Search Results (34)

Vehicle–infrastructure cooperative perception (VICP) extends the sensing capability of single-vehicle systems by integrating multi-source information from onboard and roadside sensors, thereby alleviating limitations in sensing range and field-of-view coverage. However, in complex urban environments, the robustness of such systems—particularly in terms of blind-spot coverage and feature representation—is severely affected by both static and dynamic occlusions, as well as distance-induced sparsity in point cloud data. To address these challenges, a 3D object detection framework incorporating point cloud feature enhancement and spatially adaptive fusion is proposed. First, to mitigate feature degradation under sparse and occluded conditions, a Redefined Squeeze-and-Excitation Network (R-SENet) attention module is integrated into the feature encoding stage. This module employs a dual-dimensional squeeze-and-excitation mechanism operating across pillars and intra-pillar points, enabling adaptive recalibration of critical geometric features. In addition, a Feature Pyramid Backbone Network (FPB-Net) is designed to improve target representation across varying distances through multi-scale feature extraction and cross-layer aggregation. Second, to address feature heterogeneity and spatial misalignment between heterogeneous sensing agents, a Spatial Adaptive Feature Fusion (SAFF) module is introduced. By explicitly encoding the origin of features and leveraging spatial attention mechanisms, the SAFF module enables dynamic weighting and complementary fusion between fine-grained vehicle-side features and globally informative roadside semantics. Extensive experiments conducted on the DAIR-V2X benchmark and a custom dataset demonstrate that the proposed approach outperforms several state-of-the-art methods. Specifically, Average Precision (AP) scores of 0.762 and 0.694 are achieved at an IoU threshold of 0.5, while AP scores of 0.617 and 0.563 are obtained at an IoU threshold of 0.7 on the two datasets, respectively. Furthermore, the proposed framework maintains real-time inference performance, highlighting its effectiveness and practical potential for real-world deployment. Full article

Multi-Key Fully Homomorphic Encryption (MK-FHE) is essential for secure multi-party computation but currently faces significant scalability bottlenecks due to linear computational growth and low bootstrapping throughput. To address these limitations, we propose DMBB-RCB, a novel fully homomorphic, bit-wise Dynamic Multi-Key Block-Binary Ring-Compact Bootstrapping scheme. Our contribution is threefold. First, we integrate the Block Binary Distribution into the dynamic setting, reducing the complexity of the core blind rotation operation from O(P⋅n) to O(p⋅k) (where k ≪ n) by leveraging key sparsity. Second, we implement an amortized ring packing strategy that aggregates multiple Learning with Errors (LWE) ciphertexts into the coefficients of a single Ring Learning with Errors (RLWE) polynomial, enabling the parallel refreshing of messages. Third, we introduce a Ring-Compact extraction architecture that natively translates RLWE states into Multi-Key Regev–Gentry–Sahai–Waters (RGSW) ciphertexts via scheme switching. Unlike traditional pipelines that suffer from severe network latency due to interactive multi-party key-switching after each bootstrapping, our architecture keeps the data entirely within the ring domain. This completely eliminates intermediate interaction rounds, enabling depth-unbounded homomorphic evaluations with zero interaction between participants during the computation phase (interaction is strictly reserved for the final joint decryption step). The proposed scheme supports the dynamic addition of participants without parameter re-generation. Theoretical analysis confirms that DMBB-RCB significantly reduces latency and enhances throughput compared to existing dynamic MKHE solutions. Full article

Topology identification and signal inference are cornerstone tasks in graph signal processing (GSP). Structural Equation Modeling (SEM) is particularly effective for network inference as it explicitly captures causal dependencies. However, a major bottleneck in existing SEM-based approaches is the reliance on fully observable exogenous inputs. In many practical applications, systems are driven by latent stimuli, rendering traditional estimation methods ineffective. To overcome this, we propose a novel SEM framework for the joint inference of graph topology and unknown exogenous inputs. The core innovation lies in the spatio-temporal modeling of these latent inputs: each stimulus is decomposed into a rank-one component characterized by nodal sparsity (spatial localization) and temporal piecewise smoothness (temporal persistence). This structured formulation transforms an otherwise ill-posed blind identification problem into a tractable regularized optimization task. We develop an efficient algorithm based on the Alternating Direction Method of Multipliers (ADMM) to solve the resulting convex problem. Numerical experiments on synthetic and real-world datasets demonstrate that the proposed method effectively disentangles endogenous network interactions from latent exogenous influences, outperforming baseline approaches in both topology and signal recovery. Full article

Infrared remote sensing images are often degraded by blur and stripe noise caused by satellite attitude variations, optical distortions, and electronic interference, which significantly compromise image quality and target detection performance. Existing joint deblurring and destriping methods tend to over-smooth image edges and textures, failing to effectively preserve high-frequency details and sometimes misclassifying ringing artifacts as stripes. This paper proposes a variational framework for simultaneous deblurring and destriping of infrared remote sensing images. By leveraging an adaptive structure tensor model, the method exploits the sparsity and directionality of stripe noise, thereby enhancing edge and detail preservation. During blur kernel estimation, a fidelity term orthogonal to the stripe direction is introduced to suppress noise and residual stripes. In the image restoration stage, a WCOB (Non-blind restoration based on Wiener-Cosine composite filtering) model is proposed to effectively mitigate ringing artifacts and visual distortions. The overall optimization problem is efficiently solved using the alternating direction method of multipliers (ADMM). Extensive experiments on real infrared remote sensing datasets demonstrate that the proposed method achieves superior denoising and restoration performance, exhibiting strong robustness and practical applicability. Full article

Rapid and reliable identification of large vessel occlusions and critical stenoses is essential for guiding treatment in acute ischemic stroke. Conventional MR angiography (MRA) and PET protocols are constrained by trade-offs among acquisition time, spatial resolution, and motion tolerance. A multimodal MR–PET angiography reconstruction framework is introduced that integrates joint Hankel-structured sparsity with topology-preserving multitask learning to overcome these limitations. High-resolution time-of-flight MRA and perfusion-sensitive PET volumes are reconstructed from undersampled data using a cross-modal low-rank Hankel prior coupled to a super-resolution generator optimized with adversarial, perceptual, and pixel-wise losses. Vesselness filtering and centerline continuity terms enforce preservation of fine arterial topology, while learned k-space and sinogram sampling concentrate measurements within vascular territories. Motion correction, blind deblurring, and modality-specific denoising are embedded to improve robustness under clinical conditions. A multitask output head estimates occlusion probability, stenosis localization, and collateral flow, with hypoperfusion mapping generated for dynamic PET. Evaluation on clinical and synthetically undersampled MR–PET studies demonstrated consistent improvements over MR-only, PET-only, and conventional fusion methods. The framework achieved higher image quality (MRA PSNR gains up to 3.7 dB and SSIM improvements of 0.042), reduced vascular topology breaks by over 20%, and improved large vessel occlusion detection by nearly 10% in AUROC, while maintaining at least a 40% reduction in sampling. These findings demonstrate that embedding vascular-aware priors within a joint Hankel–sparse MR–PET framework enables accelerated acquisition with clinically relevant benefits for early stroke assessment. Full article

Blind image deblurring, which requires simultaneous estimation of the latent image and blur kernel, constitutes a classic ill-posed problem. To address this, priors based on $L_2$, $L_1$, and $L_p$ regularizations have been widely adopted. Based on this foundation and combining successful experiences of previous work, this paper introduces $L_N$ regularization, a novel nonlinear sparse regularization combining the $L_p$ and $L_{\mathrm{\infty }}$ norms via nonlinear coupling. Statistical probability analysis demonstrates that $L_N$ regularization achieves stronger sparsity than traditional regularizations like $L_2$, $L_1$, and $L_p$ regularizations. Furthermore, building upon the $L_N$ regularization, we propose a novel nonlinear sparse model for blind image deblurring. To optimize the proposed $L_N$ regularization, we introduce an Adaptive Generalized Soft-Thresholding (AGST) algorithm and further develop an efficient optimization strategy by integrating AGST with the Half-Quadratic Splitting (HQS) strategy. Extensive experiments conducted on synthetic datasets and real-world images demonstrate that the proposed nonlinear sparse model achieves superior deblurring performance while maintaining completive computational efficiency. Full article

Optical remote sensing images are often partially obscured by clouds due to the inability of visible light to penetrate cloud cover, which significantly limits their subsequent applications. Most existing cloud removal methods formulate the problem using low-rank and sparse priors within a discrete representation framework. However, these approaches typically rely on manually designed regularization terms, which fail to accurately capture the complex geostructural patterns in remote sensing imagery. In response to this issue, we develop a continuous blind cloud removal model. Specifically, the cloud-free component is represented using a continuous tensor function that integrates implicit neural representations with low-rank tensor decomposition. This representation enables the model to capture both global correlations and local smoothness. Furthermore, a band-wise sparsity constraint is employed to represent the cloud component. To preserve the information in regions not covered by clouds during reconstruction, a box constraint is incorporated. In this constraint, cloud detection is performed using an adaptive thresholding strategy, and a morphological erosion function is employed to ensure accurate detection of cloud boundaries. To efficiently handle the developed model, we formulate an alternating minimization algorithm that decouples the optimization into three interpretable subproblems: cloud-free reconstruction, cloud component estimation, and cloud detection. Our extensive evaluations on both synthetic and real-world data reveal that the proposed method performs competitively against state-of-the-art cloud removal methods. Full article

Rolling element bearings serve as critical transmission components in industrial automation systems, yet their fault signatures are susceptible to interference from strong background noise, complex operating conditions, and nonlinear impact characteristics. Addressing the limitations of conventional methods in adaptive parameter optimization and weak feature enhancement, this paper proposes an innovative diagnostic framework integrating Improved Goose optimized Variational Mode Decomposition (IGOOSE-VMD), RobustICA, and CYCBD. First, to mitigate modal aliasing issues caused by empirical parameter dependency in VMD, we fuse a refraction-guided reverse learning mechanism with a dynamic mutation strategy to develop the IGOOSE. By employing an energy-feature-driven fitness function, this approach achieves synergistic optimization of the mode number and penalty factor. Subsequently, a multi-channel observation model is constructed based on optimal component selection. Noise interference is suppressed through the robust separation capabilities of RobustICA, while CYCBD introduces cyclostationarity-based prior constraints to formulate a blind deconvolution operator with periodic impact enhancement properties. This significantly improves the temporal sparsity of fault-induced impact components. Experimental results demonstrate that, compared to traditional time–frequency analysis techniques (e.g., EMD, EEMD, LMD, ITD) and deconvolution methods (including MCKD, MED, OMEDA), the proposed approach exhibits superior noise immunity and higher fault feature extraction accuracy under high background noise conditions. Full article

In rolling bearing fault diagnosis, faint fault features are often obscured by ambient noise, limiting the feature extraction capabilities of traditional methods. To address this problem, a time and frequency domain blind deconvolution method based on the generalized $L_p/L_q$ norm (G-Lp/Lq-TF) is proposed. Through an analysis of the generalized $L_p/L_q$ norm’s properties, two monotonic yet opposing sparsity-related value intervals are identified and applied separately in the time and frequency domains. The optimal selection range for p and q values is then determined. A hybrid optimization criterion is designed to enforce mutual constraints between the two intervals, ensuring an optimal solution. A convolutional neural network is utilized to serve as the blind deconvolution filter, with backpropagation-based automatic differentiation used for gradient-based optimization of filter coefficients. This approach provides adequate decision-making guidance for selecting p and q values, which was lacking in previous studies on the sparsity of the generalized $L_p/L_q$ norm. It also mitigates noise-spike sensitivity and frequency component loss when applied independently in either domain. Validation using simulated signals and three real-world bearing fault datasets confirms that the proposed method outperforms existing methods in both fault feature extraction and stability. Full article

The problem of blind image deblurring remains a challenging inverse problem, due to the ill-posed nature of estimating unknown blur kernels and latent images within the Maximum A Posteriori (MAP) framework. To address this challenge, traditional methods often rely on sparse regularization priors to mitigate the uncertainty inherent in the problem. In this paper, we propose a novel blind deblurring model based on the MAP framework that leverages Composite-Gradient Feature (CGF) variations in edge regions after image blurring. This prior term is specifically designed to exploit the high sparsity of sharp edge regions in clear images, thereby effectively alleviating the ill-posedness of the problem. Unlike existing methods that focus on local gradient information, our approach focuses on the aggregation of edge regions, enabling better detection of both sharp and smoothed edges in blurred images. In the blur kernel estimation process, we enhance the accuracy of the kernel by assigning effective edge information from the blurred image to the smoothed intermediate latent image, preserving critical structural details lost during the blurring process. To further improve the edge-preserving restoration, we introduce an adaptive regularizer that outperforms traditional total variation regularization by better maintaining edge integrity in both clear and blurred images. The proposed variational model is efficiently implemented using alternating iterative techniques. Extensive numerical experiments and comparisons with state-of-the-art methods demonstrate the superior performance of our approach, highlighting its effectiveness and real-world applicability in diverse image-restoration tasks. Full article

In the non-deterministic, large-scale navigation environment under the Mars exploration mission, there is a large space for action and many environmental states. Traditional reinforcement learning algorithms that can only obtain rewards at target points and obstacles will encounter the problems of reward sparsity and dimension explosion, making the training speed too slow or even impossible. This work proposes a deep layered learning algorithm based on the goal-driven layered deep Q-network (GDH-DQN), which is more suitable for mobile robots to explore, navigate, and avoid obstacles without a map. The algorithm model is designed in two layers. The lower layer provides behavioral strategies to achieve short-term goals, and the upper layer provides selection strategies for multiple short-term goals. Use known position nodes as short-term goals to guide the mobile robot forward and achieve long-term obstacle avoidance goals. Hierarchical execution not only simplifies tasks but also effectively solves the problems of reward sparsity and dimensionality explosion. In addition, each layer of the algorithm integrates a Hindsight Experience Replay mechanism to improve performance, make full use of the goal-driven function of the node, and effectively avoid the possibility of misleading the agent by complex processes and reward function design blind spots. The agent adjusts the number of model layers according to the number of short-term goals, further improving the efficiency and adaptability of the algorithm. Experimental results show that, compared with the hierarchical DQN method, the navigation success rate of the GDH-DQN algorithm is significantly improved, and it is more suitable for unknown scenarios such as Mars exploration. Full article

This paper explores the important role of blind source separation (BSS) techniques in separating M mixtures including N sources using a dual-sensor array, i.e., $M=2$, and proposes an efficient two-stage underdetermined BSS (UBSS) algorithm to estimate the mixing matrix and achieve source recovery by exploiting time–frequency (TF) sparsity. First, we design a mixing matrix estimation method by precisely identifying high clustering property single-source TF points (HCP-SSPs) with a spatial vector dictionary based on the principle of matching pursuit (MP). Second, the problem of source recovery in the TF domain is reformulated as an equivalent sparse recovery model with a relaxed sparse condition, i.e., enabling the number of active sources at each auto-source TF point (ASP) to be larger than M. This sparse recovery model relies on the sparsity of an ASP matrix formed by stacking a set of predefined spatial TF vectors; current sparse recovery tools could be utilized to reconstruct $N>2$ sources. Experimental results are provided to demonstrate the effectiveness of the proposed UBSS algorithm with an easily configured two-sensor array. Full article

To address the threat posed by unknown signal sources within Mobile Crowd Sensing (MCS) systems to system stability and to realize effective localization of unknown sources in long-distance scenarios, this paper proposes a unilateral branch ratio decision algorithm (UBRD). This approach addresses the inadequacies of traditional sparse localization algorithms in long-distance positioning by introducing a time–frequency domain composite block sparse localization model. Given the complexity of localizing unknown sources, a unilateral branch ratio decision scheme is devised. This scheme derives decision thresholds through the statistical characteristics of branch residual ratios, enabling adaptive control over iterative processes and facilitating multisource localization under conditions of remote blind sparsity. Simulation results indicate that the proposed model and algorithm, compared to classic sparse localization schemes, are more suitable for long-distance localization scenarios, demonstrating robust performance in complex situations like blind sparsity, thereby offering broader scenario adaptability. Full article

With the growing scarcity of spectrum resources, wideband spectrum sensing is necessary to process a large volume of data at a high sampling rate. For some applications, only second-order statistics are required for spectrum estimation. In this case, a fast power spectrum sensing solution is proposed based on the generalized coprime sampling. The solution involves the inherent structure of the sensing vector to reconstruct the autocorrelation sequence of inputs from sub-Nyquist samples, which requires only parallel Fourier transform and simple multiplication operations. Thus, it takes less time than the state-of-the-art methods while maintaining the same performance, and it achieves higher performance than the existing methods within the same execution time without the need to pre-estimate the number of inputs. Furthermore, the influence of the model mismatch has only a minor impact on the estimation performance, allowing for more efficient use of the spectrum resource in a distributed swarm scenario. Simulation results demonstrate the low complexity in sampling and computation, thus making it a more practical solution for real-time and distributed wideband spectrum sensing applications. Full article

Underdetermined blind source separation (UBSS) has garnered significant attention in recent years due to its ability to separate source signals without prior knowledge, even when sensors are limited. To accurately estimate the mixed matrix, various clustering algorithms are typically employed to enhance the sparsity of the mixed matrix. Traditional clustering methods require prior knowledge of the number of direct signal sources, while modern artificial intelligence optimization algorithms are sensitive to outliers, which can affect accuracy. To address these challenges, we propose a novel approach called the Genetic Simulated Annealing Optimization (GASA) method with Adaptive Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering as initialization, named the CYYM method. This approach incorporates two key components: an Adaptive DBSCAN to discard noise points and identify the number of source signals and GASA optimization for automatic cluster center determination. GASA combines the global spatial search capabilities of a genetic algorithm (GA) with the local search abilities of a simulated annealing algorithm (SA). Signal simulations and experimental analysis of compressor fault signals demonstrate that the CYYM method can accurately calculate the mixing matrix, facilitating successful source signal recovery. Subsequently, we analyze the recovered signals using the Refined Composite Multiscale Fuzzy Entropy (RCMFE), which, in turn, enables effective compressor connecting rod fault diagnosis. This research provides a promising approach for underdetermined source separation and offers practical applications in fault diagnosis and other fields. Full article