Synthesis of graphene oxide–methacrylic acid–sodium allyl sulfonate copolymer and its tanning properties

AbstractGraphite oxide nanosheets (GONs) and the copolymer of GONs with methacrylic acid (MAA) and sodium allyl sulfonate (SAS) (poly(GON–MAA–SAS)) were prepared. The GONs in poly(GON–MAA–SAS) are smaller and uniformly dispersed, allowing them to penetrate into collagen fibers of leather and produce better tanning effects than current nano-tanning agents. Tanning effects due to chemical bonding and nanoeffects are elucidated by measuring the shrinkage temperature (Ts) of wet and dry leather. The results indicate that poly(GON–MAA–SAS) could be used alone as a tanning agent to provide excellent mechanical properties, especially good elasticity and softness, although the Ts is slightly lower than that of chrome-tanned leather. Poly(GON–MAA–SAS) in combination with a chrome tanning agent could allow the dosage of the latter to be halved. These results indicate the potential for new nano-tanning agents to reduce the pollution caused by tanning agents

Target localization based on bistatic T/R pair selection in GNSS-based multistatic radar system

To cope with the increasingly complex electromagnetic environment, multistatic radar systems, especially the passive multistatic radar, are becoming a trend of future radar development due to their advantages in anti-electronic jam, anti-destruction properties, and no electromagnetic pollution. However, one problem with this multi-source network is that it brings a huge amount of information and leads to considerable computational load. Aiming at the problem, this paper introduces the idea of selecting external illuminators in the multistatic passive radar system. Its essence is to optimize the configuration of multistatic T/R pairs. Based on this, this paper respectively proposes two multi-source optimization algorithms from the perspective of resolution unit and resolution capability, the Covariance Matrix Fusion Method and Convex Hull Optimization Method, and then uses a Global Navigation Satellite System (GNSS) as an external illuminator to verify the algorithms. The experimental results show that the two optimization methods significantly improve the accuracy of multistatic positioning, and obtain a more reasonable use of system resources. To evaluate the algorithm performance under large number of transmitting/receiving stations, further simulation was conducted, in which a combination of the two algorithms were applied and the combined algorithm has shown its effectiveness in minimize the computational load and retain the target localization precision at the same time

Electrical Tunable Spintronic Neuron with Trainable Activation Function

Spintronic devices have been widely studied for the hardware realization of artificial neurons. The stochastic switching of magnetic tunnel junction driven by the spin torque is commonly used to produce the sigmoid activation function. However, the shape of the activation function in previous studies is fixed during the training of neural network. This restricts the updating of weights and results in a limited performance. In this work, we exploit the physics behind the spin torque induced magnetization switching to enable the dynamic change of the activation function during the training process. Specifically, the pulse width and magnetic anisotropy can be electrically controlled to change the slope of activation function, which enables a faster or slower change of output required by the backpropagation algorithm. This is also similar to the idea of batch normalization that is widely used in the machine learning. Thus, this work demonstrates that the algorithms are no longer limited to the software implementation. They can in fact be realized by the spintronic hardware using a single device. Finally, we show that the accuracy of hand-written digit recognition can be improved from 88% to 91.3% by using these trainable spintronic neurons without introducing additional energy consumption. Our proposals can stimulate the hardware realization of spintronic neural networks.Comment: 26 pages, 9 figure