Abstract: This invention relates to a demountable cassette construction system for building a structure comprising one or more demountable cassettes, wherein the or each of the one or more demountable cassettes are attachable to one or more adjacent demountable cassettes via demountable joints, wherein the demountable joints permit the one or more demountable cassettes to be removed from a structure without removal of and/or damage to the or each of the one or more adjacent demountable cassettes.

Abstract: A method of configuring a telecommunications network comprising the steps of: providing a first set of time dependent network data, a network related parameter varying with time; identifying abnormalities embedded within said first set of data; providing a first set of external event data; associating respective abnormalities with corresponding event data; generating a model to represent an impact of the, or each, class of event, represented by the event data; using the, or each, model in combination with extrapolated data to predict future variation in the network related parameter; repeating this simulation for alternative input conditions; selecting a solution from the output of one the simulations; and implementing a modification to the network corresponding to the/or each respective input condition.

Abstract: A method of configuring a telecommunications network comprising the steps of: providing a first set of time dependent network data, a network related parameter varying with time; identifying abnormalities embedded within said first set of data; providing a first set of external event data; associating respective abnormalities with corresponding event data; generating a model to represent an impact of the, or each, class of event, represented by the event data; using the, or each, model in combination with extrapolated data to predict future variation in the network related parameter; repeating this simulation for alternative input conditions; selecting a solution from the output of one the simulations; and implementing a modification to the network corresponding to the/or each respective input condition.

Abstract: A downhole tool assembly for use in a drilling string includes a motor, a flow head, and a flow restrictor. The flow head is coupled to the motor and comprises a plurality of ports permitting fluid communication therethrough. The flow restrictor is in fluid communication with the flow head and comprises a plurality of ports permitting fluid communication therethrough. The flow restrictor is stationary with respect to the flow head, which rotates. As the flow head rotates, one or more of the plurality of ports of the flow head enters into and out of alignment with one or more of the plurality of ports of the flow restrictor such that fluid flow through the ports of the flow head and the flow restrictor is constrained to a cyclic, polyrhythmic pattern optionally including at least one interval where the fluid flow is substantially blocked by the flow restrictor.

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 downhole tool assembly for use in a drilling string includes a motor, a flow head, and a flow restrictor. The flow head is coupled to the motor and comprises a plurality of ports permitting fluid communication therethrough. The flow restrictor is in fluid communication with the flow head and comprises a plurality of ports permitting fluid communication therethrough. The flow restrictor is stationary with respect to the flow head, which rotates. As the flow head rotates, one or more of the plurality of ports of the flow head enters into and out of alignment with one or more of the plurality of ports of the flow restrictor such that fluid flow through the ports of the flow head and the flow restrictor is constrained to a cyclic, polyrhythmic pattern optionally including at least one interval where the fluid flow is substantially blocked by the flow restrictor.

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: An apparatus and a method are disclosed for tracking a plurality of targets (e.g. land-based vehicles) The method can include: for a first time-step, estimating a state of each target; at a second time-step, measuring values for a state of a target; for the second time-step, estimating a state of each target using the estimated target states for the first time-step; updating the estimated target states for the second time-step using the measured values; and performing an identity management process to estimate a probability that a particular state measurement corresponds to a particular target by providing a mixing matrix, wherein an element of the mixing matrix is based on an overlap between the updated estimated target states of targets in an underlying state space of the estimated target states.

Abstract: An apparatus and a method are disclosed for tracking a plurality of targets (e.g. land-based vehicles). The method can include: for a first time-step, estimating a state of each respective target; at a second time-step, measuring values for a state of each target; for the second time-step, estimating a state of each target using the estimated target states for the first time-step; updating the estimated target states for the second time-step by performing a Joint Probabilistic Data Association process using the measured values; and performing an identity management process to estimate a probability that a particular state measurement corresponds to a particular.

Abstract: A method and apparatus for selecting a value or change in value of a measurement variable for an observation of an object, comprising: receiving models for the object defined in terms of an observation parameter and a measurement variable; selecting values of the measurement variable; for each model, determining a value of the observation parameter for each selected value; for each selected value, determining a value of an expected classification potential level using the determined values; and selecting a value of the measurement variable dependent upon the potential level values; wherein the potential level is an expected level of: the information or lack of information, and/or the certainty or uncertainty, with which the object could be classified if a measurement of the observation parameter were taken of the object at the respective value of the measurement variable.

Abstract: A method and apparatus for processing an observation of an object to be classified, the method comprising using a particle filter in which an observation of an object to be classified is used to weight particles on a plurality of models for the object. The method may further comprise performing at least one further iteration of using the particle filter to weight the particles on the plurality of models for the object using a further observation of the object to be classified. The method may further comprise providing respective classification probabilities that each respective model corresponds to the object based on the weightings of the particles of the respective model. Each of the respective particles may be divided into a respective plurality of sub-particles.

Abstract: An apparatus and method are disclosed for analysing a process. An exemplary method includes: generating a process template; and determining a probabilistic model specifying the process template. The method can include use of task nodes for tasks of the process; observables nodes for observables that may be caused by performance of the tasks; and a background activities node, wherein observables may further be caused by background activities of the background node. The method can include use of task nodes for tasks of the process; observables nodes for observables that may be caused by performance of the tasks; and a background activities node, observables may further be caused by background activities of the background node. The method can include measuring values of an observable corresponding to one of the observables nodes; and updating a probabilistic estimate of the process state using the measured values.

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 kernelised 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 kernelised data.

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 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, 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 including performing a kernel function on a given system input from the data and a further system input to provide kernelised data; and inferring a value for further system output corresponding to the further system input; wherein the step of inferring includes applying a Gaussian Process to the kernelised data. The data sources may be heterogeneous data sources.

Abstract: A discretely tunable locally seeded laser device is provided. The discretely tunable locally seeded laser device comprises: a first tunable laser comprising first discrete output modes; and, a second tunable laser arranged to receive output from the first tunable laser, but otherwise optically isolated from the first tunable laser, the second tunable laser comprising second discrete output modes, such that one first discrete output mode can optically seed an aligned second discrete output mode in a given position of one or more of the first discrete output modes and the second discrete output modes, the aligned second discrete output mode comprising a dominant output.

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: An apparatus and method are disclosed for analysing a process. An exemplary method includes: generating a process template; and determining a probabilistic model specifying the process template. The method can include use of task nodes for tasks of the process; observables nodes for observables that may be caused by performance of the tasks; and a background activities node, wherein observables may further be caused by background activities of the background node. The method can include use of task nodes for tasks of the process; observables nodes for observables that may be caused by performance of the tasks; and a background activities node, observables may further be caused by background activities of the background node. The method can include measuring values of an observable corresponding to one of the observables nodes; and updating a probabilistic estimate of the process state using the measured values.

Abstract: An apparatus and method are disclosed for tracking plural targets. The method can include: estimating a state of each target; measuring values for a state of a target, and measuring a value of an identity parameter for that target to distinguish that target from another target. A state of each target can be estimated using the estimated target states for a first time-step; and updating the estimated target states for a second time-step can include performing a Joint Probabilistic Data Association process using measured state values and measured identity parameter values, wherein estimating a state of each target can be implemented using a particle filter.