Search results for "NEURAL NETWORK"
showing 10 items of 1385 documents
Frequency spike encoding using Gabor-like receptive fields
2014
Abstract Spiking Neural Networks (SNN) are a popular field of study. For a proper development of SNN algorithms and applications, special encoding methods are required. Signal encoding is the first step since signals need to be converted into spike trains as the primary input to an SNN. We present an efficient frequency encoding system using receptive fields. The proposed encoding is versatile and it can provide simple image transforms like edge detection, spot detection or removal, or Gabor-like filtering. The proposed encoding can be used in many application areas as image processing and signal processing for detection and classification.
Disordered and Frustrated Spin Systems
2007
A brief review on the effects of quenched disorder on magnetic ordering is given. This disorder can be due to dilution of a ferro- or antiferromagnetic crystal with nonmagnetic atoms, or due to noncrystallinity (amorphous magnetic systems). This disorder in the positions of the magnetic atoms leads to disorder in the exchange interactions between spins. If the disorder is sufficiently weak, the critical temperature of magnetic ordering is somewhat decreased, and the critical behavior may change, but the nature of ordering is maintained. However, if the disorder is sufficiently strong, magnetic long-range order may disappear altogether at a percolation threshold, or a new type of order may a…
The manifestation of dipoles clustering in paraelectric phase of disordered ferroelectrics
2001
Abstract We predict the existence of Griffiths phase in the dielectrics with concentrational crossover between dipole glass (electric analog of spin glass) and ferroelectricity. The peculiar representatives of above substances are KTaO3: Li, Nb, Na or relaxor ferroelectrics like Pb1−xLaxZr0.65Ti0.35O3. Since this phase exists above ferroelectric phase transition temperature (but below that temperature for ordered substance), we call it “para-glass phase”. We assert that the difference between paraelectric and para-glass phase of above substances is the existence of clusters (inherent to “ordinary” Griffiths phase in Ising magnets) of correlated dipoles. We show that randomness play a decisi…
Performance potential for simulating spin models on GPU
2012
Graphics processing units (GPUs) are recently being used to an increasing degree for general computational purposes. This development is motivated by their theoretical peak performance, which significantly exceeds that of broadly available CPUs. For practical purposes, however, it is far from clear how much of this theoretical performance can be realized in actual scientific applications. As is discussed here for the case of studying classical spin models of statistical mechanics by Monte Carlo simulations, only an explicit tailoring of the involved algorithms to the specific architecture under consideration allows to harvest the computational power of GPU systems. A number of examples, ran…
Towards peptide-based tunable multistate memristive materials
2021
Development of new memristive hardware is a technological requirement towards widespread neuromorphic computing. Molecular spintronics seems to be a fertile field for the design and preparation of this hardware. Within molecular spintronics, recent results on metallopeptides demonstrating the interaction between paramagnetic ions and the chirality induced spin selectivity effect hold particular promise for developing fast (ns–μs) operation times. [R. Torres-Cavanillas et al., J. Am. Chem. Soc., 2020, DOI: 10.1021/jacs.0c07531]. Among the challenges in the field, a major highlight is the difficulty in modelling the spin dynamics in these complex systems, but at the same time the use of inexp…
Measurement of the single-top-quark production cross section at CDF.
2008
We report a measurement of the single top quark production cross section in 2.2 ~fb-1 of p-pbar collision data collected by the Collider Detector at Fermilab at sqrt{s}=1.96 TeV. Candidate events are classified as signal-like by three parallel analyses which use likelihood, matrix element, and neural network discriminants. These results are combined in order to improve the sensitivity. We observe a signal consistent with the standard model prediction, but inconsistent with the background-only model by 3.7 standard deviations with a median expected sensitivity of 4.9 standard deviations. We measure a cross section of 2.2 +0.7 -0.6(stat+sys) pb, extract the CKM matrix element value |V_{tb}|=0…
Using neural networks to obtain indirect information about the state variables in an alcoholic fermentation process
2020
This work provides a manual design space exploration regarding the structure, type, and inputs of a multilayer neural network (NN) to obtain indirect information about the state variables in the alcoholic fermentation process. The main benefit of our application is to help experts reduce the time needed for making the relevant measurements and to increase the lifecycles of sensors in bioreactors. The novelty of this research is the flexibility of the developed application, the use of a great number of variables, and the comparative presentation of the results obtained with different NNs (feedback vs. feed-forward) and different learning algorithms (Back-Propagation vs. Levenberg&ndash
Descriptor-type Kalman Filter and TLS EXIN Speed Estimate for Sensorless Control of a Linear Induction Motor.
2014
This paper proposes a speed observer for linear induction motors (LIMs), which is composed of two parts: 1) a linear Kalman filter (KF) for the online estimation of the inductor currents and induced part flux linkage components; and 2) a speed estimator based on the total least squares (TLS) EXIN neuron. The TLS estimator receives as inputs the state variables, estimated by the KF, and provides as output the LIM linear speed, which is fed back to the KF and the control system. The KF is based on the classic space-vector model of the rotating induction machine. The end effects of the LIMs have been considered an uncertainty treated by the KF. The TLS EXIN neuron has been used to compute, in …
Hierarchical Structure in Financial Markets
1998
I find a topological arrangement of stocks traded in a financial market which has associated a meaningful economic taxonomy. The topological space is a graph connecting the stocks of the portfolio analyzed. The graph is obtained starting from the matrix of correlation coefficient computed between all pairs of stocks of the portfolio by considering the synchronous time evolution of the difference of the logarithm of daily stock price. The hierarchical tree of the subdominant ultrametric space associated with the graph provides information useful to investigate the number and nature of the common economic factors affecting the time evolution of logarithm of price of well defined groups of sto…
Temporal and spatial persistence of combustion fronts
2002
The spatial and temporal persistence, or first-return distributions are measured for slow combustion fronts in paper. The stationary temporal and (perhaps less convincingly) spatial persistence exponents agree with the predictions based on the front dynamics, which asymptotically belongs to the Kardar-Parisi-Zhang (KPZ) universality class. The stationary short-range and the transient behavior of the fronts is non-Markovian and the observed persistence properties thus do not agree with the theory. This deviation is a consequence of additional time and length scales, related to the crossovers to the asymptotic coarse-grained behavior.