With the development of intelligent networks such as the Internet of Things, network scales
are becoming increasingly larger, and network environments increasingly complex, which …

Compressed sensing is an alternative to Shannon/Nyquist sampling for acquiring sparse or
compressible signals. Sparse coding represents a signal as a sparse linear combination of …

Sparse approximate solutions to linear equations are classically obtained via L1 norm
regularized least squares, but this method often underestimates the true solution. As an …

Most image encryption algorithms based on low-dimensional chaos systems bear security
risks and suffer encryption data expansion when adopting nonlinear transformation directly …

When recovering a sparse signal from noisy compressive linear measurements, the
distribution of the signal's non-zero coefficients can have a profound effect on recovery …

We design sparse and block sparse feedback gains that minimize the variance amplification
(ie, the H 2 norm) of distributed systems. Our approach consists of two steps. First, we …

In the past decade, sparse and low-rank recovery has drawn much attention in many areas
such as signal/image processing, statistics, bioinformatics, and machine learning. To …

In traditional compressed sensing (CS), the measurement matrix always faces problems
such as large data storage, high memory usage and a large amount of data calculation. To …

The existing ways to encrypt images based on compressive sensing usually treat the whole
measurement matrix as the key, which renders the key too large to distribute and memorize …

Most of the image encryption algorithms are the private-key cryptosystem, in which the
encryption process and the decryption process employ the same keys. To enhance the …