Abstract: 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.

Abstract: 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.

Abstract: 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 on, n=1, 2, 3 . . . , of signal values. Each observation corresponds to a respective location qn along 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 qn based on the probabilistic model (PM) and the sequence of observations, wherein the sequence of observations comprises one or more future observations on+m for 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.

Abstract: 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.

Abstract: 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.

Abstract: 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 (611–631) of the wireless environment (RN). Each submodel indicates a probability distribution (F1–F3) for signal values at one or more locations (Q1–QY) 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.