In many statistical hyperspectral unmixing approaches, the unmixing task is essentially an
optimization problem given a defined linear or nonlinear spectral mixture model. However, …

Traffic forecasting is the foundation for intelligent transportation systems. Spatiotemporal
graph neural networks have demonstrated state-of-the-art performance in traffic forecasting. …

In this paper, we propose a novel approach for spectral unmixing by unfolding the iterative
shrinkage-thresholding algorithm (ISTA) into a deep neural network architecture. Spectral …

We present further progress, in the form of analytical results, on the Wigner entropy conjecture
set forth by Van Herstraeten and Cerf [ Phys. Rev. A 104 , 042211 ( 2021 ) 10.1103/…

Self-supervised methods have recently been widely used for hyperspectral image (HSI)
denoising. As only a single noisy HSI required to be restored is used for learning, effectively …

A major challenge in hyperspectral image (HSI) classification is the small-sample-size problem.
Cross-domain information can help solve the problem. In cross-domain HSI classification…

We present a novel feature extraction method to improve classification of hyperspectral
image, leveraging graph wavelet transform to address the shortcomings of classical wavelet …

The discrimination of quantum states is a central problem in quantum information science and
technology. Meanwhile, partial post-selection has emerged as a valuable tool for quantum …

Path planning in complex environments presents a substantial research challenge for
mobile robots. This study introduces an enhanced ant colony algorithm based on islands (EACI) …

Hyperspectral Image (HSI) denoising is a crucial preprocessing step to ensure the accuracy
of the subsequent HSI analysis and interpretation. Neural network methods recently achieve …