General Electric Company

Koninklijke Philips Corporation N.v.

Harman International Industries, Inc.

Harman Becker Automotive Systems Gmbh

Weijia Lu

Lei Wang

Qiang Zhou

Zhongfeng Pan

John Petruzzello

Ajay Anand

Joel Quizhen Xue

Yinan Chen

Jie Shuai

Jianyi Zhong

Shuyan Gu

Guangjun Qin

Lifei Hu

Chong Liu

Qingfeng Xia

Shangpeng Li

The present disclosure provides a power filter. The power filter may include an overvoltage protection circuit and an LC filter circuit connected to the overvoltage protection circuit, wherein the overvoltage protection circuit includes a voltage regulator and a peak compensation circuit. The peak compensation circuit is configured to generate a gain peak at a resonance frequency of the LC filter circuit, so as to compensate for a gain dip at the voltage regulator stage occurring at the resonance frequency of the LC filter circuit.

Power filter

The present disclosure provides an audio system. The audio system includes: a target audio signal generator module including a plurality of target audio signal generators, configured to receive an audio signal and a tracking signal and generate a plurality of target audio signals based on the audio signal and tracking signal; a tracking power supply circuit module including a plurality of tracking power supply circuits, configured to receive the plurality of target audio signals and generate a plurality of power supply voltages based on the plurality of target audio signals; an audio power amplifying circuit module including a plurality of audio amplifying circuits, configured to receive the plurality of target audio signals and the plurality of power supply voltages, and amplify the plurality of target audio signals; wherein each of the plurality of tracking power supply circuits provides a corresponding power supply voltage for each of the plurality of audio amplifying circuits.

Audio system

A system for identifying arrhythmias based on cardiac waveforms includes a storage system storing a trained deep neural network system, wherein the trained deep neural system includes a trained representation neural network and a trained classifier neural network. A processing system is communicatively connected to the storage system and configured to receive cardiac waveform data for a patient, identify a time segment in the cardiac waveform data, and transform the time segment into a spectrum image. The processing system is further configured to generate, with the representation neural network, a latent representation from the spectrum image, and then to generate, with the classifier neural network, an arrhythmia classifier from the latent representation.

System and method for identifying cardiac arrhythmias with deep neural networks

An occlusion and/or reopening of an artery of a body caused by a changing pressure applied on the artery is detected. The systolic blood pressure of an artery of a body is also measured. A Doppler Ultrasound transducer attached to the exterior of the body outputs a blood flow signal indicative of a change of the blood flow in the artery caused by the changing pressure. At least one variable of a first variable indicative of the magnitude of the blood flow and a second variable indicative of the periodicity of the blood flow is derived from the blood flow signal. The occlusion and/or reopening of the artery is detected on the basis of the at least one variable. In this way, the occlusion/reopening of the artery can be detected automatically. Since the need to manually detect the occlusion and reopening of the artery by listening with a stethoscope or a Doppler probe is eliminated, the detecting result is more predictable and repeatable, and therefore also more accurate.

Method and device for detecting occlusion/reopening of an artery and system for measuring systolic blood pressure

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Shanghai, China

Weijia Lu