ABSTRACT Accurate lesion segmentation in medical image analysis is critical for diagnosis and treatment planning. However, traditional U‐shaped architectures often struggle with large variation in lesion size and blurred boundaries. To address these challenges, a model called SFIF‐Net has been proposed in this study. In particular, SFIF‐Net strengthens feature interaction through four key components: a Hierarchical Feature Aggregation (HFA) module to enable cross‐layer feature fusion guidance; a Layer‐wise Feature Aggregation (LWFA) module in skip connections for dynamic multiscale fusion; an Interactive Feature Fusion (IFF) module equipped with a Spectral Feature Migration (SFM) component in the decoder to restore fine boundaries via spatial‐frequency fusion and a Multiscale Feature Enhancement (MFE) module applied across stages to improve multilevel feature learning. Experiments have been conducted on four public datasets, which are ISIC2018, BUSI, GlaS and CVC‐ClinicDB, and four metrics (Dice, mIoU, HD95 and Specificity) are used for evaluating the model performance. Experimental results show that SFIF‐Net outperforms other popular models. It achieves the highest average Dice score across all four datasets, outperforming the second‐best models by 1.02%, 1.22%, 0.06% and 0.06%, respectively. The source code is available at https://github.com/shen123shen/SFIF‐Net‐main .

Reconfigurable intelligent surfaces (RIS) has emerged as a promising approach for efficiently enhancing communication performance via passive signal reflection. However, in high-mobility scenarios like vehicular communications, the rapidly changing channel presents challenges in acquiring instantaneous channel state information (CSI) for RIS systems with many reflectors, impacting transmission reliability. To overcome this issue, we present an innovative equivalent linear framework equipped with a low-complexity transmitter signal waveform design and receiver signal detection method for downlink communication systems, substantially enhancing stability in fast fading environments. Simulation results indicate that the proposed designs achieve higher communication reliability with low complexity, significantly improving performance in high-mobility scenarios.

In this paper, an Enhanced Self-Attention (ESA) module has been put forward for feature extraction. The proposed ESA is integrated with the recursive gated convolution and self-attention mechanism. In particular, the former is used to capture multi-order feature interaction and the latter is for global feature extraction. In addition, the location of interest that is suitable for inserting the ESA is also worth being explored. In this paper, the ESA is embedded into the encoder layer of the Transformer network for automatic speech recognition (ASR) tasks, and this newly proposed model is named ESAformer. The effectiveness of the ESAformer has been validated using three datasets, that are Aishell-1, HKUST and WSJ. Experimental results show that, compared with the Transformer network, 0.8% CER, 1.2% CER and 0.7%/0.4% WER, improvement for these three mentioned datasets, respectively, can be achieved.

A module using sliding window with deformablity, abbreviated as SWD, has been proposed for local feature enhancement. In particular, the proposed SWD module adopts windows with variable size based on the depth of the embedded network layers. Moreover, the proposed SWD module is inserted into the Transformer network, referred as LFEformer, for automatic speech recognition. Such network is particularly good at capturing both local and global features, and this is beneficial for model improvement. It is worth mentioning that the local and global features are extracted by SWD module and the attention mechanism in Transformer network, respectively. The effectiveness of the LFEformer has been validated on three widely used datasets, which are Aishell-1, HKUST and WSJ (dev93/eval92). The experimental results demonstrate that 0.5% CER, 0.8% CER and 0.7%/0.3% WER improvement can be obtained in the correspondent datasets.

In this paper, we proposed a novel motion model with Helmholtz decomposition for complex fluid flows in a filtering-based optical flow framework, where the optimization of the regularization term is treated as a filtering process, and different motion patterns can be captured by finding appropriate filter kernels based on the designed filters. In this framework, we introduce a novel optical flow method with a joint spatial filter, which is based on the Helmholtz decomposition theorem that assumes a local motion field is composed of a curl field and a divergence field. By adjusting the scale of the weights in the filter kernels and combining a curl filter with a divergence filter in a certain ratio, it can simulate different motion patterns. In addition, if the correlation between the horizontal and vertical components of the optical flow field in the filter kernel is eliminated, it will be transformed into a linear motion model. Based on this linear motion model, we also develop a novel optical flow method with an adaptive guided filter. By finding an adaptive filter kernel driven by both the input image and the guided motion field for the designed filter, it can successfully capture different motion patterns, and yield an edge-preserving smoothing optical flow field. Most importantly, the proposed optical flow model provides a new way to design the regularizers for capturing different motion patterns in complex fluid flow. In particular, the designed optical flow method with an adaptive guided filter significantly outperforms the current state-of-the-art optical flow methods in predicting complex fluid flows.

Optical flow computation for video under the dynamic illumination is a challenging issue in video multimedia applications. In this paper, we solve this issue by introducing an illumination-invariant framework for variational optical flow estimation. It consists of an illumination-invariance model that handles complex illumination changes and a data enhancement model that guarantees highly accurate optical flow estimation. In this framework, we design a log-correlation descriptor for the data term, which handles complex illumination changes by eliminating the common parameters shared by the neighboring pixels in the corresponding illumination change model while improving the accuracy of optical flow estimation by enhancing the discriminability of the data term matching. We also introduce a novel optical flow model with$L_{0}$norm regularization, which reconstructs optical flow field by a sparse flow gradient counting scheme. Different from other edge-preserving regularizers, it does not depend on local motion features, but locates important flow edges globally. Therefore, it will not cause edge blurriness due to avoiding local filtering or average operation. It is particularly effective for enhancing major flow edges while eliminating a manageable degree of low-amplitude motion structures to control smoothing and reduce oversegmentation artifacts. Even small-scale motion structures with high contrast can be preserved remarkably well. The experimental results show our method significantly outperforms previous illumination-robust optical flow methods in handling complex illumination changes, and achieves competitive evaluation results on the challenging MPI-Sintel and Kitti datasets.