Abstract: A computer-implemented method of determining a position of a vehicle within a transport network comprises obtaining track geometry data indicating track geometry of at least a part of the transport network; receiving first sensor data from an inertial measurement unit mounted to the vehicle; executing a Bayesian estimation filter algorithm to predict a position of the vehicle, wherein the Bayesian estimation filter algorithm comprises a process model, the process model comprising a strapdown inertial navigation algorithm, and wherein the strapdown inertial navigation algorithm generates data indicative of the predicted position of the vehicle based at least upon the first sensor data and the track geometry data such that the predicted position of the vehicle lies on a track defined by the track geometry data; receiving second sensor data from a sensor other than an inertial measurement unit, wherein the sensor is mounted to the vehicle; executing the Bayesian estimation filter algorithm to update the predicte

Abstract: A computer-implemented method of determining a position of a vehicle 10 within a transport network comprises: obtaining track geometry data indicating track geometry of at least a part of the transport network; determining, based upon the track geometry data, that the vehicle is approaching a junction; determining, based upon the track geometry data, a plurality of route options from the junction; generating a plurality of Bayesian estimation filter algorithms each associated with a respective one of the plurality of route options and configured to estimate a position of the vehicle based upon the track geometry data indicative of the associated route option, wherein the plurality of Bayesian estimation filter algorithms are configured to output data indicative of probabilities of the vehicle taking the associated route options; monitoring the output of the plurality of Bayesian estimation filter algorithms as the vehicle passes through the junction; and determining the route option taken by the vehicle by se

Abstract: A machine vision system comprising receiving means configured to receive image data indicative of an object to be classified where there is provided processing means with an initial neural network, the processing means configured to determine a differential equation describing the initial neural network algorithm based on the neural network parameters, and to determine a solution to the differential equation in the form of a series expansion; and to convert the series expansion to a finite series expansion by limiting the number of terms in the series expansion to a finite number; and to determine the output classification in dependence on the finite series expansion.

Abstract: A computer-implemented method of determining a position of a vehicle within a transport network comprises obtaining track geometry data indicating track geometry of at least a part of the transport network; receiving first sensor data from an inertial measurement unit mounted to the vehicle; executing a Bayesian estimation filter algorithm to predict a position of the vehicle, wherein the Bayesian estimation filter algorithm comprises a process model, the process model comprising a strapdown inertial navigation algorithm, and wherein the strapdown inertial navigation algorithm generates data indicative of the predicted position of the vehicle based at least upon the first sensor data and the track geometry data such that the predicted position of the vehicle lies on a track defined by the track geometry data; receiving second sensor data from a sensor other than an inertial measurement unit, wherein the sensor is mounted to the vehicle; executing the Bayesian estimation filter algorithm to update the predicte

Abstract: A computer-implemented method of determining a position of a vehicle 10 within a transport network comprises: obtaining track geometry data indicating track geometry of at least a part of the transport network; determining, based upon the track geometry data, that the vehicle is approaching a junction; determining, based upon the track geometry data, a plurality of route options from the junction; generating a plurality of Bayesian estimation filter algorithms each associated with a respective one of the plurality of route options and configured to estimate a position of the vehicle based upon the track geometry data indicative of the associated route option, wherein the plurality of Bayesian estimation filter algorithms are configured to output data indicative of probabilities of the vehicle taking the associated route options; monitoring the output of the plurality of Bayesian estimation filter algorithms as the vehicle passes through the junction; and determining the route option taken by the vehicle by se

Abstract: A system and method of supervising vehicle positioning of a vehicle along a guideway where the vehicle comprising a supervisory controller, at least two controllers communicatively connected with the supervisory controller, an inertial measurement unit (IMU) and a speed measurement sensor includes receiving, by the controllers, speed measurements from the speed measurement sensor and motion measurements from the inertial measurement unit. The two controllers each estimate the along-track position of the vehicle using a track constrained UKF function based on the received speed measurements and motion measurements. The system executes protection level and protection level supervision functions on the supervisory controller to validate the along-track position estimates. The protection level supervision function uses a Stanford diagram verification technique.

Abstract: A heap of a material to be leached to recover a valuable metal from the material includes an electromagnetic heating system to generate heat in situ in the heap.

Abstract: A machine vision system comprising receiving means configured to receive image data indicative of an object to be classified where there is provided processing means with an initial neural network, the processing means configured to determine a differential equation describing the initial neural network algorithm based on the neural network parameters, and to determine a solution to the differential equation in the form of a series expansion; and to convert the series expansion to a finite series expansion by limiting the number of terms in the series expansion to a finite number; and to determine the output classification in dependence on the finite series expansion.

Abstract: The present disclosure provides an apparatus for treatment of mined material. The apparatus comprises a source for generating electromagnetic radiation and a microwave inlet region for exposing fragments of the mined material to the electromagnetic radiation. Further, the apparatus comprises a reflective structure adjacent the microwave inlet region and providing, or surrounding, a passage for guiding the fragments of the mined material to the microwave inlet region. The reflective structure is arranged to attenuate penetration of the electromagnetic radiation from the microwave inlet region into the passage during throughput of the fragments of the mined material.

Abstract: A heap of a material to be leached to recover a valuable metal from the material includes an electromagnetic heating system to generate heat in situ in the heap.

Abstract: A heap of a material to be leached to recover a valuable metal from the material includes an electromagnetic heating system to generate heat in situ in the heap.

Abstract: A heap of a material to be leached to recover a valuable metal from the material includes an electromagnetic heating system to generate heat in situ in the heap.

Abstract: A method of sorting fragments of mined material that includes valuable and non-valuable materials is disclosed. The method comprising heating the fragments by heating selected materials within the fragments and thermally analysing fragments of the mined material to determine the temperature of each fragment. The method further comprises sorting fragments on the basis of the thermal analysis to separate fragments having a temperature within a temperature band defined by a range between an upper temperature limit and a lower temperature limit from fragments having a temperature outside the temperature band. The upper and lower temperature limits are selected to identify fragments as containing an amount of valuable material that is economically viable to extract based on the fragment temperature. Also disclosed are an apparatus for sorting mined material and a method of recovering valuable material by sorting in according with the sorting method.

Abstract: The present disclosure provides an apparatus for treatment of mined material. The apparatus comprises a source for generating electromagnetic radiation and a microwave inlet region for exposing fragments of the mined material to the electromagnetic radiation. Further, the apparatus comprises a reflective structure adjacent the microwave inlet region and providing, or surrounding, a passage for guiding the fragments of the mined material to the microwave inlet region. The reflective structure is arranged to attenuate penetration of the electromagnetic radiation from the microwave inlet region into the passage during throughput of the fragments of the mined material.