In this paper, we have proposed a new cryptography system which combines both the position permutation and the value transformation encryption methods. Three good features involve in this system: (1) High security evaluated with the measure of fractal dimension, (2) The content of encrypted image is sensitive to the initial key, and (3) This system can easily defense against the exhaustive search attack. Besides, for the requirement of real-time in multimedia system, we also proposed the high performance reconfigurable architecture for this system as well as the IP core generator software. The proposed IP core generator can be parameterized by the parameters of system-type, packet size, throughput and security to create the proper IP core for the applications. All the architectures generated from the IP core generator have been verified; except for the coding guideline checking, there exist 100% code coverage. According to the UMC 0.18 um cell library, we further verified all the configurations of architecture for speed, area and power consumption as well as delivering the essential scripts. The verifications of all the configurations, the throughput can be ranged between 1.59 and 2.25 Gbps with the hardware cost of 0.54 and 3.92 mm2. Compared with the existing designs, the proposed design possesses performance enough for most of multimedia system applications.

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THE PROPOSED APPROACH

Trees play an important role in maintaining environmental conditions suitable for life on the earth. To classify the tree type is very important for the forest maintenance. With the advent of high spatial resolution remote sensing sensors, our ability has greatly increased for tree type identification. Considering the amount of data in need of processing and the high computational costs required by image processing algorithms, conventional computing environments are simply impractical. Therefore, it is necessary to develop techniques and models for efficiently processing large volume of remote sensing images. In this study, a cluster computing environment was adopted to speed up the computation time. The test image was first partitioned into hundreds of manageable sub-images. Scheduled by the head node, the sub-images were then distributed to compute nodes for processing. A distributed K-mean clustering algorithm with undetermined number of class was applied to each compute node. A promising result was obtained. Compared to the field investigations, tree types of the test site were properly identified. In addition, great improvement in computation time was obtained. The distributed K-mean clustering algorithm implemented on our cluster computing environment performed much faster than stand-alone alternatives. By adding more compute nodes to our cluster computing environment, further improvement in computation time is expected.

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