A novel method to remove impulse noise from atomic force microscopy images based on Bayesian compressed sensing

• Yingxu Zhang,
• Yingzi Li,
• Zihang Song,
• Zhenyu Wang,
• Jianqiang Qian and
• Junen Yao

Beilstein J. Nanotechnol. 2019, 10, 2346–2356, doi:10.3762/bjnano.10.225

• further improve the denoising performance, machine learning [10] and neural networks [11][12] are introduced to help remove the impulse noise. First, machine learning or neural networks are used to improve the accuracy of the recognition of noisy pixels. Then, the noise pixels are replaced by the median

• density. In addition, impulse noise filtering methods using machine learning [10], support vector machines [38], or neural networks [12] encounter the same problem as the adaptive median filter. When the noise density is lower than 0.5, the values of PSNR and SSIM acquired by the proposed method remain

Nitrogen-vacancy centers in diamond for nanoscale magnetic resonance imaging applications

• Alberto Boretti,
• Lorenzo Rosa,
• Jonathan Blackledge and
• Stefania Castelletto

Beilstein J. Nanotechnol. 2019, 10, 2128–2151, doi:10.3762/bjnano.10.207

• was dispersed in cell media and then applied at a concentration of 6 μg/mL to the primary cultures while performing a routine change of cell media. ODMR from NVs within the NDs in the neural networks was used for sensing the temperature from thousands of NDs, which were probed simultaneously using a

Unipolar magnetic field pulses as an advantageous tool for ultrafast operations in superconducting Josephson “atoms”

• Daria V. Popolitova,
• Nikolay V. Klenov,
• Igor I. Soloviev,
• Sergey V. Bakurskiy and
• Olga V. Tikhonova

Beilstein J. Nanotechnol. 2019, 10, 1548–1558, doi:10.3762/bjnano.10.152

• of developments in algorithmic and adiabatic quantum computers, artificial metamaterials, and quantum neural networks. Hence, they seem to be very promising for studies of novel types of fast quantum-state control or initialization [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15]. In this work, we

• ultrafast state initialization for algorithmic quantum computers and quantum neural networks as well as in the fast control of the magnetic properties of media from Josephson meta-atoms. (a) The potential energy and the eigenfunctions (with energies E1 ,E2, E3, E4) of the three-junction qubit (described in

Beyond Moore’s technologies: operation principles of a superconductor alternative

• Igor I. Soloviev,
• Nikolay V. Klenov,
• Sergey V. Bakurskiy,
• Mikhail Yu. Kupriyanov,
• Alexander L. Gudkov and
• Anatoli S. Sidorenko

Beilstein J. Nanotechnol. 2017, 8, 2689–2710, doi:10.3762/bjnano.8.269

• mentioned localization of information and high non-linearity of Josephson junctions make superconductor circuits to be ideally suited for the implementation of unconventional computational paradigms like cellular automata [94][95], artificial neural networks [96][97][98] or quantum computing [99][100][101

Optical techniques for cervical neoplasia detection

• Tatiana Novikova

Beilstein J. Nanotechnol. 2017, 8, 1844–1862, doi:10.3762/bjnano.8.186

• watchful waiting accompanied by HPV/Pap tests or active treatment is further needed [34][35][36][37][38]. The use of spectra classification algorithms (e.g., Bayesian variable selection, neural networks, library approach, multivariate statistical analysis) may bring its own set of the problems: high

Adiabatic superconducting cells for ultra-low-power artificial neural networks

• Andrey E. Schegolev,
• Nikolay V. Klenov,
• Igor I. Soloviev and
• Maxim V. Tereshonok

Beilstein J. Nanotechnol. 2016, 7, 1397–1403, doi:10.3762/bjnano.7.130

• . We optimize their parameters for application in three-layer perceptron and radial basis function networks. Keywords: adiabatic superconductor cells; artificial neural networks; energy efficiency; Josephson effect; superconductivity; Findings Artificial neural networks (ANNs) are famous for their

The eNanoMapper database for nanomaterial safety information

• Nina Jeliazkova,
• Charalampos Chomenidis,
• Philip Doganis,
• Bengt Fadeel,
• Roland Grafström,
• Barry Hardy,
• Janna Hastings,
• Markus Hegi,
• Vedrin Jeliazkov,
• Nikolay Kochev,
• Pekka Kohonen,
• Cristian R. Munteanu,
• Haralambos Sarimveis,
• Bart Smeets,
• Pantelis Sopasakis,
• Georgia Tsiliki,
• David Vorgrimmler and
• Egon Willighagen

Beilstein J. Nanotechnol. 2015, 6, 1609–1634, doi:10.3762/bjnano.6.165

Growth and structural discrimination of cortical neurons on randomly oriented and vertically aligned dense carbon nanotube networks

• Christoph Nick,
• Sandeep Yadav,
• Ravi Joshi,
• Christiane Thielemann and
• Jörg J. Schneider

Beilstein J. Nanotechnol. 2014, 5, 1575–1579, doi:10.3762/bjnano.5.169

• between the aggregated networks. These results are in line with the results after using micro-stamped structures of carbon nanotubes, which led to the self-assembly of neural networks [28] and to the formation of neural networks on islands of unordered CNTs [12]. After 21 days in vitro the neural network