Abstract: A method for calibrating an audio amplification system can include injecting a tone having a frequency to an input path of an audio amplifier, such that an input signal provided to the audio amplifier includes the tone signal. The method can further include sampling an output signal at an output node of the audio amplifier, and sampling the input signal at an input node of the audio amplifier. The method can further include generating a correction signal based on the sampled output signal and the sampled input signal to correct for a gain variation of the audio amplifier.

Abstract: Various devices and methods are provided for machine learning-driven medical diagnostic testing. In one example, a method includes generating a patient-specific collaboration channel comprising a patient-specific dashboard and a communication thread between a care provider team monitoring a patient and a virtual healthcare assistant, storing, at the channel, text- and/or rich media-based messages on the communication thread between one or more care providers of the care provider team and the virtual healthcare assistant, at least a portion of the messages on the communication thread including patient-specific medical data, and responsive to a prompt, outputting at least a portion of the communication thread that includes the patient-specific medical data to a display device.

Abstract: In some embodiments, a calibration circuit for an audio amplification system can include a tone generator configured to provide a tone having a frequency to an input path of an audio amplifier, such that an input signal provided to the audio amplifier includes the tone, a a first sampling circuit configured to sample an output signal at an output node of the audio amplifier, and a second sampling circuit configured to sample the input signal at an input node of the audio amplifier. The calibration circuit can further include a gain adjustment circuit configured to generate a correction signal based on the sampled output signal and the sampled input signal to correct for a gain variation of the audio amplifier.

Abstract: This disclosure relates generally to a system for mitigating error in an amplifier. The system may include a loop filter; a driver; a digital-to-analog converter (DAC) configured to source and to sink current; an edge selector configured to determine a first delay between the driver receiving a signal to provide a driver output signal and the driver providing the driver output signal; a timing circuit configured to determine a second delay between the DAC receiving a signal to provide a DAC output current and the DAC providing the DAC output current; and at least one controller configured to control the DAC to source or to sink the DAC output current at a time corresponding to a beginning of the driver output signal, and control the DAC to stop sourcing or sinking the DAC output current at a time corresponding to an end of the driver output signal.

Abstract: Aspects of this disclosure relate to an asynchronous sample rate converter (ASRC) comprising a signal input configured to receive an input signal having one or more input signal values at a first sample rate, a first clock input configured to receive an input clock signal corresponding to the first sample rate, a signal output configured to provide an output signal having one or more output signal values at a second sample rate, a second clock input configured to receive an output clock signal corresponding to the second sample rate, a zero-padding circuit configured to add a plurality of zero values following at least one of the one or more input signal values, and a filter configured to generate the output signal, the output signal having one or more output signal values based on the input signal.

Abstract: In some embodiments, a calibration circuit for an audio amplification system can include a tone generator configured to provide a tone having a frequency to an input path of an audio amplifier, such that an input signal provided to the audio amplifier includes the tone, a a first sampling circuit configured to sample an output signal at an output node of the audio amplifier, and a second sampling circuit configured to sample the input signal at an input node of the audio amplifier. The calibration circuit can further include a gain adjustment circuit configured to generate a correction signal based on the sampled output signal and the sampled input signal to correct for a gain variation of the audio amplifier.

Abstract: The present invention provides methods for treating, preventing or ameliorating a fibrotic condition. The methods of the present invention include administering to a patient suffering from a fibrotic condition a therapeutically effective amount an anti-interleukin-36 receptor (anti-IL-36R) antibody.

Abstract: The present invention provides a lubricant composition comprising a base oil of lubricating viscosity, wherein the base oil comprises an ether base stock of formula (A), where: Ra and Rb are aliphatic hydrocarbyl groups and may be the same or different; the lubricant composition further comprising at least one aminic anti-oxidant and at least one phenolic anti-oxidant. In some embodiments, the ether base stock has the formula (1), where: R1 R2, R3, R4, R5 and R6 are as defined herein. The lubricant composition may be used for lubricating a surface in an internal combustion engine as well as for improving the fuel economy performance and/or piston cleanliness performance of an engine and/or a vehicle, such as an automotive vehicle associated with an internal combustion engine.

Abstract: Various devices and methods are provided for machine learning-driven medical diagnostic testing. In one example, a method includes generating a patient-specific collaboration channel comprising a patient-specific dashboard and a communication thread between a care provider team monitoring a patient and a virtual healthcare assistant, storing, at the channel, text- and/or rich media-based messages on the communication thread between one or more care providers of the care provider team and the virtual healthcare assistant, at least a portion of the messages on the communication thread including patient-specific medical data, and responsive to a prompt, outputting at least a portion of the communication thread that includes the patient-specific medical data to a display device.

Abstract: Various devices and methods are provided for machine learning-driven medical diagnostic testing. In one example, a method includes generating a patient-specific collaboration channel comprising a patient-specific dashboard and a communication thread between a care provider team monitoring a patient and a virtual healthcare assistant, storing, at the channel, text- and/or rich media-based messages on the communication thread between one or more care providers of the care provider team and the virtual healthcare assistant, at least a portion of the messages on the communication thread including patient-specific medical data, and responsive to a prompt, outputting at least a portion of the communication thread that includes the patient-specific medical data to a display device.

Abstract: A computer-implemented method for generating one or more random numbers includes configuring a mapper to feed inputs of a random number generation system using a subset of noise sources from multiple noise sources. The random number generation system generates a random number based on the inputs. The method further includes evaluating the subset of noise sources and detecting that a first noise source from the subset of noise sources has degraded in quality. The method further includes evaluating a second noise source from the available noise sources, the second noise source not being in the subset of noise sources. In response to the second noise source satisfying a predetermined threshold criterion, the first noise source is replaced with the second in the subset of noise sources for providing random bit streams to facilitate generating the random number by the random number generation system.

Abstract: A variable output data rate converter circuit preferably meets performance requirements while keeping the circuit complexity low. In some embodiments, the converter circuit may include an oversampling sigma delta modulator circuit to quantize an analog input signal at an oversampled rate, and output an sigma delta modulated signal, a transposed polynomial decimator circuit to decimate the sigma delta modulated signal, and output a first decimated signal, and an integer decimator circuit to decimate the first decimated signal by an integer factor and output a second decimated signal having a desired output data rate. The transposed polynomial decimator circuit has a transposed polynomial filter circuit and a digital phase locked loop circuit, which tracks a ratio between a sampling rate of the first decimated signal and the oversampled rate, and outputs an intersample position parameter to the transposed polynomial filter circuit.

Abstract: A computer-implemented method for generating one or more random numbers includes configuring a mapper to feed inputs of a random number generation system using a subset of noise sources from multiple noise sources. The random number generation system generates a random number based on the inputs. The method further includes evaluating the subset of noise sources and detecting that a first noise source from the subset of noise sources has degraded in quality. The method further includes evaluating a second noise source from the available noise sources, the second noise source not being in the subset of noise sources. In response to the second noise source satisfying a predetermined threshold criterion, the first noise source is replaced with the second in the subset of noise sources for providing random bit streams to facilitate generating the random number by the random number generation system.

Abstract: A variable output data rate converter circuit preferably meets performance requirements while keeping the circuit complexity low. In some embodiments, the converter circuit may include an oversampling sigma delta modulator circuit to quantize an analog input signal at an oversampled rate, and output an sigma delta modulated signal, a transposed polynomial decimator circuit to decimate the sigma delta modulated signal, and output a first decimated signal, and an integer decimator circuit to decimate the first decimated signal by an integer factor and output a second decimated signal having a desired output data rate. The transposed polynomial decimator circuit has a transposed polynomial filter circuit and a digital phase locked loop circuit, which tracks a ratio between a sampling rate of the first decimated signal and the oversampled rate, and outputs an intersample position parameter to the transposed polynomial filter circuit.

Abstract: The present invention provides a lubricant composition for an internal combustion engine comprising a base oil of lubricating viscosity, wherein the base oil comprises an ether base stock of formula (A): where: Ra and Rb are aliphatic hydrocarbyl groups and may be the same or different; wherein at least one of Ra and Rb is branched-chain alkyl, alkoxy-substituted-alkyl or cycloalkyl-substituted-alkyl; the lubricant composition further comprising at least one aminic anti-oxidant and/or at least one phenolic anti-oxidant; and wherein the total combined amount of aminic and phenolic anti-oxidant in the lubricant composition is not more than 4.0% by weight of the lubricant composition. In some embodiments, the ether base stock has the formula (1): where: R1, R2, R3, R4, R5 and R6 are as defined herein.

Abstract: The present invention provides a lubricant composition comprising a base oil of lubricating viscosity, wherein the base oil comprises an ether base stock of formula (A), where: Ra and Rb are aliphatic hydrocarbyl groups and may be the same or different; the lubricant composition further comprising at least one aminic anti-oxidant and at least one phenolic anti-oxidant. In some embodiments, the ether base stock has the formula (1), where: R1 R2, R3, R4, R5 and R6 are as defined herein. The lubricant composition may be used for lubricating a surface in an internal combustion engine as well as for improving the fuel economy performance and/or piston cleanliness performance of an engine and/or a vehicle, such as an automotive vehicle associated with an internal combustion engine.

Abstract: The present invention provides a lubricant composition comprising a base oil of lubricating viscosity, wherein the base oil comprises an ether base stock of formula (A) where: Ra and Rb are aliphatic hydrocarbyl groups and may be the different; the lubricant composition further comprising from 0.5 to 7% of total dispersant additive, by weight of the lubricant composition. In some embodiments, the ether base stock has the formula (1) where: R1, R2, R3, R4, R5 and R6 are as defined herein. The lubricant composition may be used for lubricating a surface in an internal combustion engine as well as for improving the fuel economy performance and/or piston cleanliness performance of an engine and/or a vehicle, such as an automotive vehicle associated with an internal combustion engine.

Abstract: Various devices and methods are provided for machine learning-driven medical diagnostic testing. In one example, a method includes generating a patient-specific collaboration channel comprising a patient-specific dashboard and a communication thread between a care provider team monitoring a patient and a virtual healthcare assistant, storing, at the channel, text- and/or rich media-based messages on the communication thread between one or more care providers of the care provider team and the virtual healthcare assistant, at least a portion of the messages on the communication thread including patient-specific medical data, and responsive to a prompt, outputting at least a portion of the communication thread that includes the patient-specific medical data to a display device.