Based on vertical-cavity surface-emitting lasers (VCSELs) with optical injection and polarization-rotated optical feedback, we propose and numerically demonstrate a scheme for generating synchronized random bit sequences (SRBS) for two legal users at different locations. For the scheme, the chaotic signal generated by a driving VCSEL (D-VCSEL) subject to chaotic optical injection are utilized to simultaneously drive two VCSELs (A-VCSEL and B-VCSEL) with polarization-rotated feedback, then the chaotic outputs from A (B)-VCSEL are used as physical entropy sources to generate SRBS. The simulated results show that, the SRBS at a rate up to 400 Gbits/s can be achieved after adopting suitable post-processing method. The security of this scheme can be ensured by a high-quality synchronization between A-VCSEL and B-VCSEL while relatively low-quality synchronization between D-VCSEL and A (B)-VCSEL. Moreover, the BER and the randomness of generated SRBS are also discussed.
This paper describes a novel structural adaptation artificial immune network (SAAN) clustering algorithm for texture
segmentation. In the SAAN, a new immune antibody neighborhood and an adaptive learning coefficient are presented.
The model can adaptively map input data into the antibody output space, which has a better adaptive net structure.
Images are first partitioned into a set of regions by using the watershed segmentation. Then the nonsubsampled
contourlet texture features are extracted from each watershed region as the antigens of the SAAN. Finally the antibodies
clustering results of the SAAN are combined to yield a global clustering solution by the minimal spanning tree, which
need not a predefined number of clustering. The experimental results with various texture images illustrate the
effectiveness of the proposed novel segmentation algorithm.
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