11,155 research outputs found
Incoherent multi-gap optical solitons in nonlinear photonic lattices
We demonstrate numerically that partially incoherent light can be trapped in
the spectral band gaps of a photonic lattice, creating partially incoherent
multi-component spatial optical solitons in a self-defocusing nonlinear
periodic medium. We find numerically such incoherent multi-gap optical solitons
and discuss how to generate them in experiment by interfering incoherent light
beams at the input of a nonlinear periodic medium.Comment: 9 pages, 5 figure
Impact of Spatial Filtering on Distortion from Low-Noise Amplifiers in Massive MIMO Base Stations
In massive MIMO base stations, power consumption and cost of the low-noise
amplifiers (LNAs) can be substantial because of the many antennas. We
investigate the feasibility of inexpensive, power efficient LNAs, which
inherently are less linear. A polynomial model is used to characterize the
nonlinear LNAs and to derive the second-order statistics and spatial
correlation of the distortion. We show that, with spatial matched filtering
(maximum-ratio combining) at the receiver, some distortion terms combine
coherently, and that the SINR of the symbol estimates therefore is limited by
the linearity of the LNAs. Furthermore, it is studied how the power from a
blocker in the adjacent frequency band leaks into the main band and creates
distortion. The distortion term that scales cubically with the power received
from the blocker has a spatial correlation that can be filtered out by spatial
processing and only the coherent term that scales quadratically with the power
remains. When the blocker is in free-space line-of-sight and the LNAs are
identical, this quadratic term has the same spatial direction as the desired
signal, and hence cannot be removed by linear receiver processing
Cross-Correlation in the Auditory Coincidence Detectors of Owls
Interaural time difference (ITD) plays a central role in many auditory functions, most importantly in sound localization. The classic model for how ITD is computed was put forth by Jeffress (1948). One of the predictions of the Jeffress model is that the neurons that compute ITD should behave as cross-correlators. Whereas cross-correlation-like properties of the ITD-computing neurons have been reported, attempts to show that the shape of the ITD response function is determined by the spectral tuning of the neuron, a core prediction of cross-correlation, have been unsuccessful. Using reverse correlation analysis, we demonstrate in the barn owl that the relationship between the spectral tuning and the ITD response of the ITD-computing neurons is that predicted by cross-correlation. Moreover, we show that a model of coincidence detector responses derived from responses to binaurally uncorrelated noise is consistent with binaural interaction based on cross-correlation. These results are thus consistent with one of the key tenets of the Jeffress model. Our work sets forth both the methodology to answer whether cross-correlation describes coincidence detector responses and a demonstration that in the barn owl, the result is that expected by theory
Memory in reservoirs for high dimensional input
Reservoir Computing (RC) is a recently introduced scheme to employ recurrent neural networks while circumventing the difficulties that typically appear when training the recurrent weights. The ‘reservoir’ is a fixed randomly initiated recurrent network which receives input via a random mapping. Only an instantaneous linear mapping from the network to the output is trained which can be done with linear regression. In this paper we study dynamical properties of reservoirs receiving a high number of inputs. More specifically, we investigate how the internal state of the network retains fading memory of its input signal. Memory properties for random recurrent networks have been thoroughly examined in past research, but only for one-dimensional input. Here we take into account statistics which will typically occur in high dimensional signals. We find useful empirical data which expresses how memory in recurrent networks is distributed over the individual principal components of the input
High-Performance FPGA Implementation of Equivariant Adaptive Separation via Independence Algorithm for Independent Component Analysis
Independent Component Analysis (ICA) is a dimensionality reduction technique
that can boost efficiency of machine learning models that deal with probability
density functions, e.g. Bayesian neural networks. Algorithms that implement
adaptive ICA converge slower than their nonadaptive counterparts, however, they
are capable of tracking changes in underlying distributions of input features.
This intrinsically slow convergence of adaptive methods combined with existing
hardware implementations that operate at very low clock frequencies necessitate
fundamental improvements in both algorithm and hardware design. This paper
presents an algorithm that allows efficient hardware implementation of ICA.
Compared to previous work, our FPGA implementation of adaptive ICA improves
clock frequency by at least one order of magnitude and throughput by at least
two orders of magnitude. Our proposed algorithm is not limited to ICA and can
be used in various machine learning problems that use stochastic gradient
descent optimization
Magnitude and Sign Correlations in Heartbeat Fluctuations
We propose an approach for analyzing signals with long-range correlations by
decomposing the signal increment series into magnitude and sign series and
analyzing their scaling properties. We show that signals with identical
long-range correlations can exhibit different time organization for the
magnitude and sign. We find that the magnitude series relates to the nonlinear
properties of the original time series, while the sign series relates to the
linear properties. We apply our approach to the heartbeat interval series and
find that the magnitude series is long-range correlated, while the sign series
is anticorrelated and that both magnitude and sign series may have clinical
applications.Comment: 4 pages,late
Frequency Precision of Oscillators Based on High-Q Resonators
We present a method for analyzing the phase noise of oscillators based on
feedback driven high quality factor resonators. Our approach is to derive the
phase drift of the oscillator by projecting the stochastic oscillator dynamics
onto a slow time scale corresponding physically to the long relaxation time of
the resonator. We derive general expressions for the phase drift generated by
noise sources in the electronic feedback loop of the oscillator. These are
mixed with the signal through the nonlinear amplifier, which makes them
{cyclostationary}. We also consider noise sources acting directly on the
resonator. The expressions allow us to investigate reducing the oscillator
phase noise thereby improving the frequency precision using resonator
nonlinearity by tuning to special operating points. We illustrate the approach
giving explicit results for a phenomenological amplifier model. We also propose
a scheme for measuring the slow feedback noise generated by the feedback
components in an open-loop driven configuration in experiment or using circuit
simulators, which enables the calculation of the closed-loop oscillator phase
noise in practical systems
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