Abstract: Examples herein relate to a solid state drive that includes a media, first circuitry, and second circuitry. In some examples, the first circuitry is to execute one or more commands. In some examples, the second circuitry is to receive a configuration of at one type of command, where the configuration is to define an amount of media bandwidth allocated for the at one type of command; receive a command; and assign the received command to the first circuitry for execution.

Abstract: Examples herein relate to a solid state drive that includes a media, first circuitry, and second circuitry. In some examples, the first circuitry is to execute one or more commands. In some examples, the second circuitry is to receive a configuration of at one type of command, where the configuration is to define an amount of media bandwidth allocated for the at one type of command; receive a command; and assign the received command to the first circuitry for execution.

Abstract: Examples herein relate to a solid state drive that includes a media, first circuitry, and second circuitry. In some examples, the first circuitry is to execute one or more commands. In some examples, the second circuitry is to receive a configuration of at one type of command, where the configuration is to define an amount of media bandwidth allocated for the at one type of command; receive a command; and assign the received command to the first circuitry for execution.

Abstract: Examples herein relate to a solid state drive that includes a media, a processing system, and a media command arbiter configured to permit execution of a specific allocation of storage and compute commands based on a configuration, wherein the media command arbiter is to transfer commands to the media based on the configuration. The media can be locally connected to a compute engine processing system that is configurable to perform computations on data stored in the media. The configuration can indicate a number of compute commands and storage commands that are permitted to be performed over a period of time or media bandwidth allocated to compute commands and storage commands. The processing system can include an inference engine that performs one or more of: data pattern recognition, image recognition, augmented reality overlay applications, face recognition, object recognition, or voice recognition, language translation.

Abstract: Techniques for storage and processing for distributed file systems are disclosed. In the illustrative embodiment, padding is placed between data elements in a file to be stored on a distributed file system. The file is to be split into several objects in order to be stored in the distributed file system, and the padding is used to prevent a data element from being split across two different objects. The objects are stored on data nodes, which analyze the objects to determine which data elements are present in the object as well at the location of those objects. The location of the objects is saved on the data storage device, and those locations can be used to perform queries on the data elements in the object on the data storage device itself. Such an approach can reduce transfer of data elements from data storage to local memory of the data node.

Abstract: Examples herein relate to a solid state drive that includes a media, a processing system, and a media command arbiter configured to permit execution of a specific allocation of storage and compute commands based on a configuration, wherein the media command arbiter is to transfer commands to the media based on the configuration. The media can be locally connected to a compute engine processing system that is configurable to perform computations on data stored in the media. The configuration can indicate a number of compute commands and storage commands that are permitted to be performed over a period of time or media bandwidth allocated to compute commands and storage commands. The processing system can include an inference engine that performs one or more of: data pattern recognition, image recognition, augmented reality overlay applications, face recognition, object recognition, or voice recognition, language translation.

Abstract: A method and apparatus for user activity-based dynamic power management and policy creation for mobile platforms are described. In one embodiment, the method includes the monitoring of one or more sensor values of a mobile platform device to gather sensor activity data. Once the sensor activity data is gathered, the user state may be predicted according to the gathered user activity and an updated user state model. In one embodiment, the user state model is updated according to the sensor activity data. In one embodiment, a switch occurs from the present power management policy to a new power management policy if the new user state differs from a present user state by a predetermined amount. In one embodiment, at least one time-out parameter of a selected power management policy may be adjusted to comply with a predicted user state. Other embodiments are described and claimed.

Abstract: A method and apparatus for user activity-based dynamic power management and policy creation for mobile platforms are described. In one embodiment, the method includes the monitoring of one or more sensor values of a mobile platform device to gather sensor activity data. Once the sensor activity data is gathered, the user state may be predicted according to the gathered user activity and an updated user state model. In one embodiment, the user state model is updated according to the sensor activity data. In one embodiment, a switch occurs from the present power management policy to a new power management policy if the new user state differs from a present user state by a predetermined amount. In one embodiment, at least one time-out parameter of a selected power management policy may be adjusted to comply with a predicted user state. Other embodiments are described and claimed.

Abstract: A method and apparatus for user activity-based dynamic power management and policy creation for mobile platforms are described. In one embodiment, the method includes the monitoring of one or more sensor values of a mobile platform device to gather sensor activity data. Once the sensor activity data is gathered, the user state may be predicted according to the gathered user activity and an updated user state model. In one embodiment, the user state model is updated according to the sensor activity data. In one embodiment, a switch occurs from the present power management policy to a new power management policy if the new user state differs from a present user state by a predetermined amount. In one embodiment, at least one time-out parameter of a selected power management policy may be adjusted to comply with a predicted user state. Other embodiments are described and claimed.

Abstract: A method and apparatus for user activity-based dynamic power management and policy creation for mobile platforms are described. In one embodiment, the method includes the monitoring of one or more sensor values of a mobile platform device to gather sensor activity data. Once the sensor activity data is gathered, the user state may be predicted according to the gathered user activity and an updated user state model. In one embodiment, the user state model is updated according to the sensor activity data. In one embodiment, a switch occurs from the present power management policy to a new power management policy if the new user state differs from a present user state by a predetermined amount. In one embodiment, at least one time-out parameter of a selected power management policy may be adjusted to comply with a predicted user state. Other embodiments are described and claimed.

Abstract: A method and apparatus for user activity-based dynamic power management and policy creation for mobile platforms are described. In one embodiment, the method includes the monitoring of one or more sensor values of a mobile platform device to gather user activity data. Once the user activity data is gathered, the user state may be predicted according to the gathered user activity and an updated user state model. In one embodiment, the user state model is updated according to the user activity data. In one embodiment, a switch occurs from the current power management policy to a new power management policy if the new user state differs from a current user state by a predetermined amount. In one embodiment, at least one time-out parameter of a selected power management policy may be adjusted to comply with a predicted user state. Other embodiments are described and claimed.

Abstract: A method and apparatus for user activity-based dynamic power management and policy creation for mobile platforms are described. In one embodiment, the method includes the monitoring of one or more sensor values of a mobile platform device to gather sensor activity data. Once the sensor activity data is gathered, the user state may be predicted according to the gathered user activity and an updated user state model. In one embodiment, the user state model is updated according to the sensor activity data. In one embodiment, a switch occurs from the present power management policy to a new power management policy if the new user state differs from a present user state by a predetermined amount. In one embodiment, at least one time-out parameter of a selected power management policy may be adjusted to comply with a predicted user state. Other embodiments are described and claimed.

Abstract: A method and apparatus for user activity-based dynamic power management and policy creation for mobile platforms are described. In one embodiment, the method includes the monitoring of one or more sensor values of a mobile platform device to gather user activity data. Once the user activity data is gathered, the user state may be predicted according to the gathered user activity and an updated user state model. In one embodiment, the user state model is updated according to the user activity data. In one embodiment, a switch occurs from the current power management policy to a new power management policy if the new user state differs from a current user state by a predetermined amount. In one embodiment, at least one time-out parameter of a selected power management policy may be adjusted to comply with a predicted user state. Other embodiments are described and claimed.

Abstract: A method and apparatus for user activity-based dynamic power management and policy creation for mobile platforms are described. In one embodiment, the method includes the monitoring of one or more sensor values of a mobile platform device to gather sensor activity data. Once the sensor activity data is gathered, the user state may be predicted according to the gathered user activity and an updated user state model. In one embodiment, the user state model is updated according to the sensor activity data. In one embodiment, a switch occurs from the present power management policy to a new power management policy if the new user state differs from a present user state by a predetermined amount. In one embodiment, at least one time-out parameter of a selected power management policy may be adjusted to comply with a predicted user state. Other embodiments are described and claimed.

Abstract: A multi-band antenna comprises a first conductive layer having one or more parasitic patches, a second conductive layer having a plurality of radiating patches, and a third conductive layer having a ground patch. The first, second and third conductive layers may be separated by first and second substrate layers. The second conductive layer may comprise a first radiating patch having dimensions selected to radiate signals within a first frequency spectrum and second radiating patches having dimensions selected to radiate signals within a second frequency spectrum. In wireless local area network (WLAN) embodiments, the first frequency spectrum may comprise a frequency band ranging from approximately 5.1 to 5.9 GHz, and the second frequency spectrum may comprise frequency bands ranging from approximately 2.4 to 2.5 GHz.

Abstract: Novel methods for purifying contaminated subsurface groundwater are disclosed. The method is involves contacting the contaminated subsurface groundwater with methanotrophic or heterotrophic microorganisms which produce contaminant-degrading enzymes. The microorganisms are derived from surface cultures and are injected into the ground so as to act as a biofilter. The contaminants which may be treated include organic or metallic materials and radionuclides.