Results 241 to 250 of about 103,779 (270)

A tunable beamformer for robust superdirective beamforming

2016 IEEE International Workshop on Acoustic Signal Enhancement (IWAENC), 2016
Conventional superdirective beamforming is a well-known multi-microphone enhancement method with superior directivity factor (DF). However, it suffers from an inferior white noise gain (WNG), which is expressed by high sensitivity to uncorrelated noise and array inaccuracies.
Israel Cohen   +2 more
openaire   +2 more sources

Beamforming Matrix Transformation for Random Beamforming

2011 IEEE 73rd Vehicular Technology Conference (VTC Spring), 2011
The signal to interference plus noise ratio (SINR) feedback has been utilized in random beamforming (RBF) to select users for the provision of service in multiple-input multiple-output (MIMO) systems. A large number of users are required to obtain the gain of multi-user diversity for a downlink transmission.
Hojae Lee, Sanghoon Lee, Jongrok Park
openaire   +2 more sources

Spherical Fraction Beamforming

IEEE/ACM Transactions on Audio, Speech, and Language Processing, 2020
This paper describes a beamforming method for one-eighth, quarter, and half-spaces, bounded by rigid planes. The proposed approach is based on the spherical fraction harmonic decomposition, similar to that of spherical harmonics decomposition for the whole sphere. The definition of these functions is given in detail.
Lecomte, Pierre   +2 more
openaire   +3 more sources

Compressive beamforming

The Journal of the Acoustical Society of America, 2014
Sound source localization with sensor arrays involves the estimation of the direction-of-arrival (DOA) from a limited number of observations. Compressive sensing (CS) solves such underdetermined problems achieving sparsity, thus improved resolution, and can be solved efficiently with convex optimization.
Angeliki, Xenaki   +2 more
openaire   +2 more sources

The Dynamic Beamformer

2012
Beamforming is one of the most commonly used methods for estimating the active neural sources from the MEG or EEG sensor readings. The basic assumption in beamforming is that the sources are uncorrelated, which allows for estimating each source independent of the others.
Bahramisharif, A.   +4 more
openaire   +3 more sources

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