Reversible data hiding based on RSBEMD coding and adaptive multi-segment left and right histogram shifting

By leveraging the secret data coding using the remainder storage based exploiting modification direction (RSBEMD), and the pixel change operation recording based on multi-segment left and right histogram shifting, a novel reversible data hiding (RHD) scheme is proposed in this paper. The secret data are first encoded by some specific pixel change operations to the pixels in groups. After that, multi-segment left and right histogram shifting based on threshold manipulation is implemented for recording the pixel change operations. Furthermore, a multiple embedding policy based on chess board prediction (CBP) and threshold manipulation is put forward, and the threshold can be adjusted to achieve adaptive data hiding. Experimental results and analysis show that it is reversible and can achieve good performance in capacity and imperceptibility compared with the existing methods.     

无篦条可逆锤式破碎机

首先介绍了锤式破碎机的现状及发展前景，主要阐述了无篦条可逆锤式破碎机的工作原理，较详细地介绍了本机的结构特点和性能优点。     

Synthesis of water-soluble single-walled carbon nanotubes by RAFT polymerization

Water-soluble single-walled carbon nanotubes (SWNTs) were synthesized by grafting poly(acrylamide) (PAM) from the surface of SWNT via reversible addition-fragmentation chain transfer (RAFT) polymerization. The RAFT agents were covalently attached to the SWNTs by functionalizing SWNTs with in situ generated diazonium compounds. The product was characterized by means of FT-IR, Raman, ¹H NMR, TGA and TEM. The results showed that PAM chains had successfully grafted from SWNT by RAFT polymerization. The amount of PAM grown from SWNT increased with the polymerization time. The acrylamide conversion increased linearly with the polymerization time, indicating the “living” characteristics of the RAFT polymerization. TEM was utilized to image PAM-g-SWNT, showing relatively uniform polymer coatings present on the surface of individual, debundled nanotubes.     

基于可逆函数复杂性的正反控制门可逆网络综合

提出了基于可逆函数复杂性的正反控制(PNC, Positive/Negative ControD门可逆网络综合方法。该方法根据可逆函数的输出排列,逐次交换输出向量,在交换过程中减少函数的复杂性,直至复杂性为零。每一次向量的交换对应一个独立的PNC门。利用该方法综合部分可逆函数,并将实验结果与国际上代表性文献采用的实验用例进行比较。结果表明,构造的可逆网络在可逆门数上有一定程度的改善。     

DECODE: a new method for discovering clusters of different densities in spatial data

When clusters with different densities and noise lie in a spatial point set, the major obstacle to classifying these data is the determination of the thresholds for classification, which may form a series of bins for allocating each point to different clusters. Much of the previous work has adopted a model-based approach, but is either incapable of estimating the thresholds in an automatic way, or limited to only two point processes, i.e. noise and clusters with the same density. In this paper, we present a new density-based cluster method (DECODE), in which a spatial data set is presumed to consist of different point processes and clusters with different densities belong to different point processes. DECODE is based upon a reversible jump Markov Chain Monte Carlo (MCMC) strategy and divided into three steps. The first step is to map each point in the data to its mth nearest distance, which is referred to as the distance between a point and its mth nearest neighbor. In the second step, classification thresholds are determined via a reversible jump MCMC strategy. In the third step, clusters are formed by spatially connecting the points whose mth nearest distances fall into a particular bin defined by the thresholds. Four experiments, including two simulated data sets and two seismic data sets, are used to evaluate the algorithm. Results on simulated data show that our approach is capable of discovering the clusters automatically. Results on seismic data suggest that the clustered earthquakes, identified by DECODE, either imply the epicenters of forthcoming strong earthquakes or indicate the areas with the most intensive seismicity, this is consistent with the tectonic states and estimated stress distribution in the associated areas. The comparison between DECODE and other state-of-the-art methods, such as DBSCAN, OPTICS and Wavelet Cluster, illustrates the contribution of our approach: although DECODE can be computationally expensive, it is capable of identifying the number of point processes and simultaneously estimating the classification thresholds with little prior knowledge.     

On the multi-level embedding of crypto-image reversible data hiding

Crypto-space reversible image steganography has attracted increasing attention, given its ability to embed authentication information without revealing the image content. This paper presents an efficient reversible data hiding scheme for crypto-images: a block predictor is applied to compute prediction errors, then an adaptive block mapping algorithm is utilized to compress them whose amplitudes are within a small threshold, finally, this strategy can be applied in a multi-level manner to achieve a higher embedding capacity. Due to the correlations among adjacent pixels in the block, images can be sufficiently compressed to reserve abundant space for additional data embedding. Different from the prior arts, the compression code of the image is fully encrypted. Experimental results verify that the embedded data and original image can be perfectly recovered, the security is higher compared with the state-of-the-arts, and a significant improvement in the average embedding rate is achieved on two large-scale image datasets.     

A high payload frequency-based reversible image hiding method

To provide both a high payload capacity and a good stego-image quality, a high payload frequency-based reversible image hiding (HPFRIH) method was presented in this paper. These performances can be achieved by transforming a cover image from the spatial domain into the frequency domain through Haar digital wavelet transform (HDWT) method followed by an adaptive arithmetic coding method to encode the HDWT coefficients in a high-frequency band. The HPFRIH method was used to conceal both the compressed data and the secret data in the high-frequency band. This simple method can completely reconstruct the cover image only from the stego-image. Moreover, experimental results showed that the HPFRIH method offers both impressive hiding capacity and satisfied stego-image quality.     

A reversible data hiding scheme using complementary embedding strategy

Obtaining good visual quality and high hiding capacity with reversible data hiding systems is a technically challenging problem. In this paper, we propose a simple reversible data hiding scheme that uses a complementary hiding strategy. The proposed method embeds one secret bit horizontally and vertically into one cover pixel of a grayscale cover image by decreasing odd-valued pixels and increasing even-valued pixels by one. Experimental results show that the hiding capacity measured by bit per pixel (bpp) of the proposed scheme is at least 1.21 bpp with a PSNR (peak signal-to-noise ratio) value greater than 52 dB for all standard test images. Especially in the case of four-layer embedding, the PSNR value of the proposed method is still greater than 51 dB at a hiding capacity of about 5 bpp for all standard test images. In addition, the proposed method is quite simple because it primarily uses additions and subtractions. These results indicate that the proposed scheme is superior to many existing reversible data hiding schemes introduced in the literature.     

Oil spill detection from SAR intensity imagery using a marked point process

This paper presents a new algorithm for the detection of oil spill from SAR intensity images. The proposed algorithm combines the marked point process, Bayesian inference and Markov Chain Monte Carlo (MCMC) technique. In this paper, the candidates of oil spills or dark spots in a SAR intensity image are characterized by a Poisson marked point process. The marked point process is formed by a group of random points (as a point process modelling the locations of oil spills) and a set of parameters including geometric parameters of windows centred at the random points and gamma distribution parameters (as the marks attaching to each point). As a result, the candidates of oil spills are represented by a group of windows, in which the intensities of pixels follow independent and identical gamma distribution with lower mean than that for the identical gamma distribution of the pixels out of windows. Following the Bayesian paradigm, the posterior distribution, which characterizes the locations and statistical distributions of oil spills, can be obtained up to a normalizing constant. In order to simulate from the posterior distribution and to estimate the parameters of the posterior distribution, the Revisable Jump MCMC (RJMCMC) algorithm is used. The optimal locations and sizes of dark spots are obtained by a maximum a posteriori (MAP) algorithm. The proposed approach is tested using Radarsat-1 SAR images with oil spills indicated by human analysts. The results show that the proposed approach works well and is very promising.