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In this work, a color image encryption and decryption algorithm for digital images is presented. It is based on the modular discrete derivative (MDD), a novel technique to encrypt images and efficiently hide visual information. In addition, Langton’s ant, which is a two-dimensional universal Turing machine with a high key space, is used. Moreover, a deterministic noise technique that adds security to the MDD is utilized. The proposed hybrid scheme exploits the advantages of MDD and Langton’s ant, generating a very secure and reliable encryption algorithm. In this proposal, if the key is known, the original image is recovered without loss. The method has demonstrated high performance through various tests, including statistical analysis (histograms and correlation distributions), entropy, texture analysis, encryption quality, key space assessment, key sensitivity analysis, and robustness to differential attack. The proposed method highlights obtaining chi-square values between 233.951 and 281.687, entropy values between 7.9999225223 and 7.9999355791, PSNR values (in the original and encrypted images) between 8.134 and 9.957, the number of pixel change rate (NPCR) values between 99.60851796% and 99.61054611%, unified average changing intensity (UACI) values between 33.44672377% and 33.47430379%, and a vast range of possible keys >5.8459×1072. On the other hand, an analysis of the sensitivity of the key shows that slight changes to the key do not generate any additional information to decrypt the image. In addition, the proposed method shows a competitive performance against recent works found in the literature.

In this work, a new medical image encryption/decryption algorithm was proposed. It is based on three main parts: the Jigsaw transform, Langton’s ant, and a novel way to add deterministic noise. The Jigsaw transform was used to hide visual information effectively, whereas Langton’s ant and the deterministic noise algorithm give a reliable and secure approach. As a case study, the proposal was applied to high-resolution retinal fundus images, where a zero mean square error was obtained between the original and decrypted image. The method performance has been proven through several testing methods, such as statistical analysis (histograms and correlation distributions), entropy computation, keyspace assessment, robustness to differential attack, and key sensitivity analysis, showing in each one a high security level. In addition, the method was compared against other works showing a competitive performance and highlighting with a large keyspace (>1×101,134,190.38). Besides, the method has demonstrated adequate handling of high-resolution images, obtaining entropy values between 7.999988 and 7.999989, an average Number of Pixel Change Rate (NPCR) of 99.5796%±0.000674, and a mean Uniform Average Change Intensity (UACI) of 33.4469%±0.00229. In addition, when there is a small change in the key, the method does not give additional information to decrypt the image.

This paper proposes a new approach of parameterizing the excitation signal for improving the quality of HMM-based speech synthesis system. The proposed method tries to model the excitation or residual signal by segregating the regions of the residual signal based on their perceptual importance. Initially, a study on the characteristics of the residual signal around glottal closure instant (GCI) is performed using principal component analysis (PCA). Based on the present study, and from the previous literature (Adiga and Prasanna in Proceedings of Interspeech, pp 1677–1681, 2013 ; Cabral in Proceedings of Interspeech, pp 1082–1086, 2013 ), it is concluded that the segment of the residual signal around GCI which carries perceptually important information is considered as the deterministic component and the remaining part of the residual signal is considered as the noise component. The deterministic component is compactly represented using PCA coefficients (with about 95% accuracy), and the noise component is parameterized in terms of spectral and amplitude envelopes. The proposed excitation modeling approach is incorporated in the HMM-based speech synthesis system. Subjective evaluation results show a significant improvement of quality for both female and male speakers’ speech synthesized by the proposed method, compared to three existing excitation modeling methods. Accurate parameterization of the segment of the residual signal around GCI resulted in the improvement of the quality of the synthesized speech. Synthesized speech samples of the proposed and existing source models are made available online at http://www.sit.iitkgp.ernet.in/~ksrao/parametric-hts/pcd-hts.html .

We investigate the optimal solution of systems of initial-value problems with smooth right-hand side functions f from a Hölder class F reg r , ϱ , where r ≥ 0 is the number of continuous derivatives of f , and ϱ ∈ (0, 1] is the Hölder exponent of r th partial derivatives. We consider algorithms that use n evaluations of f , the i th evaluation being corrupted by a noise δ i of deterministic or random nature. For δ ≥ 0, in the deterministic case the noise δ i is a bounded vector, ∥ δ i ∥≤ δ . In the random case, it is a vector-valued random variable bounded in average, ( E (∥ δ i ∥ q )) 1/ q ≤ δ , q ∈ [1, + ∞ ). We point out an algorithm whose L p error ( p ∈ [0, + ∞ ]) is O ( n − ( r + ϱ ) + δ ), independently of the noise distribution. We observe that the level n − ( r + ϱ ) + δ cannot be improved in a class of information evaluations and algorithms. For ε > 0, and a certain model of δ -dependent cost, we establish optimal values of n ( ε ) and δ ( ε ) that should be used in order to get the error at most ε with minimal cost.

A fundamental question of data analysis is how to distinguish noise corrupted deterministic chaotic dynamics from time-(un)correlated stochastic fluctuations when just short length data is available. Despite its importance, direct tests of chaos vs stochasticity in finite time series still lack of a definitive quantification. Here we present a novel approach based on recurrence analysis, a nonlinear approach to deal with data. The main idea is the identification of how recurrence microstates and permutation patterns are affected by time reversibility of data, and how its behavior can be used to distinguish stochastic and deterministic data. We demonstrate the efficiency of the method for a bunch of paradigmatic systems under strong noise influence, as well as for real-world data, covering electronic circuit, sound vocalization and human speeches, neuronal activity, heart beat data, and geomagnetic indexes. Our results support the conclusion that the method distinguishes well deterministic from stochastic fluctuations in simulated and empirical data even under strong noise corruption, finding applications involving various areas of science and technology. In particular, for deterministic signals, the quantification of chaotic behavior may be of fundamental importance because it is believed that chaotic properties of some systems play important functional roles, opening doors to a better understanding and/or control of the physical mechanisms behind the generation of the signals.

Uncertainty is ubiquitous and has a significant impact on individuals’ actions. Inspired by the reality, this paper considers a population affected by changing evolutionary environments with both payoff noise and demographic noise. Meanwhile, we discuss two different types of changing environments, one with deterministic fluctuations and the other with stochastic fluctuations. To explore how changing environments influence the dynamics of cooperation, we first build a theoretical model, which introduces a geometric mean instead of an arithmetic mean of growth rate to present a time-dependent performance of cooperation. Through simulation results, we find that a fluctuating environment with the two noises is beneficial for promoting cooperation, and a deterministic environment is better at promoting cooperation than a stochastic environment. Besides, both deterministic and stochastic fluctuations can make the cooperative species get a higher payoff than the defective species, which is the cause of cooperation promotion. Moreover, in an environment with stochastic noises, the type of time-dependent noise distribution has little impact on evolutionary dynamics. In addition, a high noise variance is beneficial for promoting cooperation, but not for maintaining cooperation.

Kalman filter is a commonly used method in the Global Navigation Satellite System (GNSS)/Inertial Navigation System (INS) integrated navigation system, in which the process noise covariance matrix has a significant influence on the positioning accuracy and sometimes even causes the filter to diverge when using the process noise covariance matrix with large errors. Though many studies have been done on process noise covariance estimation, the ability of the existing methods to adapt to dynamic and complex environments is still weak. To obtain accurate and robust localization results under various complex and dynamic environments, we propose an adaptive Kalman filter navigation algorithm (which is simply called RL-AKF), which can adaptively estimate the process noise covariance matrix using a reinforcement learning approach. By taking the integrated navigation system as the environment, and the opposite of the current positioning error as the reward, the adaptive Kalman filter navigation algorithm uses the deep deterministic policy gradient to obtain the most optimal process noise covariance matrix estimation from the continuous action space. Extensive experimental results show that our proposed algorithm can accurately estimate the process noise covariance matrix, which is robust under different data collection times, different GNSS outage time periods, and using different integration navigation fusion schemes. The RL-AKF achieves an average positioning error of 0.6517 m within 10 s GNSS outage for GNSS/INS integrated navigation system and 14.9426 m and 15.3380 m within 300 s GNSS outage for the GNSS/INS/Odometer (ODO) and the GNSS/INS/Non-Holonomic Constraint (NHC) integrated navigation systems, respectively.

Nonlinear vibraion isolation technique is widely employed for vibration suppression. An identification-control integrated method based on data-driven approaches is proposed for solving the optimal control law of a nonlinear time-continuous dynamic system. A dynamic surrogate model of the quasi-zero-stiffness (QZS) vibration isolation system is established by using an identification algorithm combined physical information neural network and Runge–Kutta method with the input and output signals of the original model. Two approximate optimal controllers are trained through the particle swarm optimization with ‘loser-out’ skill and the twin delayed deep deterministic policy gradient (TD3) in the sense of a self-defined objective function, where controllers communicate with the dynamic surrogate model during the training process. Then, the comprehensive performance in the condition of variable load and Gaussian noise excitation, and the displacement transmissibility are tested on the original model. The results show that the identified surrogate model can accurately reproduce the dynamic characteristics of the original model and the trained controllers are able to accomplish the control tasks successfully with a certain adaptability, further enhancing the low-frequency vibration isolation of the QZS isolator.

A recent paper of Melbourne & Stuart (2011 A note on diffusion limits of chaotic skew product flows. Nonlinearity 24, 1361-1367 (doi:10.1088/0951-7715/24/4/018)) gives a rigorous proof of convergence of a fast-slow deterministic system to a stochastic differential equation with additive noise. In contrast to other approaches, the assumptions on the fast flow are very mild. In this paper, we extend this result from continuous time to discrete time. Moreover, we show how to deal with one-dimensional multiplicative noise. This raises the issue of how to interpret certain stochastic integrals; it is proved that the integrals are of Stratonovich type for continuous time and neither Stratonovich nor Itô for discrete time. We also provide a rigorous derivation of super-diffusive limits where the stochastic differential equation is driven by a stable Lévy process. In the case of one-dimensional multiplicative noise, the stochastic integrals are of Marcus type both in the discrete and continuous time contexts.

This study investigates the use of deep reinforcement learning for active noise control (DRL-ANC) to cancel narrowband noise. The filtered-x least mean square algorithm for ANC, which includes the secondary path model in itself, has been widely used in various applications. If the path model is inaccurate due to the variations of the actual path, control performance and stability of the algorithm can be restricted. To eliminate the effect by the model inaccuracy, it is considered to remove the path model in the novel DRL-ANC strategy. A DRL approach using the deep deterministic policy gradient without any path model is adopted to learn the behavior of a physical environment including the effect of the actual secondary path in real time. However, a temporal credit assignment problem arises due to the time-delayed reward inherent in the secondary path, which means that the current action could not be evaluated by its true. To address this problem, this study proposes a novel definitions of the state and action of the RL agent, specialized in narrowband noise suppression. Additionally, a novel exploration noise is also suggested to enhance effectiveness and practicality of the learning process. Computer simulations and real-time control experiments were conducted, and the results demonstrated that the proposed DRL-ANC algorithm can robustly cope with changes in the secondary path.