This article proposes a sensorless control method for the permanent magnet synchronous motors (PMSM) combining the fifth-order generalized integral flux observer (FOGIFO) and the extended state observer-based phase-locked loop (ESO_PLL). First, due to various nonideal factors, including parameter mismatch, sampling errors, and inverter nonlinearity, the rotor flux estimated by traditional flux observers suffers the dc offset and harmonics. To solve these issues, a FOGIFO incorporating a frequency-locked loop (FLL) is proposed, and the FLL ensures that FOGIFO dynamically tracks the frequency of the back electromotive force signal. This guarantees superior integral characteristics at the central frequency point. Meanwhile, the FOGIFO pole distribution is optimized to enhance the harmonic suppression performance. Subsequently, the analysis of the conventional phase-locked loop is carried out in terms of the error transfer function. The ESO_PLL is used for rotor position and speed observation, which significantly improves the dynamic tracking performance. Moreover, since the proposed method is based on a voltage model in stationary coordinate system, it is applicable to both surface and interior PMSM, and simulations and experiments have verified its effectiveness.

Background: Metabolic dysfunction–associated fatty liver disease and steatotic liver disease (SLD) have emerged as one of the fastest-growing liver complications over recent decades; this study evaluated trends in SLD prevalence among Chinese adults and its variation across demographic groups. Methods: A multicenter, retrospective study analyzed health examination data from 600,536 Chinese adults across 10 provinces between 2015 and 2023. Participants (≥18 y) with complete data on age, sex, weight, waist circumference, ultrasound, and biochemical tests were included. SLD was defined as hepatic steatosis with at least 1 metabolic disorder. Age-adjusted SLD prevalence was calculated using direct standardization, and trends were assessed through Joinpoint regression. Logistic regression models examined associations between SLD and demographic/metabolic factors. Results: Of 501,017 participants (mean age: 43.39±12.89 y, 54.8% male), the age-standardized SLD prevalence rose from 36.24% in 2015 to 48.11% in 2023 (estimated annual percentage change=3.38, p=0.002). Metabolic dysfunction–associated SLD (MASLD) prevalence also increased from 30.01% to 43.17% (estimated annual percentage change=4.48, p<0.001). Subgroup analysis revealed significant increases in individuals aged 18–40, and higher prevalence in older adults, with the highest risk in those aged 41–64. Males had a higher risk than females (53.0% vs. 18.6%, adjusted prevalence ratio=1.97). Participants from North China had a higher risk of both SLD and MASLD compared with South China. Laborers had a lower risk of SLD compared with farmers, while administrative personnel had a higher risk of MASLD. Conclusions: The prevalence of SLD and MASLD significantly increased from 2015 to 2023, with notable variations across demographic groups, highlighting the need for targeted public health strategies to address the growing burden of SLD in China.

The current greedy iterative pursuit algorithms for sparse channel estimation in an orthogonal frequency division multiplexing (OFDM) system based on compressive sensing (CS) or distributed CS (DCS) have the disadvantages of relying on channel priori information as halting condition and having a low support searching efficiency. Under DCS framework, this letter proposes a unique halting condition for greedy algorithms by exploiting the delay correlation between adjacent symbol channels. Additionally, we present a segmented pruning strategy that supports to select multiple atoms in a single iteration to improve the support searching efficiency. Simulation results show that our algorithm can achieve more robust sparsity-adaptive channel estimation with reduced computational complexity compared to the traditional methods.

In this paper, we investigate the decoding performance and corresponding decoding strategies for polar-coded orthogonal frequency division multiplexing (OFDM) system operating under multipath fading channels. Multipath fading channels lead to frequency selective fading of the OFDM signal, resulting in varying levels of noise interference across different subcarriers after equalization. Furthermore, frequency selective fading induces burst errors in the equalized information, thereby significantly degrading the decoding performance. To address these issues, we propose two schemes in this paper to enhance the decoding performance of polar-coded OFDM system. Firstly, we integrate the channel state information of each subcarrier into the generation of soft information for the polar codes decoder, in order to alleviate the degradation of decoding performance caused by variations in signal-to-noise ratio across subcarriers during OFDM demodulation. Secondly, we introduce a packet interleaving design after the polar codes encoder to enhance the robustness of polar codes against burst errors. Simulation results demonstrate that the combined application of these two decoding strategies significantly outperforms the conventional scheme in terms of transmission performance over multipath fading channels. The findings of this study suggest that, within the context of future wireless communication systems utilizing Polar-OFDM, the concurrent implementation of our two proposed decoding strategies is essential for achieving robust error correction performance in the OFDM system.

This article presents a robust sensorless control strategy for surface-mounted permanent magnet synchronous motors. The approach integrates an enhanced full-order supertwisting sliding mode observer (STA-FSMO) with a feedforward phase-locked loop utilizing a harmonic-resonant proportional–integral (HR-PI) controller featuring a tunable forward gain. Conventional sliding mode observers often exhibit high-frequency chattering and limited resilience against linearly growing perturbations, degrading estimation accuracy and convergence. To address these limitations, the proposed STA-FSMO employs a dual-layer correction mechanism: linear feedback ensures rapid response during transients, while a nonlinear supertwisting action guarantees smooth, finite-time convergence near equilibrium. This design effectively suppresses linearly growing disturbances and ensures robust estimation under dynamic conditions. Rigorous Lyapunov-based analysis confirms global stability, finite-time convergence, and robustness against parameter uncertainties. The HR-PI controller mitigates inverter-induced back-EMF harmonics, particularly the fifth and seventh components, while its tunable forward gain quantitatively enhances dynamic performance metrics, such as overshoot and rise time. Parameter sensitivity analysis verifies bounded estimation errors under stator resistance and inductance variations. Extensive experimental results validate the method’s superior accuracy, robustness, and practical feasibility, including critical low-speed and zero-to-reverse loaded operations.

Optical object detection under fog-induced atmospheric degradation remains a challenging problem for terrestrial sensing and monitoring systems. Atmospheric scattering reduces image contrast and attenuates high-frequency edge and texture features that are important for precise object localization, while standard downsampling in convolutional neural networks (CNNs) further amplifies this information loss during feature extraction. Existing spatial-domain methods largely improve pixel appearance or feature refinement without explicitly preserving fog-weakened high-frequency edge and texture features during feature extraction. To address this issue, we propose WFSCA-YOLO, a frequency-aware and feature-preserving detection framework with cross-domain fusion between frequency-domain details and spatial semantic responses. The framework introduces the Wavelet-driven Frequency–spatial Co-awareness Block (WFSCA-Block) into YOLOv8, where the Discrete Wavelet Transform (DWT) is used to decompose feature maps into multi-directional high-frequency subbands and preserve high-frequency edge and texture features degraded by atmospheric scattering. A Cross-Domain Feature Selector (CDFS) is further designed to adaptively recalibrate the fusion of frequency-domain details and spatial semantic responses under varying visibility conditions. Experiments on synthetic and real-world degraded optical benchmarks from near-ground scenes, namely Foggy Cityscapes and RTTS, show that WFSCA-YOLO consistently outperforms representative state-of-the-art methods, achieving 50.3% mAP@50 on Foggy Cityscapes (2.1 percentage points above the baseline) and a mean mAP@50 of 79.28% on RTTS over three independent runs. Under a unified FP32 batch-1 inference benchmark, WFSCA-YOLO runs at 134.76 FPS on an RTX 4090D, indicating real-time capability with only a slight latency increase relative to the YOLOv8-s baseline. These results indicate that preserving high-frequency edge and texture features is an effective strategy for robust perception under degraded visibility and offers practical potential for terrestrial sensing and monitoring platforms.