Sparse modeling for signal processing and machine learning, in general, has been at the
focus of scientific research for over two decades. Among others, supervised sparsity-aware …

Massive multiple-input multiple-output (MIMO) integrated with millimeter-wave (mmWave)
can provide unprecedented performance improvement for realizing future wireless …

Benefitting from the vast spatial degrees of freedom, the amalgamation of integrated sensing
and communication (ISAC) and massive multiple-input multiple-output (MIMO) is expected to …

Computational phenotyping allows for unsupervised discovery of subgroups of patients as
well as corresponding co‐occurring medical conditions from electronic health records …

In this paper, we propose a novel two-stage uplink channel estimation strategy with reduced
pilot overhead and error propagation for reconfigurable intelligent surface (RIS)-aided multi …

Tensor network (TN) has demonstrated remarkable efficacy in the compact representation of
high-order data. In contrast to the TN methods with pre-determined structures, the recently …

Multivariable time-series prediction based on the denoising diffusion probabilistic model
(DDPM) highlights a major challenge in improving prediction robustness and ensuring …

Reconstructing ocean sound speed field (SSF) from limited and noisy measurements/
estimates is crucial for many ocean acoustic applications, including underwater tomography …

Accurately reconstructing a three-dimensional (3D) ocean sound speed field (SSF) is
essential for various ocean acoustic applications, but the sparsity and uncertainty of sound …

The so-called block-term decomposition (BTD) tensor model, especially in its rank-version,
has been recently receiving increasing attention due to its enhanced ability of representing …