Parkinson's Disease (PD) is often characterized by abnormal gait patterns, which can be objectively and quantitatively diagnosed using Vertical Ground Reaction Force (VGRF) signals. Previous studies have demonstrated the effectiveness of deep learning in VGRF signal analysis. However, the inherent graph structure of VGRF signals has not been adequately considered, limiting the representation of dynamic gait characteristics. To address this, we propose a Multi-Scale Adaptive Directed Graph Neural Network (MS-ADGNN) approach to distinguish the gaits between Parkinson's patients and healthy controls. This method models the VGRF signal as a multi-scale directed graph, capturing the distribution relationships within the plantar sensors and the dynamic pressure conduction during walking. MS-ADGNN integrates an Adaptive Directed Graph Network (ADGN) unit and a Multi-Scale Temporal Convolutional Network (MSTCN) unit. ADGN extracts spatial features from three scales of the directed graph, effectively capturing local and global connectivity. MSTCN extracts multi-scale temporal features, capturing short to long-term dependencies. The proposed method outperforms existing methods on three widely used datasets. In cross-dataset experiments, the average improvements in terms of accuracy, F1-score, and geometric mean are 2.46$\%$, 1.25$\%$, and 1.11$\%$ respectively. Meanwhile, in 10-fold cross-validation experiments, the improvements are 0.78$\%$, 0.83$\%$, and 0.81$\%$ respectively.

In recent years, numerous neuroscientific studies have demonstrated that specific brain regions are associated with human emotional responses, with these regions exhibiting variability across individuals and emotions. To effectively leverage these neural patterns, we propose an Adaptive Progressive Attention Graph Neural Network (APAGNN), which dynamically models the spatial relationships among brain regions during emotional processing. APAGNN employs three specialized expert modules that progressively analyze brain topology. The first expert captures global brain connectivity patterns, the second extracts localized regional features, and the third focuses on emotion-related channel interactions. This progressive refinement strategy enables hierarchical feature extraction from coarsegrained to fine-grained neural representations. Furthermore, a weight generator integrates the outputs of all three experts, adaptively balancing their contributions for final emotion recognition. Extensive experiments conducted on SEED, SEED-IV and MPED datasets demonstrate that our method significantly improves EEG emotion recognition performance, achieving superior results compared to baseline methods.

Hierarchical decoders are widely employed in semantic segmentation architectures, where feature upsampling and fusion are critical at each layer. However, inaccuracies in feature position restoration during upsampling and misalignments in multi-scale feature fusion limit model performance. To address these issues, we propose the Aligned Feature Aggregation Upsampler (AFAU), a novel upsampling module. AFAU consists of two types of Aligned Feature Aggregation Modules (AFAM), which partition feature maps from both the encoder and decoder into multiple aligned patches. By applying attention mechanisms between corresponding patches, AFAM facilitates more effective contextual information fusion. Additionally, AFAM corrects feature position inaccuracies by calculating the similarity between linear embeddings of low-level (query) features and high-level features. This process allows semantic information aggregation guided by fine-grained high-resolution features. We conducted extensive experiments across multiple advanced semantic segmentation frameworks, demonstrating the universality and efficiency of AFAU. Notably, our results indicate that decoders incorporating AFAU significantly outperform SegFormer across all sizes of MiT encoders on the ADE20K and Cityscapes datasets.

Recently, Huang and Zhang [1], [2] introduced the derivative as a fundamental structure of cyclic codes, based on which the derivative decoding was further presented. It is well known that some cyclic codes constructed based on finite geometries form a special class of low-density parity-check (LDPC) codes, which perform well under the sum-product algorithm. In this paper, we study the derivative structure of Euclidean geometry (EG) codes, with a special interest in EG-LDPC codes. It is proved that for µ = m −2, the derivative ascendant of the extended (µ,s)th-order EG code of length 2msis the extended (µ+1,s)th-order twofold EG code of length 2ms. As a subcode of EG-LDPC code, the derivative descendant of the extend (1,s)th-order twofold EG code is characterized in terms of the roots of the generator polynomial. The new code relationship suggests that the twofold EG code can leverage the sum-product decoding of the derivative descendant for its derivative decoding.

In this paper, we present an efficient decoding algorithm for a class of Reed-Solomon (RS) codes over Fermat field$\mathbb{F}_{2^{r}+1}$. We show that the Fermat number transform can be used to speed up the syndrome computation and the Chien search. The implementation architectures are designed for the two blocks. The key equation is then derived. When using the RS code in practice, there arises the issue that a$(2^{r}+1)$-ary symbol is less efficiently represented by a tuple of$(r+1)$bits. We present a nested coding scheme based on RS code and single parity-check (SPC) code to harness the inefficiency. A modified Wagner algorithm is proposed for decoding the inner (nonlinear) code and is proved to be an ML decoding over the BPSK-modulated AWGN channel. Simulation results show that the proposed RS-SPC nested coding scheme yields a considerable performance gain compared to the stand-alone RS coding scheme.

GCN-based methods have achieved remarkable performance in skeleton-based action recognition. However, existing methods have not explicitly attempted to remove temporal and spatial redundancy that might introduce additional computational costs. Inspired by the fact that humans always tend to glimpse at overall motion and then zoom into the most important spatio-temporal regions, we propose a Spatio Temporal Focused Dynamic Network (STFD-Net) trained with reinforcement learning for skeleton-based action recognition. Specifically, we first propose a global extractor with Skeleton Pooling Module (SPM) to enable the network to focus on overall motion information with a refined skeleton structure. Then, a local extractor, containing pair-wise part partition, tubelet proposal network, and Partition-Grouped Module (PGM), is proposed to extract local motion details as a complement to the overall motion information. Finally, the dynamic classifier utilizes a recurrent neural network to dynamically terminate the process once the network is adequately confident. Extensive experiments have demonstrated that the proposed network achieves SOTA level performance with lower computational cost on the NTU 60 and NTU 120 dataset.

Vertical Ground Reaction Force (VGRF) signal obtained from foot-worn sensors, also known as plantar data, provides a highly informative and detailed representation of an individual's gait features. Existing methods, such as CNNs, LSTMs and Transformers, have revealed the efficiency of deep learning in Parkinson's Disease (PD) diagnosis using VGRF signal. However, the intrinsic topologic graph and pressure transmission characteristics of plantar data are overlooked in those approaches, which are essential features for gait analysis. In this paper, we propose to construct a plantar directed topologic graph to fully exploit the plantar topology in gait circles. It can facilitate the expression of gait information by representing sensors as nodes and pressure transmissions as directional edges. Accordingly, an Adaptive Spatio-Temporal Directed Graph Neural Network (AST-DGNN) is proposed to extract the connection features of the plantar directed topologic graph. Each AST-DGNN Unit includes an Adaptive Directed Graph Network (ADGN) block and a Temporal Convolutional Network (TCN) block. In order to capture both local and global spatial relationships among sensor nodes and pressure transmission edges, the ADGN block performs message passing on the plantar directed topologic graph in an adaptive manner. To capture the temporal features of sensor nodes and pressure transmission edges, the TCN block defines a temporal feature extraction process for each node and edge in the graph. Moreover, the data augmentation is introduced for plantar data to improve the generalization ability of the AST-DGNN. Experimental results on Ga, Ju, and Si datasets demonstrate that the proposed method outperforms the existing methods under both cross-dataset validation and mixed-data cross-validation. Especially in cross-dataset validation, there is an average improvement of 2.13%, 7.73%, and 12.27% in accuracy, F1 score, and G-mean, respectively.

The challenge of image impulse noise removal is to restore spatial details from damaged pixels using remaining ones in random locations. Most existing methods use all uncontaminated pixels within a local window to estimate the centred noisy one via a statistic way. These kinds of methods have two defects. First, all noisy pixels are treated as independent individuals and estimated by their neighbours one by one, with the correlation between their true values ignored. Second, the image structure as a natural feature is usually ignored. This study proposes a new denoising framework, in which all noisy pixels are jointly restored via non‐uniform sampling and supervised piecewise autoregressive modelling based super‐resolution. In this method, the noisy pixels are jointly estimated in groups through solving a well‐designed optimisation problem, in which image structure feature is considered as an important constraint. Another contribution is that piecewise autoregressive model is not simply adopted but carefully designed so that all noise‐free pixels can be used to supervise the model training and optimisation problem solving for higher accuracy. The experimental results demonstrate that the proposed method exhibits good denoising performance in a large noise density range (10–90%).

A (v,g)-cage is a (not necessarily unique) smallest v-regular graph of girth g. On such a graph, a nonbinary (2,v)-regular low-density parity-check (LDPC) code can be defined such that the Tanner graph has girth 2g and the code length achieves the minimum possible. In this paper, we focus on two aspects of this class of codes, structural property and code optimization. We find that, in addition to those found previously, many cages can be used to construct structured LDPC codes. We show that all cages with even girth can be structured as protograph-based codes, many of which have block-circulant Tanner graphs. We also find that four cages with odd girth can be structured as protograph-based codes with block-circulant Tanner graphs. For code optimization, we develop an ontology-based approach. All possible inter-connected cycle patterns that lead to low symbol-weight codewords are identified to put together the ontology. By doing so, it becomes handleable to estimate and optimize distance spectrum of equivalent binary image codes. We further analyze some known codes from the Consultative Committee for Space Data Systems recommendation and design several new codes. Numerical results show that these codes have reasonably good minimum bit distance and perform well under iterative decoding.

A model of visual masking, which reveals the visibility of stimuli in the human visual system (HVS), is useful in perceptual based image/video processing. The existing visual masking function mainly considers luminance contrast, which always overestimates the visibility threshold of the edge region and underestimates that of the texture region. Recent research on visual perception indicates that the HVS is sensitive to orderly regions that possess regular structures and insensitive to disorderly regions that possess uncertain structures. Therefore, structural uncertainty is another determining factor on visual masking. In this paper, we introduce a novel pattern masking function based on both luminance contrast and structural uncertainty. Through mimicking the internal generative mechanism of the HVS, a prediction model is firstly employed to separate out the unpredictable uncertainty from an input image. In addition, an improved local binary pattern is introduced to compute the structural uncertainty. Finally, combining luminance contrast with structural uncertainty, the pattern masking function is deduced. Experimental result demonstrates that the proposed pattern masking function outperforms the existing visual masking function. Furthermore, we extend the pattern masking function to just noticeable difference (JND) estimation and introduce a novel pixel domain JND model. Subjective viewing test confirms that the proposed JND model is more consistent with the HVS than the existing JND models.

We develop a novel psychovisually motivated edge-based low-bit-rate image codec. It offers a compact description of scale-invariant second-order statistics of natural images, the preservation of which is crucial to the perceptual quality of coded images. Although being edge based, the codec does not explicitly code the edge geometry. To save bits on edge descriptions, a background layer of the image is first coded and transmitted, from which the decoder estimates the trajectories of significant edges. The edge regions are then refined by a residual coding technique based on edge dilation and sequential scanning in the edge direction. Experimental results show that the new image coding technique outperforms the existing ones in both objective and perceptual quality, particularly at low bit rates.

Adaptive Directional Lifting (ADL) has been successfully implemented in image compression and denoising due to the feature of simple structure and flexible directional selectivity. However, image denoising by means of ADL introduces many visual artifacts caused by Gibbs phenomena due to the lack of translation invariance. In this paper, we propose a translation invariant directional lifting (TI-DL) by employing the cycle-spinning based technique to reduce artifacts in denoising results. Moreover, the inefficiency and high computational complexity of the orientation estimation technique in ADL strongly influences the performance. In order to achieve better denoising results, in this paper, 2-D Gabor filters are adopted for orientation estimation to achieve better orientation estimation results with lower complexity. Experimental results demonstrate that the proposed method achieves state-of-art denoising performance in terms of both objective (PSNR) and subjective (SSIM) evaluation.

We propose a novel edge-based low bit-rate image codec. Although being edge based, the codec does not explicitly code the edge geometry. To avoid spending high bit budget on edges, a background layer of the image is first coded and transmitted, from which the decoder estimates the trajectories of significant edges. The edge regions are then refined by a residual coding technique based on edge dilation and sequential scanning in the edge direction. Experimental results show that the new image coding technique outperforms existing ones in both objective and perceptual quality, particularly at low bit rates.