Abstract: Systems and computer-implemented methods are used for analyzing battery information. The battery information may be acquired from both passive data acquisition and active data acquisition. Active data may be used for feature extraction and parameter identification responsive to the input data relative to an electrical equivalent circuit model to develop geometric-based parameters and optimization-based parameters. These parameters can be combined with a decision fusion algorithm to develop internal battery parameters. Analysis processes including particle filter analysis, neural network analysis, and auto regressive moving average analysis can be used to analyze the internal battery parameters and develop battery health metrics. Additional decision fusion algorithms can be used to combine the internal battery parameters and the battery health metrics to develop state-of-health estimations, state-of-charge estimations, remaining-useful-life predictions, and end-of-life predictions for the battery.

Abstract: Systems and computer-implemented methods are used for analyzing battery information. The battery information may be acquired from both passive data acquisition and active data acquisition. Active data may be used for feature extraction and parameter identification responsive to the input data relative to an electrical equivalent circuit model to develop geometric-based parameters and optimization-based parameters. These parameters can be combined with a decision fusion algorithm to develop internal battery parameters. Analysis processes including particle filter analysis, neural network analysis, and auto regressive moving average analysis can be used to analyze the internal battery parameters and develop battery health metrics. Additional decision fusion algorithms can be used to combine the internal battery parameters and the battery health metrics to develop state-of-health estimations, state-of-charge estimations, remaining-useful-life predictions, and end-of-life predictions for the battery.

Abstract: Systems and computer-implemented methods are used for analyzing battery information. The battery information may be acquired from both passive data acquisition and active data acquisition. Active data may be used for feature extraction and parameter identification responsive to the input data relative to an electrical equivalent circuit model to develop geometric-based parameters and optimization-based parameters. These parameters can be combined with a decision fusion algorithm to develop internal battery parameters. Analysis processes including particle filter analysis, neural network analysis, and auto regressive moving average analysis can be used to analyze the internal battery parameters and develop battery health metrics. Additional decision fusion algorithms can be used to combine the internal battery parameters and the battery health metrics to develop state-of-health estimations, state-of-charge estimations, remaining-useful-life predictions, and end-of-life predictions for the battery.

Abstract: Apparatuses and methods relating to power line communication are disclosed, and in particular, to an adaptive frequency band for power line communication. A power line communication device is disclosed. The power line communication device comprises a cutoff frequency estimator configured for estimating a cutoff frequency for communication in a power line communication network, and a processor operably coupled with the cutoff frequency estimator. The processor is configured to adaptively select boundaries of a frequency band in response to the estimated cutoff frequency. Other apparatuses and methods are disclosed.

Abstract: A GPS receiver circuit (300) for reducing insertion loss. The receiver circuit (300) includes a switch (304) for diverting input signals between a filtered pathway (312) and a short circuit pathway (310) to an amplifier (314). The amplifier (314) output feeds an automatic gain controller (316) that senses a noise level in the output of the amplifier (314) and adjusts a gain of the amplifier (314) in response to the noise level. The receiver circuit (300) also includes a threshold detector (306) with an input coupled to the output of the automatic gain controller (316) and an output coupled to the switch (304) for selecting between the filtered pathway (312) and the short circuit pathway (310), thereby removing the filter (312) from the signal path if not needed.

Abstract: A wireless device lighting system provides backlighting of a display (104) and/or a user interface (1526) of a wireless device (100). A user interface (1526) includes a plurality of buttons (112) for entering information and at least one multicolor LED for emitting light. The at least one multicolor LED is located behind the plurality of buttons (112). The user interface (1526) further includes a connector for connecting the user interface (1526) to the wireless device (100) and a circuit board for mounting the at least one multicolor LED and the connector. The user interface (1526) further includes a light pipe for allowing light emitted from the at least one multicolor LED to be emitted from the buttons (112), the light pipe located between the at least one multicolor LED and the buttons (112). The user interface (1526) further includes a selector for allowing a user to define a color of light for emission by the at least one multicolor LED.

Abstract: An apparatus and method for monitoring a process involve development and application of a statistically qualified neuro-analytic (SQNA) model to accurately and reliably identify process change. The development of the SQNA model is accomplished in two stages: deterministic model adaption and stochastic model modification of the deterministic model adaptation. Deterministic model adaption involves formulating an analytic model of the process representing known process characteristics, augmenting the analytic model with a neural network that captures unknown process characteristics, and training the resulting neuro-analytic model by adjusting the neural network weights according to a unique scaled equation error minimization technique. Stochastic model modification involves qualifying any remaining uncertainty in the trained neuro-analytic model by formulating a likelihood function, given an error propagation equation, for computing the probability that the neuro-analytic model generates measured process output.