Chaotic systems have been widely employed in constructing hash functions because of their nonlinear characteristics. Nonetheless, some chaotic hash functions are intricately designed, significantly increasing their computational overhead, and some can only generate a single hash value of fixed length, thus lacking flexibility. To overcome the above problems, a novel hash function based on a 2D linear cross-coupled hyperchaotic map (HF-2DLCHM) is introduced and has a parallel feedback structure. Compared to the typical 1D chaotic maps, 2DLCHM has superior dynamic complexity, allowing HF-2DLCHM to resist phase space reconstruction attacks. A parallelizable structure is introduced, enhancing computational efficiency through concurrent processing of operational units. Simultaneously, the feedback mechanism is incorporated to augment the diffusion effect, ensuring better mixing and distribution of information. Moreover, by controlling the size of the input parameter T, the scheme can generate a hash value of [Formula: see text] bits. The experimental results illustrate that this scheme exhibits distribution, confusion, diffusion and collision resistance characteristics approaching their nearly ideal benchmarks while maintaining an acceptable speed. Therefore, this scheme holds substantial practical potential in the domain of data security and privacy protection.
Tackling the shortcomings of slow convergence, imprecision, and entrapment in local optima inherent in traditional meta-heuristic algorithms, this study presents the enhanced artificial hummingbird algorithm with chaotic traversal flight (CEAHA), which employs chaotic ergodicity within the foundational framework of the conventional artificial hummingbird algorithm. This approach implements chaotic motion within local regions of the solution space, ensuring a thorough exploration of potential optima and preventing algorithmic stagnation at local maxima by guaranteeing a non-repetitive traversal of all search states. This study also analyzes the intrinsic mechanisms by which eight different chaotic mappings affect optimization performance, from the perspectives of invariant measures and traversal efficiency of ergodic chaotic motion. In comparative tests with 21 meta-heuristic algorithms on the CEC2014, CEC2019, and CEC2022 benchmark suites across various dimensions, CEAHA demonstrates superior optimization performance. Furthermore, the practicability and robustness of CEAHA have been confirmed in mechanical design optimization problems through 4 engineering instances: pressure vessel, gear trains, speed reducers, and piston levers.
In this paper, we investigate a novel synchronization method, which consists of n ( n ≥ 2) cascade‐coupled chaotic systems. Furthermore, as the number of chaotic systems decreases from n to 2, the proposed synchronization will transform into bidirectional coupling synchronization. Based on Lyapunov stability theory, a general criterion is proposed for choosing the appropriate coupling parameters to ensure cascading synchronization. Moreover, 4 Lü systems are taken as an example and the corresponding numerical simulations demonstrate the effectiveness of our idea.
Forest fires pose a catastrophic threat to Earth’s ecology as well as threaten human beings. Timely and accurate monitoring of forest fires can significantly reduce potential casualties and property damage. Thus, to address the aforementioned problems, this paper proposed an unmanned aerial vehicle (UAV) based on a lightweight forest fire recognition model, Fire-Net, which has a multi-stage structure and incorporates cross-channel attention following the fifth stage. This is to enable the model’s ability to perceive features at various scales, particularly small-scale fire sources in wild forest scenes. Through training and testing on a real-world dataset, various lightweight convolutional neural networks were evaluated on embedded devices. The experimental outcomes indicate that Fire-Net attained an accuracy of 98.18%, a precision of 99.14%, and a recall of 98.01%, surpassing the current leading methods. Furthermore, the model showcases an average inference time of 10 milliseconds per image and operates at 86 frames per second (FPS) on embedded devices.
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