An efficient and noise resistive selective image encryption scheme for gray images based on chaotic maps and DNA complementary rules

A novel image encryption algorithm in streaming mode is proposed which exhaustively employs an entire set of DNA complementary rules alongwith one dimensional chaotic maps. The proposed algorithm is highly efficient due to encrypting the subset of digital image which contains 92.125 % of information. DNA addition operation is carried out on this MSB part. The core idea of the proposed scheme is to scramble the whole image by means of piecewise linear chaotic map (PWLCM) followed by decomposition of image into most significant bits (MSB) and least significant bits (LSB). The logistic sequence is XORed with the decoded MSB and LSB parts separately and finally these two parts are combined to get the ciphered image. The parameters for PWLCM, logistic map and selection of different DNA rules for encoding and decoding of both parts of an image are derived from 128-bit MD5 hash of the plain image. Simulated experimental results in terms of quantitative and qualitative ways prove the encryption quality. Efficiency and robustness against different noises make the proposed cipher a good candidate for real time applications.     

Pure spatial rich model features for digital image steganalysis

The SRM (Spatial Rich Model) is a very effective steganalysis method. It uses statistics of neighboring noise residual samples as features to capture the dependency changes caused by embedding. Because the noise residuals are the high-frequency components of image and closely tied to image content, the residuals of different types of image regions have different statistical properties and effectiveness for steganalysis. In this paper, the effectiveness of the residuals is investigated. Then the effectiveness of the statistics collected from different types of neighboring residual samples is investigated from the FLD (Fisher Linear Discriminant) viewpoint, and ineffective, effective and high-effective neighboring residual samples are defined. The ineffective neighboring residual samples are not likely to change during embedding, and if they are counted in statistics, they may mix the features with noise and make the features impure. Pure SRM features are extracted based on neighboring noise residual sample selection strategy. Furthermore, multi-order statistical features are proposed to increase the statistical diversity. Steganalysis performances of the statistical features collected from different types of neighboring residual samples are investigated on three content adaptive steganographic algorithms. Experimental results demonstrate that the proposed method can achieve a more accurate detection than SRM.     

Consensus in second‐order Markovian jump multi‐agent systems via impulsive control using sampled information with heterogenous delays

This paper investigates the consensus problem of second‐order Markovian jump multi‐agent systems with delays. Both network‐induced random delay and node‐induced state delay are considered, where the network‐induced random delay, subjected to a Markov chain, exists in the switching signal and the node‐induced state delay, related to switching topologies, is heterogeneous between any two linked agents. In order to reduce communication and control energy, an impulsive protocol is proposed, where each agent only can get delayed relative positions to neighbors and the velocity of itself at impulsive instants. By performing three steps of model transformation and introducing a mapping for two independent Markov chains, the consensus problem of the original continuous‐time system is equivalent to the stability problem of a discrete‐time expand error system with two Markovian jumping parameters and a necessary and sufficient criterion is derived. A numerical example is given to illustrate the effectiveness of the theoretical result.@@@@This work is supported by the National Natural Science Foundation of China under Grants 61374171, 61572210, and 51537003, the Fundamental Research Funds for the Central Universities (2015TS030), and the Program for Changjiang Scholars and Innovative Research Team in University (IRT1245).     

High-capacity quantum key distribution using Chebyshev-map values corresponding to Lucas numbers coding

We propose an approach that achieves high-capacity quantum key distribution using Chebyshev-map values corresponding to Lucas numbers coding. In particular, we encode a key with the Chebyshev-map values corresponding to Lucas numbers and then use k-Chebyshev maps to achieve consecutive and flexible key expansion and apply the pre-shared classical information between Alice and Bob and fountain codes for privacy amplification to solve the security of the exchange of classical information via the classical channel. Consequently, our high-capacity protocol does not have the limitations imposed by orbital angular momentum and down-conversion bandwidths, and it meets the requirements for longer distances and lower error rates simultaneously.     

基于粗糙集的光电装备延伸保修策略评估研究

光电装备延伸保修策略选择中最重要工作就是评估待选择的策略;为选出最合适策略,提高决策的科学性和合理性,克服不同指标之间的属性不一致性和以往指标确定主观性过大的问题,提出基于粗糙集和模糊集理论的综合评估模型;根据粗糙集理论和经验,采用属性指标重要度计算属性评估指标权重;与专家模糊指标评估矩阵相结合,得到最终综合评估值,选定最优策略;通过具体实例应用分析,验证方法的合理性和可行性。     

改进PSO优化参数的LSSVM燃煤锅炉Nox排放预测

为了提高燃煤锅炉NOX排放浓度预测的准确度,更好地进行氮氧化物的污染监测,提出了一种结合最小二乘支持向量机(Least squares support vector machines,LSSVM)和改进的粒子群优化算法(Particle swarm optimization,PSO)的预测方法。依据LSSVM预测原理及其参数选择的不确定性,采用一种改进的PSO优化算法对模型参数进行寻优,建立锅炉燃烧NOX排放特性模型,并与另两种方法结果进行比较。结果表明:LSSVM是一种有效的建模方法,有较高的拟合度;改进的PSO与LSSVM结合可改善模型的预测精度和泛化能力,在NOX排放浓度预测方面明显优于其他两种参数优化算法,对NOX排放预测有指导意义。