A system and method for monitoring a patient suspected of experiencing a state of unconsciousness are provided. In certain aspects, the method includes assembling physiological data, obtained from a plurality of sensors placed on a subject, into sets of time-series data, separating, from the sets of time-series data, a plurality of electroencephalogram signals, and determining, from the plurality of electroencephalogram signals, at least one of frequency information and amplitude information. The method can also include identifying, using the at least one of the frequency information and the amplitude information, spatiotemporal signatures indicative of a likelihood of arousing the patient to consciousness by applying an external stimulus and generating a report indicating the likelihood of arousing the patient to consciousness by applying the external stimulus.

Systems and methods are provided that are directed to generating video sequences including physio-realistic avatars. In examples, an albedo for an avatar is received, a sub-surface skin color associated with the albedo is modified based on physiological data associated with physiologic characteristic, and an avatar based on the albedo and the modified sub-surface skin color is rendered. The rendered avatar may then be synthesized in a frame of video. In some examples, a video including the synthesized avatar may be used to train a machine learning model to detect a physiological characteristic. The machine learning model may receive a plurality of video segments, where one or more of the video segments includes a synthetic physio-realistic avatar generated with the physiological characteristic. The machine learning model may be trained using the plurality of video segments. The trained model may be provided to a requesting entity.

A circuit for measuring a passive component includes a first comparator, a second comparator, a third comparator and a counter. The first comparator compares a first voltage and a first reference voltage to generate a first comparison signal. The second comparator compares a second voltage and a second reference voltage to generate a second comparison signal. The third comparator compares a third voltage and a third reference voltage to generate a third comparison signal. The counter counts the number of pulses according to the third comparison signal. The number of pulses corresponds to time for the third voltage to drop to the third reference voltage, and the first comparison signal and the second comparison signal are used to control the drop of the third voltage.

A device, system, and method for facilitating a sleep cycle in a subject during a pandemic or peri COVID vaccination period, comprising determining a current awake or sleep stage of a person; automatically defining a desired sleep cycle pattern, dependent on the current awake or sleep stage of the person; generating an audio or optical stimulation pattern by an automated processor; and entraining brainwaves of the brain of the person with the stimulation pattern corresponding to the desired sleep cycle pattern, to thereby induce a sleep cycle in the person according to the sleep cycle pattern. When sleep patterns are normalized, a SARS-Cov-2 vaccination may be administered to the person.

A system and a method for image blurring processing are provided. The method includes: obtaining an image and a depth information corresponding to the image; identifying an object and a background in the image according to the depth information, wherein the object includes a plurality of object pixels and the background includes a plurality of background pixels; defining a number of an edge pixel spaced between the object and the background; performing a blurring processing on the edge pixel and the background pixels respectively by using a plurality of masks with different sizes; combining the plurality of object pixels, the edge pixel after the blurring processing, and the background pixels after the blurring processing to generate an output image; and outputting the output image.

The invention described provides a way to use video image to estimate the human artery blood pressure reducing or completely eliminating the need for human contact (non invasive). Since video images can be stored and transmitted, the estimation of the blood pressure can be performed locally, remotely and in real time or offline.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for training and deploying machine-learned compact representations of radio frequency (RF) signals. One of the methods includes: determining a first RF signal to be compressed; using an encoder machine-learning network to process the first RF signal and generate a compressed signal; calculating a measure of compression in the compressed signal; using a decoder machine-learning network to process the compressed signal and generate a second RF signal that represents a reconstruction of the first RF signal; calculating a measure of distance between the second RF signal and the first RF signal; and updating at least one of the encoder machine-learning network or the decoder machine-learning network based on (i) the measure of distance between the second RF signal and the first RF signal, and (ii) the measure of compression in the compressed signal.

Systems and methods are provided for receiving a two-dimensional (2D) image comprising a 2D object; identifying a contour of the 2D object; generating a three-dimensional (3D) mesh based on the contour of the 2D object; and applying a texture of the 2D object to the 3D mesh to output a 3D object representing the 2D object.

A system includes an expert graphical user interface configured to: present a list of reliability measures of an industrial machine, facilitate a selection by a user of a reliability measure from the list of reliability measures, present a representation of a smart band data collection template associated with the reliability measure selected by the user, and a data routing and collection system configured to, in response to a user indication of acceptance of the smart band data collection template, collect data from a plurality of sensors in an industrial environment in response to a data value from one of the plurality of sensors being detected outside of an acceptable range of data values.

A method for initiating and automatically improving model-driven operations in a low-data scenario includes creating a regression model using pre-operation data prior to initiating the model-driven operations, using the regression model to initiate and perform the model-driven operations during an operational stage, collecting operational data during the operational stage, creating a first artificial neural network model using the operational data, transitioning from using the regression model to perform the model-driven operations to using the first artificial neural network model to perform the model-driven operations responsive to the operational data satisfying a first sufficiency threshold.