In complex environments of wireless communication, to improve the communication anti-interference performance, quasi-synchronous frequency-hopping (FH) communication system requires low-hit-zone (LHZ) FH sequence sets with optimal Hamming correlation (HC) properties and flexible parameters. In this paper, based upon cyclotomy theory, two classes of LHZ FH sequence sets with flexible parameters not included in the literatures are designed, and the periodic HC properties of which are analyzed. It turns out that the designed LHZ FH sequence sets are optimal on the maximum periodic HC. And thus the new constructions can be used to provide optimal FH sequence sets for quasi-synchronous FH communication system.

Using no‐hit‐zone frequency‐hopping (FH) sequences with wide gap (WG NHZ FH sequences), the quasi‐synchronous (QS) FH communication systems not only have good capability of preventing muti‐path interference but also have strong capabilities of resisting the enemy's intentional interferences in the complicated electromagnetic interference conditions. This paper designs a new class of optimal WG NHZ FH sequence set which was not presented previously. It significantly meet the needs of high reliable QS FH communication under complicated electromagnetic interference conditions.

Personalized route recommendations require comprehensive consideration of multiple complex factors, including visitor preferences, dwell time, and historical trajectories. Existing methods often suffer from low clustering efficiency as well as insufficient route relevance and accuracy when dealing with high-dimensional temporal and spatial data. To address these issues, this study proposes a personalized tourism route recommendation model, termed the Granular-based Multi-scale Fusion Network with Temporal and Spatial Convolutional Networks and Multi-Head Attention (GMF-Net). The model employs an iterative splitting-and-merging strategy based on granular sphere optimization spectral clustering to generate m granules. As a result, the clustering time complexity is reduced from O(n³) to O(m³), where m is much smaller than n. GMF-Net optimizes the hyperparameters of the granular sphere spectral clustering via the Black-Winged Kite Optimization technique, achieving clustering accuracy of up to 81% for related visitor groups. Following clustering, temporal and spatial convolutional networks are employed to capture patterns of visitor behavior, while multi-scale feature fusion is used to analyze spatial correlations among scenic locations. Experiments were conducted using over 1.04 million trajectory data points collected from the Qingcheng Mountain Scenic Area in Sichuan Province, spanning from January 2014 to December 2022. The experimental results demonstrate that GMF-Net achieves a route recommendation accuracy of 85.2%, outperforming the current state-of-the-art (SOTA) method by 3.7 percentage points (81.5%). These findings validate the effectiveness and applicability of GMF-Net in complex tourism scenarios.The source code is available on https://github.com/xxf-ai/GMF-Net.