaluation of a global relationship by means of aggregation of the evaluations of said local relationships.

Method and apparatus for automatic comparison of data sequences using local and global relationships

The invention relates to a sensor system and several method for the capacitive measurement of electromagnetic signals having a biological origin. Such a sensor system comprises a capacitive electrode device (), an electrode shielding element () which surrounds the electrode device () at least in part in order to shield the same () from interfering external electromagnetic fields, and a signal processing device () for processing electromagnetic signals that can be detected by means of the electrode device (). According to the invention, additional shielding means () three-dimensionally surround the electrode device () and the electrode shielding element () at least in part in order to block out interfering external electromagnetic fields. The changes in the electrode capacity of the capacitive sensor system are determined with the aid of several methods which particularly use the inventive sensor system in order to take said changes into account when the test signals are evaluated.

Sensor system and methods for the capacitive measurement of electromagnetic signals having a biological origin

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Berlin, Germany

Klaus-Robert Mueller