Future Wei Technologies, Inc.

DaQi Ren

Li Li

Masood Mortazavi

Chi Young Ku

Jianhuan Wen

A system for object recognition in an autonomous vehicle includes non-transitory memory storage for storing instructions and an image feature database. One or more processors are in communication with the memory. The one or more processors execute the instructions to download a road object database associated with routes between a departure point and a destination. Sensor data is received from a plurality of sensors of the autonomous vehicle and compared to data in the road object database. If the sensor data is not found in the road object database, the sensor data is compared to data in the image feature database. If the sensor data is not found in the image feature database, it is transmitted over a wireless channel to an Internet-coupled database. The sensor data is interpreted based on at least one of the road object database, the image feature database, or the Internet-coupled database.

Object recognition in autonomous vehicles

A device comprises a memory storage comprising instructions and one or more processors in communication with the memory. The one or more processors execute the instructions to access data for a plurality of previous time periods for a client of a plurality of clients, the data for each previous time period of the plurality of previous time periods including resource usage data for a plurality of jobs of the client, apply a time series analysis algorithm to the accessed data to predict a distribution of characteristics of resource usage for a future time period for the client, based on the predicted distribution of characteristics of resource usage, simulate a number of future predicted workloads for the future time period for the client, and based on the simulated number of future predicted workloads, automatically reserve a set of resources for the future time period for the client.

Method of dynamic resource allocation for public clouds

A computer-implemented method of deploying a cloud application comprises accessing, by one or more processors, a first factor graph that represents a first component of the cloud application, the first factor graph comprising a first set of nodes; accessing a second factor graph that represents a second component of the cloud application, the second factor graph comprising a second set of nodes; determining a third set of nodes that are present in both the first set of nodes and the second set of nodes; joining, by the one or more processors, the first factor graph and the second factor graph into a third factor graph, wherein the joining includes unifying the third set of nodes in the first factor graph and the second factor graph; based on the third factor graph, selecting computing resources; and deploying at least a portion of the cloud application to the selected computing resources.

Mechanisms for programmable composition of factor graphs

A method includes creating communication agents in each worker node in a distributed processing cluster, creating communication agents in nodes other than the distributed processing cluster as redundant nodes, the worker nodes and redundant nodes forming a communicator group, numbering and identifying each worker node in the distributed processing cluster using the communication agents, detecting a distributed processing worker node fault using the communication agent between nodes of the distributed processing cluster, spawning a new worker node via the communication agent from one of the redundant nodes in the communicator group for use as a new worker node, rebuilding the new worker node by using the communication agent to operate with the distributed processing cluster, and starting the new worker node to join the distributed processing cluster to work with the other worker nodes utilizing the communication agents for communications between the worker nodes in the communicator group.

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Saratoga, CA, United States of America

DaQi Ren