The complex-coefficient filter-based phase-locked loop is widely used in state estimation for the constant frequency grid system. However, its performance under power systems with a variable frequency is generally unacceptable and related applications are consequently rare. This letter proposes an adaptive enhanced complex-coefficient filter-based phase-locked loop (AECCF-PLL) that accurately estimates the operational state of variable frequency grids. By deriving the relationship between model parameters and frequency, a frequency adaptive module is established to dynamically correct the model parameters in real time. Furthermore, a stability analysis of the system is provided to meet the requirements of stability and fast response for state estimation in more electric aircraft variable frequency grids and the computational workload is relatively small. Experimental tests show strong evidence for the effectiveness and validity of the proposed AECCF-PLL method.

To address the complexities of crane hook operating environments, the challenges faced by large-scale object detection algorithms on edge devices, and issues such as frame rate mismatch causing image delays, this paper proposes a faster, lighter, and more efficient object detection algorithm called FLE-YOLO. Firstly, the FasterNet is used as the backbone for feature extraction, and the Triplet Attention mechanism is integrated to effectively emphasize target information while maintaining network lightweightness effectively. Additionally, the Slim-neck module is introduced in the neck connection layer, utilizing a lightweight convolutional network GSconv to further streamline the network structure without compromising recognition accuracy. Lastly, the Dyhead module is employed in the head section to unify multiple attention operations, improve the ability to resist interference from small objects and complex backgrounds. Experimental evaluations on public datasets VOC2012 and COCO2017 demonstrate the effectiveness of our proposed algorithm in terms of lightweight design and detection accuracy. Experimental evaluations were also conducted using images of crane hooks captured under complex operating conditions. The results demonstrate that compared to the original algorithm, the proposed approach achieves a reduction in computational complexity to 19.4 GFLOPs, an increase in FPS to 142.857 f/s, and the precision reached 97.3%. Additionally, the AP₅₀ reaches 98.3%, reflecting 0.6% improvement. Ultimately, the testing carried out at the construction site successfully facilitated the identification and tracking of hooks, thereby ensuring the safety and efficiency of tower crane operations.

Phonon polaritons in natural anisotropic crystals hold great promise for infrared nano-optics. However, the direct electrical control of these polaritons is difficult, preventing the development of active polaritonic devices. Here we propose the heterostructures of graphene on a biaxial crystal ({\alpha}-phase molybdenum trioxide) slab and theoretically study the hybridized plasmon-phonon polaritons with dependence on the Fermi level of graphene from three aspects: dispersion relationships, iso-frequency contours, and the quantum spin Hall effects. We demonstrate the distinct wavelength tunability of the plasmon-phonon polaritons modes and the optical topologic transitions from open (hyperbolic) to closed (bow-tie-like) iso-frequency contours as the increase of the Fermi level of graphene. Furthermore, we observe the tunable quantum spin Hall effects of the plasmon-phonon polaritons, manifesting propagation direction switching by the Fermi level tuning of the graphene. Our findings open opportunities for novel electrically tunable polaritonic devices and programmable quantum optical networks.