Apr 18, 2017 · In this paper, we study the problem of recovering N -dimensional (particularly N\geq 3 ) exponential signals from partial observations, and ...

Apr 6, 2016 · In this paper, we study the problem of recovering N-dimensional (particularly N\geq 3) exponential signals from partial observations, and ...

In this paper, we study the problem of recovering $N$ -dimensional (particularly $N\geq 3$ ) exponential signals from partial observations, and formulate this ...

Abstract—Signals are generally modeled as a superposition of exponential functions in spectroscopy of chemistry, biology and medical imaging.

The full signal is reconstructed by simultaneously exploiting the CANDECOMP/PARAFAC (CP) tensor decomposition and the exponential structure of the associated ...

The completion of matrices with missing values under the rank constraint is a non-convex optimization problem. A popular convex relaxation is based on ...

Our design starts from constraining the low rank property of the Hankel matrix which is arranged from exponential signals and adopts matrix factorization-based ...

The design of our network starts from constraining the low-rank property of the Hankel matrix which is arranged from exponential signals and adopts matrix ...

Hankel Matrix Nuclear Norm Regularized Tensor Completion for N -dimensional Exponential Signals ... Signals are generally modeled as a superposition of ...

Apr 25, 2024 · Xiaobo Qu : Hankel Matrix Nuclear Norm Regularized Tensor Completion for N-dimensional Exponential Signals. IEEE Trans. Signal Process. 65 ...