Abstract: Methods and systems are provided for ranking search results and generating a presentation. In some implementations, a search system generates a presentation based on a search query. In some implementations, a search system ranks search results based on data stored in a knowledge graph. In some implementations, a search system identifies a modifying concept such as a superlative in a received search query, and determines ranking properties based on the modifying concept.

Abstract: An electronic device may have a power system with a battery. The device may include power management circuitry that helps distribute power from the battery to components within the device. To prevent an excessive load from being applied to the battery and the battery from dropping below a cut-off voltage, power management circuitry may control power consumption by components in the device. Power consumption models in the power management circuitry may be used to ensure that maximum allowable power consumption levels are not exceeded. To help accurately and quickly manage power consumption decisions, each component may have characteristic power consumption values that characterize the power consumption profile of the component.

Abstract: Based on changes in a battery (e.g., age, temperature) of an electronic device, battery power prediction and correction logic of the electronic device may correct a power capability and/or regulate power associated with the battery. For example, the battery power prediction and correction logic may operate the battery to supply up to a maximum of a power capability with an applied correction factor based on a voltage measurement and a cutoff voltage associated with the battery.

Abstract: This application relates to methods and apparatus for predicting power and energy availability of a battery. The prediction is made based on a given amount of time, which represents a period in which the battery may be required to operate. Additionally, a learning cycle is incorporated to update a battery model of the battery with certain parameters. The battery model is updated by introducing a time-varying current to the battery and analyzing the voltage response of the battery. A model-based predictive algorithm is used in combination with the battery model to predict battery output parameters based on variables derived from the learning cycle and additional inputs supplied to the model-based predictive algorithm. After one or more iterations, or using a simplified model-based equation, the model-based predictive algorithm can provide an accurate prediction for the maximum current that the battery can supply for a predetermined period of time.

Abstract: An electronic device may have a power system with a battery. The device may include power management circuitry that helps distribute power from the battery to components within the device. To prevent an excessive load from being applied to the battery and the battery from dropping below a cut-off voltage, power management circuitry may control power consumption by components in the device. Power consumption models in the power management circuitry may be used to ensure that maximum allowable power consumption levels are not exceeded. To help accurately and quickly manage power consumption decisions, each component may have characteristic power consumption values that characterize the power consumption profile of the component.

Abstract: This application relates to methods and apparatus for predicting power and energy availability of a battery. The prediction is made based on a given amount of time, which represents a period in which the battery may be required to operate. Additionally, a learning cycle is incorporated to update a battery model of the battery with certain parameters. The battery model is updated by introducing a time-varying current to the battery and analyzing the voltage response of the battery. A model-based predictive algorithm is used in combination with the battery model to predict battery output parameters based on variables derived from the learning cycle and additional inputs supplied to the model-based predictive algorithm. After one or more iterations, or using a simplified model-based equation, the model-based predictive algorithm can provide an accurate prediction for the maximum current that the battery can supply for a predetermined period of time.

Abstract: A method, system (100), and computer program product for age compensation for a gas sensor (101) are provided. The method includes determining an impedance spectrum for the gas sensor (101) by an impedance analyzer (103); determining a set of principle parameters based on the impedance spectrum by a principle parameter identifier (104); constructing an age compensation model for the gas sensor (101) using the set of principle parameters by an age compensation modeler (105); and applying the age compensation model to an output of the gas sensor (101). Gas sensor (101) failure detection is also provided.