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Compressed sensing (CS) is a novel theory for simultaneous data sampling and compression. The block compressed sensing can reduce the computation complexity and storage space for compressed sensing. In this paper, the weight-block compressed sensing technique coupled with the edge information is presented for improving the reconstructed image quality. Firstly, we segment the original image into block by block. Based on the edge characteristic of every sub-block, we will select the different measurements that needed for each block. This algorithm can preserve the edge and reduce the aliasing in comparison to the traditional block-compressed sensing. Experimental results show that the proposed algorithm can improve the PSNR comparing with the usual method. [ABSTRACT FROM PUBLISHER]

The aim of our paper is to prove the Triple Representation Theorem, which was established by Jenca in the setting of complete lattice effect algebras, for a special class of homogeneous effect algebras, namely TRT-effect algebras. This class includes complete lattice effect algebras, sharply dominating Archimedean atomic lattice effect algebras and homogeneous ortho complete effect algebras. [ABSTRACT FROM PUBLISHER]

Several data hiding techniques have been proposed in the literature in recent years for binary images. However most of them suffer from image distortion. One such data hiding technique introduced in [7] is based on optimization and dynamic programming. The technique uses block pattern encoding and the resulting images not only contain more data bits, but are also shown to be optimally distortion minimizing. Although the technique is quite effective and produces good results, time taken in encryption is abnormally high with O(n2) complexity. In this paper we have proposed a simpler method for data hiding in binary images that reduces the time complexity of the algorithm to O(n) while keeping the distortion low. We use a twofold encryption by inserting the data in the image using block pattern encoding table similar to [7] and then using XOR operation to increase the security of the data. The proposed algorithm follows a greedy approach which takes into consideration the local minimum cost of bit replacement leading to a significant difference in the execution time. [ABSTRACT FROM PUBLISHER]

Key Frame Extraction is a key step for video content analysis and video retrieval. According to the motion of object and camera, a method of key frame extraction based on frame blocks differential accumulation with double thresholds is proposed. Firstly, image frames are partitioned into equal-sized sub-images. Then the blocks in current frames are compared with the corresponding blocks in last extracted key frame to detect weather their contents changed. At last, the key frames of the video can be determined by the number of blocks whose contents changed a lot. The experiment results show that this method has a satisfactory efficiency. [ABSTRACT FROM PUBLISHER]

In the case of network applications become more common and computer network security issues become more prominent, intercepting and analyzing network packets is important. However, network environment traffic is large. How to intercept and analyze network packet effectively is a crucial issue. This paper research the process of NDIS intermediate driver capture packets in the Windows platform and propose a mode of shared memory blocks that transport data packets to the application layer software to analyze. This way can improve the efficiency of communication between kernel layer and application layer and reduce the probability of losses during the packets transmission. [ABSTRACT FROM PUBLISHER]

In order to effectively reduce the storage volume of complex image data in high-resolution synthetic aperture radar (SAR), blocked spectrum compressive framework [1][2] based on Root-MUSIC algorithm is proposed. In this paper block-wise processing [3] of SAR image method is introduced, which can effectively reduce the storage space of measurement matrix. Gaussian random matrix is employed as observation matrix [4][5] to obtain observed value of each sub-block. Model parameters can be calculated by means of Root-MUSIC algorithm. Spectrum signal is reconstructed from small amount of measurements. Simulation results with real spatial-sparse SAR image demonstrate that data storage capacity can be reduced to as low as 1.17%, which validate the effectiveness of the method. [ABSTRACT FROM PUBLISHER]