An adaptive image steganographic scheme based on Noise Visibility Function and an optimal chaotic based encryption method

Steganography is the science of hiding secret message in an appropriate digital multimedia in such a way that the existence of the embedded message should be invisible to anyone apart from the sender or the intended recipient. This paper presents an irreversible scheme for hiding a secret image in the cover image that is able to improve both the visual quality and the security of the stego-image while still providing a large embedding capacity. This is achieved by a hybrid steganography scheme incorporates Noise Visibility Function (NVF) and an optimal chaotic based encryption scheme. In the embedding process, first to reduce the image distortion and to increase the embedding capacity, the payload of each region of the cover image is determined dynamically according to NVF. NVF analyzes the local image properties to identify the complex areas where more secret bits should be embedded. This ensures to maintain a high visual quality of the stego-image as well as a large embedding capacity. Second, the security of the secret image is brought about by an optimal chaotic based encryption scheme to transform the secret image into an encrypted image. Third, the optimal chaotic based encryption scheme is achieved by using a hybrid optimization of Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) which is allowing us to find an optimal secret key. The optimal secret key is able to encrypt the secret image so as the rate of changes after embedding process be decreased which results in increasing the quality of the stego-image. In the extracting process, the secret image can be extracted from the stego-image losslessly without referring to the original cover image. The experimental results confirm that the proposed scheme not only has the ability to achieve a good trade-off between the payload and the stego-image quality, but also can resist against the statistics and image processing attacks.     

A Least Squares Approach to Escalator Algorithms for Adaptive Filtering

In this paper, we introduce an escalator (ESC) algorithm based on the least squares (LS) criterion. The proposed algorithm is relatively insensitive to the eigenvalue spread ratio (ESR) of an input signal and has a faster convergence speed than the conventional ESC algorithms. This algorithm exploits the fast adaptation ability of least squares methods and the orthogonalization property of the ESC structure. From the simulation results, the proposed algorithm shows superior convergence performance.     

Outlier-resistant methods for estimation and model fitting

Least squares is perhaps the most widely used technique for model fitting. In this article, we illustrate the poor performance of least squares when there are spurious values, or outliers, in a sequence of measurements. A brief overview of three well-known classes of robust alternatives to the least-squares mean is presented. For robust regression, a recent proposal called least median squares (LMS) is decribed. LMS regression is compared to least-squares regression in an example involving the estimation of optical fiber geometry. References are provided for software that is available for robust estimation techniques surveyed in this article.     

Closed-loop automatic tuning of PID controller for nonlinear systems

In this paper, a robust control system is first proposed which is suitable for the control of a class of nonlinear systems. A parallel connection of a relay to a proportional integral derivative (PID) controller collectively forms the robust controller. The relay ensures robust control by providing a high feedback gain, but it also induces a control chattering phenomenon. Instead of viewing chattering as an undesirable yet inevitable feature, the chattering signals are used as natural excitation signals for identifying an equivalent PID controller using the recursive least squares algorithm. No other explicit input signal is required. Analysis is provided on the stability properties of the control scheme. Simulation results for the level control of fluid in a spherical tank using the scheme are presented.     

CXFTV2: A Fortran subroutine for the discrete least squares convex approximation

A Fortan subroutine calculates the least squares approximation to n data values containing random errors subject to non-negative second divided differences (convexity). The method employs a dual active set quadratic programming technique that allows several concavities of an iterate to be corrected simultaneously, which is a distinctive feature of this calculation. A B-spline representation of the iterates reduces each active set calculation to an unconstrained minimization with fewer variables that requires only O(n) computer operations. Details in these techniques including the data structure that establishes the implementation of the method are specified. Numerical testing on a variety of data sets indicates that the subroutine is particularly efficient, terminating after a small number of active set changes, the subroutine being suitable for large numbers of data. A numerical example and its output is provided to help the use of the software.     

用最小二乘法恢复逐行倒相副载波

介绍ＰＡＬ制彩色全电视信号中色同步信号编码原理及恢复逐行倒相副载波的过程中，两种计算色同步信号相位角的方法。并且在实验的基础上得出：最小二乘法在ＰＡＬ制电视信号的副载波相位角计算中不仅可以满足精度要求，而且简便灵活。     

折反光学系统最佳红外像面位置的研究

较深入地研究了折反光学系统的红外像面,为确定实际的红外像面提出了一个新方法—计算从可见光边缘无色的弥散圆到红外最佳像面的距离。实践证明,这是确定这类光学系统最佳像面的一个方便、可靠的方法。     

Performance analysis of the forgetting factor RLS algorithm

An analysis is given of the performance of the standard forgetting factor recursive least squares (RLS) algorithm when used for tracking time-varying linear regression models. Three basic results are obtained: (1) the ‘P-matrix’ in the algorithm remains bounded if and only if the (time-varying) covariance matrix of the regressors is uniformly non-singular; (2) if so, the parameter tracking error covariance matrix is of the order O(μ + γ²/μ), where μ = 1 - λ, λ is the forgetting factor and γ is a quantity reflecting the speed of the parameter variations; (3) this covariance matrix can be arbitrarily well approximated (for small enough μ) by an expression that is easy to compute.     

基于最小二乘支持向量机的声波测距系统消噪处理

针对声波测距系统噪声复杂,淹没在噪声中的回波难以检测的问题,以机器统计学习理论为基础,采用最小二乘支持向量机(LS-SVM)建立系统模型,实现了声波测距系统异常值的预测和噪声的消除,并与传统的时间序列分析方法建立的自回归滑动平均求和模型(ARIMA)的消噪效果进行了仿真对比。仿真结果表明,利用最小二乘支持向量机建立的模型预测精度高,能有效地抑制声波测距系统中的噪声。