Since the rolling bearing fault signal captured by a vibration sensor contains a large amount of background noise, fault features cannot be accurately extracted. To address this problem, a rolling bearing fault feature extraction algorithm based on improved pelican optimization algorithm (IPOA)–variable modal decomposition (VMD) and multipoint optimal minimum entropy deconvolution adjustment (MOMEDA) methods is proposed. Firstly, the pelican optimization algorithm (POA) was improved using a reverse learning strategy for dimensional-by-dimensional lens imaging and circle mapping, and the optimization performance of IPOA was verified. Secondly, the kurtosis-square envelope Gini coefficient criterion was used to select the optimal modal components from the decomposed components of the signal, and MOMEDA was used to process the optimal modal components in order to obtain the optimal deconvolution signal. Finally, the Teager energy operator (TEO) was employed to demodulate and analyze the optimally deconvoluted signal in order to enhance the transient shock component of the original fault signal. The effectiveness of the proposed method was verified using simulated and actual signals. The results showed that the proposed method can accurately extract failure characteristics in the presence of strong background noise interference.

The topic of joint angle and frequency estimation (JAFE) has aroused extensive interests in the past decades. Current estimation algorithms mainly rely on the Nyquist sampling criterion. In order not to cause ambiguity for parameter estimation, the space–time intervals must be smaller than given thresholds, which results in complicated hardware costs and a huge computational burden. This paper aims to reduce the complexity for JAFE, and a novel sparsity-aware framework is proposed. Unlike the current uniform sampling architectures, the incoming narrow-band singles are sampled by a series of space–time coprime samplers. An improved rotational invariance estimator is introduced, which offers closed-form solutions for both angle and frequency estimation. The mathematical treatments indicate that our methodology is inherent in larger spatial/temporal aperture than the uniform sampling architectures; hence, it provides more accurate JAFE compared to alternative approaches relying on uniform sampling. Additionally, it attains nearly the same complexity as the current rotational invariance approach. Numerical results agree with the theoretical advantages of our methodology.

Ship detection in synthetic aperture radar (SAR) imagery plays a crucial role in maritime surveillance, military reconnaissance, and maritime rescue operations. Although current deep learning approaches have made significant advancements, they still face significant challenges in balancing detection accuracy and efficiency, handling complex background clutter, and detecting small targets. To address these issues, this paper proposes YOLO-SARDet—a lightweight ship detection algorithm based on an improved YOLOv11n architecture. The proposed method comprises three core components: (1) an efficient Cross-Stage Partial-DualFlowBlock feature extraction module that enhances multi-scale feature representation while reducing computational cost through partitioned processing and dual-scale aggregation; (2) a novel Focused Diffusion Feature Pyramid Network, integrated with a Cross-Resolution Feature Fusion Module, which leverages parallel depthwise separable convolutions to enable effective cross-scale feature fusion; and (3) a Dynamic Task-Aligned Detection Head that improves detection accuracy via task decomposition and spatial alignment mechanisms. Extensive experiments on two SAR ship detection benchmarks—the SAR Ship Detection Dataset and the High-Resolution SAR Images Dataset—demonstrate that YOLO-SARDet achieves AP _50:95 scores of 72.5% and 67.7%, respectively, with notable improvements of 2.7% and 5.6% in the small-object detection metric (AP _s ). With only 2.59 million parameters, the model outperforms various mainstream YOLO variants and advanced detection methods while maintaining strong real-time applicability, thereby validating its superior trade-off between accuracy and efficiency.