Intel Corporation

Ramot at Tel-aviv University Ltd.

Other

Technion Research & Development Foundation Limited

The Medical Research, Infrastructure and Health Services Fund of the Tel Aviv Medical Center

Alex Bronstein

Michael Bronstein

Ron Kimmel

Erez Sperling

Aviad Zabatani

Vitaly Surazhsky

Ohad Menashe

David Haim Silver

Sagy Bareket

Sagi Ben Moshe

Barak Freedman

Ofir Mulla

Evgeny Tsizin-Goldman

Emanuel Marom

Harel Haim

Sagi BenMoshe

Shlomo Selim Rakib

Yael Yaniv

Asaf Matatyaou

Talma Hendler

Mordekhay Medvedovsky

Noam Keidar

Or Litany

Tal Remez

Yoseff Shachar

Zachi Karni

Ohad Manashe

Tomer Gazit

Gal Eidelsztein

An example apparatus includes: a camera to record an image; memory to store instructions; and a processor in circuit with the memory, the processor to execute the instructions to: determine a depth based on: (a) the image and (b) a calibration parameter of the camera; and adjust the calibration parameter based on a temperature of the camera and the depth.

Online compensation of thermal distortions in a stereo depth camera

A method comprising: at a training stage, training a machine learning algorithm on a training set comprising: (i) Heart Rate Variability (HRV) parameters extracted from temporal beat activity samples, wherein at least some of said samples include a representation of a Ventricular Fibrillation (VF) event, (ii) labels associated with one of: a first period of time immediately preceding a VF event in a temporal beat activity sample, a second period of time immediately preceding the first period of time in a temporal beat activity sample, and all other periods of time in a temporal beat activity sample; at an inference stage, receiving, as input, a target HRV parameters representing temporal beat activity in a subject; and applying said machine learning algorithm to said target HRV parameters, to predict an onset time of a VF event in said subject.

Predicting ventricular fibrillation

A diagnostic system includes an array of electrodes, which are coupled to a body surface of a living subject at different, respective positions in proximity to a region of interest within the body. A switched impedance network applies varying loads to the electrodes. A processor is coupled to receive and measure electrical signals from the electrodes as a function of the varying loads, and to analyze the measured signals so as to compute a local electrical characteristic of one or more locations within the region of interest.

Impedance-enriched electrophysiological measurements

Example range estimation apparatus disclosed herein include a first signal processor to estimate a signal power parameter and a noise power parameter of a LIDAR system based on first data to be output from a light capturing device of the LIDAR system. Disclosed example range estimation apparatus also include a second signal processor to generate templates corresponding to different possible propagation delays associated with second data to be output from the light capturing device, the second data associated with a modulated light beam projected by the LIDAR system, the templates generated based on the signal power parameter and the noise power parameter, and the second data to have a higher sampling rate and a lower quantization resolution than the first data. In some examples, the second signal processor is also to determine, based on the templates, an estimated propagation delay associated with the second data.

Range estimation for light detecting and ranging (LIDAR) systems

Apparatus, including a set of N electrodes (), configured to be located in proximity to an epidermis () of a subject, and to acquire signals generated by electric sources within the subject. The apparatus also includes a set of M channels, configured to transfer the signals, where M is less than N, and a switch (), configured to select, repetitively and randomly, M signals from the N electrodes and to direct the M signals to the M channels. The apparatus further includes a processor (), configured to activate the switch, and to receive and analyze the M signals from the M channels so as to determine respective positions of the electric sources within the subject.

Biosignal acquisition method and algorithms for wearable devices

A method and apparatus for auto range control are described. In one embodiment, the apparatus comprises a projector configured to project a sequence of light patterns on an object; a first camera configured to capture a sequence of images of the object illuminated with the projected light patterns; a controller coupled to the projector and first camera and operable to receive the sequence of images and perform range control by controlling power of the sequence of light patterns being projected on the object and exposure time of a camera based on information obtained from the sequence of images captured by the camera.

Auto range control for active illumination depth camera

Example range estimation apparatus disclosed herein include a first signal processor to process first data output from a light capturing device of a LIDAR system to estimate signal and noise power parameters of the LIDAR system. Disclosed example range estimation apparatus also include a second signal processor to generate templates corresponding to different possible propagation delays associated with second data output from the light capturing device while a modulated light beam is projected by the LIDAR system, the templates generated based on the signal and noise power parameters, and the second data having a higher sampling rate and a lower quantization resolution than the first data. In some examples, the second signal processor also cross-correlates the templates with the second data to determine an estimated propagation delay associated with the second data, the estimated propagation delay convertible to an estimated range to an object that reflected the modulated light beam.

Arrangements (e.g., apparatus, system, method, article of manufacture) for reconstructing a depth image of a scene. Some embodiments include: a processor; and a non-transitory computer readable medium to store a set of instructions for execution by the processor, the set of instructions to cause the processor to perform various operations. Operations include: collecting multiple data sets for a code-modulated light pulse reflected from an object in a scene, with each data set associated with a direction in a set of directions for the reflected code-modulated light pulse; assigning a fitness value to each data set based on one or more parameters of a model; and reconstructing a depth image providing a depth at each direction based on a corresponding data set and fitness value, the depth to correspond with a round-trip delay time of the code-modulated light pulse.

Range reconstruction using shape prior

A method of processing an image is disclosed. The method comprises decomposing the image into a plurality of channels, each being characterized by a different depth-of-field, and accessing a computer readable medium storing an in-focus dictionary defined over a plurality of dictionary atoms, and an out-of-focus dictionary defined over a plurality of sets of dictionary atoms, each set corresponding to a different out-of-focus condition. The method also comprises computing one or more sparse representations of the decomposed image over the dictionaries.

Method and system for processing an image

A diagnostic method includes applying a vibration at a selected frequency to a location within a brain of a living subject (). Electrical signals resulting from the vibration are measured at multiple positions on a scalp of the subject. The measured electrical signals are processed in order to compute an electrical gain matrix between the location within the brain and the positions on the scalp. Electroencephalogram (EEG) signals are measured at the multiple positions on the scalp. The EEG signals are filtered using the gain matrix in order to identify brain electrical activity originating from the location.

Localizing electrical activity in the brain using vibration of the cerebral cortex

An example apparatus for calibrating texture cameras includes an image receiver to receive a depth image from a depth camera and a color image from a texture camera. The apparatus also includes a feature extractor to extract features from the depth image and the color image. The apparatus further includes a feature tester to detect that the extracted features from the depth image and the color image exceed a quality threshold. The apparatus includes a misalignment detector to detect a misalignment between the extracted features from depth image and the extracted features from color image exceeds a misalignment threshold. The apparatus also further includes a calibrator to modify calibration parameters for the texture camera to reduce the detected misalignment between the extracted features from the depth image and the extracted features from the color image below a misalignment threshold.

Calibrating texture cameras using features extracted from depth images

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Haifa, Israel

Alex Bronstein