3,717 research outputs found
Ultrahigh dielectric constant of thin films obtained by electrostatic force microscopy and artificial neural networks
Copyright 2012 American Institute of Physics. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the American Institute of Physics.A detailed analysis of the electrostatic interaction between an electrostatic force microscope tip and a thin film is presented. By using artificial neural networks, an equivalent semiinfinite sample has been described as an excellent approximation to characterize the whole thin film sample. A useful analytical expression has been also developed. In the case of very small thin film thicknesses (around 1 nm), the electric response of the material differs even for very high dielectric constants. This effect can be very important for thin materials where the finite size effect can be described by an ultrahigh thin filmdielectric constant.This work was supported by TIN2010-196079. G.M.S. acknowledges support from the Spanish Ramón y Cajal Program
Constructive Multiuser Interference in Symbol Level Precoding for the MISO Downlink Channel
This paper investigates the problem of interference among the simultaneous
multiuser transmissions in the downlink of multiple antennas systems. Using
symbol level precoding, a new approach towards the multiuser interference is
discussed along this paper. The concept of exploiting the interference between
the spatial multiuser transmissions by jointly utilizing the data information
(DI) and channel state information (CSI), in order to design symbol-level
precoders, is proposed. In this direction, the interference among the data
streams is transformed under certain conditions to useful signal that can
improve the signal to interference noise ratio (SINR) of the downlink
transmissions. We propose a maximum ratio transmission (MRT) based algorithm
that jointly exploits DI and CSI to glean the benefits from constructive
multiuser interference. Subsequently, a relation between the constructive
interference downlink transmission and physical layer multicasting is
established. In this context, novel constructive interference precoding
techniques that tackle the transmit power minimization (min power) with
individual SINR constraints at each user's receivers is proposed. Furthermore,
fairness through maximizing the weighted minimum SINR (max min SINR) of the
users is addressed by finding the link between the min power and max min SINR
problems. Moreover, heuristic precoding techniques are proposed to tackle the
weighted sum rate problem. Finally, extensive numerical results show that the
proposed schemes outperform other state of the art techniques.Comment: Submitted to IEEE Transactions on Signal Processin
Symbol Based Precoding in The Downlink of Cognitive MISO Channels
This paper proposes symbol level precoding in the downlink of a MISO
cognitive system. The new scheme tries to jointly utilize the data and channel
information to design a precoding that minimizes the transmit power at a
cognitive base station (CBS); without violating the interference temperature
constraint imposed by the primary system. In this framework, the data
information is handled at symbol level which enables the characterization the
intra-user interference among the cognitive users as an additional source of
useful energy that should be exploited. A relation between the constructive
multiuser transmissions and physical-layer multicast system is established.
Extensive simulations are performed to validate the proposed technique and
compare it with conventional techniques.Comment: CROWNCOM 201
Non-linear frequency and amplitude modulation of a nano-contact spin torque oscillator
We study the current controlled modulation of a nano-contact spin torque
oscillator. Three principally different cases of frequency non-linearity
( being zero, positive, and negative) are investigated.
Standard non-linear frequency modulation theory is able to accurately describe
the frequency shifts during modulation. However, the power of the modulated
sidebands only agrees with calculations based on a recent theory of combined
non-linear frequency and amplitude modulation.Comment: 4 pages, 4 figure
Nonlinear envelope equation for broadband optical pulses in quadratic media
We derive a nonlinear envelope equation to describe the propagation of
broadband optical pulses in second order nonlinear materials. The equation is
first order in the propagation coordinate and is valid for arbitrarily wide
pulse bandwidth. Our approach goes beyond the usual coupled wave description of
phenomena and provides an accurate modelling of the evolution of
ultra-broadband pulses also when the separation into different coupled
frequency components is not possible or not profitable
Comparing Deep Recurrent Networks Based on the MAE Random Sampling, a First Approach
Recurrent neural networks have demonstrated to be good at tackling prediction problems, however due to their high sensitivity to
hyper-parameter configuration, finding an appropriate network is a tough task. Automatic hyper-parameter optimization methods have emerged to find the most suitable configuration to a given problem, but these methods are not generally adopted because of their high computational cost. Therefore, in this study we extend the MAE random sampling, a low-cost method to compare single-hidden layer architectures, to multiple-hidden-layer ones. We validate empirically our proposal and show that it is possible to predict and compare the expected performance of an hyper-parameter configuration in a low-cost way.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech.
This research was partially funded by Ministerio de Economı́a, Industria y Competitividad, Gobierno de España, and European Regional Development Fund grant numbers TIN2016-81766-REDT (http://cirti.es) and TIN2017-88213-R (http://6city.lcc.uma.es)
Can Self-Organizing Maps accurately predict photometric redshifts?
We present an unsupervised machine learning approach that can be employed for
estimating photometric redshifts. The proposed method is based on a vector
quantization approach called Self--Organizing Mapping (SOM). A variety of
photometrically derived input values were utilized from the Sloan Digital Sky
Survey's Main Galaxy Sample, Luminous Red Galaxy, and Quasar samples along with
the PHAT0 data set from the PHoto-z Accuracy Testing project. Regression
results obtained with this new approach were evaluated in terms of root mean
square error (RMSE) to estimate the accuracy of the photometric redshift
estimates. The results demonstrate competitive RMSE and outlier percentages
when compared with several other popular approaches such as Artificial Neural
Networks and Gaussian Process Regression. SOM RMSE--results (using
z=z--z) for the Main Galaxy Sample are 0.023, for the
Luminous Red Galaxy sample 0.027, Quasars are 0.418, and PHAT0 synthetic data
are 0.022. The results demonstrate that there are non--unique solutions for
estimating SOM RMSEs. Further research is needed in order to find more robust
estimation techniques using SOMs, but the results herein are a positive
indication of their capabilities when compared with other well-known methods.Comment: 5 pages, 3 figures, submitted to PAS
Artificial Neural Network based on SQUIDs: demonstration of network training and operation
We propose a scheme for the realization of artificial neural networks based
on Superconducting Quantum Interference Devices (SQUIDs). In order to
demonstrate the operation of this scheme we designed and successfully tested a
small network that implements a XOR gate and is trained by means of examples.
The proposed scheme can be particularly convenient as support for
superconducting applications such as detectors for astrophysics, high energy
experiments, medicine imaging and so on.Comment: 10 pages, 6 figure
Intelligent search for distributed information sources using heterogeneous neural networks
As the number and diversity of distributed information sources on the Internet exponentially increase, various search services are developed to help the users to locate relevant information. But they still exist some drawbacks such as the difficulty of mathematically modeling retrieval process, the lack of adaptivity and the indiscrimination of search. This paper shows how heteroge-neous neural networks can be used in the design of an intelligent distributed in-formation retrieval (DIR) system. In particular, three typical neural network models - Kohoren's SOFM Network, Hopfield Network, and Feed Forward Network with Back Propagation algorithm are introduced to overcome the above drawbacks in current research of DIR by using their unique properties. This preliminary investigation suggests that Neural Networks are useful tools for intelligent search for distributed information sources
Micromagnetic understanding of stochastic resonance driven by spin-transfertorque
In this paper, we employ micromagnetic simulations to study non-adiabatic
stochastic resonance (NASR) excited by spin-transfer torque in a
super-paramagnetic free layer nanomagnet of a nanoscale spin valve. We find
that NASR dynamics involves thermally activated transitions among two static
states and a single dynamic state of the nanomagnet and can be well understood
in the framework of Markov chain rate theory. Our simulations show that a
direct voltage generated by the spin valve at the NASR frequency is at least
one order of magnitude greater than the dc voltage generated off the NASR
frequency. Our computations also reproduce the main experimentally observed
features of NASR such as the resonance frequency, the temperature dependence
and the current bias dependence of the resonance amplitude. We propose a simple
design of a microwave signal detector based on NASR driven by spin transfer
torque.Comment: 25 pages 8 figures, accepted for pubblication on Phys. Rev.
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