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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 sensing, which can complete image encryption and compression at the same time. The Lyapunov exponent, bifurcation diagram and complexity of the new three-dimensional chaotic system are analyzed. The performance analysis shows that the chaotic system has two positive Lyapunov exponents and high complexity. In the encryption scheme, a new chaotic system is used as the measurement matrix for compressed sensing, and Arnold is used to scrambling the image further. The proposed method has better reconfiguration ability in the compressible range of the algorithm compared with other methods. The experimental results show that the proposed encryption scheme has good encryption effect and image compression capability.
Logic gate functions built with nonvolatile resistive
switching
and thermoresponsive memory based on biologic proteins were investigated.
The “NAND” and “NOR” functions of logic
gates in soya protein devices have been built at room temperature
by their nonvolatile ternary WORM resistive switching behaviors. Furthermore,
heating the devices from room temperature to 358 K results in a switch
from tristable state to bistable state WORM resistive switching behavior,
indicating that the thermoresponsiveness can be efficiently memorized.
The biologic transient nonvolatile memory device consisting of soya
protein is illustrated. This device exhibits a long data retention
time (104 s) and significant HRS/LRS ratio (∼105); the transient response of the current to voltage of an
as-fabricated device is also explored. The soya protein based memory
device on a gelatin film substrate is also assessed to validate the
feasibility of degradation and biological compatibility for the implantable
biological electronic device, that is, innoxious and avirulent to
the human body. This can offer alternative avenues for exploring prospective
bioelectronic devices.
Remote-sensing images constitute an important means of obtaining geographic information. Image super-resolution reconstruction techniques are effective methods of improving the spatial resolution of remote-sensing images. Super-resolution reconstruction networks mainly improve the model performance by increasing the network depth. However, blindly increasing the network depth can easily lead to gradient disappearance or gradient explosion, increasing the difficulty of training. This report proposes a new pyramidal multi-scale residual network (PMSRN) that uses hierarchical residual-like connections and dilation convolution to form a multi-scale dilation residual block (MSDRB). The MSDRB enhances the ability to detect context information and fuses hierarchical features through the hierarchical feature fusion structure. Finally, a complementary block of global and local features is added to the reconstruction structure to alleviate the problem that useful original information is ignored. The experimental results showed that, compared with a basic multi-scale residual network, the PMSRN increased the peak signal-to-noise ratio by up to 0.44 dB and the structural similarity to 0.9776.
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