Abstract: The present invention relates to treating a surface with a polymer and compositions comprising it for preventing microbial reinfection especially that of virus and bacteria on the surface for a long period of time after the surface has been treated. The present invention also relates to certain liquid cleansing compositions, sanitizers, hand-held spray devices and towelettes comprising the polymer for delivering the above benefit.

Abstract: Systems and methods for deriving pressures outside of a blood inlets and blood outlets of an intracardiac blood pump assembly, and pressure differentials therebetween. Pressures outside of a blood inlet may be derived based on one or more readings from a pressure sensor placed within a blood inlet, one or more readings from a differential pressure sensor configured to measure pressure differential across a wall of the pump housing or cannula, and speed of the pump motor. Pressure differentials between a blood inlet and blood outlet may be derived based on one or more readings from the differential pressure sensor and speed of the pump motor. Pressures outside of a blood outlet may be derived based on a derived pressure outside of a blood inlet and a derived pressure differential between the blood inlet and the blood outlet.

Abstract: A system for monitoring fetal health data and mother health data comprises a belly-covering garment that is configured to at least partially cover a belly and to hold one or more sensor modules directly adjacent to the belly. One or more sensor modules disposed within the belly-covering garment. The one or more sensor modules comprise a pulse-oximeter sensor that gathers pulse oximetry data from the mother through contact with the belly. The one or more sensor modules also comprise an accelerometer sensor that gathers movement data from the mother. Additionally, the one or more sensor modules comprise a fetal sensor that gathers health data from a fetus within the belly.

Abstract: A first digital signal processor (DSP) associated with a first user outputs audio to the first user via first headphones, and captures speech from the first user via a first microphone. Similarly, a second DSP associated with a second user outputs audio to the second user via second headphones, and captures speech from the second user via a second microphone. The first DSP is coupled to the second DSP in order to allow the first and second users to share music and communicate with one another. The first user may speak into the first microphone, and the first and second DSPs may then interoperate to output that speech to the second user without substantially disrupting audio output to the second user. Each of the first and second users may also select between first and second audio sources that may be coupled to the first and second DSPs, respectively.

Abstract: A technology for detecting fetal movement. In one example an electrocardiogram (ECG) dataset can be obtained where the ECG dataset contains fetal heart rate (FHR) values acquired from a pregnant subject, and the ECG dataset is for a segment of time during which an FHR is monitored using an ECG monitor. An FHR baseline can be calculated for the FHR values in the ECG dataset, and the ECG dataset can be analyzed to identify an accelerated FHR value that exceeds the FHR baseline that is followed in time in the ECG dataset by a decelerated FHR value that is less than or equal to the FHR baseline. Identifying the accelerated FHR value followed in time in the ECG dataset by the decelerated FHR value in the ECG dataset may indicate a fetal movement.

Abstract: A technique for estimating a set of parameter values for a lumped parameter model of a loudspeaker. The technique includes determining, via a neural network model, a first set of parameter values for the LPM of the loudspeaker based on an audio input signal and a first measured response of a loudspeaker that corresponds to the audio input signal. The technique further includes generating a first LPM response based on the first set of parameter values and comparing the first LPM response to the first measured response of the loudspeaker to determine a first error value. The technique further includes generating, via the neural network model, a second set of parameter values for the LPM of the loudspeaker based on the first error value.

Abstract: A technique for controlling a loudspeaker system with an artificial neural network includes filtering, with a deconvolution filter, a measured system response of a loudspeaker and a reverberant environment in which the loudspeaker is disposed to generate a filtered response, wherein the measured system response corresponds to an audio input signal applied to the loudspeaker while the loudspeaker is disposed in the reverberant environment. The techniques further include generating, via a neural network model, an initial neural network output based on the audio input signal, comparing the initial neural network output to the filtered response to determine an error value, and generating, via the neural network model, an updated neural network output based on the audio input signal and the error value.

Abstract: In an audio system, an audio signal is preprocessed to provide an input signal to a fast detector and a slow detector, the input signal comprising alert signals and ambient sounds. The slow detector determines the ambient sound level of the input signal which is output to an alert signal detector. The alert signal detector uses the ambient sound level to compute an adaptive threshold level using an adaptive threshold function. The fast detector determines the envelope level of the input signal which is output to the alert signal detector. The alert signal detector compares the envelope level to the adaptive threshold level to determine if an alert signal is present in the input signal. The adaptive threshold level varies depending on the ambient sound level of the input signal and the alert signal detection of the audio system automatically adapts to changing acoustic environments having different ambient sound levels.

Abstract: A technique for controlling a loudspeaker system with an artificial neural network includes filtering, with a deconvolution filter, a measured system response of a loudspeaker and a reverberant environment in which the loudspeaker is disposed to generate a filtered response, wherein the measured system response corresponds to an audio input signal applied to the loudspeaker while the loudspeaker is disposed in the reverberant environment. The techniques further include generating, via a neural network model, an initial neural network output based on the audio input signal, comparing the initial neural network output to the filtered response to determine an error value, and generating, via the neural network model, an updated neural network output based on the audio input signal and the error value.

Abstract: A technique for controlling a loudspeaker system with an artificial neural network includes filtering, with a deconvolution filter, a measured system response of a loudspeaker and a reverberant environment in which the loudspeaker is disposed to generate a filtered response, wherein the measured system response corresponds to an audio input signal applied to the loudspeaker while the loudspeaker is disposed in the reverberant environment. The techniques further include generating, via a neural network model, an initial neural network output based on the audio input signal, comparing the initial neural network output to the filtered response to determine an error value, and generating, via the neural network model, an updated neural network output based on the audio input signal and the error value.

Abstract: An improved method for processing and extracting krill oil from krill meal/fresh krill. The proposed method for processing and extracting of krill oil from krill meal/fresh krill can be a very simple, cost effective and productive approach. The krill oil obtained herein can meet industrial standards and requirements of a wide range of applications including nutraceuticals and pharmaceutical applications. The method can be’ alternatively adapted to process and extract a wide range of marine oils including, but not limited to, Fish, Salmon, Shrimp, Cod and other marine product varieties.

Abstract: An audio processing system for a listening device includes an input device, a voice activity detector and a ratio-based attenuator. The input device is configured to receive a first audio signal emanating from an environment and including a signal of interest. The voice activity detector is configured to generate a control signal in response to the first audio signal. The ratio-based attenuator is configured to receive the control signal and determine whether the signal level of the first audio signal exceeds the signal level of an audio signal received from an audio playback device by at least a target difference. If so, then the audio level of the playback audio signal is maintained. Otherwise, the audio level of the playback audio signal is adjusted, where, at the adjusted value, the first signal level exceeds the playback signal level by at least the target difference.

Abstract: An audio system is described that corrects for linear and nonlinear distortions. The system can include a physical loudspeaker system responsive to an audio input signal, an adaptive circuit, e.g., with a recurrent neural network, to correct for non-linear distortions from the loudspeaker.

Abstract: Digital filters for processing an input audio signal for extending the audio capabilities and reducing distortion of a loudspeaker. Such extension may include a general sound pressure level (SPL) extension or a more targeted low frequency (LF) extension. This may be accomplished by modifying an original frequency response of the loudspeaker to achieve a particular extension, and applying a digital linear filter based on the modified frequency response to the audio signal. A nonlinear digital filter may also be applied to the audio signal reduce nonlinear distortions of the loudspeaker. The nonlinear digital filter may be based on an inverse of an electro-mechanical model of the loudspeaker. In this manner, a loudspeaker may be driven close to its maximum theoretical power capability without increasing distortion in the loudspeaker output.

Abstract: An audio system is described that corrects for linear and nonlinear distortions. The system can include a physical loudspeaker system responsive to an audio input signal, an adaptive circuit, e.g., with a recurrent neural network, to correct for non-linear distortions from the loudspeaker.

Abstract: A dynamic processor for compressing an incoming audio signal is provided. The processor includes an adaptive detector, an adaptive module, and an amplifier. The adaptive detector includes a first adaptive filter and is configured to apply a transfer function to the incoming audio signal to generate a detector output signal. The adaptive module includes a second adaptive filter and is configured to generate a control signal based on the detector output signal. The amplifier is configured to receive the control signal from the adaptive module and to generate a compressed audio signal based on the control signal.

Abstract: In an audio system, an audio signal is preprocessed to provide an input signal to a fast detector and a slow detector, the input signal comprising alert signals and ambient sounds. The slow detector determines the ambient sound level of the input signal which is output to an alert signal detector. The alert signal detector uses the ambient sound level to compute an adaptive threshold level using an adaptive threshold function. The fast detector determines the envelope level of the input signal which is output to the alert signal detector. The alert signal detector compares the envelope level to the adaptive threshold level to determine if an alert signal is present in the input signal. The adaptive threshold level varies depending on the ambient sound level of the input signal and the alert signal detection of the audio system automatically adapts to changing acoustic environments having different ambient sound levels.

Abstract: A corrector is configured to transform audio signals to compensate for unwanted distortion characteristics of a loudspeaker. A tuning filter is configured to transform audio signals to incorporate desired distortion characteristics associated with a target loudspeaker. By chaining together the tuning filter and the corrector, an audio signal can be modified so that the loudspeaker, when outputting the audio signal, has response characteristics of the target loudspeaker.

Abstract: In one embodiment, a pre-correcting engine generates a pre-corrected signal that may be used to mitigate air path distortions in sounds generated by loudspeaker systems that include loudspeakers coupled to waveguides. In operation, the pre-correcting engine virtually propagates a target signal in a reverse direction from the output of the waveguide to the output of a driver. Notably, the pre-correcting engine implements a propagation distortion compensation model that, when applied to the target signal, corrects for the effects of air path distortions on the target signal. The pre-correcting engine then transmits the pre-corrected signal to the driver. As the driver drives the waveguide based on the pre-corrected signal, effects of the pre-corrected distortions negate effects of air path distortions and the loudspeaker system generates high-fidelity sounds.

Abstract: A technique for controlling a loudspeaker system with an artificial neural network includes filtering, with a deconvolution filter, a measured system response of a loudspeaker and a reverberant environment in which the loudspeaker is disposed to generate a filtered response, wherein the measured system response corresponds to an audio input signal applied to the loudspeaker while the loudspeaker is disposed in the reverberant environment. The techniques further include generating, via a neural network model, an initial neural network output based on the audio input signal, comparing the initial neural network output to the filtered response to determine an error value, and generating, via the neural network model, an updated neural network output based on the audio input signal and the error value.