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This paper proposes a new chaotic image encryption algorithm. Firstly, an original phased composite chaotic map is used. The comparative study shows that the map cryptographic characteristics are better than the Logistic map, and the map is used as the controller of Fisher-Yates scrambling. Secondly, with the higher complexity of the fractional-order five-dimensional cellular neural network system, it is used as a diffusion controller in the encryption process. And mix the secret key, mapping and plaintext, we can obtain the final ciphertext. Finally, the comparative experiments prove that the proposed algorithm improves the encryption efficiency, has good security performance, and can resist common attack methods., Competing Interests: The authors have declared that no competing interests exist.

Based on the analysis of the theoretical calculation model of axial resolution of optical coherence tomograph for the posterior segment of the human eye, a set of testing device for measuring its axial resolution is designed and developed. In view of a commercial ophthalmic optical coherence tomograph in clinical use, its axial resolution is calculated to be 5.07 μm theoretically, and the actual measurement value is 5.45 μm. The uncertainty of the detection device is evaluated and the result is (5.45±0.10) μm. The measurement error introduced by the testing device is very small. Meanwhile, the axial resolution measured by the testing device meets the requirements of the instrument(≤ 6 μm).

Purpose: The aim of our study is to explore the impacts of different deconvolution algorithms on correlations between CBF, MTT, CBV, TTP, Tmax from MR perfusion (MRP) and angiography cerebral circulation time (CCT)., Methods: Retrospectively, 30 patients with unilateral carotid stenosis, and available pre-stenting MRP and angiography were included for analysis. All MRPs were conducted in a 1.5-T MR scanner. Standard singular value decomposition, block-circulant, and two delay-corrected algorithms were used as the deconvolution methods. All angiographies were obtained in the same bi-plane flat-detector angiographic machine. A contrast bolus of 12mL was administrated via angiocatheter at a rate of 8mL/s. The acquisition protocols were the same for all cases. CCT was defined as the difference between time to peak from the cavernous ICA and the parietal vein in lateral view. Pearson correlations were calculated for CCT and CBF, MTT, CBV, TTP, Tmax., Results: The correlation between CCT and MTT was highest with Tmax (r=0.65), followed by MTT (r=0.60), CBF (r=-0.57), and TTP (r=0.33) when standard singular value decomposition was used. No correlation with CBV was noted., Conclusions: MRP using a singular value decomposition algorithm confirmed the feasibility of quantifying cerebral blood flow deficit in steno-occlusive disease within the angio-room. This approach might further improve patient safety by providing immediate cerebral hemodynamics without extraradiation and iodine contrast., (Copyright © 2016 Elsevier Masson SAS. All rights reserved.)

In this article, the controllability of Boolean networks via input controls under Harvey's update scheme is investigated. First, the model of Boolean control networks under Harvey's stochastic update is proposed, by means of semi-tensor product approach, which is converted into discrete-time linear representation. And, a general formula of control-depending network transition matrix is provided. Second, based on discrete-time dynamics, controllability of the proposed model is analytically discussed by revealing the necessary and sufficient conditions of the reachable sets, respectively, for three kinds of controls, i.e., free Boolean control sequence, input control networks, and close-loop control. Examples are showed to demonstrate the effectiveness and feasibility of the proposed scheme.

In efforts to discover disease mechanisms and improve clinical diagnosis of tumors, it is useful to mine profiles for informative genes with definite biological meanings and to build robust classifiers with high precision. In this study, we developed a new method for tumor-gene selection, the Chi-square test-based integrated rank gene and direct classifier (χ(2)-IRG-DC). First, we obtained the weighted integrated rank of gene importance from chi-square tests of single and pairwise gene interactions. Then, we sequentially introduced the ranked genes and removed redundant genes by using leave-one-out cross-validation of the chi-square test-based Direct Classifier (χ(2)-DC) within the training set to obtain informative genes. Finally, we determined the accuracy of independent test data by utilizing the genes obtained above with χ(2)-DC. Furthermore, we analyzed the robustness of χ(2)-IRG-DC by comparing the generalization performance of different models, the efficiency of different feature-selection methods, and the accuracy of different classifiers. An independent test of ten multiclass tumor gene-expression datasets showed that χ(2)-IRG-DC could efficiently control overfitting and had higher generalization performance. The informative genes selected by χ(2)-IRG-DC could dramatically improve the independent test precision of other classifiers; meanwhile, the informative genes selected by other feature selection methods also had good performance in χ(2)-DC.

In this paper, dynamics of asynchronous multiple-valued networks (AMVNs) are investigated based on linear representation. By semitensor product of matrices, we convert AMVNs into the discrete-time linear representation. A general formula to calculate all of network transition matrices of a specific AMVN is achieved. A necessary and sufficient algebraic criterion to determine whether a given state belongs to loose attractors of length s is proposed. Formulas for the numbers of attractors in AMVNs are provided. Finally, algorithms are presented to detect all of the attractors and basins. Examples are shown to demonstrate the feasibility of the proposed scheme.

TL- φ algorithms are newly developed explicit structure-dependent integration algorithms utilized for solving the temporally discretized equations of motion. In contrast to the existing algorithms, the most significant improvement of TL- φ algorithms is in diminishing the amount of period errors by introducing a precorrection coefficient φ into the integration parameters of TL- φ algorithms, which is related to the critical frequency of a system. In the previous work, the fundamental frequency of the system is deemed to be the critical frequency, so that φ is a constant scaling corresponding to the fundamental frequency for both single-degree-of-freedom (SDOF) and multi-degree-of-freedom (MDOF) systems. However, for a MDOF system, the first mode may not contribute to the total response more than other ones under a given external excitation, calculating φ only by the fundamental frequency will underestimate the contribution of the higher-frequency modes to structural dynamics. In this paper, choices of the critical frequency for φ when applying TL- φ algorithms to MDOF systems are investigated thoroughly. By considering the initial structural properties of the system and the frequency characteristics of the external excitation simultaneously, a calculation criterion of φ for MDOF systems under specific external excitations is proposed. Four numerical examples with different initial structure properties and loading conditions are designed, and the results demonstrate that the proposed criterion can be potentially used to solve structural dynamic problems of MDOF systems with a more desirable numerical dispersion performance. [ABSTRACT FROM AUTHOR]

Because quantum computing can break encryption systems based on mathematical models, this paper proposes a new color quantum image encryption algorithm based on hyperchaotic system and quantum revolving gate. First, the classic image is diffused, the matrix generated by the four-wing chaotic system is serialized, and XORed with the key and plaintext to obtain the semi-ciphertext image. Then, the quantum rotation method is used to perform scrambling operations on images quantized using the improved novelty enhanced quantum representation (NEQR) model. The angle of rotation is determined by the sequence generated by the piecewise chaotic map. Finally, the experimental results and performance analysis prove that the algorithm has high security performance. [ABSTRACT FROM AUTHOR]

Direct integration algorithms are effective methods to solve the temporally discretized differential equations of motion for structural dynamics. Numerous researchers have worked out various algorithms to achieve desirable properties of explicit expression, unconditional stability, and controllable numerical dissipation. However, studies involving the numerical dispersion of integration algorithms are limited. In this paper, a precorrected bilinear transformation from a continuous domain to a discrete domain associating with pole-matching based on the control theory is utilized to develop a new family of explicit structure-dependent integration algorithms, referred to as TL- φ algorithms. In contrast to the existing algorithms, the significant improvement of the proposed method is that it can control the amount of numerical dispersion by an additional parameter related to the critical frequency of the structure. Stability, energy dissipation, and numerical dispersion properties of the proposed algorithms for both linear and nonlinear systems are fully studied. It is shown that the proposed family of algorithms is unconditionally stable for linear systems while only conditionally stable for nonlinear systems. Though the numerical dissipation property of the TL- φ algorithms is quite similar to that of other well-developed methods, its ability to minimize the period errors when compared with other methods makes it beneficial to the accuracy of the numerical simulation of dynamic responses. Four numerical examples are used to investigate the improved performance of the new method, and the results show that the proposed algorithms can be potentially used to solve linear and nonlinear structural dynamic problems with desirable numerical dispersion performance. [ABSTRACT FROM AUTHOR]

In this paper, a new chaotic image encryption algorithm based on pseudo-random bit sequence and DNA plane is proposed. The coupled map lattice (CML) is applied to design a pseudo-random bit sequence generation (PBSG) system and use the system to generate the random sequence needed in the encryption process. The initial values and parameters of the system are generated by the SHA-256 hash algorithm combined with given keys. Firstly, the plane image is decomposed into four DNA planes in combination with the DNA encoding rules, and then the four DNA planes are subjected to row circular permutation and column circular permutation. After that, the diffusion operation on each DNA plane is performed. Finally, the four DNA planes are decoded and then combined into a pixel matrix, that is, the final cipher image is obtained. Throughout the encryption process, the choice of DNA encoding and decoding rules is determined by the PBSG system. Simulation results and security analysis show that the algorithm not only has good encryption effect, but also can resist various classic attacks, and has excellent security performance. [ABSTRACT FROM AUTHOR]

A novel algebraic approach is proposed to study dynamics of asynchronous random Boolean networks where a random number of nodes can be updated at each time step (ARBNs). In this article, the logical equations of ARBNs are converted into the discrete-time linear representation and dynamical behaviors of systems are investigated. We provide a general formula of network transition matrices of ARBNs as well as a necessary and sufficient algebraic criterion to determine whether a group of given states compose an attractor of length in ARBNs. Consequently, algorithms are achieved to find all of the attractors and basins in ARBNs. Examples are showed to demonstrate the feasibility of the proposed scheme. [ABSTRACT FROM AUTHOR]

Fuzzy Cognitive Maps (FCMs) as knowledge-based tools are widely implemented for knowledge representation and reasoning. To reach final equilibrium states is essential for the results of causal reasoning in practical applications by using FCMs. However, the dynamics and the stabilization problem of FCMs are still not adequately studied so far. In this article, the algebraic dynamics of k -valued fuzzy cognitive maps and its stabilization is analytically and quantitatively investigated. Based on the technique of semi-tensor product (STP), k -valued FCMs are firstly converted into discrete-time linear representation. General formulas are obtained to calculate the number of fixed points and limit cycles of finite-state FCMs, and a necessary and sufficient criterion to determine attractors is proposed, by means of which an algorithm for calculating all attractors and their basins are achieved. Furthermore, the stabilization problem of k -valued FCMs is discussed. By introducing controls to certain concepts in FCMs, the system can be globally stabilized in equilibrium states. The related necessary and sufficient criterion is proposed and the corresponding method to achieve the global stabilization of FCMs under constant controls is given. Examples are shown to demonstrate the effectiveness and feasibility of the proposed scheme. [ABSTRACT FROM AUTHOR]

• An algorithm is designed to perform quantitative analysis for the temporal features. • A model is constructed to obtain optimal observability-constrained feature subsets. • A spatial-temporal ensemble mechanism provides adaptability under missing data. • The method employs spatial-temporal information to reduce effects of PMU loss. Transient stability assessment (TSA) plays an important role in the design and operation of power system. With the widespread deployment of phasor measurement units (PMUs), the machine learning-based method has attracted much attention for its speed and generalization. However, the generalization will deteriorate if some features are missing due to PMU failure. In this paper, a spatial-temporal adaptive TSA method is proposed to handle the missing data issue. By developing an optimal PMU clusters searching model based on temporal feature importance, and by constructing an ensemble mechanism of long short-term memory (LSTM) for the optimal PMU clusters, the spatial-temporal information is utilized adaptively. Therefore, the aim of maintaining the robustness of TSA performance under any possible PMU failure event is achieved. The proposed approach is demonstrated on New England 39-bus power system. Compared with existing methods, the proposed method achieves state-of-art performance in both accuracy and response time under missing data conditions. In addition, the proposed method is more robust in the case of PMU failure than others. [ABSTRACT FROM AUTHOR]