Bar-ilan University

Tg-17, Inc.

Nec Corporation

Nec Corporation of America

Rambam Med-tech Ltd.

Joseph Keshet

Ron Asher

Zcharia Baratz

Jun Furukawa

Toshinori Araki

Kazuma Ohara

Hikaru Tsuchida

Jacob Cohen

Takuma Amada

Kazuya Kakizaki

Yaacob Aizer

Ran Banker

Olga Peled

Shir Aviv-Reuven

Italy Fisher

Alma Cohen

Systems and methods of predicting glottal insufficiency by at least one hardware processor including receiving a voice recording comprising a phonation by a subject, analysis of the voice recording to calculate a fundamental frequency contour curve of the phonation, and measurement of at least one of (i) a time period from a start of the phonation until the contour curve reaches a settled level, (ii) a slope of the contour curve during the time period, and (iii) an area under the contour curve during that time period. In certain embodiments, the processor subsequently, determines a glottal closure insufficiency in the subject based on these measurements.

Predicting glottal insufficiency using frequency analysis

There is provided a system for computing a secure statistical classifier, comprising: at least one hardware processor executing a code for: accessing code instructions of an untrained statistical classifier, accessing a training dataset, accessing a plurality of cryptographic keys, creating a plurality of instances of the untrained statistical classifier, creating a plurality of trained sub-classifiers by training each of the plurality of instances of the untrained statistical classifier by iteratively adjusting adjustable classification parameters of the respective instance of the untrained statistical classifier according to a portion of the training data serving as input and a corresponding ground truth label, and at least one unique cryptographic key of the plurality of cryptographic keys, wherein the adjustable classification parameters of each trained sub-classifier have unique values computed according to corresponding at least one unique cryptographic key, and providing the statistical classifier, wherein the statistical classifier includes the plurality of trained sub-classifiers.

Systems and methods for generating and applying a secure statistical classifier

A goal of the disclosure is to provide real-time adjustment of a deep learning-based tracking system to track a moving individual without using a labeled set of training data. Disclosed are systems and methods for tracking a moving individual with an autonomous drone. Initialization video data of the specific individual is obtained. Based on the initialization video data, real-time training of an input neural network is performed to generate a detection neural network that uniquely corresponds to the specific individual. Real-time video monitoring data of the specific individual and the surrounding environment is captured. Using the detection neural network, target detection is performed on the real-time video monitoring data and a detection output corresponding to a location of the specific individual within a given frame of the real-time video monitoring data is generated. Based on the detection output, first tracking commands are generated to maneuver and center the camera on the location of the specific individual.

Systems and methods for real-time adjustment of neural networks for autonomous tracking and localization of moving subject

Disclosed are technologies for autonomous tracking. An initial coordinate of a beacon device carried by a user is registered as a dead reckoning waypoint with a drone configured to track the user. The drone receives IMU measurements from the beacon as the user moves. For each IMU measurement, a displacement vector characterizing user movement is calculated. Estimated beacon locations are calculated by dead reckoning, based on the displacement vectors and the dead reckoning waypoint. Later, an updated dead reckoning waypoint is calculated by obtaining the current location coordinate of the drone and performing optical triangulation to determine a relative position of the user with respect to the drone. The updated dead reckoning waypoint does not depend on previously estimated beacon locations, and accumulated IMU/estimation error is eliminated. Tracking continues, where subsequent estimated locations of the beacon are calculated by dead reckoning based on the updated dead reckoning waypoint.

Systems and methods for autonomous machine tracking and localization of mobile objects

Growing community of inventors

Tel Aviv, Israel

Joseph Keshet