Global Navigation Satellite System (GNSS) coordinate time series contains obvious seasonal signals, which mainly manifest as a superposition of annual and semi-annual oscillations. Accurate extraction of seasonal signals is of great importance for understanding various geophysical phenomena. In this paper, a Weighted Nuclear Norm Minimization (WNNM) is proposed to extract the seasonal signals from the GNSS coordinate time series. WNNM assigns different weights to different singular values that enable us to estimate an approximate low rank matrix from its noisy matrix. To address this issue, the low rank characteristics of the Hankel matrix induced by GNSS coordinate time series was investigated first, and then the WNNM is applied to extract the seasonal signals in the GNSS coordinate time series. Meanwhile, the residuals have been analyzed, obtaining the estimation of the uncertainty of velocity. To demonstrate the effectiveness of the proposed algorithm, a number of tests have been carried out on both simulated and real GNSS dataset. Experimental results indicate that the proposed scheme offers preferable performances compared with many state-of-the-art methods.

Multimodal sentiment analysis must combine textual, visual, and acoustic evidence whose reliability varies across utterances. We propose PGMCR, which uses a predicted three-way polarity distribution to compose sample-level modality-reliability weights and applies a training-only consistency constraint to text-preserving visual and acoustic perturbations. Experiments on CMU-MOSI and CMU-MOSEI show that polarity-guided fusion is the principal architectural contribution, while the text-guided refinement and consistency branch have metric-dependent effects. The learned weights vary across polarity groups, but proxy-corruption diagnostics also reveal strong dependence on textual evidence and non-negligible polarity-estimation errors. The results support polarity as a useful conditioning signal for multimodal fusion while delimiting the scope of the nonverbal robustness claim.

Precise livestock detection is fundamental to smart agriculture; however, real-world environments often present challenges such as high object density, severe occlusion, and ambiguous boundaries. To address feature integrity degradation under such conditions, this paper proposes DOLD-Net: a model for densely occluded livestock detection utilizing global–local feature collaboration. First, this paper develops a Dual-Branch Occlusion-Aware Network (DBOAN) backbone to mitigate feature confusion stemming from object aggregation. The DBOAN models both local gradient flows and global topological structures. By integrating these two aspects, the network preserves fine-grained details while maintaining coherent spatial relationships. To further resolve semantic discontinuities in occluded regions, a Context-Guided Focus Propagation (CGFP) mechanism is designed. The CGFP constructs a global core representation through long-range dependency modeling. This representation propagates discriminative semantic priors in a top-down manner across feature hierarchies. Furthermore, a Frequency–Spatial Anti-occlusion Module (FSAM) decouples entangled representations in the frequency domain to sharpen ambiguous boundaries. Additionally, a Context-Aware Occlusion Fusion Module (CAOFM) compensates for local information loss through adaptive cross-scale feature interactions. Extensive experiments on four public datasets demonstrate that DOLD-Net achieves a superior balance between accuracy and efficiency, attaining AP scores of 57.7%, 67.5%, 52.5%, and 62.5%, respectively. The source code is publicly available at https://github.com/Jiakaida/DOLD-Net .

Despite significant advances, multimodal sentiment analysis still faces critical challenges in modeling complex cross-modal interactions and extracting discriminative sentiment features. To address these limitations, this paper proposes a hierarchical multimodal sentiment analysis framework. Specifically, a cross-modal feature enhancement module is first introduced to capture deep correlations among textual, visual, and acoustic modalities via cross-attention mechanisms, thereby obtaining context-aware fused representations. Subsequently, an attention-gated feature disentanglement approach is employed to effectively separate sentiment-relevant information from content-specific features within the fused representations; an independence loss is further imposed to enforce orthogonality between these two feature subsets, thereby mitigating noise induced by repetitive visual frames and textual stop words. Finally, all disentangled features are integrated to facilitate high-level sentiment reasoning through a multimodal logical inference module, where supervised contrastive loss is incorporated to enhance the discriminability of sentiment expressions. Extensive experiments conducted on two public benchmarks, CMU-MOSI and CMU-MOSEI, demonstrate that the proposed framework achieves improvements of 2–6% across multiple evaluation metrics compared with state-of-the-art methods.

Object detection in unmanned aerial vehicle (UAV)-based visual sensing is important for aerial monitoring and intelligent perception. However, UAV imagery often contains small and densely distributed objects, complex backgrounds, and substantial scale variations. Repeated downsampling weakens the boundary and localization cues of small objects, while query initialization based mainly on classification responses may overlook objects with weak semantic features. To address these challenges, we propose TQ-DETR, a target-guided end-to-end detector based on RT-DETRv2. A Target-Oriented Branch (TOB) learns class-agnostic objectness priors to provide reliable foreground spatial guidance independent of category confidence. Based on this prior, Dynamic Query Allocation (DQ) supplements semantic queries with objectness-guided candidates under a fixed query budget, improving candidate coverage for small objects with weak classification responses. In addition, Objectness-Guided Multiscale Boundary Fusion (OG-MBF) selectively extracts and injects target-relevant shallow boundary information while suppressing high-frequency background interference, thereby recovering fine-grained localization cues without adding an extra Transformer feature level. Experiments on VisDrone2019 show that, over three independent runs, TQ-DETR achieved an AP of 30.21 ± 0.51% and an AP50 of 50.28 ± 0.65%, outperforming RT-DETRv2-R18 by 1.79% and 2.75%, respectively, demonstrating improved detection of small and densely distributed UAV targets.