With the ever-growing reliance on cloud computing, efficient resource allocation is crucial for maximising the effective use of provisioned resources from cloud service providers. Proactive resource management is therefore critical for minimising cos...
Traditional lie detection technologies, such as the polygraph and event-related potential (ERP)-based approaches, often face limitations in real-world applicability due to their sensitivity to psychological states and the complex, nonlinear nature of...
Electroencephalography (EEG) is widely used in brain-computer interfaces (BCIs) for decoding motor imagery (MI) signals. However, existing methods remain limited in extracting multi-scale local spatiotemporal features and effectively integrating them...
Accurate segmentation of water bodies from high-resolution remote sensing imagery is crucial for water resource management and ecological monitoring. However, small and morphologically complex water bodies remain difficult to detect due to scale vari...
With the rise in small-scale distributed photovoltaic (PV) power generation technology, the behind-the-meter PV problem has greatly increased the difficulty of power system regulation and management and accurate net-load forecasting is of great signi...
Accurate segmentation of thyroid nodules in ultrasound images is crucial for the diagnosis of thyroid cancer and preoperative planning. However, the segmentation of thyroid nodules is challenging due to their irregular shape, blurred boundary, and un...
Medical image segmentation is essential for clinical diagnosis and treatment planning. Despite the notable performance of convolutional neural networks (CNNs), they encounter difficulties in capturing long-range relationships crucial for precise boun...
Currently, most images captured by high-pixel devices such as mobile phones, camcorders, and drones are in RGGB format. However, image quality in extremely dark scenes often needs improvement. Traditional methods for processing these dark RGGB images...
Reliable medical image segmentation remains challenging because models must preserve fine boundary details while maintaining global semantic consistency. CNNs capture local structures effectively but have limited long-range modeling ability, whereas...
Target detection plays a crucial role in the intelligent interpretation of remote sensing images and has a wide range of potential applications. However, in the presence of targets with high aspect ratios and significant scale variations in remotely...
With the rapid development of the aviation industry, engines operate under extreme conditions of high temperature, high pressure, and high vibration, making them prone to surface damage such as ablation. Ablation not only affects the structural integ...
Building extraction from disaster scenes is critical for emergency response and post-disaster assessment. Unlike conventional static remote sensing imagery, multi-phase disaster imagery contains scenes spanning early, middle, and late disaster stages...
Remote sensing object detection (RSOD) faces challenges such as large variations in target scale, diverse orientations, and complex backgrounds. Existing approaches struggle to simultaneously balance local feature extraction and global context modeli...
Grain quality inspection is crucial for seed stored, with image segmentation playing a key role in this process. However, existing methods face challenges such as high computational costs, expensive data annotation, and data privacy concerns, which h...
20 September 2026
Atrial fibrillation (AF) detection from short single-lead electrocardiogram (ECG) recordings requires a balance between rhythm discrimination and computational efficiency. This study compares a one-dimensional ResNet-34 (1D-ResNet34), a Temporal Conv...
This paper proposes a novel hybrid deep learning framework for direction-of-arrival (DOA) estimation using a uniform linear array. Direction of Arrival estimation is a fundamental problem in array signal processing with critical applications in radar...
Accurate segmentation of abdominal organs in Computed Tomography (CT) underpins radiotherapy planning, surgical planning, and disease monitoring. Existing benchmarks rank architectures by a single aggregate Dice score, without per-organ statistical t...
Accurate segmentation of lesion regions is essential for skin cancer diagnosis. As dermoscopic images of skin lesions demonstrate different sizes, diverse shapes, fuzzy boundaries, and so on, accurate segmentation still faces great challenges. To add...
Infrared small target detection (IRSTD) is hindered by low signal-to-noise ratios, minute object scales, and strong target–background similarity. Although long-range skip fusion is exploited in SCTransNet, the global context is insufficiently c...
Speech separation, as an important research direction in audio signal processing, has been widely studied by the academic community since its emergence in the mid-1990s. In recent years, with the rapid development of deep neural network technology, s...
The growth of aquaculture poses significant challenges for disease management, impacting economic sustainability and global food security. Traditional diagnostics are slow and require expertise, while current deep learning models, including CNNs and...
Accurate glomerular segmentation in renal pathological images is a key challenge for chronic kidney disease diagnosis and assessment. Due to the high visual similarity between pathological glomeruli and surrounding tissues in color, texture, and morp...
Satellite remote sensing plays an essential role in wildfire monitoring due to its large-scale observation capability. However, fire targets in satellite imagery are typically extremely small, sparsely distributed, and embedded in complex backgrounds...
Surface roughness, critically measured by the Arithmetical Mean Roughness (Ra), is a vital determinant of workpiece functional performance. Traditional contact-based measurement methods are inefficient and unsuitable for online inspection. While mach...
Recent vision backbones increasingly rely on sophisticated modules, whereas long-used operators such as residual connections, activations, and normalization layers remain less explored for lightweight redesign. This paper revisits these operators and...
Wildfires have become increasingly frequent and intense due to climate change, posing severe threats to ecosystems, infrastructure, and human lives. As a result, accurate wildfire spread prediction is critical for effective risk mitigation, resource...
In hyperspectral image classification (HSIC), each pixel contains information across hundreds of contiguous spectral bands; therefore, the ability to perform long-distance modeling that stably captures and propagates these long-distance dependencies...
High-resolution remote sensing imagery is crucial for ship extraction in ocean-related applications. Existing object detection and semantic segmentation methods for ship extraction have limitations: the former cannot precisely obtain ship shapes, whi...
Water body segmentation is essential for flood monitoring, water resource management, and ecological and environmental protection. Recent advances in deep learning and remote sensing have substantially improved the automation and accuracy of water bo...
Multimodal sentiment analysis (MSA) faces key challenges such as incomplete modality inputs, long-range temporal dependencies, and suboptimal fusion strategies. To address these, we propose MGMR-Net, a Mamba-guided multimodal reconstruction and fusio...
The accurate recognition of pig behaviors in intensive farming is crucial for health monitoring and growth assessment. To address multi-scale recognition challenges caused by perspective distortion (non-frontal camera angles), this study proposes MAC...
Medical image segmentation still faces three critical challenges: insufficient joint modeling of local details and long-range dependencies, the high computational burden of transformer-based architectures for high-resolution inputs, and performance d...
Underwater image enhancement is a challenging task due to the unique optical properties of water, which often lead to color distortion, low contrast, and detail loss. At the present stage, the methods based on the CNN have the problem of insufficient...
Remote sensing image change captioning (RSICC) aims to describe changes between bi-temporal remote sensing (RS) images in natural language. The existing methods, typically based on traditional encoder–decoder architectures or multi-task collabo...
High-order phase defocus occurs in ISAR imaging of maneuvering targets. Traditional methods are limited by motion model mismatches, whereas existing deep imaging relies on complex-valued convolutions or self-attention mechanisms, whose excessive comp...
Accurate pest classification is critical for precision agriculture, yet existing deep learning methods face challenges including computational inefficiency from uniform sample processing and inadequate modeling of complex feature relationships. This...
Accurate medical image segmentation remains challenging when lesions have large-scale variation, weak boundaries, and strong background interference. Vision Mamba provides efficient long-range modeling, but current Mamba-based U-shaped networks are s...
Compressed sensing (CS) is a powerful technique that can reduce data size while maintaining high reconstruction quality, which makes it particularly valuable in high-dimensional image applications. However, many existing methods have difficulty balan...
Background/Objectives: Robust brain-tumor segmentation on contrast-enhanced MRI remains limited by scanner-dependent intensity shifts, scarce annotations, and evaluation protocols that may leak patient-specific information. We propose BA-SwinMamba, a...
Camellia oleifera leaf disease segmentation under natural field conditions is important for precision plant protection but remains challenging because lesions often show small target areas, blurred boundaries, uneven illumination, complex backgrounds...
Hyperspectral image super-resolution (HSI SR) focuses on enhancing the spatial resolution of HSIs while preserving their inherent spectral information. Existing single-image HSI SR methods still suffer from blurred spatial edges and spectral distorti...
Hyperspectral imaging, a key technology in remote sensing, captures rich spectral information beyond the visible spectrum, rendering it indispensable for advanced classification tasks. However, with developments in hyperspectral imaging, spatial&ndas...
Accurate semantic segmentation of high-resolution images captured by unmanned aerial vehicles (UAVs) is crucial for applications in environmental monitoring, urban planning, and precision agriculture. However, challenges such as class imbalance, smal...
Breast ultrasound imaging functions as a vital radiation-free detection tool for breast cancer, yet its low contrast, speckle noise, and interclass variability make automated interpretation difficult. In this paper, we introduce UltraScanNet as a spe...
Low-light images commonly suffer from insufficient contrast, noise accumulation, and colour shifts, which impair human perception and subsequent visual tasks. We propose MambaDPF-Net—a dual-path fusion framework based on the retinal effect, adh...
Accurate segmentation of tunnel lining joints is essential for intelligent tunnel inspection, yet it remains challenging because of texture interference, occlusion-induced discontinuity, and the slender topology of joints in complex in situ environme...
Automatic License Plate Recognition (LPR) is an essential technology in modern intelligent transportation systems, facilitating the identification of vehicles without manual intervention. It supports a wide range of applications, including traffic mo...
Semantic segmentation of urban meshes plays an increasingly crucial role in the analysis and understanding of 3D environments. Most existing large-scale urban mesh semantic segmentation methods focus on integrating multi-scale local features but stru...
Background/Objectives: Electroencephalography (EEG), with its high temporal resolution, non-invasiveness, and cost-effectiveness, provides a suitable modality for investigating task-related signal patterns associated with Visual Sustained Attention (...
Salient object detection in optical remote sensing images remains challenging due to complex backgrounds, blurred boundaries, small objects, unstable foreground–background contrast, and dense object distributions. Existing convolution-based met...
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