Abstract: A computing device is made up of a number of watertight, interengaging, modular computing units immersed in a tank (4) of pure water (41). Each of the computing units has a housing, a data processor within the housing and at least one external electrical power connector connected to the data processor and provided on the housing. Each housing presents a plurality of facets, with external electrical power connectors arranged on different facets of the housing, for the transfer of electrical power with adjacent modular computing units. Each external electrical power connector is of platinum or plated with platinum; and the water (41) has an electrical conductivity of 5×10?3 S/m or less. The computing units interengage to form ball shapes, defining square holes 14a in the faces of the balls to allow water (41) to pass through the system by convection flow.

Abstract: To record events, a vector A is taken of n measurements An. Each measurement An is converted into a binary vector Bn using a binning process. Measurements in vector Bn, are transformed using a kernel operation, to make a vector Cn. All vectors Cn are concatenated into a single vector D. A binary vector M is created with P bits set. A date and vector A are recorded against vector M in a database Z. Vector D is associated into a correlation matrix memory R against vector M. To recall events, the above steps are repeated up to concatenating all vectors Cn into single vector D. Vector D is then used to access a correlation matrix memory R to recall an association vector S, and vector S is applied directly or indirectly to database Z to recover the or each date associated with vector S. This may facilitate the monitoring of assets such as generators, gas turbines, motors, and larger assets such as trains, boats and planes, to ensure that they continue to operate reliably.

Abstract: A subject (1) is illuminated by a light source (2) and observed by a pair of cameras (3). The outputs of the cameras (3) are input to an image processor (4), operated under the control of a controller (5), which also controls operation of the light source (2). The light source (2) transmits radiation in a frequency range that is limited substantially to a region of increased opacity in the atmospheric transmission spectrum. This may enable the illumination to be quite clearly discriminated, even at relatively great distances and in bright daylight.

Abstract: To record events, a vector A is taken of n measurements An. Each measurement An is converted into a binary vector Bn using a binning process. Measurements in vector Bn, are transformed using a kernel operation, to make a vector Cn. All vectors Cn are concatenated into a single vector D. A binary vector M is created with P bits set. A date and vector A are recorded against vector M in a database Z. Vector D is associated into a correlation matrix memory R against vector M. To recall events, the above steps are repeated up to concatenating all vectors Cn into single vector D. Vector D is then used to access a correlation matrix memory R to recall an association vector S, and vector S is applied directly or indirectly to database Z to recover the or each date associated with vector S. This may facilitate the monitoring of assets such as generators, gas turbines, motors, and larger assets such as trains, boats and planes, to ensure that they continue to operate reliably.

Abstract: A subject (1) is illuminated by a light source (2) and observed by a pair of cameras (3). The outputs of the cameras (3) are input to an image processor (4), operated under the control of a controller (5), which also controls operation of the light source (2). The light source (2) transmits radiation in a frequency range that is limited substantially to a region of increased opacity in the atmospheric transmission spectrum. This may enable the illumination to be quite clearly discriminated, even at relatively great distances and in bright daylight.

Abstract: A telecommunications fault location and diagnostic system employs a remote test unit (RTU) to collect system parameter data. The RTU is operatively coupled to a trained neural network, which receives the system parameter data from the RTU. The neural network is trained using pre-screened historical fault data, which is stored in a database. Once trained, the neural network classifies the RTU data into one of a predetermined number of fault probabilities.