A novel satellite image encryption algorithm based on hyperchaotic systems and Josephus problem

With the rapid growth of the number of Earth observation satellite (EOS) supporting critical applications, it is required to improve the security techniques to protect the sensitive data and images during the transmission between the satellites and the ground stations. This paper introduces a new satellite image encryption algorithm based on the Linear Feedback Shift Register (LFSR) generator, SHA 512 hash function, hyperchaotic systems, and Josephus problem. LFSR generates a matrix that is used to construct the 512-bits value of the hash function. These bits are used to set the initial values and parameters of the proposed encryption algorithm. Firstly, the six dimensions (6-D) hyperchaotic system is divided into three parts, where every two equations are considered as one part. Secondly, the 1-D hyperchaotic logistic-tent system is considered as the controller to select one part. The selected part is used to generate a matrix that is XORed with the original image. Thirdly, the scrambling operation by Josephus sequences is applied to the output of the previous step by scrambling the rows and the columns according to the selected part to produce the pre-encrypted image. Finally, if the number of iterations is less than the required number which is considered as a parameter of the secret key, the previous operations will be repeated in the pre-encrypted image; otherwise, the pre-encrypted image is considered as the final cipher image. Experimental and analyses results show that the proposed algorithm has good performance in terms of high level of security, large enough key-space, tolerance to Single Event Upsets (SEU) as well as low time complexity.     

Variational Mode Decomposition for Rotating Machinery Condition Monitoring Using Vibration Signals

The failure of rotating machinery applications has major time and cost effects on the industry.Condition monitoring helps to ensure safe operation and also avoids losses.The signal processing method is essential for ensuring both the efficiency and accuracy of the monitoring process.Variational mode decomposition(VMD)is a signal processing method which decomposes a non-stationary signal into sets of variational mode functions(VMFs)adaptively and non-recursively.The VMD method offers improved performance for the condition monitoring of rotating machinery applications.However,determining an accurate number of modes for the VMD method is still considered an open research problem.Therefore,a selection method for determining the number of modes for VMD is proposed by taking advantage of the similarities in concept between the original signal and VMF.Simulated signal and online gearbox vibration signals have been used to validate the performance of the proposed method.The statistical parameters of the signals are extracted from the original signals,VMFs and intrinsic mode functions(IMFs)and have been fed into machine learning algorithms to validate the performance of the VMD method.The results show that the features extracted from VMD are both superior and accurate for the monitoring of rotating machinery.Hence the proposed method offers a new approach for the condition monitoring of rotating machinery applications.     

Evaluation and improvement of coastal GNSS reflectometry sea level variations from existing GNSS stations in Taiwan

Global sea level rise due to an increasingly warmer climate has begun to induce hazards, adversely affecting the lives and properties of people residing in low-lying coastal regions and islands. Therefore, it is important to monitor and understand variations in coastal sea level covering offshore regions. Signal-to-noise ratio (SNR) data of Global Navigation Satellite System (GNSS) have been successfully used to robustly derive sea level heights (SLHs). In Taiwan, there are a number of continuously operating GNSS stations, not originally installed for sea level monitoring. They were established in harbors or near coastal regions for monitoring land motion. This study utilizes existing SNR data from three GNSS stations (Kaohsiung, Suao, and TaiCOAST) in Taiwan to compute SLHs with two methods, namely, Lomb–Scargle Periodogram (LSP)-only, and LSP aided with tidal harmonic analysis developed in this study. The results of both methods are compared with co-located or nearby tide gauge records. Due to the poor quality of SNR data, the worst accuracy of SLHs derived from traditional LSP-only method exceeds 1?m at the TaiCOAST station. With our procedure, the standard deviations (STDs) of difference between GNSS-derived SLHs and tide gauge records in Kaohsiung and Suao stations decreased to 10?cm and the results show excellent agreement with tide gauge derived relative sea level records, with STD of differences of 7?cm and correlation coefficient of 0.96. In addition, the absolute GNSS-R sea level trend in Kaohsiung during 2006–2011 agrees well with that derived from satellite altimetry. We conclude that the coastal GNSS stations in Taiwan have the potential of monitoring absolute coastal sea level change accurately when our proposed methodology is used.     

Automatic detection of geomagnetic sudden commencement via time–frequency clusters

One of global processes in ionosphere–thermosphere–magnetosphere system is the geomagnetic storms. It is of great importance to develop an algorithm that auto-detects sudden commencement because it could be an indicator of onset of the geomagnetic storm. Automatic detection of geomagnetic sudden commencement is based on time–frequency clusters generated by spectrogram. Proposed algorithm is tested on data set collected from stations belong to the international real-time magnetic observatory network (INTERMAGNET). Maximum standard deviation of algorithm detection times is observed to be one minute of the corresponding arrival times published by National Geophysical Data Center (NGDC).     

Interaction of tryptophan and phenylalanine with metal ferrocyanides and its relevance in chemical evolution

The interaction of two naturally occurring aromatic alpha-amino acids, namely, tryptophan and phenylalanine, with zinc, nickel, cobalt, and copper ferrocyanides has been studied. Both amino acids showed a high adsorption affinity toward metal ferrocyanides at neutral pH (7.0). Adsorption trends followed the Langmuir adsorption isotherm. Values of the Langmuir constants K(L) and X(m) suggest tryptophan is a better adsorbate than phenylalanine. Zinc ferrocyanide showed the highest adsorption, while the minimum adsorption was found in the case of copper ferrocyanide. Infrared spectral studies of adsorbate, adsorbent, and adsorption adducts indicate that adsorption occurs because of the interaction of adsorbate molecules with outer divalent metal ions present in the lattice of metal ferrocyanides. The present investigation supports the hypothesis that metal ferrocyanides might have concentrated the biomonomers on their surface in primeval seas during the course of chemical evolution.     

含中央分层缺陷层合板的压缩性能研究

通过考察层合板厚度(含铺层形式)、分层位置与大小等因素对压缩强度的影响,并采用局部屈曲、分层扩展以及软化夹杂等3种模型对含分层层合板的压缩强度进行了计算和破坏机理分析。得出如下结果:厚板对分层缺陷不敏感,中等厚度和薄层合板比较敏感;缺陷的位置和大小对层合板压缩强度的影响没有明显的规律可循;3种模型中软化夹杂模型效果较好,说明大部分含缺陷层合板的压缩过程具有分层扩展、基体开裂等损伤交杂在一起的特点,这导致了试件的破坏。     

大型壁板数控钻铆的三点快速调平算法

 针对传统机翼壁板铆接过程中托架调平所存在的问题，提出一种基于有限曲面加工区域的三点调平算法。利用机翼表面上待钻铆点附近三点的坐标来表征该铆接区域的空间姿态，通过钻铆系统的几何结构模型所确定的坐标变化矩阵，逆解出调平所需的各运动副位移增量。在钻铆系统结构模型和机翼曲面方程的基础上建立三点选取的优化模型，并利用外点罚函数法确定其优化解。西安飞机工业(集团)责任有限公司ARJ21新支线飞机的机翼壁板试验件数控钻铆结果表明，该算法能够实现较高的加工精度和调平效率。     

Modeling and control for cooperative transport of a slung fluid container using quadrotors

In this paper, dynamic modeling and control problem for transfer of a sloshing liquid container suspended through rigid massless links from a team of quadrotors are investigated. By the proposed solution, pose of the slung container and fluid sloshing modes are stabilized appropriately. Dynamics of the container-liquid-quadrotors system is modeled by Euler-Lagrange method. Fluid slosh dynamics is included using multi-mass-spring model. According to derived model, a proper control law is designed for a system with three or more quadrotors. Implementing the proposed control law, quadrotors can control pose of the container, directions of the links and liquid sloshing modes simultaneously. Stability of closed loop system of tracking errors and sloshing modes are demonstrated using a theory of singularly perturbed systems and Lyapunov stability theorem. Also, the capability of the proposed feedback control laws in solving a formerly organized transport problem of a liquid filled container has been demonstrated in simulations. Moreover, priority of the proposed control scheme to an existing slung load controller in the literature is demonstrated.     

Systematic examination of the geomagnetic storm sudden commencement using multi resolution analysis

Geomagnetic storm sudden commencements (SSC) contain a wealth of information which is useful in many applications. It is important to point out that the SSCs used in this study are sampled at the rate of one sample per second in order get use of such high resolution data. In this paper, two studies are made on the geomagnetic SSC and an SSC onset automatic detection algorithm is introduced. The first study is about finding the relationship between the SSC rise time and its amplitude. Where it is found that there is a positive correlation between the amplitude and the amplitude gradient which is the amplitude divided by rise time. The second study is checking the spectrum of the SSC, starting from its onset until the end of the SSC rise time. This check had proved that the SSC contains both low and high frequency regions. This led us to introduce a new term, namely the SSC variation rate (VR). This VR is defined as the maximum rate of change of the field in the higher-frequency region of the SSC. These two studies were the guide to build an SSC automatic detector of one sample per second data using multi resolution analysis (MRA) of the discrete wavelet transform (DWT). The data set contains 134 SSCs with different VRs that were collected from the Circum-pan Pacific Magnetometer Network (CPMN). It is found that the standard deviation of the detection error is 41 s and that the average error is 9 s. From the calculated error distribution function, it is found that the detection error is within the range of −1.5 to 3 min. The detection process, as will be shown in the article, takes 70 s for one station and 3 min if the decision is related to the detection(s) of other stations. These results demonstrate the superiority of the proposed algorithm over other algorithms in which the detection error ranges between −8 and 5 min and the detection process takes 2–10 min. In addition to being faster and more accurate than the other algorithms, the proposed algorithm is the first algorithm that automatically detects the SSC onset times from high-resolution data unlike previous studies that focused on determining the SSC times automatically using one-minute resolution data.