Search Results (389)

The increasing adoption of digital technologies, robotic systems, and IoT applications in sectors such as medicine, agriculture, and industry drives a surge in data generation and necessitates secure and efficient encryption. For resource-constrained systems, lightweight yet robust cryptographic algorithms are critical. This study addresses the security demands of IoRT systems by proposing an enhanced chaos-based encryption method. The approach integrates the lightweight structure of NIST-standardized Ascon-AEAD128 with the randomness of the Zaslavsky map. Ascon-AEAD128 is widely used on many hardware platforms; therefore, it must robustly resist both passive and active attacks. To overcome these challenges and enhance Ascon’s security, we integrate into Ascon the keys and nonces generated by the Zaslavsky chaotic map, which is deterministic, nonperiodic, and highly sensitive to initial conditions and parameter variations.This integration yields a chaos-based Ascon variant with a higher encryption security relative to the standard Ascon. In addition, we introduce exploratory variants that inject non-repeating chaotic values into the initialization vectors (IVs), the round constants (RCs), and the linear diffusion constants (LCs), while preserving the core permutation. Real-time tests are conducted using Raspberry Pi 3B devices and ROS 2–based IoRT robots. The algorithm’s performance is evaluated over 100 encryption runs on 12 grayscale/color images and variable-length text transmitted via MQTT. Statistical and differential analyses—including histogram, entropy, correlation, chi-square, NPCR, UACI, MSE, MAE, PSNR, and NIST SP 800-22 randomness tests—assess the encryption strength. The results indicate that the proposed method delivers consistent improvements in randomness and uniformity over standard Ascon-AEAD128, while remaining comparable to state-of-the-art chaotic encryption schemes across standard security metrics. These findings suggest that the algorithm is a promising option for resource-constrained IoRT applications. Full article

In the pursuit of sustainable development, optimizing water resources management while maintaining ecological balance is crucial. This study introduces a Chaos-enhanced Harris Hawks Optimizer (CEHHO) aimed at optimizing natural flow patterns in cascade reservoirs. First, an ecological scheduling model considering ensuring guaranteed output is established based on the similarity of ecological flows. Subsequently, the CEHHO algorithm is proposed, which uses tilted skew chaos mapping for population initialization, improving the quality of the initial population. In the exploration phase, an adaptive strategy enhances the efficiency of group search algorithms, enabling effective navigation of the complex solution space. A random difference mutation strategy, combined with the Q-learning algorithm, mitigates premature convergence and maintains algorithmic diversity. Comparative analysis with the existing technology under different typical hydrological frequency shows that the search accuracy and convergence efficiency of the proposed method are significantly improved. Under the guaranteed output limit of 1000 MW, the proposed method enhances the optimal, median, mean, and worst values by 293.92, 493.23, 422.14, and 381.15, respectively, compared to the HHO. Furthermore, the results of the multi-purpose guaranteed output scenario highlight the superior detection and exploitation capabilities of this algorithm. These findings highlight the great potential of the proposed method for practical engineering applications, providing a reliable tool for optimizing water resources management while maintaining ecological balance. Full article

This study conducts an in-depth analysis of the dynamical characteristics and solitary wave solutions of the integrable Zhanbota-IIA equation through the lens of planar dynamic system theory. This research applies Lie symmetry to convert nonlinear partial differential equations into ordinary differential equations, enabling the investigation of bifurcation, phase portraits, and dynamic behaviors within the framework of chaos theory. A variety of analytical instruments, such as chaotic attractors, return maps, recurrence plots, Lyapunov exponents, Poincaré maps, three-dimensional phase portraits, time analysis, and two-dimensional phase portraits, are utilized to scrutinize both perturbed and unperturbed systems. Furthermore, the study examines the power frequency response and the system’s sensitivity to temporal delays. A novel classification framework, predicated on Lyapunov exponents, systematically categorizes the system’s behavior across a spectrum of parameters and initial conditions, thereby elucidating aspects of multistability and sensitivity. The perturbed system exhibits chaotic and quasi-periodic dynamics. The research employs the maximum Lyapunov exponent portrait as a tool for assessing system stability and derives solitary wave solutions accompanied by illustrative visualization diagrams. The methodology presented herein possesses significant implications for applications in optical fibers and various other engineering disciplines. Full article

In this study, the complete discrimination system for the polynomial method (CDSPM) is employed to analyze the integrable Gardner Equation (IGE). Through a traveling wave transformation, the model is reduced to a nonlinear ordinary differential equation, enabling the derivation of a wide class of exact solutions, including trigonometric, hyperbolic, rational, and Jacobi elliptic functions. For example, a bright soliton solution is obtained for parameters $A=1.3$, $\beta =-0.1$, and $\gamma =0.8$. Qualitative analysis reveals diverse phase portraits, indicating the presence of saddle points, centers, and cuspidal points depending on parameter values. Chaos and quasi-periodic dynamics are investigated via Poincaré maps and time-series analysis, where chaotic patterns emerge for values like ${\nu }_1=1.45$, ${\nu }_2=2.18$, ${\Xi }_0=4$, and $\lambda =2\pi$. Sensitivity analysis confirms the model’s sensitivity to initial conditions $\chi =2.2,2.4,2.6$, reflecting real-world unpredictability. Additionally, the energy balance method (EBM) is applied to approximate periodic solutions by conserving kinetic and potential energies. These results highlight the IGE’s ability to capture complex nonlinear behaviors relevant to fluid dynamics, plasma waves, and nonlinear optics. Full article

The growing digitization of healthcare has amplified concerns about the privacy and security of medical images, as conventional encryption methods often fail to provide sufficient protection. To address this gap, we propose a privacy-enhancing image encryption algorithm that integrates SHA-256 hashing, block-wise processing (16 × 16 with zero-padding), DNA encoding with XOR operations, and logistic map-driven key generation into a unified framework. This synergistic design balances efficiency and robustness by embedding data integrity verification, ensuring high sensitivity to initial conditions, and achieving strong diffusion through dynamic DNA rules. Experimental results confirm that the scheme achieves high NPCR (0.997), UACI (0.289), entropy (7.995), and PSNR (27.89 dB), outperforming comparable approaches while maintaining scalability to large image formats and robustness under compression (JPEG quality factors 90 and 70). These findings demonstrate that the proposed method offers an efficient and resilient solution for securing medical images, ensuring confidentiality, integrity, and practical applicability in real-world healthcare environments. Full article

Deterministic chaos in optically injected semiconductor lasers (OILs) has attracted significant attention due to its relevance in secure communications, entropy generation, and photonic applications. However, existing studies often rely on low-resolution parameter sweeps or include noise contributions that obscure the intrinsic nonlinear dynamics. To address this gap, we investigate a noise-free OIL model and construct high-resolution chaos maps across the injection strength and frequency detuning parameter space. Chaos is characterized using two complementary approaches for computing the largest Lyapunov exponent: the Rosenstein time-series method and the exact variational method. This dual approach provides reliable and reproducible detection of deterministic chaotic regimes and reveals a rich attractor landscape with alternating bands of periodicity, quasi-periodicity, and chaos. The novelty of this work lies in combining high-resolution mapping with rigorous chaos indicators, enabling fine-grained identification of dynamical transitions. The results not only deepen the fundamental understanding of nonlinear laser dynamics but also provide actionable guidelines for exploiting or avoiding chaos in photonic devices, with potential applications in random chaos-based communications, number generation, and optical security systems. Full article

Although many chaos-based cryptosystems have been proposed over the past decade, they have yet to gain traction in real-world applications. A key reason for this is that most designs rely on security through obscurity, with unnecessarily complex structures that hinder cryptanalysis and formal evaluation. In this paper, we challenge this trend by showing that chaos-based ciphers can be constructed using conventional, well-understood cryptographic design paradigms without sacrificing performance. First, we present a minimalistic image encryption scheme based on the substitution–permutation network (SPN), demonstrating that it satisfies widely accepted criteria for evaluating chaos-based ciphers. We further show that simple, low-dimensional chaotic maps are sufficient to eliminate statistical biases and that variations in the underlying map have a negligible impact. Second, we propose a chaos-based Feistel block cipher (CFBC) grounded in the generalized Feistel network, enabling standard security evaluation through differential cryptanalysis. As a direct comparison with existing chaos-based image ciphers, we apply CFBC in cipher block chaining (CBC) mode to image encryption. Experimental results show that CFBC achieves a statistical performance comparable to that of state-of-the-art image ciphers. Our findings reinforce the idea that chaos-based cryptosystems need not rely on overly complex constructions and can instead adopt established principles to become more analyzable and robust. Full article

The Honey Badger Algorithm (HBA) is a recently proposed metaheuristic optimization algorithm inspired by the foraging behavior of honey badgers. The search mechanism of this algorithm is divided into two phases: a mining phase and a honey-seeking phase, effectively emulating the processes of exploration and exploitation within the search space. Despite its innovative approach, the Honey Badger Algorithm (HBA) faces challenges such as slow convergence rates, an imbalanced trade-off between exploration and exploitation, and a tendency to become trapped in local optima. To address these issues, we propose an enhanced version of the Honey Badger Algorithm (HBA), namely the Multi-Strategy Honey Badger Algorithm (MSHBA), which incorporates a Cubic Chaotic Mapping mechanism for population initialization. This integration aims to enhance the uniformity and diversity of the initial population distribution. In the mining and honey-seeking stages, the position of the honey badger is updated based on the best fitness value within the population. This strategy may lead to premature convergence due to population aggregation around the fittest individual. To counteract this tendency and enhance the algorithm’s global optimization capability, we introduce a random search strategy. Furthermore, an elite tangential search and a differential mutation strategy are employed after three iterations without detecting a new best value in the population, thereby enhancing the algorithm’s efficacy. A comprehensive performance evaluation, conducted across a suite of established benchmark functions, reveals that the MSHBA excels in 26 out of 29 IEEE CEC 2017 benchmarks. Subsequent statistical analysis corroborates the superior performance of the MSHBA. Moreover, the MSHBA has been successfully applied to four engineering design problems, highlighting its capability for addressing constrained engineering design challenges and outperforming other optimization algorithms in this domain. Full article

Mine water hazards represent one of the principal threats to safe coal mine operations; therefore, accurately predicting mine water inflow is critical for drainage system design and water hazard mitigation. Because mine water inflow is governed by the combined influence of multiple hydrogeological factors and thus exhibits pronounced non-linear characteristics, conventional approaches are inadequate in terms of forecasting accuracy and medium- to long-term predictive capability. To address this issue, this study proposes a Chaos-Autoformer-based method for predicting mine water inflow. First, the univariate inflow series is mapped into an m-dimensional phase space by means of phase-space reconstruction from chaos theory, thereby fully preserving its non-linear features; the reconstructed vectors are then used to train and forecast inflow with an improved Chaos-Autoformer model. On top of the original Autoformer architecture, the proposed model incorporates a Chaos-Attention mechanism and a Lyap-Dropout scheme, which enhance sensitivity to small perturbations in initial conditions and complex non-linear propagation paths while improving stability in long-horizon forecasting. In addition, the loss function integrates the maximum Lyapunov exponent error and earth mode decomposition (EMD) indices so as to jointly evaluate dynamical consistency and predictive performance. An empirical analysis based on monitoring data from the Liangshuijing Coal Mine for 2022–2025 demonstrates that the trained model delivers high accuracy and stable performance. Ablation experiments further confirm the significant contribution of the chaos-aware components: when these modules are removed, forecasting accuracy declines to only 76.5%. Using the trained model to predict mine water inflow for the period from June 2024 to June 2025 yields a root mean square error (RMSE) of 30.73 m³/h and a coefficient of determination (R²) of 0.895 against observed data, indicating excellent fitting and predictive capability for medium- to long-term tasks. Extending the forecast to July 2025–November 2027 reveals a pronounced annual cyclical pattern in future mine water inflow, with markedly higher inflow in summer than in winter and an overall slowly declining trend. These findings show that the Chaos-Autoformer can achieve high-precision medium- and long-term predictions of mine water inflow, thereby providing technical support for proactive deployment and refined management of mine water hazard prevention. Full article

This paper presents a comprehensive dynamical analysis of a nonlinear oscillator subjected to both deterministic and stochastic excitations. Utilizing a diverse suite of analytical tools—including phase portraits, Poincaré sections, Lyapunov exponents, recurrence plots, Fokker–Planck equations, and sensitivity diagnostics—we investigate the transitions between quasi-periodicity, chaos, and stochastic disorder. The study reveals that quasi-periodic attractors exhibit robust topological structure under moderate noise but progressively disintegrate as stochastic intensity increases, leading to high-dimensional chaotic-like behavior. Recurrence quantification and Lyapunov spectra validate the transition from coherent dynamics to noise-dominated regimes. Poincaré maps and sensitivity analysis expose multistability and intricate basin geometries, while the Fokker–Planck formalism uncovers non-equilibrium steady states characterized by circulating probability currents. Together, these results provide a unified framework for understanding the geometry, statistics, and stability of noisy nonlinear systems. The findings have broad implications for systems ranging from mechanical oscillators to biological rhythms and offer a roadmap for future investigations into fractional dynamics, topological analysis, and data-driven modeling. Full article

The dynamics of signal transmission in neuronal networks remain incompletely understood. In this study, we propose a novel Rulkov neuronal network model that incorporates Q-learning, a reinforcement learning method, to establish efficient signal transmission pathways. Using a simulated neuronal network, we focused on a key parameter that modulates both the intrinsic dynamics of individual neurons and the input signals received from active neighbors. We investigated how variations in this parameter affect signal transmission efficiency by analyzing changes in attenuation rate, as well as the maximum and minimum firing intervals of the start and goal neurons. Our simulations revealed that signal transmission efficiency between distant neurons was significantly impaired in the parameter region, where a chaotic attractor and an attractor of the eight-periodic points are observed to co-exist. A key finding was that low-frequency oscillatory bursts, while failing long-distance transmission, were capable of amplifying signals in neighboring neurons. Furthermore, we observed variation in signal transmission even when individual neuron dynamics remained similar. This variability, despite similar presynaptic activity, is a biologically significant phenomenon, and it is argued that it may contribute to the flexibility and robustness of information processing. These findings are discussed in the context of their biological implications. Full article

This study introduces a memristive Hopfield neural network (M-HNN) model to investigate electromagnetic radiation impacts on neural dynamics in complex electromagnetic environments. The proposed framework integrates a magnetic flux-controlled memristor into a three-neuron Hopfield architecture, revealing significant alterations in network dynamics through comprehensive nonlinear analysis. Numerical investigations demonstrate that memristor-induced electromagnetic effects induce distinctive phenomena, including coexisting attractors, transient chaotic states, symmetric bifurcation diagrams and attractor structures, and constant chaos. The proposed system can generate more than 12 different attractors and extends the chaotic region. Compared with the chaotic range of the baseline Hopfield neural network (HNN), the expansion amplitude reaches 933%. Dynamic characteristics are systematically examined using phase trajectory analysis, bifurcation mapping, and Lyapunov exponent quantification. Experimental validation via a DSP-based hardware implementation confirms the model’s operational feasibility and consistency with numerical predictions, establishing a reliable platform for electromagnetic–neural interaction studies. Full article

Image encryption plays a critical role in ensuring the confidentiality and integrity of visual information, particularly in applications involving secure transmission and storage. While traditional cryptographic algorithms like AES are widely used, they may not fully exploit the properties of image data, such as high redundancy and spatial correlation. In recent years, chaotic systems have emerged as promising candidates for lightweight and secure encryption schemes, but comprehensive comparisons between different chaotic maps and standardized methods are still lacking. This study investigates the use of three classical chaotic systems—Henon, tent, and logistic maps—for image encryption, and evaluates their performance both visually and statistically. The research is motivated by the need to assess whether these well-known chaotic systems, when used with proper statistical sampling, can match or surpass conventional methods in terms of encryption robustness and complexity. We propose a key generation method based on chaotic iterations, statistically filtered for independence, and apply it to a one-time-pad-like encryption scheme. The encryption quality is validated over a dataset of 100 JPEG images of size $512\times 512$, using multiple evaluation metrics, including MSE, PSNR, NPCR, EQ, and UACI. Results are benchmarked against the AES algorithm to ensure interpretability and reproducibility. Our findings reveal that while the AES algorithm remains the fastest and most uniform in histogram flattening, certain chaotic systems, such as the tent and logistic maps, offer comparable or superior results in visual encryption quality and pixel-level unpredictability. The analysis highlights that visual encryption performance does not always align with statistical metrics, underlining the importance of multi-faceted evaluation. These results contribute to the growing body of research in chaos-based image encryption and provide practical guidelines for selecting encryption schemes tailored to specific application requirements, such as efficiency, visual secrecy, or implementation simplicity. Full article

We explore the concept of scaling invariance in a type of dynamical systems that undergo a transition from regularity to chaos. The systems are described by a two-dimensional, nonlinear mapping that preserves the area in the phase space. The key variables are the action and the angle, as usual from Hamiltonian systems. The transition is influenced by a control parameter giving the form of the order parameter. We observe a scaling invariance in the average squared action within the chaotic region, providing evidence that this change from regularity (integrability) to chaos (non-integrability) is akin to a second-order or continuous phase transition. As the order parameter approaches zero, its response against the variation in the control parameter (susceptibility) becomes increasingly pronounced (indeed diverging), resembling a phase transition. Full article

The rapid development of Internet technology, while providing convenient services for users, has also aroused deep concern among the public about the issue of privacy leakage during image data transmission. To address this situation, this article proposes a color image encryption algorithm based on RNA extended dynamic coding and quantum chaos (CIEA-RQ). This algorithm significantly improves the ability of the system to withstand cryptographic attacks by introducing RNA extended dynamic encoding with 384 encoding rules. The employed quantum chaotic map improves the randomness of chaotic sequences and increases the key space. First, the algorithm decomposes the plaintext image into bit planes and obtains two parts, high 4-bit and low 4-bit planes, based on different weights of information. Then, the high 4-bit planes are partitioned into blocks and scrambled, and the scrambled planes are confused using RNA extended coding rules. Meanwhile, the low 4-bit planes employ a lightweight XOR operation to improve encryption efficiency. Finally, the algorithm performs cross-iterative diffusion on the processed high 4-bit and low 4-bit planes and then synthesizes a color ciphertext image. Experimental simulations and security assessments demonstrate the superior numerical statistical outcomes of the CIEA-RQ. According to the criteria of cryptanalysis, it can effectively resist known-plaintext attacks and chosen-plaintext attacks. Therefore, the CIEA-RQ presented in this article serves as an efficient digital image privacy safeguard technique, promising extensive applications in image secure transmission for the upcoming generation of networks. Full article