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In this paper, we propose a novel and robust local image descriptor to resolve the problem of complex illumination variations on low-level feature matching. Many classic local feature descriptors are invariant to linear illumination or monotonous intensity shift, but cannot handle more complex nonlinear illumination changes, which often occur due to the different time of exposure, the viewing direction, different types of light-surface interactions and other varying brightness changes. The presented descriptor that extracts the histograms of oriented gradients and Uniform Symmetric-Local Binary Pattern (US-LBP) features for each feature point neighborhood in the scale space. Extensive experiments show that the proposed descriptor outperforms many state-of-the-art descriptors such as SIFT, ORB, SURF and FREAK under the problem of local image matching, especially demonstrating the effectiveness of our method under complex illumination changes.

A feedback canceller model was built to cancel the feedback interference which caused by the coupling paths between the antennas of the transmitter and receiver in on-channel repeater. The normalized delay least square mean algorithm was presented to improve the capability of feedback canceller. The improved method could not only cancel echo signal in a low processing delay when relaying, but also get a fast convergence rate and better maladjustment performance. With this algorithm, on-channel repeater may be applied in many wireless broadcasting systems with different guard intervals. Simulation results show that, when the antenna isolation is lower than the gain of repeater, the improved feedback canceller could eliminate the multi-path echo signal to an allowable level, and it could increase the antenna isolation above 20dB.