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Given a sequence of frames, the main steps involved in the estimation direction model are (i) computation of optical flow between each two successive frames resulting in a set of motion vectors, (ii) grouping of motion vectors in the corresponding block, and (iii) circular clustering of the motion vector orientation in each block.
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The transmitter obtains AOC by adopting a popular spatial-spectrum estimation direction-finding method [26, 27] or from a database with information concerning the AOC.
The three estimators are capable of decoupling polarization estimation from direction finding.
Unlike the self-initiating MUSIC estimator in [5] decoupling the polarization estimation from direction finding via the spatio-polarizational beamforming, we approach to that by the transformation matrix as given in (24).
All participants completed the testing tasks in the following order: landmark familiarity rating, stop-point ordering, action recall, distance estimation, and direction estimation.
However, their beamspace for each source has to be designed separately to decouple the polarization estimation from direction finding.
The human estimation of direction of arrival can be modeled by two important binaural auditory cues [5]: interaural time difference (ITD) and interaural level difference (ILD).
The focus of this paper is on the use of the binaural cue ILD and estimation of direction of arrival on the horizontal plane.
Estimation of direction of arrival (DOA) from data collected by sensor arrays is of fundamental importance to a variety of applications such as radar, sonar, wireless communications, geophysics and biomedical engineering.
In this contribution, an unbiased, efficient, and robust practical technique is presented for estimation of direction dependent as well as orientation averaged two-point correlation functions in three-dimensional (3D) microstructures from the measurement performed on vertical metallographic planes.
In Table 1, the estimation of direction nonlinearity is tested in a two-dimensional case where the prior has identity covariance matrix and the measurement function is mathbf{y} = e^{mathbf{a}^{T} mathbf{x}}.
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