Ekahau Oy

Aalto University Foundation

University of Helsinki

Altum Technologies Oy

Bayes Information Technology, Ltd.

Petri Jukka Myllymäki

Kimmo Valtonen

Henry Tirri

Petri Kontkanen

Pauli Misikangas

Antti Tuominen

Hannes Wettig

Jussi Lahtinen

Teemu Roos

Ari Salmi

Timo Rauhala

Edward Hæggström

Jaakko Tapani Peltonen

Giulio Jacucci

Samuel Kaski

Tuukka Juhani Ruotsalo

Dorota Glowacka

Jussi Mika Antero Lahtinen

Tomi Viljam Silander

Arto Klami

Manuel J A Eugster

Tomi Silander

Disclosed are methods for cleaning of devices, such as heat exchangers, in particular to methods wherein machine learning systems, such as trained neural networks are used for indicating the fouling status the during the cleaning processes.

Method for cleaning of a device

A search system configured to predict further search intents of a user and to perform exploratory further searches and produce a number of search features and associated relevances and divergence quantifiers for displaying by user equipment at least two-dimensional so as to allow the user to identify relationship of various diverging search terms and to rapidly direct the searching towards information the existence of which may have been previously unknown to the user. Some of the search features can be concealed and shown to the user only if the user magnifies the corresponding area on a display showing the search features returned by the search engine. Files matching to varying degree with the present and predicted further searches are shown to the user with respective lists of search features.

Low-dimensional information discovery and presentation system, apparatus and method

A target device's location is estimated by a location estimation module (LEM) that comprises a probabilistic model (PM) for a plurality of sample points, each of which comprises a sample location and an expected distribution of signal values at that sample point. The location estimation module (LEM) makes a sequence (OS) of observations o, n=1, 2, 3 . . . , of signal values. Each observation corresponds to a respective location qalong the target device's path. The sequence of observations and the respective location constitute a hidden Markov model. The module estimates the target device's location qbased on the probabilistic model (PM) and the sequence of observations, wherein the sequence of observations comprises one or more future observations ofor which m is a positive integer. In other words, the target device's location is estimated, at least partially, based on knowledge of its future.

Sequence-based positioning technique

A method for estimating a receiver's location in a wireless communication environment having several channels. Each channel has at least one signal parameter that varies with location differently from the other channels. A set of calibration data is determined for each calibration point, each set including the location and at least one measured signal parameter for each of several channels. The calibration data serve as a basis for a statistical model of the signal parameters versus a receiver's location. A set of observed signal parameters is determined, the set including at least one signal parameter for each of several channels at the receiver's location. A location estimate approximating the location of the receiver is determined on the basis of the statistical model and the set of observed signal parameters.

Location estimation in wireless telecommunication networks

A method for determining an error estimate concerning a target device's location. The target device moves and communicates in a wireless environment using signals having at least one measurable signal value. A probabilistic model of the wireless environment indicates a probability distribution for signal values at several sample points in the wireless environment. A set of observations of signal values is made and the target device's location is estimated based on the probabilistic model and the set of observations. The error estimate is determined as a combination of products over several sample points. Each product comprises a probability distribution for the sample point in question being the target device's location and a distance function between the sample point in question and the target device's estimated location.

Error estimate concerning a target device's location operable to move in a wireless environment

A model construction module (MCM) for constructing a probabilistic model (PM) of a wireless environment (RN) in which a target device (T) communicates using signals that have a measurable signal value (x), such as signal strength. The model construction module forms several submodels () of the wireless environment (RN). Each submodel indicates a probability distribution (F&#x2013;F) for signal values at one or more locations (Q&#x2013;Q) in the wireless environment. The module combines the submodels to a probabilistic model (PM) of the wireless environment (RN), such that the probabilistic model indicates a probability distribution for signal values at several locations in the wireless environment. Alternatively, the model may insert new locations to a single model based on a combination of existing locations. The combination of submodels or existing locations includes combining the inverse cumulative distribution functions of the submodels or existing locations.

Probabilistic model for a positioning technique

A method and system for transforming a multivariate data domain into a low-dimensional visual representation. Probabilistic models of the data domain are utilized, and at least one probabilistic model is used to produce at least one predictive distribution. The predictive distributions are used as inputs to the visualization process, where the multidimensional space is converted to a low-dimensional space. In this process data vectors are considered similar, for example, if the corresponding instances of a predictive distribution, conditioned with the variable value assignments found in the data vectors, are similar. Consequently, similarity is not defined directly using the physical properties of the data vectors, but indirectly through the probabilistic predictive model(s).

Visualization method and visualization system

Growing community of inventors

Helsinki, Finland

Petri Jukka Myllymäki