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This work introduces a novel geometrical and analytical modeling framework for predicting the stiffness properties of knitted composites, a class of materials known for their complex architectures and versatile mechanica...

Tensor network formalisms have emerged as powerful tools for simulating quantum state evolution. While widely applied in the study of optical quantum circuits, such as boson sampling, existing tensor network approaches f...

Abstract Direction of arrival algorithms are used, in general, to estimate a number of incident plane waves on the antenna array and their angles of incidence. In this paper, firstly, a new approach representing a more e...

Abstract Efficiently and accurately extracting key information from videos has become a core challenge in the current computer vision. Traditional methods rely on manual features and shallow models, making it difficult t...

With advances in aerospace technology, high-resolution optical imagery from satellites, such as China’s Gaofen series, is vital for climate monitoring and agriculture. However, cloud cover significantly impedes th...

Abstract The Broad Learning System (BLS) provides an effective framework for nonlinear mapping, offering advantages over traditional deep neural networks through its expanded input node architecture. While BLS has demons...

Moving towards a sustainable future, optimising energy systems and resources involves increasingly sophisticated decision-making processes that require balancing technical efficiency with societal, economic and environme...

Recently, in the image super-resolution (SR) domain, Transformers have outperformed Convolution Neural Networks (CNNs) with reduced computational complexity and parameters by modeling long-range dependencies input-depend...

Aiming at the problem of power quality disturbance (PQD) classification, classification sub-models based on one-dimension CNN in the time domain (original signal), frequency domain (Fourier transform) and time-frequency ...

Digital health increasingly relies on artificial intelligence for clinical decision-making, patient management, and distributed learning, yet two persistent challenges remain: the computational complexity of optimization...

Abstract Multiple-input multiple-output non-orthogonal multiple access (MIMO-NOMA) systems assisted by reconfigurable intelligent surfaces (RIS) have become a viable way to improve next-generation wireless networks. Howe...

Buildings serve as a key indicator of human land use, and building change detection plays a crucial role in urban growth and shrinkage monitoring, land use planning, and urban development assessment and management. Curre...

Abstract Point cloud registration is the process of determining the optimal rigid body transformation matrix based on the corresponding point pairs between the source point cloud and the target point cloud to align the p...

Formulation of the problem. In scientific research in sociology, education, physics, chemistry and other sciences, methods of correlation analysis are used. Correlation coefficients are used to determine the existence of...

Convolutional neural networks (CNNs) and visual Transformers have become the dominant approaches for hyperspectral image (HSI) classification due to their powerful feature representation capabilities. However, CNNs are l...

Abstract The growing deployment of grid-connected voltage source inverters (GC-VSIs) in the smart grids poses some significant issues that include harmonic distortion, grid impedance fluctuations, nonlinear loading, and ...

This study addresses the under-explored dynamic characteristics of unmanned ships in trajectory planning by constructing a constraint space in the navigation state-phase plane. First, we employ a path-velocity decoupling...

Soilless cultivation systems such as hydroponics rely on continuous and accurate monitoring of physicochemical parameters to maintain optimal plant growth conditions. In practice, however, hydroponic operations are const...

In response to the limitations of traditional planning methods for Autonomous Surface Vehicles (ASVs), which heavily rely on predefined map representations and neglect kinematic feasibility, this paper proposes a novel k...

Abstract Traffic flow prediction is a fundamental building block for Intelligent Transportation Systems (ITS), enabling proactive congestion mitigation, adaptive traffic signal control, and reliable route guidance. Howev...

Abstract Medical image segmentation is fundamental for delineating lesion and organ boundaries in clinical workflows. While UNet-based models remain widely used, CNN-dominant designs are limited in modeling long-range co...

Abstract Background Optimization of biotechnological processes is traditionally limited by time-consuming trial-and-error approaches and the complexity of simultaneously optimizing multiple, often conflicting objectives....

Soft robotics is a rapidly evolving field that leverages compliant and deformable materials to create robotic systems capable of safe human interaction, environmental adaptability, and bio-inspired motion. Unlike rigid r...

Probabilistic proposals of Language of Thoughts (LoTs) can explain learning across different domains as statistical inference over a compositionally structured hypothesis space. While frameworks may differ on how a LoT m...