Control of Rabinovich chaotic system using sliding mode control

This paper investigates the control of the continuous time Rabinovich chaotic system with the sliding mode control method. Based on the properties of the sliding mode theory, the controllers are designed and added to the nonlinear Rabinovich system. Numerical simulations show that the Rabinovich chaotic system can be regulated to its equilibrium points in the state space by using the sliding mode controllers, which verifies all the theoretical analyses. Simulation results of the proposed sliding mode control strategy have been also compared with the passive control method, and their performances are discussed. Copyright © 2013 John Wiley & Sons, Ltd.     

Chaotic Oscillators Derived from Sinusoidal Oscillators Based on the Current Feedback Op Amp

A collection of novel chaotic oscillators displaying behavior similar to that of the chaotic Colpitts oscillator and requiring the same number and type of energy storage elements is proposed. The oscillators use as an active element the current feedback op amp (CFOA) mostly employed as a current negative impedance converter (INIC). Nonlinearity is introduced through a two-terminal voltage-controlled nonlinear device with an antisymmetric driving-point characteristic. The chaos generators are designed based on sinusoidal oscillators that have been modified for chaos in a semi-systematic manner. By using CFOAs, several attractive features are attained, in particular suitability for high frequency operation. Systems of third- and fourth-order ordinary differential equations describing the chaotic behaviors are derived. Experimental results, PSpice circuit simulations and numerical simulations of the derived mathematical models are included.     

魔方变换及其在数字图像加密中的应用

提出了一种新的数字图像置乱方法：魔方变换，结合非线性动力学系统的Logistic映射。设计了基于魔方变换的图像加密／解密算法，首先由密钥生成得到自然数据混沌序列，以此序列为参数对图像矩阵进行魔方变换后得到加密图像，解密算法是加密算法的逆过程，实验结果表明，该算法能够得到令人满意的结果。     

基于混沌理论的工业过程控制

通过改变系统李雅普诺夫指数对工业过程控制 中的混沌现象进行控制，并提出了一种确定控制区域的方法．理论分析和仿真实验结果表明 ，该控制策略是有效的，可以实现系统的快速稳定．     

一个新混沌系统的自适应滑模变结构控制

研究了一个新混沌系统的控制问题．基于自适应滑模变结构控制的方法，用该控制律，即使系统存在输入饱和及外界扰动，也可以将混沌系统的状态渐进稳定到指定的平衡点．该控制律对外界扰动俱有鲁棒性．数字仿真表明，其控制效果极好．     

Robust Watermarking Algorithm for Medical Images Based on Non-Subsampled Shearlet Transform and Schur Decomposition

With the development of digitalization in healthcare, more and more information is delivered and stored in digital form, facilitating people’s lives significantly. In the meanwhile, privacy leakage and security issues come along with it. Zero watermarking can solve this problem well. To protect the security of medical information and improve the algorithm’s robustness, this paper proposes a robust watermarking algorithm for medical images based on Non-Subsampled Shearlet Transform (NSST) and Schur decomposition. Firstly, the low-frequency subband image of the original medical image is obtained by NSST and chunked. Secondly, the Schur decomposition of low-frequency blocks to get stable values, extracting the maximum absolute value of the diagonal elements of the upper triangle matrix after the Schur decomposition of each low-frequency block and constructing the transition matrix from it. Then, the mean of the matrix is compared to each element’s value, creating a feature matrix by combining perceptual hashing, and selecting 32 bits as the feature sequence. Finally, the feature vector is exclusive OR (XOR) operated with the encrypted watermark information to get the zero watermark and complete registration with a third-party copyright certification center. Experimental data show that the Normalized Correlation (NC) values of watermarks extracted in random carrier medical images are above 0.5, with higher robustness than traditional algorithms, especially against geometric attacks and achieve watermark information invisibility without altering the carrier medical image.     

隧道爆破振动信号混沌分形特征研究

为了分析隧道延时爆破时滞信号动力系统的非线性特征,在对新悬泉寺隧道爆破信号准确采集的基础上,运用混沌理论研究了经频率切片小波变换后各频带重构子信号的混沌动力学行为。通过子信号吸引子相轨迹特征对隧道爆破信号系统状态进行了直观描述和定量分析。结果表明:隧道爆破振动信号具有混沌特征,不同频带子信号吸引子在二维相空间一定区域内具有特定层次结构且永不封闭椭圆形轨迹。随着频率的增大,混沌吸引子在相空间的形态表现为椭圆轨迹的长/短轴之比逐渐减小,混沌吸引子在相空间沿长轴方向收缩,沿短轴扩展并最终趋于稳定,信号高频噪声具有弱混沌特征。混沌吸引子形态的演化过程有助于爆破信号幅值、能量信息的精确提取和主频有效判别,为隧道爆破信号非线性特征提取提供了新的思路。     

参数不确定离散混沌系统的模糊脉冲控制

研究了参数不确定离散混沌系统的控制问题.通过Takagi-Sugeno(TS)模糊动态模型和脉冲控制技术,建立了参数不确定离散混沌系统的Takagi-Sugeno模糊脉冲控制模型,然后利用矩阵分析和Lyapunov稳定性理论,得到了参数不确定离散混沌系统控制的一个充分条件,最后通过实例证实了该结果的正确性,相比传统的控制方法,基于Takagi-Sugeno模型的模糊脉冲控制方法具有一定的优越性.     

SBOOSP for Massive Devices in 5G WSNs Using Conformable Chaotic Maps

The commercialization of the fifth-generation (5G) wireless network has begun. Massive devices are being integrated into 5G-enabled wireless sensor networks (5G WSNs) to deliver a variety of valuable services to network users. However, there are rising fears that 5G WSNs will expose sensitive user data to new security vulnerabilities. For secure end-to-end communication, key agreement and user authentication have been proposed. However, when billions of massive devices are networked to collect and analyze complex user data, more stringent security approaches are required. Data integrity, non-repudiation, and authentication necessitate special-purpose subtree-based signature mechanisms that are pretty difficult to create in practice. To address this issue, this work provides an efficient, provably secure, lightweight subtree-based online/offline signature procedure (SBOOSP) and its aggregation (Agg-SBOOSP) for massive devices in 5G WSNs using conformable chaotic maps. The SBOOSP enables multi-time offline storage access while reducing processing time. As a result, the signer can utilize the pre-stored offline information in polynomial time. This feature distinguishes our presented SBOOSP from previous online/offline-signing procedures that only allow for one signature. Furthermore, the new procedure supports a secret key during the pre-registration process, but no secret key is necessary during the offline stage. The suggested SBOOSP is secure in the logic of unforgeability on the chosen message attack in the random oracle. Additionally, SBOOSP and Agg-SBOOSP had the lowest computing costs compared to other contending schemes. Overall, the suggested SBOOSP outperforms several preliminary security schemes in terms of performance and computational overhead.     

Cuckoo Optimized Convolution Support Vector Machine for Big Health Data Processing

Big health data collection and storing for further analysis is a challenging task because this knowledge is big and has many features. Several cloud-based IoT health providers have been described in the literature previously. Furthermore, there are a number of issues related to time consumed and overall network performance when it comes to big data information. In the existing method, less performed optimization algorithms were used for optimizing the data. In the proposed method, the Chaotic Cuckoo Optimization algorithm was used for feature selection, and Convolutional Support Vector Machine (CSVM) was used. The research presents a method for analyzing healthcare information that uses in future prediction. The major goal is to take a variety of data while improving efficiency and minimizing process time. The suggested method employs a hybrid method that is divided into two stages. In the first stage, it reduces the features by using the Chaotic Cuckoo Optimization algorithm with Levy flight, opposition-based learning, and distributor operator. In the second stage, CSVM is used which combines the benefits of convolutional neural network (CNN) and SVM. The CSVM modifies CNN’s convolution product to learn hidden deep inside data sources. For improved economic flexibility, greater protection, greater analytics with confidentiality, and lower operating cost, the suggested approach is built on fog computing. Overall results of the experiments show that the suggested method can minimize the number of features in the datasets, enhances the accuracy by 82%, and decrease the time of the process.