Search Results (58)

Existing chaos-based color image encryption algorithms still face several challenges, including insufficient dynamical complexity of low-dimensional chaotic maps, residual boundary regularity caused by fixed block partitioning, and limited diffusion among RGB channels. To address these issues, this paper proposes a color image encryption algorithm based on a three-dimensional sine-bilinear fully coupled chaotic map (3D-SBFCM). The proposed map integrates sinusoidal modulation, linear coupling, and bilinear cross-coupling within a mod-1 mapping framework, thereby improving the complexity and pseudorandomness of the generated chaotic sequences. In addition, a residual-feasibility-constrained dynamic rectangular partitioning mechanism is developed to generate reversible non-uniform image blocks and reduce the structural regularity associated with fixed-size partitioning. Based on this partitioning structure, inter-block permutation among same-size blocks and intra-block two-dimensional permutation are performed to weaken both global and local spatial correlations. Plaintext-related initialization, dynamic S-box substitution, and forward-backward cross-channel diffusion are further incorporated into the overall permutation-diffusion framework to enhance plaintext sensitivity, nonlinear confusion, and perturbation propagation across RGB channels. Experimental results demonstrate that the proposed algorithm effectively conceals the statistical characteristics of plaintext images, with information entropy values higher than 7.999 for all color channels and NPCR/UACI values close to their theoretical expectations. The algorithm also shows satisfactory robustness against cropping and noise attacks. These results indicate that the proposed method provides an effective and secure solution for color image encryption. Full article

Accurate fault diagnosis of rotating machinery is critical for ensuring the reliability of the energy, industrial, and transportation sectors. However, conventional methods face significant challenges, including the susceptibility of the Snow Ablation Optimizer (SAO) to local optima, the instability of Multiscale Differential Symbolic Entropy (MDSE) with short time series, and the non-adaptability of Support Vector Machine parameters. To address these issues, this study proposes a parameter-adaptive fault diagnosis framework integrating an improved SAO with Adaptive Refined Composite Multiscale Differential Symbolic Entropy (Adaptive-RCMDSE). First, the Logistic Sine Cosine strategy (LSC) is introduced to enhance SAO’s global search capability, forming the LSC-SAO algorithm. Subsequently, an Adaptive-RCMDSE method is developed wherein LSC-SAO optimizes the control parameter to significantly improve feature stability for short time series. Furthermore, an Adaptive Support Vector Machine (Adaptive-SVM) model is constructed, employing LSC-SAO to automatically tune the penalty factor and kernel parameters for precise fault identification. Finally, validation is performed on gearbox, ball bearing, and axle box bearing datasets. Results indicate that the proposed method achieves superior diagnostic performance, with average accuracies of 99.70%, 99.29%, and 99.28%, respectively, outperforming existing methods. This work provides an effective and robust solution for intelligent health monitoring of rotating machinery. Full article

Discrete memristive chaotic maps are promising for secure communications due to their digital compatibility, yet existing designs face limitations, including narrow hyperchaotic ranges and a single type of chaotic attractor. This paper proposes a family of 2D hyperchaotic maps by coupling a discrete exponential memristor with four 1D seed maps. Theoretical analysis reveals that the exponential memristor induces non-hyperbolic fixed points and periodicity with respect to the memristor’s initial charge, enabling controlled coexistence of both homogeneous and heterogeneous multistable attractors. Numerical simulations show two positive Lyapunov exponents (LEs) and broad hyperchaotic regions; the memristor-coupled Sine map achieves a maximum LE of 0.4963 and spectral entropy (SE) of 0.8915, outperforming representative cosine- and quadratic-based benchmarks. A pseudorandom number generator (PRNG) passes all National Institute of Standards and Technology (NIST) SP 800-22 tests. STM32F407-based hardware experiments confirm physical realizability, and an image encryption application demonstrates near-ideal entropy (7.9883) and strong differential attack resistance. These results establish the discrete exponential memristor as an effective nonlinearity for enriching chaos complexity and hardware-oriented security primitives. Full article

With the rapid advancement of information technology, the secure transmission and storage of digital images have garnered increasing attention. To safeguard image information from theft and enhance security during network transmission, a novel image encryption algorithm based on a two-dimensional chaotic system named the two-dimensional sine-cubic modular map (2D-SCMM) and a rotating dial model is proposed. First, the 2D-SCMM is designed, and comprehensive dynamic analyses along with randomness assessments are conducted. Second, in the scrambling phase, a diagonal cyclic-shift transformation is employed to dynamically update the distribution of pixel positions. Third, during the diffusion phase, inspired by the dial phone, the rotating dial model is utilized to achieve dynamic pixel updates. Finally, extensive testing and comparative analyses reveal that image pixels are evenly distributed, the average entropy value for grayscale images is 7.9993, and the correlation coefficients approach 0. Meanwhile, the encryption algorithm is highly secure against various attacks, such as noise attacks and cropping attacks. Full article

To resolve the inherent conflict between high robustness and strict reversibility in frequency-domain image encryption, as well as to eliminate data expansion caused by floating-point errors, this paper presents a novel lossless frequency-domain image encryption scheme via 3D exponential hyper-chaos and integer lifting wavelet transform (ILWT). Firstly, a 3D hyper-chaotic exponential sine map (3D-HESM) is constructed by introducing nonlinear exponential coupling, providing a high-entropy keystream source with wider chaotic ranges than traditional maps. Secondly, to guarantee lossless reconstruction, the ILWT is employed to diffuse image coefficients in the frequency domain. By integrating modular arithmetic into the lifting steps, this transform confines coefficients within the finite integer ring, effectively solving the data expansion problem while maintaining perfect mathematical reversibility. Thirdly, an adaptive key generation protocol is designed by fusing SHA-512 with Singular Value Decomposition (SVD). Leveraging the geometric stability of singular values, this mechanism establishes a balance between extreme sensitivity to plaintext alterations and tolerance to channel noise. Experimental results and security analyses demonstrate that the proposed scheme achieves a vast key space and resists differential attacks. Furthermore, it exhibits superior robustness against data cropping and noise interference compared to state-of-the-art methods, validating its suitability for secure and lossless image transmission. Full article

With the rapid growth of digital image transmission, ensuring data security has become increasingly important. However, existing chaos-based image encryption algorithms often suffer from insufficient chaotic randomness and weak integration between chaotic dynamics and encryption mechanisms. To address these issues, a novel image encryption scheme based on a two-dimensional hyperbolic–exponential Sine–Logistic map (2D-HESLM) is proposed. A Sierpiński carpet-inspired scrambling strategy and a cascaded diffusion mechanism are designed to enhance permutation and diffusion performance based on the 2D-HESLM. The experimental results show that the information entropy value is 7.9980, while NPCR and UACI are approximately averaged 99.6147% and 33.4672%, respectively, with correlation coefficients close to zero. These results demonstrate the effectiveness and security of the proposed scheme. Full article

Artificial neural network and neuron models have made significant contributions to the area of neurodynamics. Investigating the dynamics of artificial neurons and neural networks is vital in developing brain-like systems and understanding how the brain functions. Neural network models and memristive neurons are currently demonstrating a lot of promise in the study of neurodynamics. In order to model the dynamics of biological synapses, this study explores the complex dynamical behavior of a discrete fractional Hopfield-type neural network using a flux-controlled memristive element with periodic memductance. Hyperbolic tangent and sine are the heterogeneous activation functions that are implemented in the proposed system to improve nonlinearity and replicate various forms of brain activity. Stability and bifurcation analyses are used to illustrate the nonlinear dynamical nature of the constructed network model. We examine how the fractional order $\left(\nu \right)$ and periodical memductance aspects influence the dynamics of the system to emphasize the emerging complex phenomena like multi-layered dynamics and the presence of several distinct dynamical states throughout the system variables. Randomness and complexity of the time series data for the proposed system are illustrated with the help of approximate entropy analysis. These findings could help researchers better understand brain-like memory networks, neuromorphic computers, and the theoretical study of neurological and mental abilities. The study of multi-layer attractors can be useful in advanced sensory devices, neuromorphic devices, and secure communication. Full article

The illegal wildlife trade (IWT) poses a significant global challenge that threatens biodiversity and ecosystem balance. This study addresses these complexities by proposing the Integrated Ecological Intervention Optimization Model (IEIOM). The model integrates three core metrics—habitat area, crime rate, and quantity of IWT—while incorporating multidimensional analysis and predictive modeling across ecological, social, and economic dimensions. To enhance predictive accuracy, we employed nonlinear regression, grey prediction, and autoregressive models. These predictive insights, combined with empirical data, were integrated into a multi-index intervention optimization framework using a sum-of-sines function. A simulated annealing algorithm was subsequently applied to achieve global optimization. Results indicate that the proposed IEIOM outperforms the traditional entropy weight method by providing a more dynamic, data-driven weight allocation. The optimal weights prioritized crime suppression (50%), habitat protection (28%), and trade regulation (22%), underscoring the critical roles of law enforcement and environmental preservation. Sensitivity analysis further demonstrated that technological innovation, community collaboration, and public awareness are pivotal to successful interventions. Overall, the IEIOM provides a robust decision-support tool for policymakers, enabling effective resource allocation to combat IWT and contributing to long-term sustainable development. Full article

In contemporary communication systems, digital images occupy an irreplaceable role; however, the privacy-related risks attendant to their prevalent application have grown increasingly salient. This paper presents an image encryption scheme integrating a novel two-dimensional Ackley-Sine chaotic map (2D-ASM) with dynamic DNA operations. First, a two-dimensional Ackley-Sine chaotic map, constructed based on the Ackley function and sine function, is designed and validated through a series of chaotic indicators. Results demonstrate that 2D-ASM exhibits superior chaotic properties compared to several existing state-of-the-art chaotic maps, with its maximum Lyapunov exponent (LE) exceeding 23, Permutation Entropy (PE) close to 1 in the full parameter range, and correlation dimension (CD) significantly higher than comparative chaotic systems. The proposed 2D-ASM-based image encryption scheme leverages the SHA-256 hash value of the plaintext image and four external keys to jointly generate the initial conditions and parameters of the 2D-ASM chaotic system, thereby ensuring a sufficiently large key space of 2²⁵⁶. Subsequently, chaotic sequences generated by 2D-ASM are employed to permute and diffuse the plaintext image, followed by dynamic DNA coding, operations, and decoding to obtain the encrypted image. Security analyses and comparisons with several existing representative algorithms confirm that the proposed encryption scheme achieves excellent encryption performance: the Number of Pixels Change Rate (NPCR) is above 99.6%, the Unified Average Changing Intensity (UACI) approaches 33.4%, and the information entropy of ciphertext images reaches 7.999 or higher. The scheme can effectively resist various potential attacks, including statistical and differential attacks, and outperforms representative algorithms in pixel correlation reduction and anti-interference performance. Full article

To achieve accurate voltage sag prediction and early warning, thereby improving power quality, a hybrid voltage sag prediction framework is proposed by integrating Kernel Entropy Component Analysis (KECA) with an improved Northern Goshawk Optimization (NGO) algorithm for hyperparameter tuning of a CNN-LSTM model. First, to address the limitations of the original NGO, such as proneness to falling into local optima and high randomness of the initial population distribution, a refraction-opposition-based learning mechanism is introduced to enhance population diversity and expand the search space. Furthermore, a sine–cosine strategy (SCA) with nonlinear weight coefficients is integrated into the exploration phase to dynamically adjust the search step size, optimizing the balance between global exploration and local exploitation, thereby boosting convergence speed and accuracy. The improved algorithm (SCNGO) is then utilized to optimize the hyperparameters of the CNN-LSTM model. Second, KECA is applied to voltage-sag-related data to extract key features and eliminate redundant information, and the resulting dimensionally reduced data are fed as input to the SCNGO-CNN-LSTM model to further improve prediction performance. Experimental results demonstrate that the SCNGO-CNN-LSTM model outperforms other comparative models significantly across multiple evaluation metrics. Compared with NGO-CNN-LSTM, GWO-CNN-LSTM, and the original CNN-LSTM, the proposed method achieves a mean squared error (MSE) reduction of 53.45%, 44.68%, and 66.76%, respectively. The corresponding root mean squared error (RMSE) is decreased by 25.33%, 18.61%, and 36.92%, while the mean absolute error (MAE) is reduced by 81.23%, 77.04%, and 86.06%, respectively. These results confirm that the proposed framework exhibits superior feature representation capability and significantly improves voltage sag prediction accuracy. Full article

In communication environments with limited computing resources, securely and efficiently transmitting image data has become a challenging problem. However, most existing image data protection schemes are based on high-dimensional chaotic systems as key generators, which suffer from issues such as high algorithmic complexity and large computational overhead. To address this, this paper presents new designs for a 1D Sine Fractional Chaotic Map (1D-SFCM) as a random sequence generator and provides mathematical proofs related to the boundedness and fixed points of this model. Furthermore, this paper improves the traditional 2D compressive sensing (2DCS) algorithm by using the newly designed 1D-SFCM map to generate a chaotic measurement matrix, which can effectively enhance the quality of image recovery and reconstruction. Moreover, referring to the principle of gene mutation in biogenetics, this paper designs an image encryption algorithm based on DNA base substitution. Finally, the security of the proposed encryption scheme and the quality of image compression and reconstruction are verified through indicators such as key space, information entropy, and Number of Pixel Change Rate (NPCR). Full article

The notion of twist fields has played a fundamental role in many-body physics. It is used to construct the so-called disorder parameter for the study of phase transitions in the classical Ising model of statistical mechanics, it is involved in the Jordan–Wigner transformation in quantum chains and bosonisation in quantum field theory, and it is related to measures of entanglement in many-body quantum systems. I provide a pedagogical introduction to the notion of twist field and the concepts at its roots, and review some of its applications, focussing on the 1 + 1 dimension. This includes locality and extensivity, internal symmetries, semi-locality, the standard exponential form and HEGT fields, path-integral defects and Riemann surfaces, topological invariance, and twist families. Additional topics touched upon include renormalisation and form factors in relativistic quantum field theory, tau functions of integrable PDEs, thermodynamic and hydrodynamic principles, and branch-point twist fields for entanglement entropy. One-dimensional quantum systems such as chains (e.g., quantum Heisenberg model) and field theory (e.g., quantum sine-Gordon model) are the main focus, but I also explain how the notion applies to equilibrium statistical mechanics (e.g., classical Ising lattice model), and how some aspects can be adapted to one-dimensional classical dynamical systems (e.g., classical Toda chain). Full article

Entropy-based image thresholding is one of the most widely used segmentation techniques in image processing. The Tsallis and Masi entropies are information measures that can capture long-range interactions in various physical systems. On the other hand, Shannon entropy is more appropriate for short-range correlations. In this paper, we have improved a thresholding technique based on Tsallis and Shannon formulas by using Masi entropy. Specifically, we replace the Tsallis information measure with Masi’s one, obtaining better results than the original methodology. As the proposed method depends on an entropic parameter, we designed a thresholding algorithm that incorporates a simulated annealing procedure for parameter optimization. Then, we compared our results with thresholding methods that use just Masi (or Tsallis), or a combination of them, Shannon, Sine, and Hill entropies. The comparison is enriched with a kernel version of a support vector machine, as well as a discussion of our proposal in relation to deep learning approaches. Quantitative measures of segmentation accuracy demonstrated the superior performance of our method in infrared, nondestructive testing (NDT), as well as RGB images from the BSDS500 dataset. Full article

With the rapid advancement of information technology, as critical information carriers, images are confronted with significant security risks. To ensure the image security, this paper proposes an image encryption algorithm based on a dynamic rhombus transformation and digital tube model. Firstly, a two-dimensional hyper-chaotic system is constructed by combining the Sine map, Cubic map and May map. The analysis results demonstrate that the constructed hybrid chaotic map exhibits superior chaotic characteristics in terms of bifurcation diagrams, Lyapunov exponents, sample entropy, etc. Secondly, a dynamic rhombus transformation is proposed to scramble pixel positions, and chaotic sequences are used to dynamically select transformation centers and traversal orders. Finally, a digital tube model is designed to diffuse pixel values, which utilizes chaotic sequences to dynamically control the bit reversal and circular shift operations, and the exclusive OR operation to diffuse pixel values. The performance analyses show that the information entropy of the cipher image is 7.9993, and the correlation coefficients in horizontal, vertical, and diagonal directions are 0.0008, 0.0001, and 0.0005, respectively. Moreover, the proposed algorithm has strong resistance against noise attacks, cropping attacks, and exhaustive attacks, effectively ensuring the security of images during storage and transmission. Full article

The Fermi condensation flows in the sine-Gordon–Thirring model with two impurities coupling are investigated in this paper; these matter flows can be induced by the Ricci flow perturbation in the two-dimensional string $\sigma$ model. The Ricci flow perturbation equations are derived according to the Gauss–Codazzi equations, and the two-loop asymptotic perturbation solution of the cigar soliton is reduced by using a small parameter expansion method. Moreover, the thermodynamic quantities on the cigar soliton background are obtained by using the variational functional integrals method. Subsequently, the Fermi condensation flows varying with the momentum scale $\lambda$ are analyzed and discussed. We find that the energy density, the correlation function, and the condensation fluctuations will decrease, but the entropy will increase monotonically. The Fermi condensed matter can maintain thermodynamic stability under the Ricci flow perturbation. Full article