Progression on spectrum sensing for cognitive radio networks: A survey, classification, challenges and future research issues
It is widely believed that the advances of networking technologies will reshape the future of
telecommunication system. The continuous growth in data traffic originated by mobile users …
telecommunication system. The continuous growth in data traffic originated by mobile users …
A tutorial on sparse signal reconstruction and its applications in signal processing
Sparse signals are characterized by a few nonzero coefficients in one of their transformation
domains. This was the main premise in designing signal compression algorithms …
domains. This was the main premise in designing signal compression algorithms …
Image reconstruction in electrical impedance tomography based on structure-aware sparse Bayesian learning
Electrical impedance tomography (EIT) is developed to investigate the internal conductivity
changes of an object through a series of boundary electrodes, and has become increasingly …
changes of an object through a series of boundary electrodes, and has become increasingly …
Active surveillance via group sparse Bayesian learning
The key to the effective control of a diffusion system lies in how accurately we could predict
its unfolding dynamics based on the observation of its current state. However, in the real …
its unfolding dynamics based on the observation of its current state. However, in the real …
[HTML][HTML] Image encryption scheme with compressed sensing based on new three-dimensional chaotic system
Y Xie, J Yu, S Guo, Q Ding, E Wang - Entropy, 2019 - mdpi.com
In this paper, a new three-dimensional chaotic system is proposed for image encryption. The
core of the encryption algorithm is the combination of chaotic system and compressed …
core of the encryption algorithm is the combination of chaotic system and compressed …
Low-rank Hankel matrix completion for robust time-frequency analysis
In this paper, we develop a novel method to enable robust sparsity-based time-frequency
representation of multi-component frequency modulated signals in the presence of burst …
representation of multi-component frequency modulated signals in the presence of burst …
Unlocking signal processing with image detection: a frequency hopping detection scheme for complex EMI environments using STFT and CenterNet
Z Chen, Y Shi, Y Wang, X Li, X Yu, Y Shi - IEEE Access, 2023 - ieeexplore.ieee.org
Accurate detection and parameter estimation of frequency hopping (FH) signals remain
challenging in FH signal-based transmission systems. This study proposes a scheme …
challenging in FH signal-based transmission systems. This study proposes a scheme …
Approaching sub-nyquist boundary: Optimized compressed spectrum sensing based on multicoset sampler for multiband signal
Compressed spectrum sensing naturally pursues the use of fewer sampling resources to
achieve spectrum support reconstruction and signal recovery. The theoretical lower …
achieve spectrum support reconstruction and signal recovery. The theoretical lower …
Binary sparse signal recovery with binary matching pursuit
J Wen, H Li - Inverse Problems, 2021 - iopscience.iop.org
In numerous applications from communications and signal processing, we often need to
acquire a K-sparse binary signal from sparse noisy linear measurements. In this work, we …
acquire a K-sparse binary signal from sparse noisy linear measurements. In this work, we …
Deep residual learning in modulation recognition of radar signals using higher-order spectral distribution
Automatically recognizing intra-pulse modulation of radar signals is a significant survival
technique in electronic intelligence systems. To avoid the dependence on feature selection …
technique in electronic intelligence systems. To avoid the dependence on feature selection …