• Topology-aware dynamic high-order graph learning proves robustness & high performance. • Dynamic high-order graph module enables boundary & spatial topology perception. • Spectral-spatial image convolution module reduces redundant band & mine local feature. • Spectral-spatial feature fusion module captures rich spectral-spatial features. Hyperspectral image (HSI) classification is challenging due to intra-class spectral variability caused by mixed pixels at land cover boundaries in complex spatial distributions. Additionally, the classification model easily suffers from overfitting due to high-dimensional features and limited available samples. Recently, GCNs have emerged to handle these challenges, as they enable convolution on non-Euclidean data with arbitrary structures and modeling topological relationships between nodes. However, existing GCNs either use predefined adjacency matrices that cannot be updated throughout the training process or perform feature aggregation only between the central node and its first-order neighbors, which hinders the building of spatial topology. This paper introduces a novel topology-aware dynamic high-order graph learning (TDHGL) to address the aforementioned issues. Specifically, we first introduce a spectral-spatial image convolution module (SICM) to reduce spectral redundancy and extract local spectral-spatial features. Then, we design a dynamic high-order graph module (DHGM) to extract the spatial topology of the HSI. It allows for dynamic updating of adjacency matrices and facilitates high-order message interactions between the central node and its higher-order neighbors, mitigating classification inaccuracies induced by intra-class spectral variability while enhancing the TDHGL’s generalization capacity. Finally, the spectral-spatial feature fusion module (SFFM) aggregates the local spectral-spatial features with spatial topology to capture rich spectral-spatial features. Our TDHGL achieves overall accuracies of 94.37 %, 97.17 %, 95.87 %, and 98.21 % on the Indian Pines, Salinas, University of Pavia, and WHU-Hi-LongKou datasets, respectively, and outperforms other state-of-the-art methods.

Visual grounding for remote sensing (RSVG) aims to detect objects in remote sensing scenes based on textual descriptions. While existing methods perform well on RSVG datasets, they are limited to single-object predictions, making them unsuitable for multi-object candidate category datasets. Open-set methods can be applied to both RSVG and candidate datasets, but their use in remote sensing remains rare. To bridge this gap, we introduce the open-set approach to RSVG and propose Efficient Grounding DINO, using Grounding DINO as a baseline. Open-set methods rely on two key modules: cross-modality fusion and label assignment. Existing cross-modality fusion methods simultaneously update text and multi-scale visual features, which hampers the model’s ability to generalize under different texts and increases learning complexity. Existing methods predict a single object, allowing direct use as a positive example for loss calculation, while open-set methods for multi-objects require one-to-one matching to assign positive and negative samples. However, background interference in the RSVG datasets causes frequent misassignments, slowing model convergence. We address these issues with two innovations: the multi-scale image-to-text fusion module (MSITFM), which updates text features using self-attention to maintain independence from visual features and employs scale-specific cross-attention for multi-scale visual feature fusion to reduce learning complexity, achieving a 3% parameter and 21.6% GFLOPs reduction. Text confidence matching (TCM) incorporates IoU-based confidence into label assignment to reduce mismatches and enhance model performance. Experiments on DIOR-RSVG, RSVG-HR, and DOTA datasets validate the effectiveness of our approach.

Image fusion aims to combine the complementary features of different modalities to produce an informative fused image. Due to the different imaging mechanisms, information conflicts may arise from infrared and visible source images. Existing infrared and visible fusion methods are devoted to preserving the features of source images as much as possible. However, handling conflicting information is often overlooked. Thus, we leverage the powerful generative priors of diffusion and propose a dual-diffusion structure, termed DSFuse, to handle conflicting information and achieve feature fidelity during image fusion processing. Diffusion modules are introduced to guide the fusion network to understand the meaningful information of the source image easily. First, the fusion network is used to retain features in the fused image as much as possible. Then, diffusion modules are used to reconstruct source images from noise based on the output of the fusion network. Finally, feedback from the diffusion modules forces the fusion network to aggregate modality information to ensure fidelity; the high quality of the fusion result is also profitable for a better reconstruction of diffusion modules, forming a positive feedback loop. In addition, we release a new dataset for infrared/visible fusion to support the fusion network training and evaluation, named the multiscene infrared and visible (MSIV) images dataset. Extensive experiments demonstrate that DSFuse outperforms other state-of-the-art (SOTA) fusion methods.

Envisioned as one of the most promising technologies, holographic multiple-input multiple-output (H-MIMO) recently attracts notable research interests for its great potential in expanding wireless possibilities and achieving fundamental wireless limits. Empowered by the nearly continuous, large and energy-efficient surfaces with powerful electromagnetic (EM) wave control capabilities, H-MIMO opens up the opportunity for signal processing in a more fundamental EM-domain, paving the way for realizing holographic imaging level communications in supporting the extremely high spectral efficiency and energy efficiency in future networks. In this article, we propose a generalized EM-domain near-field channel modeling and study its capacity limit of point-to-point H-MIMO systems that equips arbitrarily placed surfaces in a line-of-sight (LoS) environment. Two effective and computational-efficient channel models are established from their integral counterpart, where one is with a sophisticated formula but showcases more accurate, and another is concise with a slight precision sacrifice. Furthermore, we unveil the capacity limit using our channel model, and derive a tight upper bound based upon an elaborately built analytical framework. Our result reveals that the capacity limit grows logarithmically with the product of transmit element area, receive element area, and the combined effects of 1/d2mn, 1/d4mn, and 1/d6mnover all transmit and receive antenna elements, wheredmnindicates the distance between each transmit elementnand receive elementm. Particularly, 1/d6mndominates in the near-field region whereas 1/d2mndominates in the far-field region. Numerical evaluations validate the effectiveness of our channel models, and showcase the slight disparity between the upper bound and the exact capacity, which is beneficial for predicting practical system performance.

Graph convolutional network (GCN) gains increasing attention in the hyperspectral image (HSI) classification by the ability to flexibly capture arbitrarily irregular objects. However, due to expensive computation, the graph construction is usually based on superpixel-wise nodes, which ignore the subtle pixel-wise features. In contrast, the convolution neural network (CNN) can mine pixel-wise spectral-spatial features but is limited to capturing local features in small square windows. In this paper, we design a new CNN and GCN collaborative network to simultaneously introduce pixel- and patch-wise contextual information. Concretely, we use the depthwise separable convolution to perform pixel-wise local feature extraction. To further mine the long-range contextual information between land covers, we concatenate a GCN. Finally, we further fuse the complementary features and decode them to obtain the classification map. Extensive experiments reveal that our method achieves competitive performance.

Object detection in remote sensing is a challenging task due to the arbitrary orientations of objects and the vast variation in the number of objects within a single image. For instance, one image may contain hundreds of small vehicles, while another may only have a single football field. Recently, DEtection TRansformer (DETR) and its variants have achieved great success in object detection by setting a fixed number of object queries and using bipartite graph matching for one-to-one label assignment. However, we have observed that bipartite graph matching can result in relative redundancy of object queries when the number of objects changes dramatically in an image. This relative redundancy can cause two problems: slower convergence during training and redundant bounding boxes during inference. To analyze the aforementioned problems, we proposed a metric, Redundancy of Object Query (ROQ), to quantitatively analyze the redundancy. Through experiments, we discovered that the reason for the two issues is the difficulty in distinguishing between high-quality negative samples and positive samples. In this paper, we proposed Efficient-Matching Oriented Object Detection with Transformers(EMO2-DETR) consisting of three dedicated components to address the aforementioned issues. Specifically, Reassign Bipartite Graph Matching (RBGM) is proposed to extract high-quality negative samples from the negative samples. And Ignored Sample Predicted Head (ISPH) is proposed to predict high-quality negative samples. Then, Reassigned Hungarian loss is used to better involve high-quality negative samples in the update of model parameters. Extensive experiments on DOTAv1 and DOTAv1.5 datasets demonstrated that our proposed method achieves competitive results.

For nonlinear systems, Nonlinear Model Predictive Control (NMPC) is preferred to linear Model Predictive Control(MPC) since the nonlinear dynamics of the plant and the control performance index can be incorporated directly. In certain applications the computational resources available for calculating the control solution are severely restricted or the solution is required at high frequency. To overcome these computational challenges this paper presents a computationally efficient update scheme for NMPC using the Forward Dif-ference Generalized Minimum RESidual (FDGMRES) method with a neuro-fuzzy nonlinear dynamic model to describe the plant. Following a description of the FDGMRES approach and a simple case study, an evaluation of the algorithms computational performance is presented using the example of a reference tracking controller for control of a nonlinear Continuously Stirred Tank Reactor (CSTR) system. The online execution time of the FDGMRES algorithm based controller is compared in real time with the more conventional approach of the Sequential Quadratic Programming (SQP) algorithm using Rapid Controls Prototyping hardware.

In this paper, we investigate the hybrid beamforming design for the full-duplex (FD) millimeter-wave (mmWave) multiple-input multiple-output (MIMO) system, aiming to maximize the overall spectral efficiency (SE) and cancel the self-interference (SI) simultaneously. The coupling transmit power constraints, unit-modulus constraints, and strong SI make the problem under study highly nonconvex. To solve the complicated problem, we first transform it into an equivalent yet more tractable weighted minimum mean square error minimization problem and then propose an effective two-stage hybrid beam-forming design. In the first stage, we propose to extend the known matrix approximation method to the FD scenario to obtain the initial hybrid beamforming design. In the second stage, we propose to use the block coordinate descent method to update the digital beamformers and combiners while retaining the obtained analog solutions, which sufficiently exploits the easily adjustable property of digital solutions to cancel the residual SI and compensate for the SE loss. We further analyze the computational complexity of the proposed design. Numerical results demonstrate that our proposed two-stage hybrid beamforming design is superior to conventional related methods.

Compressive subspace learning (CSL) with the exploitation of space diversity has found a potential performance improvement for wideband spectrum sensing (WBSS). However, previous works mainly focus on either exploiting antenna auto-correlations or adopting a multiple-input multiple-output (MIMO) channel without considering the spatial correlations, which will degrade their performances. In this paper, we consider a spatially correlated MIMO channel and propose two CSL algorithms (i.e., mCSLSACC and vCSLACC) which exploit antenna cross-correlations, where the mCSLSACC utilizes an antenna averaging temporal decomposition, and the vCSLACC uses a spatial-temporal joint decomposition. For both algorithms, the conditions of statistical covariance matrices (SCMs) without noise corruption are derived. Through establishing the singular value relation of SCMs in statistical sense between the proposed and traditional CSL algorithms, we show the superiority of the proposed CSL algorithms. By further depicting the receiving correlation matrix of MIMO channel with the exponential correlation model, we give important closed-form expressions for the proposed CSL algorithms in terms of the amplification of singular values over traditional CSL algorithms. Such expressions provide a possibility to determine optimal algorithm parameters for high system performances in an analytical way. Simulations validate the correctness of this work and its performance improvement over existing works in terms of WBSS performance.

System scaling becomes essential and indispensable for distributed storage systems due to the explosive growth of data volume. Considering that fault-protection is a necessity in large-scale distributed storage systems, and Cauchy Reed-Solomon (CRS) codes are widely deployed to tolerate multiple simultaneous node failures, this paper studies the scaling problem of distributed storage systems with CRS codes. In particular, we formulate the scaling problem with an optimization model in which both the post-scaling encoding matrix and the data migration policy are assumed to be unknown in advance. To minimize the I/O overhead, we propose a three-phase optimization scaling scheme for CRS codes. Specifically, we first derive the optimal post-scaling encoding matrix under a given data migration policy, then optimize the data migration process using the selected post-scaling encoding matrix, and finally exploit the Maximum Distance Separable (MDS) property to further optimize the designed data migration process. Our scaling scheme requires minimal data movement while achieving uniform data distribution. Moreover, it requires to read fewer data blocks than conventional minimum data migration schemes, but still guarantees the minimum amount of migrated data. To validate the efficiency of our scheme, we implement it atop a networked file system. Extensive experiments show that our scaling scheme uses less scaling time than the basic scheme.