Quantum error correction (QEC) is crucial for achieving reliable quantum computation. Among topological QEC codes, color codes can correct bit-flip and phase-flip errors simultaneously, enabling efficient resource utilization. However, existing decod...
This paper proposes a resource optimized cascaded quantum surface repetition code architecture integrated with a Union Find (UF) enhanced hybrid decoder, which suppresses biased noise and improves the scalability of quantum error correction through s...
Quantum error correction is a prerequisite for quantum computing; however, the performance critically depends on the accuracy of the decoding algorithm. To address these challenges, we propose a hybrid decoding architecture, BP + UF + BP. The protoco...
Background/Objectives: Accurate liver and tumor detection and segmentation are crucial in diagnosis of early-stage liver malignancies. As opposed to manual interpretation, which is a difficult and time-consuming process, accurate tumor detection usin...
Semantic segmentation is a very popular topic in modern computer vision, and it has applications in many fields. Researchers have proposed a variety of architectures for semantic image segmentation. The most common ones exploit an encoder–decoder str...
To address the inherent limitations in the robustness of fully autonomous unmanned aerial vehicle (UAV) visual perception and the high cognitive workload associated with manual control, this paper proposes a human-in-the-loop brain–computer int...
Accurate semantic segmentation of disease spots is critical in the evaluation and treatment of cucumber leaf damage. To solve the problem of poor segmentation accuracy caused by the imbalanced feature fusion of SegFormer, the Efficient Channel Attent...
Text-guided segmentation combines visual information with semantic descriptions, but semantic conditioning alone does not explicitly supervise the separation of foreground and background representations. We combine a VGG16-based encoder–decoder...
Using deep learning or artificial intelligence (AI) in research with animals is a new interdisciplinary area of research. In this study, we have explored the potential of thermal imaging and AI in pig research. Thermal cameras play a vital role in ob...
Accurately estimating the breast muscle weight of broilers is important for poultry production. However, existing related methods are plagued by cumbersome processes and limited automation. To address these issues, this study proposed an efficient me...
In the realm of remote sensing image analysis, the task of road extraction poses significant complexities, especially in the context of intricate scenes and diminutive targets. In response to these challenges, we have developed a novel deep learning...
Routine inspections of transmission lines are essential for maintaining the reliability of the power grid. Airborne LiDAR technology provides detailed 3D corridor data for automated hazard detection, such as vegetation encroachment and structural ano...
Shipwreck segmentation in side-scan sonar imagery remains challenging. This study investigates whether repeating a frozen encoder block can improve the Segment Anything Model (SAM) for this task and how to select the block without labelled validation...
Automated colorectal tumor segmentation on routine computed tomography (CT) remains technically challenging because of limited soft-tissue contrast, heterogeneous tumor morphology, complex bowel anatomy, and marked imbalance between tumor and backgro...
Semantic segmentation of metallographic micrographs is a key task for quantitative microstructural analysis in additive manufacturing, yet it remains challenging due to phase heterogeneity, complex morphologies, and the scarcity of annotated data. Th...
Accurate brain tumor segmentation in magnetic resonance imaging (MRI) is essential for diagnosis, treatment planning, and therapy monitoring. Conventional deep learning models often struggle with large variations in tumor shape, size, and contrast, a...
The contamination of marine ecosystems by oil spills poses a significant threat to the marine environment, necessitating the prompt and effective implementation of measures to mitigate the associated damage. Satellites offer a spatial and temporal ad...
Background/Objectives: Automatic segmentation of fetal anatomical structures in prenatal ultrasound images is challenging because of low tissue contrast, noise caused by speckles, anatomical variability and the fact that structures are only partially...
Accurate retinal vessel segmentation supports quantitative vascular analysis in assessing ocular and systemic diseases. Yet, its clinical scalability is constrained by limited pixel-level annotations and domain shift across heterogeneous fundus datas...
The rapid development of unmanned aerial vehicles (UAVs) and deep learning has accelerated the application of semantic segmentation in precision agriculture (SSPA). A key driver of this progress lies in multimodal fusion, which leverages complementar...
Metallic blocks and components are indispensable across the aerospace, energy, and heavy-engineering industries, where undetected internal flaws such as cracks, voids, and inclusions may precipitate catastrophic structural failure. Reliable nondestru...
Precise segmentation of kidney tumors in medical images is crucial for diagnosis, treatment planning, and prognosis assessment. In this work, we present a newly proposed hybrid deep learning model that combines the merits of U-Net and the Swin Transf...
Reliable visual delineation of ice accretion on aircraft wing leading edges remains challenging because accreted ice often exhibits low visual contrast, translucent and irregular morphology, and ambiguous boundaries. To address these challenges, this...
Bandwidth-limited aerial–ground sensing requires an explicit trade-off among payload size, decoded-map quality, and downstream route utility. This paper evaluates a strict RGB-D edge-perception interface in which the receiver accesses only a qu...
Image-derived descriptors of fermentation foam may complement conventional brewing-process measurements, but they require evaluation at the level of independent fermentation batches. We derived image-based estimates of foam height and foam-free expos...