Nanjing University of Aeronautics and Astronautics

Jie Hao

Qiang Wu

Xue Yu

Ran Wang

Lei Zhang

Qi Zhu

Jing Chen

Yuhang Gu

An endogenous dynamic defense architecture-based multi-objective service function chain deployment method solves a problem of multi-objective deployment by constructing an endogenous dynamic defense architecture, in which a basic mode includes using moving target defense to ensure the security of VNFs, and an enhanced mode includes using mimic defense to perform security protection on the VNFs; in a construction module, a sub-pool division algorithm is proposed to divide a heterogeneous replica pool into a plurality of sub-pools, and VNFs are selected from the sub-pools so as to constitute a heterogeneous replica set; in a scheduling module, a replica VNF dynamic scheduling deployment algorithm is proposed, a deployment set is selected from the heterogeneous replica set for deployment, and is sent to a processing module; the input module replicas an input and distributes same to the processing module.

Endogenous dynamic defense architecture-based multi-objective service function chain deployment method

A road recognition method and system based on a seed point includes: obtaining a remote sensing image; obtaining a grayscale image; inserting a seed point in the grayscale image; searching for four initial road boundary points using the seed point as a reference point; obtaining a smallest bounding rectangle search box formed by the four initial road boundary points; obtaining a plurality of candidate search boxes based on the obtained search boxes; determining whether a sum of squares of grayscale differences between the plurality of candidate search boxes and the obtained search box is greater than a preset threshold; and if yes, stopping searching and completing road recognition; or if not, selecting a new search box from the plurality of candidate search boxes, and retrieving a plurality of candidate search boxes based on the new search box.

Road recognition method and system based on seed point

An efficient parallelization and deployment method of a multi-objective service function chain based on a CPU+DPU platform solves the problem of multi-objective deployment by constructing a heterogeneous computing architecture composed of an orchestrator and a server based on a CPU+DPU structure; the orchestrator is responsible for receiving an SFC request from a network operator; an SFC deployment algorithm based on deep reinforcement learning is operated, including a parallel strategy, a VNF topological order strategy and a DPU processing strategy to obtain an optimal deployment scheme of each request; then a resource management module is invoked to manage resources; and finally, a driver module is invoked to transmit the deployment scheme to a server for placement, and the server completes the deployment of SFC by using the CPU or the DPU respectively according to the deployment scheme.

Efficient parallelization and deployment method of multi-objective service function chain based on CPU + DPU platform

The present disclosure relates to a semantic segmentation method and system for a remote sensing image fusing GIS data. The method includes: obtaining a first remote sensing data training set and first GIS data; preprocessing the first remote sensing data training set to obtain a second remote sensing data training set; preprocessing the first GIS data based on the second remote sensing data training set to obtain second GIS data; performing data enhancement on the second remote sensing data training set to obtain a third remote sensing data training set; training a semantic segmentation model based on the third remote sensing data training set and the second GIS data; and performing semantic segmentation on the remote sensing image to be segmented based on the first GIS data and the trained semantic segmentation model to obtain a semantic set.

Semantic segmentation method and system for remote sensing image fusing GIS data

An indoor visible light positioning method and system based on a single LED lamp. The system includes an LED communication module and a smartphone module. The LED communication module sends its coordinates and shape data to the smartphone module. The smartphone module includes an inertial measurement unit (IMU) and a camera. The IMU is configured to obtain movement data of a smartphone. The camera is configured to shoot video streams of the LED lamp. Computing processing is performed on center point coordinates of the LED lamp and IMU data in the video streams, and constraints are provided by using a homography matrix of ellipses in the video streams, to obtain accurate location information of the smartphone, and provide location-based services such as navigation and query for a user.

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Jiangsu, China

Jie Hao