Sap Se

Huawei Technologies Co., Limited

Marvell International Limited

Future Wei Technologies, Inc.

Bsh Hausgeräte Gmbh

Nanjing Ruiying Runze Biopharmaceutical Technology Co., Inc.

Jun Liu

Jing Liu

Yu Zhang

Katherine Zhao

Ming Li

Hao Wu

Qi Zhang

Hongxin Li

Qiwei Zhang

Tao Wang

Yinling Ni

Chuntao Wang

Yonggang Xu

Jianping Xie

Yongxun Li

Junpeng Hu

Siyi Chen

Yu Li

Xiuhua Zhang

Tao Liu

Henrik Birke

Fei Lu

Dixin Shi

Ting He

Guohong Qin

A network device includes a forwarding plane and an artificial intelligence (AI) circuit. The forwarding plane is coupled to the AI circuit. The AI circuit is configured to process data about network packets from the forwarding plane using a first AI algorithm. The forwarding plane may be directly coupled to the AI circuit, and the forwarding plane may further pre-process the network packets to obtain the data about the network packets.

Network device and method for processing data about network packets

A method of batch processing implemented using a pre-classification mechanism of a network interface controller is provided. The method includes receiving, by the pre-classification mechanism of the network interface controller, rules for populating a first receive queue and a second receive queue, receiving, by the pre-classification mechanism, packets corresponding to a first protocol intermingled with packets corresponding to a second protocol, and populating, by the pre-classification mechanism of the network interface controller, the first receive queue exclusively with the packets corresponding to the first protocol and the second receive queue exclusively with the packets corresponding to the second protocol based on the rules such that a central processing unit is able to obtain a first batch of the packets from the first receive queue and process each of the packets from the first batch using a same node graph path.

Packet batch processing with graph-path based pre-classification

Described herein is a framework to monitor traffic congestion. In accordance with one aspect of the framework, the framework receives vehicle data from vehicle data sources located in a region of interest. The framework may determine a sample size and an average speed for an edge of the region of interest based on the vehicle data. Congestion probability may then be determined based on the sample size and average speed. A report may be presented based on the congestion probability.

Monitoring traffic congestion

System and methods are provided for performing image noise reduction. Color estimation of a pixel in an image is performed, wherein the image is stored in a data structure in a non-transitory computer-readable storage medium and the pixel has an initial pixel value. A pixel-block size is selected for pixel block similarity calculations based at least in part on the color estimation. One or more first pixel blocks associated with the pixel of the selected pixel-block size are determined. One or more first pixel block average values for the one or more first pixel blocks are calculated. A weighted average of the first pixel block average values is calculated. The data structure is updated by replacing the initial pixel value of the pixel with the weighted average of the first pixel black average values.

System and method for reducing image noise

A framework for identifying parking areas from vehicle trajectory data is described herein. Vehicle trajectory data collected from a sensor network having a plurality of sensor stations for detecting vehicles is provided. The vehicle trajectory data is pre-processed to generate a tracking table of vehicles and analyzed to determine an interstation parking area between first and second sensor stations of interest based on input parameters from a user.

Identification of vehicle parking using data from vehicle sensor network

Growing community of inventors

Nanjing, China

Jun Liu