When two-dimensional electron systems are subjected to magnetic fields at low temperatures, they can exhibit interesting ...

Brain-inspired spiking neural networks bring real-time AI to edge devices, boosting performance, reducing power use, and ...

Abstract: Deep Neural Networks (DNN) have realized significant achievements ... In this paper, we propose a state-vector aware test case selection method for RNN models, namely DeepVec, which reduces ...

FP has a very complex degradation process. Merging with Fourier transforms and pupil aperture scanning causes difficulty in reconstructing high-resolution images by the commonly used deep neural ...

Comparative Study of Pattern Recognition, Neural Networks, and Statistical Regression Approaches ... in the proposed methods is to infer the density functions of the feature vector in the relevant and ...

This high-performance, USB-powered vector network analyzer is in stock and available to Order Now. The 2-port, 40 GHz VNA400 utilizes a combination of high dynamic range and ultra-fast measurement to ...

This useful study examines the neural ... network’s performance and neural state structure, even so, the ring-like structure and especially the target-velocity dependent tilting remained in a degree.

To install the latest stable version of these plugins, check out the PennyLane installation guide. If you wish to install the latest development version, instructions for building from source are also ...

DL is a sub-field of ML and AI that utilize neural networks (NNs) and their superior nonlinear approximation capabilities to learn underlying structures and patterns within high ... as inputs and ...

the emotional brain pattern was obtained. According to the research of Kasabov (Kasabov, 2014), 471 nods (coordinate point) were introduced into the spatiotemporal self-backpropagation spiking neural ...