Artificial stochastic neural network on the base of double quantum wells

2021 ◽  
Author(s):  
O. V. Pavlovsky ◽  
V. I. Dorozhinsky ◽  
S. D. Mostovoy
Keyword(s):  
Neural Network ◽  
Quantum Wells ◽  
Integration Method ◽  
Monte Carlo Integration ◽  
Quantum Mechanical ◽  
Double Quantum ◽  
Quantum Mechanical System ◽  
Convolutional Network ◽  
Self Potential

In this paper, we consider a model of an artificial neural network based on quantum-mechanical particles in [Formula: see text] potential. These particles play the role of neurons in our model. To simulate such a quantum-mechanical system, the Monte Carlo integration method is used. A form of the self-potential of a particle as well as two interaction potentials (exciting and inhibiting) are proposed. Examples of simplest logical elements (such as AND, OR and NOT) are shown. Further, we show an implementation of the simplest convolutional network in framework of our model.

Many-body Effects and the Role of Indirect Excitons in Asymmetric InGaAs/GaAs Double Quantum Wells

2016 ◽  
Author(s):  
Christopher L. Smallwood ◽  
Takeshi Suzuki ◽  
Travis M. Autry ◽  
Rohan Singh ◽  
Matthew W. Day ◽  
...  
Keyword(s):  
Quantum Wells ◽  
Double Quantum ◽  
Many Body ◽  
Indirect Excitons ◽  
Many Body Effects

Evidence for condensation of excitons in double quantum wells

1996 ◽  
Vol 166 (7) ◽  
pp. 801-803 ◽  
Author(s):  
L.V. Butov ◽  
A. Zrenner ◽  
M. Hagn ◽  
G. Abstreiter ◽  
G. Boehm ◽  
...  
Keyword(s):  
Quantum Wells ◽  
Double Quantum

scholarly journals Quantum postulate vs. quantum nonlocality: on the role of the Planck constant in Bell’s argument

2021 ◽  
Vol 51 (1) ◽  
Author(s):  
Andrei Khrennikov
Keyword(s):  
Quantum Nonlocality ◽  
Quantum Mechanical ◽  
Quantum Foundations ◽  
Planck Constant ◽  
Variable Model ◽  
Basic Principles ◽  
Fundamental Feature ◽  
Hidden Variable ◽  
Bell Model

AbstractWe present a quantum mechanical (QM) analysis of Bell’s approach to quantum foundations based on his hidden-variable model. We claim and try to justify that the Bell model contradicts to the Heinsenberg’s uncertainty and Bohr’s complementarity principles. The aim of this note is to point to the physical seed of the aforementioned principles. This is the Bohr’s quantum postulate: the existence of indivisible quantum of action given by the Planck constant h. By contradicting these basic principles of QM, Bell’s model implies rejection of this postulate as well. Thus, this hidden-variable model contradicts not only the QM-formalism, but also the fundamental feature of the quantum world discovered by Planck.

scholarly journals Towards 6G IoT: Tracing Mobile Sensor Nodes with Deep Learning Clustering in UAV Networks

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3936
Author(s):  
Yannis Spyridis ◽  
Thomas Lagkas ◽  
Panagiotis Sarigiannidis ◽  
Vasileios Argyriou ◽  
Antonios Sarigiannidis ◽  
...  
Keyword(s):  
Deep Learning ◽  
Heuristic Method ◽  
Sensor Nodes ◽  
Convolutional Network ◽  
Received Signal Strength Indicator ◽  
Anchor Nodes ◽  
Time Required ◽  
Rf Signal ◽  
Deep Learning Model

Unmanned aerial vehicles (UAVs) in the role of flying anchor nodes have been proposed to assist the localisation of terrestrial Internet of Things (IoT) sensors and provide relay services in the context of the upcoming 6G networks. This paper considered the objective of tracing a mobile IoT device of unknown location, using a group of UAVs that were equipped with received signal strength indicator (RSSI) sensors. The UAVs employed measurements of the target’s radio frequency (RF) signal power to approach the target as quickly as possible. A deep learning model performed clustering in the UAV network at regular intervals, based on a graph convolutional network (GCN) architecture, which utilised information about the RSSI and the UAV positions. The number of clusters was determined dynamically at each instant using a heuristic method, and the partitions were determined by optimising an RSSI loss function. The proposed algorithm retained the clusters that approached the RF source more effectively, removing the rest of the UAVs, which returned to the base. Simulation experiments demonstrated the improvement of this method compared to a previous deterministic approach, in terms of the time required to reach the target and the total distance covered by the UAVs.

scholarly journals Role of dislocations in nitride laser diodes with different indium content

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Agata Bojarska-Cieślińska ◽  
Łucja Marona ◽  
Julita Smalc-Koziorowska ◽  
Szymon Grzanka ◽  
Jan Weyher ◽  
...  
Keyword(s):  
Quantum Wells ◽  
Defect Density ◽  
Laser Diodes ◽  
Diffusion Path ◽  
Vapour Phase ◽  
Indium Content ◽  
Nonradiative Recombination ◽  
Light Emitters ◽  
Vapour Phase Epitaxy

AbstractIn this work we investigate the role of threading dislocations in nitride light emitters with different indium composition. We compare the properties of laser diodes grown on the low defect density GaN substrate with their counterparts grown on sapphire substrate in the same epitaxial process. All structures were produced by metalorganic vapour phase epitaxy and emit light in the range 383–477 nm. We observe that intensity of electroluminescence is strong in the whole spectral region for devices grown on GaN, but decreases rapidly for the devices on sapphire and emitting at wavelength shorter than 420 nm. We interpret this behaviour in terms of increasing importance of dislocation related nonradiative recombination for low indium content structures. Our studies show that edge dislocations are the main source of nonradiative recombination. We observe that long wavelength emitting structures are characterized by higher average light intensity in cathodoluminescence and better thermal stability. These findings indicate that diffusion path of carriers in these samples is shorter, limiting the amount of carriers reaching nonradiative recombination centers. According to TEM images only mixed dislocations open into the V-pits, usually above the multi quantum wells thus not influencing directly the emission.

scholarly journals A Multi-Scale Feature Extraction-Based Normalized Attention Neural Network for Image Denoising

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 319
Author(s):  
Yi Wang ◽  
Xiao Song ◽  
Guanghong Gong ◽  
Ni Li
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Image Denoising ◽  
Color Image ◽  
Rapid Development ◽  
Similarity Index ◽  
Structural Similarity ◽  
Convolutional Network ◽  
Scale Feature ◽  
Multi Scale

Due to the rapid development of deep learning and artificial intelligence techniques, denoising via neural networks has drawn great attention due to their flexibility and excellent performances. However, for most convolutional network denoising methods, the convolution kernel is only one layer deep, and features of distinct scales are neglected. Moreover, in the convolution operation, all channels are treated equally; the relationships of channels are not considered. In this paper, we propose a multi-scale feature extraction-based normalized attention neural network (MFENANN) for image denoising. In MFENANN, we define a multi-scale feature extraction block to extract and combine features at distinct scales of the noisy image. In addition, we propose a normalized attention network (NAN) to learn the relationships between channels, which smooths the optimization landscape and speeds up the convergence process for training an attention model. Moreover, we introduce the NAN to convolutional network denoising, in which each channel gets gain; channels can play different roles in the subsequent convolution. To testify the effectiveness of the proposed MFENANN, we used both grayscale and color image sets whose noise levels ranged from 0 to 75 to do the experiments. The experimental results show that compared with some state-of-the-art denoising methods, the restored images of MFENANN have larger peak signal-to-noise ratios (PSNR) and structural similarity index measure (SSIM) values and get better overall appearance.

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