Abstract: Subject matter disclosed herein may relate to wireless energy harvesting devices and may relate more particularly to system-on-a-chip testing for wireless energy harvesting devices.

Abstract: Briefly, example methods, apparatuses, and/or articles of manufacture are disclosed that may be implemented, in whole or in part, using one or more computing devices to facilitate and/or support one or more operations and/or techniques for creating and/or enhancing hardware obscurity via one or more randomization points, such as implemented in connection with one or more computing and/or communication networks and/or protocols.

Abstract: Briefly, example methods, apparatuses, and/or articles of manufacture are disclosed that may be implemented, in whole or in part, using one or more computing devices to facilitate and/or support one or more operations and/or techniques for an autonomous pseudo-random seed generator (APRSG) for embedded computing devices, which may include IoT-type devices, such as implemented in connection with one or more computing and/or communication networks and/or protocols.

Abstract: Subject matter disclosed herein may relate to wireless energy harvesting devices and may relate more particularly to system-on-a-chip testing for wireless energy harvesting devices.

Abstract: Briefly, example methods, apparatuses, and/or articles of manufacture are disclosed that may be implemented, in whole or in part, using one or more computing devices to enhance capabilities of peer devices. In an implementation, at least one agent to: identify one or more learnable capabilities enabled by one or more parameters that are accessible via receipt of one or more message at the one or more communication devices from one or more other computing devices; and determine a utility of augmenting at least one of the one or more learning engines with at least one of the one or more learnable capabilities.

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: Briefly, example methods, apparatuses, and/or articles of manufacture are disclosed that may be implemented, in whole or in part, using one or more computing devices to facilitate and/or support one or more operations and/or techniques for an autonomous pseudo-random seed generator (APRSG) for embedded computing devices, which may include IoT-type devices, such as implemented in connection with one or more computing and/or communication networks and/or protocols.

Abstract: Briefly, example methods, apparatuses, and/or articles of manufacture are disclosed that may be implemented, in whole or in part, using one or more computing devices to facilitate and/or support one or more operations and/or techniques for creating and/or enhancing hardware obscurity via one or more randomization points, such as implemented in connection with one or more computing and/or communication networks and/or protocols.

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: Briefly, example methods, apparatuses, and/or articles of manufacture are disclosed that may be implemented, in whole or in part, using one or more computing devices to facilitate and/or support one or more operations and/or techniques for autonomous verification of circuit design for IoT-type devices, which may include, for example, IoT-type devices operating in resource constrained or like environments.

Abstract: Subject matter disclosed herein may relate to at least one processor to establish a communication connection between a portable computing device and a particular external device, the at least one processor to initiate transmission of one or more signal packets representative of one or more communication, security, and/or cryptographic parameters to the particular external device responsive at least in part to the establishing the communication connection.

Abstract: Subject matter disclosed herein may relate to at least one processor to selectively authorize requests from one or more external devices to read from and/or write to particular bio-ledger entries and/or particular biosphere ledger entries based, at least in part, on security tokens provided by the one or more external devices and based, at least in part, on first and/or second sets of particular authorizations.

Abstract: Subject matter disclosed herein may relate to systems, devices, and/or processes for processing and/or communicating signals and/or states representative of epigenetic content.

Abstract: Subject matter disclosed herein may relate to systems, devices, and/or processes for processing and/or communicating signals and/or states representative of omic and/or environmental content.

Abstract: Subject matter disclosed herein may relate to systems, devices, and/or processes for processing signals and/or states representative of behavioral content in a computing device.

Abstract: Subject matter disclosed herein may relate to systems, devices, and/or processes for processing signals and/or states representative of omic content and/or behavioral content.

Abstract: Subject matter disclosed herein may relate to systems, devices, and/or processes for processing and/or partitioning signals and/or states representative of omic content.

Abstract: Various implementations described herein are directed to a memory device. The memory device includes a first interleaving circuit that receives data words and generates a first error correction code based on the received data words. The memory device includes a second interleaving circuit that receives the data words and generates a second error correction code based on the received data words as a complement to the first error correction code. The second interleaving circuit interleaves data bits from multiple different data words and stores modified data words based on the multiple different data words.