Future Wei Technologies, Inc.

Huawei Technologies Co., Limited

Xiaotian Yin

Jian Li

Lifeng Liu

Yingxuan Zhu

Jun Zhang

Yongxiang Tao

Yucen Liu

Dayao Liang

Collaborative local positioning techniques, such as could be used with smart phones, surveying equipment, or other mobile devices, are described. The techniques include independent techniques for distance measurement between devices, the relative positioning of devices, and an absolute orienting of the devices without use of fixed anchor points and through on-will participation of the devices to provide privacy. Distances between devices are determined by use of a sequence of positioning signals, such as a magnetic signal, each of which include an identifier of the transmitting device, the amplitude of the signal as transmitted, and the position of the signal within the sequence. The amplitude of the positioning signal as received can be compared to the amplitude as transmitted to obtain a distance.

Methods and system for collaborative local positioning

A computer implemented method for self-learning of a control system. The method includes creating an initial knowledge base. The method learns first principles using the knowledge base. The method creates initial control commands derived from the knowledge base. The method generates constraints for the control commands. The method performs constrained reinforcement learning by executing the control commands with the constraints and observing feedback to improve the control commands. The method enriches the knowledge base based on the feedback.

System and method of efficient, continuous, and safe learning using first principles and constraints

A computer-implemented method of answering questions comprises: receiving, by one or more processors, a query; based on the query, generating, by the one or more processors, a matrix; based on the matrix, modifying, by the one or more processors, a dynamic memory; based on the matrix, determining, by the one or more processors, a first response from the dynamic memory; based on the matrix, determining, by the one or more processors, a second response from a database; based on the first response and the second response, determining, by the one or more processors, a third response; and in response to the query, providing, by the one or more processors, the third response.

Dynamic semantic networks for language understanding and question answering

Methods, apparatus, and systems are provided for integrated driver expression recognition and vehicle interior environment classification to detect driver condition for safety. A method includes obtaining an image of a driver of a vehicle and an image of an interior environment of the vehicle. Using a machine learning method, the images are processed to classify a condition of the driver and of the interior environment of the vehicle. The machine learning method includes general convolutional neural network (CNN) and CNN with adaptive filters. The adaptive filters are determined based on influence of filters. The classification results are combined and compared with predetermined thresholds to determine if a decision can be made based on existing information. Additional information is requested by self-motivated learning if a decision cannot be made, and safety is determined based on the combined classification results. A warning is provided to the driver based on the safety determination.

Integrated system for detection of driver condition

Various embodiments include systems and methods structured to provide recognition of an object in an image using a learning module trained using decomposition of the object into components in a number of training images. The training can be based on an overall objectness score of the object, an objectness score of each component of the object, a pose of the object, and a pose of each component of the object for each training image input. Additional systems and methods can be implemented in a variety of applications.

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Belmont, MA, United States of America

Xiaotian Yin