Abstract: Embodiments of the present invention provide a human-machine interaction method of determining an intended target of an object in relation to a user interface, comprising determining a three-dimensional location of the object at a plurality of time intervals, determining a metric associated with each of a plurality of items of the user interface, the metric indicative of the respective item being the intended target of the object, wherein the metric is determined based upon a model and the location of the object in three dimensions at the plurality of time intervals, and determining, using a Bayesian reasoning process, the intended target from the plurality of items of the user interface based on the metric associated with each of the plurality of items.

Abstract: A signal processing apparatus and method are provided for separating a plurality of mixture signals from a MIMO system to iteratively obtain a plurality of output signals. The plurality of mixture signals are a response of the MIMO system to a plurality of source signals. The signal processing apparatus comprises a plurality of blind source separators including a first blind source separator based on a first blind source separation technique or algorithm and a second blind source separator based on a second blind source separation technique or algorithm, wherein the first blind source separator is configured to compute a first plurality of preliminary output signals on the basis of a first set of coefficients describing the MIMO system and wherein the second blind source separator is configured to compute a second plurality of preliminary output signals on the basis of a second set of coefficients describing the MIMO system.

Abstract: This invention relates to methods, apparatus, and computer program code for processing acoustic signal data to determine where an object has been tapped with a stylus, finger nail or the like. The method involved storing a set of labelled training data comprising digitized waveforms from a sensor for taps at a plurality of different locations. The labelled training data is then processed to determine mean value and covariance data for the waveforms, which is afterwards used in conjunction with a digitized waveform of a tap at an unknown location to identify the location of the tap. Preferably the covariance is decomposed into a plurality of basis functions for each region each with a respective weighting, which are used to represent captured data for an unknown tap and parameters of the representation are classified to locate the tap.

Abstract: A signal processing apparatus and method are provided for separating a plurality of mixture signals from a MIMO system to iteratively obtain a plurality of output signals. The plurality of mixture signals are a response of the MIMO system to a plurality of source signals. The signal processing apparatus comprises a plurality of blind source separators including a first blind source separator based on a first blind source separation technique or algorithm and a second blind source separator based on a second blind source separation technique or algorithm, wherein the first blind source separator is configured to compute a first plurality of preliminary output signals on the basis of a first set of coefficients describing the MIMO system and wherein the second blind source separator is configured to compute a second plurality of preliminary output signals on the basis of a second set of coefficients describing the MIMO system.

Abstract: Embodiments of the present invention provide a human-machine interaction method of determining an intended target of an object in relation to a user interface, comprising determining a three-dimensional location of the object at a plurality of time intervals, determining a metric associated with each of a plurality of items of the user interface, the metric indicative of the respective item being the intended target of the object, wherein the metric is determined based upon a model and the location of the object in three dimensions at the plurality of time intervals, and determining, using a Bayesian reasoning process, the intended target from the plurality of items of the user interface based on the metric associated with each of the plurality of items.

Abstract: This invention relates to methods, apparatus, and computer program code for processing acoustic signal data to determine where an object has been tapped with a stylus, finger nail or the like. The method involved storing a set of labelled training data comprising digitized waveforms from a sensor for taps at a plurality of different locations. The labelled training data is then processed to determine mean value and covariance data for the waveforms, which is afterwards used in conjunction with a digitized waveform of a tap at an unknown location to identify the location of the tap. Preferably the covariance is decomposed into a plurality of basis functions for each region each with a respective weighting, which are used to represent captured data for an unknown tap and parameters of the representation are classified to locate the tap.

Abstract: A method is provided of extracting desired signals st from contaminated signals yt measured via respective communication channels. The system comprising the desired signals st and the channels is modelled as a state space model. In the model, the desired signals have time-varying characteristics which vary more quickly than second time-varying characteristics of the channels.

Abstract: A method is provided of extracting desired signals st from contaminated signals yt measured via respective communication channels. The system comprising the desired signals st and the channels is modelled as a state space model. In the model, the desired signals have time-varying characteristics which vary more quickly than second time-varying characteristics of the channels.

Abstract: A method is provided of extracting desired signals st from contaminated signals yt measured via respective communication channels. The system comprising the desired signals st and the channels is modelled as a state space model. In the model, the desired signals have time-varying characteristics which vary more quickly than second time-varying characteristics of the channels.