An improved complex multi-task Bayesian compressive sensing approach for compression and reconstruction of SHM data

TL;DR: In this article , a deep learning method for vibration data reconstruction by using deep convolutional generative adversarial networks (DCGAN) is proposed, which is composed of a generator G and a discriminator D. The proposed DCGAN approach has several appealing properties.

TL;DR: Li et al. as discussed by the authors proposed a hybrid deep learning and autoregressive model with attention mechanism (DL-AR-ATT) framework to accurately reconstruct structural responses considering data correlations, which can simultaneously model the linear and nonlinear structure of data.

TL;DR: This paper proposes a deep learning-based compressed sensing reconstruction algorithm using Temporal Convolutional Networks (TCNs) for efficient EMG signal recovery, outperforming traditional methods like OMP, BP, and MCo under various compression ratios.

TL;DR: In this paper , the authors proposed an enhanced error reduction method, exploited as a post-processing scheme for signal reconstruction, which constructs an autoregression model, whose residuum increases to correspond to the reconstruction error based on empirical observations.

TL;DR: In this article, the Straight Line Case is used to fit a straight line by least squares, and the Durbin-Watson Test is used for checking the straight line fit.

TL;DR: The theory of compressive sampling, also known as compressed sensing or CS, is surveyed, a novel sensing/sampling paradigm that goes against the common wisdom in data acquisition.

TL;DR: It is demonstrated theoretically and empirically that a greedy algorithm called orthogonal matching pursuit (OMP) can reliably recover a signal with m nonzero entries in dimension d given O(m ln d) random linear measurements of that signal.