Abstract: A method for filtering adversarial noise from an input signal is provided. The method comprises receiving an input signal which has an unknown level of adversarial noise. The input signal is filtered with a neural network to remove noise from the received input signal, thereby producing a filtered signal. A confidence value is calculated, the confidence value being associated with the filtered signal, and indicative of a level of trust relating to the filtered signal. The filtered signal and the confidence value may then be output.

Abstract: A method comprising the steps of obtaining image data captured at an image sensor using a focus configuration. A distance is determined for one or more objects in the image data, based on the focus configuration and a sharpness characteristic of the image data of the object. A depth map is then generated based on the determined distance.

Abstract: Broadly speaking, embodiments of the present technique provide apparatus and methods for improved wear-levelling in (volatile and non-volatile) memories. In particular, the present wear-levelling techniques comprise moving static memory states within a memory, in order to substantially balance writes across all locations within the memory.

Abstract: Disclosed subject matter relates generally to forming a set of training parameters applicable to detection of two or more entities between and/or among a distribution of entities from a plurality of digitally stored observations.

Abstract: A computer-implemented method includes generating, using a scene generator, first candidate scene data; obtaining reference scene data corresponding to a predetermined reference scene; processing the first candidate scene data and the reference scene data, using a scene discriminator, to generate first discrimination data for estimating whether each of the first candidate scene data and the reference scene data corresponds to a predetermined reference scene; updating a set of parameter values for the scene discriminator using the first discrimination data; generating, using the scene generator, second candidate scene data; processing the second candidate scene data, using the scene discriminator with the updated set of parameter values for the scene discriminator, to generate second discrimination data for estimating whether the second candidate scene data corresponds to a predetermined reference scene; and updating a set of parameter values for the scene generator using the second discrimination data.

Abstract: A computer-implemented method includes generating, using a scene generator, first candidate scene data; obtaining reference scene data corresponding to a predetermined reference scene; processing the first candidate scene data and the reference scene data, using a scene discriminator, to generate first discrimination data for estimating whether each of the first candidate scene data and the reference scene data corresponds to a predetermined reference scene; updating a set of parameter values for the scene discriminator using the first discrimination data; generating, using the scene generator, second candidate scene data; processing the second candidate scene data, using the scene discriminator with the updated set of parameter values for the scene discriminator, to generate second discrimination data for estimating whether the second candidate scene data corresponds to a predetermined reference scene; and updating a set of parameter values for the scene generator using the second discrimination data.

Abstract: The present disclosure relates generally to Internet of Things (IoT)-type devices and, more particularly, to infrastructure for validating updates via a network of IoT-type devices.

Abstract: The present disclosure relates generally to Internet of Things (IoT)-type devices and, more particularly, to infrastructure for validating updates via a network of IoT-type devices.

Abstract: A method comprising the steps of obtaining image data captured at an image sensor using a focus configuration. A distance is determined for one or more objects in the image data, based on the focus configuration and a sharpness characteristic of the image data of the object. A depth map is then generated based on the determined distance.

Abstract: Broadly speaking, embodiments of the present technique provide an apparatus and methods for improved wear-levelling in non-volatile memory (NVM) devices. In particular, the present wear-levelling techniques operate on small blocks within a memory device, at a finer scale/granularity than that used by common wear-levelling techniques which often remap large blocks (e.g. several kilobytes) of data.

Abstract: Subject matter disclosed herein may relate to buffers, and may relate more particularly to non-volatile buffers for memory operations.

Abstract: Broadly speaking, embodiments of the present technique provide apparatus and methods for improved wear-levelling in (volatile and non-volatile) memories. In particular, the present wear-levelling techniques comprise moving static memory states within a memory, in order to substantially balance writes across all locations within the memory.

Abstract: A device controller and method are provided for performing a plurality of write transactions atomically within a non-volatile data storage device. Each transaction specifies a logical address and the method comprises creating an address translation map for the logical addresses specified by the plurality of write transactions, by referencing an address translation record within the non-volatile data storage device to determine for each logical address a corresponding physical address within the data storage device. Further, if the corresponding physical address indicated in the address translation record already contains valid data, the logical address is remapped to a new physical address in the address translation map. However, at this point the address translation record as stored in the data storage device is not updated. Instead, the plurality of write transactions are performed using the logical address to physical address mapping in the address translation map.

Abstract: Broadly speaking, embodiments of the present technique provide an apparatus and methods for improved wear-levelling in non-volatile memory (NVM) devices. In particular, the present wear-levelling techniques operate on small blocks within a memory device, at a finer scale/granularity than that used by common wear-levelling techniques which often remap large blocks (e.g. several kilobytes) of data.

Abstract: A device controller and method are provided for performing a plurality of write transactions atomically within a non-volatile data storage device. Each transaction specifies a logical address and the method comprises creating an address translation map for the logical addresses specified by the plurality of write transactions, by referencing an address translation record within the non-volatile data storage device to determine for each logical address a corresponding physical address within the data storage device. Further, if the corresponding physical address indicated in the address translation record already contains valid data, the logical address is remapped to a new physical address in the address translation map. However, at this point the address translation record as stored in the data storage device is not updated. Instead, the plurality of write transactions are performed using the logical address to physical address mapping in the address translation map.