Search Results (327)

User authentication is crucial for safeguarding access to digital systems and services. Biometric authentication serves as a strong and user-friendly alternative to conventional security methods such as passwords and PINs, which are often susceptible to breaches. This study proposes a deep learning-based multimodal biometric system that combines fingerprint (FP) and finger vein (FV) modalities to improve accuracy and security. The system explores three fusion strategies: feature-level fusion (combining feature vectors from each modality), score-level fusion (integrating prediction scores from each modality), and a hybrid approach that leverages both feature and score information. The implementation involved five pretrained convolutional neural network (CNN) models: two unimodal (FP-only and FV-only) and three multimodal models corresponding to each fusion strategy. The models were assessed using the NUPT-FPV dataset, which consists of 33,600 images collected from 140 subjects with a dual-mode acquisition device in varied environmental conditions. The results indicate that the hybrid-level fusion with a dominant score weight (0.7 score, 0.3 feature) achieved the highest accuracy (99.79%) and the lowest equal error rate (EER = 0.0018), demonstrating superior robustness. Overall, the results demonstrate that integrating deep learning with multimodal fusion is highly effective for advancing scalable and accurate biometric authentication solutions suitable for real-world deployments. Full article

Advances in plaque imaging have transformed cardiovascular diagnostics through detailed characterization of atherosclerotic plaques beyond traditional stenosis assessment. This review outlines the clinical applications of varying modalities, including dual-layer spectral CT, photon-counting CT, dual-energy CT, and CT-derived fractional flow reserve (CT-FFR). These technologies offer improved spatial resolution, tissue differentiation, and functional assessment of coronary lesions. Additionally, artificial intelligence has emerged as a powerful tool to automate plaque detection, quantify burden, and refine risk prediction. Collectively, these innovations provide a more comprehensive approach to coronary artery disease evaluation and support personalized management strategies. Full article

To improve the identification of drought-affected areas in winter wheat, this paper proposes a lightweight network called MF-FusionNet based on multimodal fusion of RGB images and vegetation indices (NDVI and EVI). A multimodal dataset covering various drought levels in winter wheat was constructed. To enable deep fusion of modalities, a Lightweight Multimodal Fusion Block (LMFB) was designed, and a Dual-Coordinate Attention Feature Extraction module (DCAFE) was introduced to enhance semantic feature representation and improve drought region identification. To address differences in scale and semantics across network layers, a Cross-Stage Feature Fusion Strategy (CFFS) was proposed to integrate multi-level features and enhance overall performance. The effectiveness of each module was validated through ablation experiments. Compared to traditional single-modal methods, MF-FusionNet achieved higher accuracy, recall, and F1-score—improved by 1.35%, 1.43%, and 1.29%, respectively—reaching 96.71%, 96.71%, and 96.64%. A basis for real-time monitoring and precise irrigation management under winter wheat drought stress was provided by this study. Full article

Infrared and visible image fusion (IVIF) endeavors to amalgamate the thermal radiation characteristics from infrared images with the fine-grained texture details from visible images, aiming to produce fused outputs that are more robust and information-rich. Among the existing methodologies, those based on generative adversarial networks (GANs) have demonstrated considerable promise. However, such approaches are frequently constrained by their reliance on homogeneous discriminators possessing identical architectures, a limitation that can precipitate the emergence of undesirable artifacts in the resultant fused images. To surmount this challenge, this paper introduces HCSPNet, a novel GAN-based framework. HCSPNet distinctively incorporates heterogeneous dual discriminators, meticulously engineered for the fusion of disparate source images inherent in the IVIF task. This architectural design ensures the steadfast preservation of critical information from the source inputs, even when faced with scenarios of image degradation. Specifically, the two structurally distinct discriminators within HCSPNet are augmented with adaptive salient information distillation (ASID) modules, each uniquely structured to align with the intrinsic properties of infrared and visible images. This mechanism impels the discriminators to concentrate on pivotal components during their assessment of whether the fused image has proficiently inherited significant information from the source modalities—namely, the salient thermal signatures from infrared imagery and the detailed textural content from visible imagery—thereby markedly diminishing the occurrence of unwanted artifacts. Comprehensive experimentation conducted across multiple publicly available datasets substantiates the preeminence and generalization capabilities of HCSPNet, underscoring its significant potential for practical deployment. Additionally, we also prove that our proposed heterogeneous dual discriminators can serve as a plug-and-play structure to improve the performance of existing GAN-based methods. Full article

Hyperspectral imaging for seed vigor detection faces the challenges of handling high-dimensional spectral data, information loss after dimensionality reduction, and low feature differentiation between vigor levels. To address the above issues, this study proposes an improved dynamic optimize MDS (DO-MDS) dimensionality reduction algorithm based on multidimensional scaling transformation. DO-MDS better preserves key features between samples during dimensionality reduction. Secondly, a dual-stream spectral collaborative attention (DSCA) module is proposed. The DSCA module adopts a dual-modal fusion approach combining global feature capture and local feature enhancement, deepening the characterization capability of spectral features. This study selected commonly used rice seed varieties in Zhejiang Province and constructed three individual spectral datasets and a mixed dataset through aging, spectral acquisition, and germination experiments. The experiments involved using the DO-MDS processed datasets with a convolutional neural network embedded with the DSCA attention module, and the results demonstrate vigor discrimination accuracy rates of 93.85%, 93.4%, and 96.23% for the Chunyou 83, Zhongzao 39, and Zhongzu 53 datasets, respectively, achieving 94.8% for the mixed dataset. This study provides effective strategies for spectral dimensionality reduction in hyperspectral seed vigor detection and enhances the differentiation of spectral information for seeds with similar vigor levels. Full article

Image registration constitutes the fundamental basis for the joint interpretation of synthetic aperture radar (SAR) and optical images. However, robust image registration remains challenging due to significant regional heterogeneity in remote sensing scenes (e.g., co-existing urban and marine areas within a single image). To overcome this challenge, this article proposes a novel optical–SAR image registration method named Gradient and Standard Deviation Feature Weighted Fusion (GDWF). First, a Block-local standard deviation (Block-LSD) operator is proposed to extract block-based feature points with regional adaptability. Subsequently, a dual-modal feature description is developed, constructing both gradient-based descriptors and local standard deviation (LSD) descriptors for the neighborhoods surrounding the detected feature points. To further enhance matching robustness, a confidence-weighted feature fusion strategy is proposed. By establishing a reliability evaluation model for similarity measurement maps, the contribution weights of gradient features and LSD features are dynamically optimized, ensuring adaptive performance under varying conditions. To verify the effectiveness of the method, different optical and SAR datasets are used to compare it with the currently advanced algorithms MOGF, CFOG, and FED-HOPC. The experimental results demonstrate that the proposed GDWF algorithm achieves the best performance in terms of registration accuracy and robustness among all compared methods, effectively handling optical–SAR image pairs with significant regional heterogeneity. Full article

Accurate feature matching between optical and synthetic aperture radar (SAR) images remains a significant challenge in remote sensing due to substantial modality discrepancies in texture, intensity, and geometric structure. In this study, we proposed an attention-context-aware deep learning framework (ACAMatch) for robust and efficient optical–SAR image registration. The proposed method integrates a structure-enhanced feature extractor, RS2FNet, which combines dual-stage Res2Net modules with a bi-level routing attention mechanism to capture multi-scale local textures and global structural semantics. A context-aware matching module refines correspondences through self- and cross-attention, coupled with a confidence-driven early-exit pruning strategy to reduce computational cost while maintaining accuracy. Additionally, a match-aware multi-task loss function jointly enforces spatial consistency, affine invariance, and structural coherence for end-to-end optimization. Experiments on public datasets (SEN1-2 and WHU-OPT-SAR) and a self-collected Gaofen (GF) dataset demonstrated that ACAMatch significantly outperformed existing state-of-the-art methods in terms of the number of correct matches, matching accuracy, and inference speed, especially under challenging conditions such as resolution differences and severe structural distortions. These results indicate the effectiveness and generalizability of the proposed approach for multimodal image registration, making ACAMatch a promising solution for remote sensing applications such as change detection and multi-sensor data fusion. Full article

To noninvasively and precisely discriminate among the growth stages of multiple cultivars of navel oranges simultaneously, the fusion of the technologies of near-infrared (NIR) hyperspectral imaging (HSI) combined with machine vision (MV) and deep learning is employed. NIR reflectance spectra and hyperspectral and RGB images for 740 Gannan navel oranges of five cultivars are collected. Based on preprocessed spectra, optimally selected hyperspectral images, and registered RGB images, a dual-branch multi-modal feature fusion convolutional neural network (CNN) model is established. In this model, a spectral branch is designed to extract spectral features reflecting internal compositional variations, while the image branch is utilized to extract external color and texture features from the integration of hyperspectral and RGB images. Finally, growth stages are determined via the fusion of features. To validate the availability of the proposed method, various machine-learning and deep-learning models are compared for single-modal and multi-modal data. The results demonstrate that multi-modal feature fusion of HSI and MV combined with the constructed dual-branch CNN deep-learning model yields excellent growth stage discrimination in navel oranges, achieving an accuracy, recall rate, precision, F1 score, and kappa coefficient on the testing set are 95.95%, 96.66%, 96.76%, 96.69%, and 0.9481, respectively, providing a prominent way to precisely monitor the growth stages of fruits. Full article

Background/Objectives: Childhood obesity represents a growing public health concern. It is closely associated with obstructive sleep apnoea (OSA), which impairs nocturnal breathing and significantly affects neurocognitive and cardiovascular health. This review aims to analyse differences in fat distribution, anthropometric parameters, and instrumental assessments of paediatric OSA compared to adult OSA to improve the diagnostic characterisation of obese children. Methods: narrative review. Results: While adenotonsillar hypertrophy (ATH) remains a primary cause of paediatric OSA, the increasing prevalence of obesity has introduced distinct pathophysiological mechanisms, including fat accumulation around the pharynx, reduced respiratory muscle tone, and systemic inflammation. Children exhibit different fat distribution patterns compared to adults, with a greater proportion of subcutaneous fat relative to visceral fat. Nevertheless, cervical and abdominal adiposity are crucial in increasing upper airway collapsibility. Recent evidence highlights the predictive value of anthropometric and body composition indicators such as neck circumference (NC), neck-to-height ratio (NHR), neck-to-waist ratio (NWR), fat-to-muscle ratio (FMR), and the neck-to-abdominal-fat percentage ratio (NAF%). In addition, ultrasound assessment of lateral pharyngeal wall (LPW) thickness and abdominal fat distribution provides clinically relevant information regarding anatomical contributions to OSA severity. Among imaging modalities, dual-energy X-ray absorptiometry (DXA), bioelectrical impedance analysis (BIA), and air displacement plethysmography (ADP) have proven valuable tools for evaluating body fat distribution. Conclusions: Despite advances in the topic, a validated predictive model that integrates these parameters is still lacking in clinical practice. Polysomnography (PSG) remains the gold standard for diagnosis; however, its limited accessibility underscores the need for complementary tools to prioritise the identification of children at high risk. A multimodal approach integrating clinical, anthropometric, and imaging data could support the early identification and personalised management of paediatric OSA in obesity. Full article

Background/Objectives: Degenerative joint diseases (DJDs) involve intricate molecular disruptions within bone, cartilage, and synovial tissues, often preceding overt radiographic changes. These tissues exhibit complex biomolecular architectures and their degeneration leads to microstructural disorganization and inflammation that are challenging to detect with conventional imaging techniques. This review aims to synthesize recent advances in imaging, computational modeling, and sequencing technologies that enable high-resolution, non-invasive characterization of joint tissue health. Methods: We examined advanced modalities including high-resolution MRI (e.g., T1ρ, sodium MRI), quantitative and dual-energy CT (qCT, DECT), and ultrasound elastography, integrating them with radiomics, deep learning, and multi-scale modeling approaches. We also evaluated RNA-seq, spatial transcriptomics, and mass spectrometry-based proteomics for omics-guided imaging biomarker discovery. Results: Emerging technologies now permit detailed visualization of proteoglycan content, collagen integrity, mineralization patterns, and inflammatory microenvironments. Computational frameworks ranging from convolutional neural networks to finite element and agent-based models enhance diagnostic granularity. Multi-omics integration links imaging phenotypes to gene and protein expression, enabling predictive modeling of tissue remodeling, risk stratification, and personalized therapy planning. Conclusions: The convergence of imaging, AI, and molecular profiling is transforming musculoskeletal diagnostics. These synergistic platforms enable early detection, multi-parametric tissue assessment, and targeted intervention. Widespread clinical integration requires robust data infrastructure, regulatory compliance, and physician education, but offers a pathway toward precision musculoskeletal care. Full article

Continuous, single-mode imaging systems fail to deliver true-color high-resolution imagery around the clock under extreme lighting. High-fidelity color and signal-to-noise ratio imaging across the full day–night cycle remains a critical challenge for surveillance, navigation, and environmental monitoring. We present a competitive dual-mode imaging platform that integrates a 155 mm f/6 telephoto daytime camera with a 52 mm f/1.5 large-aperture low-light full-color night-vision camera into a single, co-registered 26 cm housing. By employing a sixth-order aspheric surface to reduce the element count and weight, our system achieves near-diffraction-limited MTF (>0.5 at 90.9 lp/mm) in daylight and sub-pixel RMS blur < 7 μm at 38.5 lp/mm under low-light conditions. Field validation at 0.0009 lux confirms high-SNR, full-color capture from bright noon to the darkest nights, enabling seamless switching between long-range, high-resolution surveillance and sensitive, low-light color imaging. This compact, robust design promises to elevate applications in security monitoring, autonomous navigation, wildlife observation, and disaster response by providing uninterrupted, color-faithful vision in all lighting regimes. Full article

Multispectral object detection faces challenges in effectively integrating complementary information from different modalities in complex environmental conditions. This paper proposes SDRFPT-Net (Spectral Dual-stream Recursive Fusion Perception Target Network), a novel architecture that integrates three key innovative modules: (1) the Spectral Hierarchical Perception Architecture (SHPA), which adopts a dual-stream separated structure with independently parameterized feature extraction paths for visible and infrared modalities; (2) the Spectral Recursive Fusion Module (SRFM), which combines hybrid attention mechanisms with recursive progressive fusion strategies to achieve deep feature interaction through parameter-sharing multi-round recursive processing; and (3) the Spectral Target Perception Enhancement Module (STPEM), which adaptively enhances target region representation and suppresses background interference. Extensive experiments on the VEDAI, FLIR-aligned, and LLVIP datasets demonstrate that SDRFPT-Net significantly outperforms state-of-the-art methods, with improvements of 2.5% in mAP50 and 5.4% in mAP50:95 on VEDAI, 11.5% in mAP50 on FLIR-aligned, and 9.5% in mAP50:95 on LLVIP. Ablation studies further validate the effectiveness of each proposed module. The proposed method provides an efficient and robust solution for multispectral object detection in remote sensing image interpretation, making it particularly suitable for all-weather monitoring applications from aerial and satellite platforms, as well as in intelligent surveillance and autonomous driving domains. Full article

For data-intensive applications like edge AI and image processing, we present a new reconfigurable 8T SRAM-based in-memory computing (IMC) macro designed for high-performance and energy-efficient operation. This architecture mitigates von Neumann limitations through numerous major breakthroughs. We built a new architecture with an adjustable capacitance array to substantially increase the multiply-and-accumulate (MAC) engine’s accuracy. It achieves 10–20 TOPS/W and >95% accuracy for 4–10-bit operations and is robust across PVT changes. By supporting binary and ternary neural networks (BNN/TNN) with XNOR-and-accumulate logic, a dual-mode inference engine further expands capabilities. With sub-5 ns mode switching, it can achieve up to 30 TOPS/W efficiency and >97% accuracy. In-memory Hamming error correction is implemented directly using integrated XOR circuitry. This technique eliminates off-chip ECC with >99% error correction and >98% MAC accuracy. Machine learning-aided co-optimization ensures sense amplifier dependability. To ensure CMOS compatibility, the macro may perform Boolean logic operations using normal 8T SRAM cells. Comparative circuit-level simulations show a 31.54% energy efficiency boost and a 74.81% delay reduction over other SRAM-based IMC solutions. These improvements make our macro ideal for real-time AI acceleration, cryptography, and next-generation edge computing, enabling advanced compute-in-memory systems. Full article

By integrating information from RGB images and depth images, the feature perception capability of a defect detection algorithm can be enhanced, making it more robust and reliable in detecting subtle defects on printed circuit boards. On this basis, inspired by the concept of differential amplification, we propose a novel and general weighted feature fusion method within the YOLO11 dual-stream detection network framework, which we name CM-YOLO. Based on the differential amplification approach, we introduce a Differential Amplification Weighted Fusion (DAWF) module, which separates multimodal features into common-mode and differential-mode features to preserve and enhance modality-specific characteristics. Then, the SE-Weighted Fusion module is used to fuse the common-mode and differential-mode features.In addition, we introduce a Cross-Attention Spatial and Channel (CASC) module into the detection network to enhance feature extraction capability. Extensive experiments show that the proposed CM-YOLO method achieves a mean Average Precision (mAP) of 0.969, demonstrating the accuracy and effectiveness of CM-YOLO. Full article

This paper investigates the super-resolution reconstruction technology of coarse granular particle images for embankment construction in earth/rock dams based on Real-ESRGAN, aiming to improve the quality of low-resolution particle images and enhance the accuracy of particle shape analysis. The paper begins with a review of traditional image super-resolution methods, introducing Generative Adversarial Networks (GAN) and Real-ESRGAN, which effectively enhance image detail recovery through perceptual loss and adversarial training. To improve the generalization ability of the super-resolution model, the study expands the morphological database of earth/rock dam particles by employing a multi-modal data augmentation strategy, covering a variety of particle shapes. The paper utilizes a dual-stage degradation model to simulate the image degradation process in real-world environments, providing a diverse set of degraded images for training the super-resolution reconstruction model. Through wavelet transform methods, the paper analyzes the edge and texture features of particle images, further improving the precision of particle shape feature extraction. Experimental results show that Real-ESRGAN outperforms other traditional super-resolution algorithms in terms of edge clarity, detail recovery, and the preservation of morphological features of particle images, particularly under low-resolution conditions, with significant improvement in image reconstruction. In conclusion, Real-ESRGAN demonstrates excellent performance in the super-resolution reconstruction of coarse granular particle images for embankment construction in earth/rock dams. It can effectively restore the details and morphological features of particle images, providing more accurate technical support for particle shape analysis in civil engineering. Full article