Current synthetic aperture radar (SAR) image recognition systems suffer from significant degradation when the systems are trained with synthetic images but tested with real measured images. To address this issue, the project is aimed at developing a quasi-supervised-learning approach for SAR image recognition. The key idea is transfer learning with quasi-supervised training procedures.
Principal Component Analysis (PCA) has been widely used in computer vision and machine learning applications due to its excellent performance in compression, feature extraction and feature representation. However, PCA suffers from severely degraded performance when outliers exist in datasets. To address this issue, the project is intended to develop a robust PCA algorithm, capable of mitigating outliers. The key idea is to leverage a popularity index for each sample so that outliers will contribute little in finding the projection matrix of the PCA.
Traditional application-driven (i.e., fault, SLA, or DoS attack detection) “pull” model (SNMP) based network management is expensive and slow for network problem detection, isolation, and root cause analysis. Especially, the recent federation of novel softwareisation and virtualization architectures, as well as Internet of Things (IoT) technologies, require better management over the heterogeneous systems and services. The project adopts “push” based open source forwarding (streaming) network management technologies by using P4 (Programming Protocol-Independent Packet Processors) Inband Network Telemetry (INT).