2012-03-11

A decorrelation of the wave-particle phase is added in order to model stochasticity of the system (e.g. due to collisions between particles). Based on the nonlinear description with added phase decorrelation, a quasilinear version of the model has been developed, where the phase decorrelation has been replaced by a quasilinear diffusion coefficient in particle energy.

A decorrelation transform can potentially bring a significant performance gain by boosting the energy diversity in signal representation. There are many theories on the purpose of neural adaptation, but evidence remains elusive. Here, we discuss the recent work by Benucci et al. (Nat Neurosci 16: 724–729, 2013), who measured for the Measurement of azimuthal decorrelation angle between the leading jet and the scattered lepton in deep inelastic scattering at HERA (first preliminary presentation) I.Pidhurskyi 1, M.Shchedrolosiev , J.Nam2, A.Quintero2, B. Surrow2 1Taras Shevchenko National University of Kyiv, 2Temple University 19 June 2019 1 The efficiency of multiresolution decomposition using mor-phological filters for 3D volume image decorrelation in lossless compression and computational complexity are evaluated. More efficient decorrelation is performed using morphological wavelets for multiresolution data representa-tion than using Laplacian style pyramidal decomposition A decorrelation of the wave-particle phase is added in order to model stochasticity of the system (e.g. due to collisions between particles).

the signal It presents an efficient and flexible algorithm called magic decorrelation which is superior to existing algorithms both in terms of the generality of application, and Mar 29, 2021 In previous work, we introduced an efficient bulk motion phase compensation technique for spectral domain OCTA devices [16]. Spectral domain I have learned in many articles that decorrelating the components of an image ( e.g. the color transform) can improves the coding efficiency. But none explains why.

by LS neurons instead enables them to perform temporal decorrelation 76 . suggest that efficient coding of natural stimuli through temporal decorrelation

Decorrelation noise is generally uncorrelated in space but can be correlated in time. The highly correlated data sets often produce quite bland color images. Decorrelation stretching requires three bands for input. These bands should be stretched The simulation results depicted that, the matrix norm is able to determine the efficiency of estimation and is proportional to the uncertainties of the system.

step, a rigorous and efficient method for transforming heights between different conservative and perhaps even pessimistic estimate of the decorrelation of

Decorrelation efficiency is the metric used to determine transforms that give highly-uncorrelated outputs. Scalar quantizers are applied to transform outputs to extract uniformly distributed bit sequences to which secret keys are bound. A set of transforms that perform well in terms of the Se hela listan på frontiersin.org The decorrelation performance of LAMBDA algorithm, LLL algorithm and Seysen algorithm are analyzed with evaluation indexes, i.e., condition number, orthogonal defect and S(A).

2011-01-01 Basic theories of efficiency (e.g., decorrelation, sparseness, independence, etc.) are likely to be insufficient to account for the more complex nonlinear representations found in higher levels. In this chapter, we take a closer look at how efficiency might be defined. In particular, we consider three forms of efficiency: representational Request PDF | Impact of Decorrelation on Search Efficiency of Ambiguity Resolution | The decorrelation performance of LAMBDA algorithm, LLL algorithm and Seysen algorithm are analyzed with An influential theory of visual processing asserts that retinal center-surround receptive fields remove spatial correlations in the visual world, producing ganglion cell spike trains that are less redundant than the corresponding image pixels. For bright, high-contrast images, this decorrelation would enhance coding efficiency in optic nerve fibers of limited capacity. Audio processing methods may involve receiving audio data corresponding to a plurality of audio channels. The audio data may include a frequency domain representation corresponding to filterbank coefficients of an audio encoding or processing system.
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Another method of increasing the efficiency of the MAFA method is based on the application of the integer decorrelation matrix to transform observation equations into equivalent, but better Learning good representations is a long standing problem in reinforcement learning (RL). One of the conventional ways to achieve this goal in the supervised setting is through regularization of the parameters. Extending some of these ideas to the RL setting has not yielded similar improvements in learning. In this paper, we develop an online regularization framework for decorrelating features Some authors proposed to use Perceptual Evaluation of Speech Quality (PESQ) to assess the efficiency of an algorithm. However, this technique is not a real subjective measure and as suggested by Kitawaki and Yamada (2007), PESQ was verified for evaluating speech distorted by codecs, filtering, variable delay, and short localized distortions.

In this chapter, we take a closer look at how efficiency might be defined. In particular, we consider three forms of efficiency: representational Request PDF | Impact of Decorrelation on Search Efficiency of Ambiguity Resolution | The decorrelation performance of LAMBDA algorithm, LLL algorithm and Seysen algorithm are analyzed with An influential theory of visual processing asserts that retinal center-surround receptive fields remove spatial correlations in the visual world, producing ganglion cell spike trains that are less redundant than the corresponding image pixels. For bright, high-contrast images, this decorrelation would enhance coding efficiency in optic nerve fibers of limited capacity.
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Abstract: The decorrelation performance of LAMBDA algorithm, LLL algorithm and Seysen algorithm are analyzed with evaluation indexes, i.e., condition number, orthogonal defect and S(A).Moreover, relationships between decorrelation performance of the above algorithms and ambiguity search efficiency are evaluated using theoretical and practical validation, respectively.

It plays an important role in the least-squares ambiguity decorrelation adjustment (Lambda) method. 2020-02-26 The efficient coding hypothesis was proposed by Horace Barlow in 1961 as a theoretical model of sensory coding in the brain. Within the brain, neurons communicate with one another by sending electrical impulses referred to as action potentials or spikes.

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A correlation coefficient is a numerical measure of some type of correlation, meaning a statistical relationship between two variables. The variables may be two columns of a given data set of observations, often called a sample, or two components of a multivariate random variable with a known distribution.

A purely quasilinear Monte Carlo model, which is typically av E Tholerus · 2016 — A relatively weak phase decorrelation also diminishes frequency Another model, describing the efficiency of fast wave current drive, has However, the multiplexing efficiency drops by only 2.4 dB, due to the de-correlation effect of the hand partly compensating the loss in total efficiency. Mer Multiplexing efficiency of MIMO antennas with user effects. This page in English.

Decorrelation is a general term for any process that is used to reduce autocorrelation within a signal, or cross-correlation within a set of signals, while preserving other aspects of the signal. A frequently used method of decorrelation is the use of a matched linear filter to reduce the autocorrelation of a signal as far as possible. Since the minimum possible autocorrelation for a given

One of the conventional ways to achieve this goal in the supervised setting is through regularization of the parameters. Extending some of these ideas to the RL setting has not yielded similar improvements in learning. In this paper, we develop an online regularization framework for decorrelating features Some authors proposed to use Perceptual Evaluation of Speech Quality (PESQ) to assess the efficiency of an algorithm. However, this technique is not a real subjective measure and as suggested by Kitawaki and Yamada (2007), PESQ was verified for evaluating speech distorted by codecs, filtering, variable delay, and short localized distortions. Basic theories of efficiency (e.g., decorrelation, sparseness, independence, etc.) are likely to be insufficient to account for the more complex nonlinear representations found in higher levels. In this chapter, we take a closer look at how efficiency might be defined.

2012; Sangouard et al.