Abstract: A method and apparatus for processing data, the data including a set of one or more system inputs; and a set of one or more system outputs; wherein each system output corresponds to a respective system input. Each system input can include a plurality of data points, such that at least one of these data points is from a different data source to at least one other of those data points. The method includes performing a kernel function on a given system input from the data and a further system input to provide kernelized data; and inferring a value indicative of a significance of data from a particular data source; wherein the inferring includes applying a regression technique to the kernelized data.

Abstract: A method and apparatus for processing data, the data including: a set of one or more system inputs; and a set of one or more system outputs; wherein each system output corresponds to a respective system input; each system input includes a plurality of data points, a first data point in the plurality and a second data point in the plurality being from a same raw data source, and the first data point being pre-processed using a different pre-processing method relative to a pre-processing method used to pre-process the second data point, the method including: for each of the first and second data points, inferring a value indicative of a significance of the pre-processing method used to pre-process that data point; wherein the inferring includes performing a machine learning algorithm on a given system input from the data and a further system input.

Abstract: The method includes receiving a plurality of target observations from a respective plurality of sensors and using the target observations to compute target state estimates. Each of the target state estimates is assessed to check if it suffers from one of a set of modelled possible fault types. The target state estimates are adjusted to compensate for any modelled fault type when it is assessed to be suffering from that fault type. A reliability value is computed for each of the target state estimates and the target state estimates are fused together based on the computed reliability values to produce a fused target state estimate.

Abstract: A method and apparatus for processing data, the data including: a set of one or more system inputs; and a set of one or more system outputs; wherein each system output corresponds to a respective system input; each system input includes a plurality of data points, a first data point in the plurality and a second data point in the plurality being from a same raw data source, and the first data point being pre-processed using a different pre-processing method relative to a pre-processing method used to pre-process the second data point, the method including: for each of the first and second data points, inferring a value indicative of a significance of the pre-processing method used to pre-process that data point; wherein the inferring includes performing a machine learning algorithm on a given system input from the data and a further system input.

Abstract: A method of estimating states of a plurality of targets tracked by a plurality of sensors. The method includes obtaining a plurality of data sets based on measurements taken by a plurality of sensors, each data set including information on states of a plurality of targets. The method then generates a plurality of target correlation data sets describing correlation of the targets within a first data set and at least one other data set. A Generalised Covariance Union (GCU) technique is used to merge at least some of the plurality of target correlation data sets to generate an estimate of the states of the plurality of targets.

Abstract: The method includes receiving a plurality of target observations from a respective plurality of sensors and using the target observations to compute target state estimates. Each of the target state estimates is assessed to check if it suffers from one of a set of modelled possible fault types. The target state estimates are adjusted to compensate for any modelled fault type when it is assessed to be suffering from that fault type. A reliability value is computed for each of the target state estimates and the target state estimates are fused together based on the computed reliability values to produce a fused target state estimate.

Abstract: A smoking article such as a cigarette, or a smoke filter or other component thereof, comprises a catalyst for the oxidation of carbon monoxide to carbon dioxide. The catalyst comprises gold, and an oxide of zinc or titanium. Titania and mixed oxides of zinc and copper or aluminum are preferred (TiO2, ZnO, ZnAlOx or CuZnOx). The gold preferably comprises species in the metallic state)(Au0), together with species in an oxidized state (Au?+).

Abstract: A method of estimating states of a plurality of targets tracked by a plurality of sensors. The method includes obtaining a plurality of data sets based on measurements taken by a plurality of sensors, each data set including information on states of a plurality of targets. The method then generates a plurality of target correlation data sets describing correlation of the targets within a first data set and at least one other data set. A Generalised Covariance Union (GCU) technique is used to merge at least some of the plurality of target correlation data sets to generate an estimate of the states of the plurality of targets.

Abstract: A marine riser, particularly but not exclusively for use in connecting a subsea well installation to a dynamically positioned servicing vessel, comprises at least one section formed from rigid pipe and at least one section formed from flexible pipe. Preferably, the upper and lower sections (10, 12) are formed from fixed lengths of flexible pipe and the central section is formed from a plurality of lengths of rigid pipe which may be assembled to make up any required length. The riser may be deployed from a moonpool of the vessel and serves to accommodate movements of the vessel on the surface. This allows the use of a dynamically positioned service vessel rather than a conventional drilling rig.

Abstract: An apparatus for laying marine pipelines of the type in which the pipeline is assembled along a horizontal axis on board a pipelaying vessel prior to being launched therefrom; in which the pipeline is plastically bent to a desired launch angle and, in most cases, at least partially straightened thereafter. In preferred embodiments, the pipeline is bent through an angle greater than 90.degree.. In the most preferred embodiments, the pipeline is diverted through an angle of substantially 270.degree., plus or minus an angle allowing variation in the launch angle from the vertical, and crosses itself prior to entering the water. The pipeline (102) is diverted by being passed around a wheel-like diverter structure (104), the pipeline being assembled in multiple fabrication lines to produce stalks which are aligned and connected in a central firing line prior to being fed around the diverter structure. Other variations are also described.

Abstract: Methods and apparatus for laying marine pipelines of the type in which the pipeline is assembled along a horizontal axis on board a pipe laying vessel prior to being launched therefrom; in which the pipeline is plastically bent to a desired launch angle and, in most cases, at least partially straightened thereafter. In preferred embodiments, the pipeline is bent through an angle greater than 90.degree.. In the most preferred embodiments, the pipeline is diverted, initially upwardly, through an angle of substantially 180.degree. sheave (28), prior to being bent downwards to the desired launch angle by an adjustable angle ramp (30). Other variations are also described.