Future Wei Technologies, Inc.

Lei Rao

Jian Li

Ron-Chung Hu

Jiafeng Zhu

John R Sopka

Yinglong Xia

Kandarp Patel

A method implemented in a network element (NE) configured to operate as an Ensemble Information Broker (EIB) within a distributed data and energy storage internet architecture comprising collecting energy data indicating a flow of energy, an amount of energy consumed and generated by devices; collecting human presence data; collecting human activity data; predicting future energy consumption requirements and generation by employing prediction algorithms and analyzing the collected data; generating a set of control commands based on the predicted future energy consumption requirements and energy generation as applied to a cost function; transmitting the set of control commands to the corresponding devices; transmitting a broadcast message to determine an external NE to establish as a friend connection based on a user preference; transmitting a request to establish a friend connection with the determined NE; and transmitting the human presence data to the external NE when the friend connection is established.

Communication between distributed information brokers within a data and energy storage internet architecture

A computer-executed method is disclosed for collective navigation of distributed virtual reality (VR) devices. The method obtains a source vertex and a destination vertex for a VR device. The source vertex and the destination vertex include vertices of a graph model of a navigable space having a plurality of vertices. The vertices represent a point within the navigable space and the plurality of edges represent a path segment between two corresponding vertices. A subset of possible vertices, selected from the plurality of vertices, is determined for a navigable path. A vertex traffic potential is determined for each vertex of the subset of possible vertices. The navigable path, including one or more consecutive path segments selected to minimize both segment path lengths and vertex traffic potentials, is determined from the source vertex to the destination vertex.

Collective navigation for virtual reality devices

An apparatus comprises: a receiver configured to receive an image; and a processor coupled to the receiver and configured to: obtain pixels of the image; reorder the pixels to create reordered pixels; generate a first sub-image and a second sub-image using the reordered pixels; generate a first instruction to store the first sub-image in a first sub-image node; and generate a second instruction to store the second sub-image in a second sub-image node; and a transmitter coupled to the processor and configured to transmit the first instruction and the second instruction to a database.

Image storage and reconstruction based on sub-images

According to a method for selecting a path, the vehicle may receive driving condition of an area from a server, where the driving condition indicates that the area comprises a trouble area. Based on the driving condition of the area, the vehicle may calculate a first path and a second path, each of which is from a first point to a second point. The vehicle may obtain an effect value on vehicle driving associated with a change of weather. Based on the effect value and some other information, the vehicle may calculate a first weighted value for the first path and a second weighted value for the second path. Based on the first weighted value and the second weighted value, the vehicle may select a path. Because the effect value is used to select a path, the path selection in this embodiment may be associated with the change of weather.

Path selection for autonomous vehicles

A system and method for performing self-learning for adaptively achieving passenger comfort enhancement in an autonomous vehicle. The system comprises a plurality of sensor inputs. Each sensor input provides data representative of voice responses and image responses from a passenger in the vehicle. A controller is coupled to the plurality of sensor inputs. The controller generates and updates a reward function that includes a plurality of driving state transitions. The reward function is updated based on destination information and the data representative of the voice and image responses. The controller further generates a goal function that determines an optimized driving state transition updates the goal function based on the updated reward function and a previous goal function. The controller also generates a vehicle speed control signal, based on the updated goal function, to control the speed of the autonomous vehicle.

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Mountain View, CA, United States of America

Lei Rao