Qiang Wang
10 Papers
12 Citations
Qiang Wang is an academic researcher. The author has contributed to research in topics: Computer science & Compressed sensing. The author has an hindex of 2, co-authored 9 publications.
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Papers
Compressive sensing reconstruction for vibration signals based on the improved fast iterative shrinkage-thresholding algorithm
TL;DR: The improved fast iterative shrinkage-thresholding algorithm (IFISTA) is proposed to improve the reconstruction effect of vibration signals by extracting information from the unstable signals in the process of iteration and protecting the feature coefficients from shrinkage during iteration.
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Analog Continuous-Time Filter Designing for Morlet Wavelet Transform Using Constrained L2-Norm Approximation
Qiang Wang,Chen Meng,Cheng Wang +2 more
TL;DR: Under the proposed scheme, the impulse response of the linear time-invariant system is used to approximate the Morlet wavelet function and an analog continuous-time filter is designed based on the Gm-C integrator and orthonormal ladder topology.
Analog-to-Information Conversion for Nonstationary Signals
Qiang Wang,Chen Meng,Cheng Wang +2 more
TL;DR: This paper proposes a novel analog-to-information conversion architecture to achieve the sub-Nyquist sampling for nonstationary signals, and presents a multi-channel sampling system to sample the signals in time-frequency domain.
Compressed Sensing Reconstruction of Radar Echo Signal Based on Fractional Fourier Transform and Improved Fast Iterative Shrinkage-Thresholding Algorithm
TL;DR: In this article, a fast iterative shrinkage thresholding reconstruction algorithm based on protection coefficients is proposed to optimize the processing of radar echo, which can effectively reduce the data rate of high-resolution radar imaging systems and solve the problem of collecting, storing, and transmitting large amounts of data in radar systems.
Adaptive Cluster Structured Sparse Bayesian Learning with Application to Compressive Reconstruction for Chirp Signals
TL;DR: An adaptive cluster structured sparse Bayesian learning algorithm is proposed to alleviate the requirements on the prior knowledge by exploiting and incorporating the local structure of the sparse matrix into the reconstruction model and applies an adaptive mechanism in variable estimation to avoid the model mismatch problem.
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