Gearboxes are the most widely used elements for transferring speed and power in many
industrial machines. High-precision gearbox fault diagnosis is quite significant for keeping …

The key operation in stochastic neural networks, which have become the state-of-the-art
approach for solving problems in machine learning, information theory, and statistics, is a …

Memristive devices are novel electronic devices, which resistance can be tuned by an
external voltage in a non-volatile way. Due to their analog resistive switching behavior, they …

The neural network enables efficient solutions for Nondeterministic Polynomial-time (NP)
hard problems, which are challenging for conventional von Neumann computing. The …

In this work, the Cu: ZnO based memristors were fabricated and modelled and its biological
synaptic characteristics were realized. Phenomenon similar to long-term potentiation and …

In this study, carbon fibers (C f) coated with Ba 0. 6 Sr 0. 4 TiO 3 (BST)(BST@ C f) were
prepared by magnetron sputtering and subsequently heated in nitrogen to produce oxygen …

The in-memory computing paradigm with emerging memory devices has been recently
shown to be a promising way to accelerate deep learning. Resistive processing unit (RPU) …

In the neural network context, used in a variety of applications, binarised networks, which
describe both weights and activations as single‐bit binary values, provide computationally …

In this work, doping C and codoping C and N into the Sb 2 Te 3 traditional chalcogenide
were investigated to reduce the writing current of the phase-change device using a …

Resistive random-access memory (RRAM) devices are very versatile with applications
ranging from digital nonvolatile memories to analog synapses and integrate-fire neurons …