Abstract: Systems, apparatuses and methods may provide technology for intelligent drive wear management. The technology may include determining a difference between a wear value derived for a first solid state storage drive and a wear value derived for a second solid state storage drive, and if the difference in wear value exceeds a wear skew threshold, swapping content between the first drive and the second drive. The technology may also include sorting an array of solid state storage drives into a plurality of drive groups based on a wear value derived for each drive, and determining, for a first pair of drives in a drive group, a difference in wear value between the drives in the first pair. Respective pairs of drives in a drive group may be selected based on the drive wear value and a drive rotation counter value associated with each drive.

Abstract: Systems, apparatuses and methods may provide for technology that dynamically tunes platform features based on virtual machine runtime requirements. In one example, a first virtual machine and a second virtual machine of a cloud server platform may each be associated with one or more logical cores. The first virtual machine may have a first configuration to efficiently support a first feature setting arrangement on the associated logical cores. The second virtual machine may have a different second configuration to efficiently support a different second feature setting arrangement on the different associated logical cores. Feature settings that are specific to an application associated with a virtual machine may be determined based on application runtime requirements. Such determined feature settings may be stored as a bit mask in control fields of a virtual machine control and enforced on the logical cores associated with a given virtual machine.

Abstract: Systems, apparatuses and methods may provide for technology that dynamically tunes platform features based on virtual machine runtime requirements. In one example, a first virtual machine and a second virtual machine of a cloud server platform may each be associated with one or more logical cores. The first virtual machine may have a first configuration to efficiently support a first feature setting arrangement on the associated logical cores. The second virtual machine may have a different second configuration to efficiently support a different second feature setting arrangement on the different associated logical cores. Feature settings that are specific to an application associated with a virtual machine may be determined based on application runtime requirements. Such determined feature settings may be stored as a bit mask in control fields of a virtual machine control and enforced on the logical cores associated with a given virtual machine.

Abstract: Systems, apparatuses and methods may provide for technology that dynamically tunes platform features based on virtual machine runtime requirements. In one example, a first virtual machine and a second virtual machine of a cloud server platform may each be associated with one or more logical cores. The first virtual machine may have a first configuration to efficiently support a first feature setting arrangement on the associated logical cores. The second virtual machine may have a different second configuration to efficiently support a different second feature setting arrangement on the different associated logical cores. Feature settings that are specific to an application associated with a virtual machine may be determined based on application runtime requirements. Such determined feature settings may be stored as a bit mask in control fields of a virtual machine control and enforced on the logical cores associated with a given virtual machine.

Abstract: Systems, apparatuses and methods may provide technology for intelligent drive wear management. The technology may include determining a difference between a wear value derived for a first solid state storage drive and a wear value derived for a second solid state storage drive, and if the difference in wear value exceeds a wear skew threshold, swapping content between the first drive and the second drive. The technology may also include sorting an array of solid state storage drives into a plurality of drive groups based on a wear value derived for each drive, and determining, for a first pair of drives in a drive group, a difference in wear value between the drives in the first pair. Respective pairs of drives in a drive group may be selected based on the drive wear value and a drive rotation counter value associated with each drive.

Abstract: Systems, apparatuses and methods may provide for technology that dynamically tunes platform features based on virtual machine runtime requirements. In one example, a first virtual machine and a second virtual machine of a cloud server platform may each be associated with one or more logical cores. The first virtual machine may have a first configuration to efficiently support a first feature setting arrangement on the associated logical cores. The second virtual machine may have a different second configuration to efficiently support a different second feature setting arrangement on the different associated logical cores. Feature settings that are specific to an application associated with a virtual machine may be determined based on application runtime requirements. Such determined feature settings may be stored as a bit mask in control fields of a virtual machine control and enforced on the logical cores associated with a given virtual machine.

Abstract: A method and apparatus are described for specifying arbitrary words in a rule-based grammar, such as a context-free grammar. A wildcard identifier is used in a context-free grammar file in place of a predefined category of words. Artificial phoneme combinations that represent generic words in a speech engine's vocabulary database, and that represent pronunciations of the predefined category of words are defined by rules. A speech engine uses the artificial phoneme combinations to determine acceptable words to listen to, and generates a results object comprising generic words corresponding to the artificial phoneme combinations, as well as non-generic words that do not exist in a given CFG, where the generic and non-generic words exist in the speech engine's vocabulary database. The word having the highest confidence level that is not a generic word is selected as the word most likely to have been spoken by the user.

Abstract: Wireless device audible alert control signal generating systems and methods are presented. The control signal may be used to either enable or disable the audible alert of a wireless device. The signal may be generated as a function of the motion of an object entering or exiting a quiet zone. An embodiment for a system in accordance with the invention includes a transmitter to transmit a control signal to either disable or enable an audible alert of a wireless device. An antenna may be operatively coupled to the transmitter. A motion sensor to detect objects passing through a field of detection may be used to trigger the emission of the control signal. A controller operatively coupled to both the motion sensor and transmitter may command the transmitter to transmit the control signal as a function of a direction of motion detected by the motion sensor. An embodiment for a method in accordance with the invention includes detecting an object in an object detection area.

Abstract: A method and apparatus are described for specifying arbitrary words in a rule-based grammar, such as a context-free grammar. A wildcard identifier is used in a context-free grammar file in place of a predefined category of words. Artificial phoneme combinations that represent generic words in a speech engine's vocabulary database, and that represent pronunciations of the predefined category of words are defined by rules. A speech engine uses the artificial phoneme combinations to determine acceptable words to listen to, and generates a results object comprising generic words corresponding to the artificial phoneme combinations, as well as non-generic words that do not exist in a given CFG, where the generic and non-generic words exist in the speech engine's vocabulary database. The word having the highest confidence level that is not a generic word is selected as the word most likely to have been spoken by the user.