The Experts below are selected from a list of 66897 Experts worldwide ranked by ideXlab platform
Walter Kellermann - One of the best experts on this subject based on the ideXlab platform.
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design of robust two dimensional polynomial beamformers as a Convex Optimization Problem with application to robot audition
Workshop on Applications of Signal Processing to Audio and Acoustics, 2017Co-Authors: Hendrik Barfuss, Markus Bachmann, Michael Buerger, Martin Schneider, Walter KellermannAbstract:We propose a robust two-dimensional polynomial beamformer design method, formulated as a Convex Optimization Problem, which allows for flexible steering of a previously proposed data-independent robust beamformer in both azimuth and elevation direction. As an exemplary application, the proposed two-dimensional polynomial beamformer design is applied to a twelve-element microphone array, integrated into the head of a humanoid robot. To account for the effects of the robot's head on the sound field, measured head-related transfer functions are integrated into the Optimization Problem as steering vectors. The two-dimensional polynomial beamformer design is evaluated using signal-independent and signal-dependent measures. The results confirm that the proposed polynomial beamformer design approximates the original fixed beamformer design very accurately, which makes it an attractive approach for robust real-time data-independent beamforming.
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design of robust superdirective beamformers as a Convex Optimization Problem
International Conference on Acoustics Speech and Signal Processing, 2009Co-Authors: Edwin Mabande, Adrian Schad, Walter KellermannAbstract:Broadband data-independent beamforming designs aiming at constant beamwidth often lead to superdirective beamformers for low frequencies, if the sensor spacing is small relative to the wavelengths. Superdirective beamformers are extremely sensitive to spatially white noise and to small errors in the array characteristics. These errors are nearly uncorrelated from sensor to sensor and affect the beamformer in a manner similar to spatially white noise. Hence the White Noise Gain (WNG) is a commonly used measure for the robustness of beamformer designs. In this paper, we present a method which incorporates a constraint for the WNG into a least-squares beamformer design and still leads to a Convex Optimization Problem that can be solved directly, e.g. by Sequential Quadratic Programming. The effectiveness of this method is demonstrated by design examples.
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ICASSP - Design of robust superdirective beamformers as a Convex Optimization Problem
2009 IEEE International Conference on Acoustics Speech and Signal Processing, 2009Co-Authors: Edwin Mabande, Adrian Schad, Walter KellermannAbstract:Broadband data-independent beamforming designs aiming at constant beamwidth often lead to superdirective beamformers for low frequencies, if the sensor spacing is small relative to the wavelengths. Superdirective beamformers are extremely sensitive to spatially white noise and to small errors in the array characteristics. These errors are nearly uncorrelated from sensor to sensor and affect the beamformer in a manner similar to spatially white noise. Hence the White Noise Gain (WNG) is a commonly used measure for the robustness of beamformer designs. In this paper, we present a method which incorporates a constraint for the WNG into a least-squares beamformer design and still leads to a Convex Optimization Problem that can be solved directly, e.g. by Sequential Quadratic Programming. The effectiveness of this method is demonstrated by design examples.
Boris Polyak - One of the best experts on this subject based on the ideXlab platform.
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CDC - Robust eigenvector of a stochastic matrix with application to PageRank
2012 IEEE 51st IEEE Conference on Decision and Control (CDC), 2012Co-Authors: Anatoli Juditsky, Boris PolyakAbstract:We discuss a definition of robust dominant eigenvector of a family of stochastic matrices. Our focus is on application to ranking Problems, where the proposed approach can be seen as a robust alternative to the standard PageRank technique. The robust eigenvector computation is reduced to a Convex Optimization Problem. We also propose a simple algorithm for robust eigenvector approximation which can be viewed as a regularized power method with a special stopping rule.
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Robust eigenvector of a stochastic matrix with application to PageRank
2012 IEEE 51st IEEE Conference on Decision and Control (CDC), 2012Co-Authors: Anatoli Juditsky, Boris PolyakAbstract:We discuss a definition of robust dominant eigenvector of a family of stochastic matrices. Our focus is on application to ranking Problems, where the proposed approach can be seen as a robust alternative to the standard PageRank technique. The robust eigenvector computation is reduced to a Convex Optimization Problem. We also propose a simple algorithm for robust eigenvector approximation which can be viewed as a regularized power method with a special stopping rule.
Edwin Mabande - One of the best experts on this subject based on the ideXlab platform.
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design of robust superdirective beamformers as a Convex Optimization Problem
International Conference on Acoustics Speech and Signal Processing, 2009Co-Authors: Edwin Mabande, Adrian Schad, Walter KellermannAbstract:Broadband data-independent beamforming designs aiming at constant beamwidth often lead to superdirective beamformers for low frequencies, if the sensor spacing is small relative to the wavelengths. Superdirective beamformers are extremely sensitive to spatially white noise and to small errors in the array characteristics. These errors are nearly uncorrelated from sensor to sensor and affect the beamformer in a manner similar to spatially white noise. Hence the White Noise Gain (WNG) is a commonly used measure for the robustness of beamformer designs. In this paper, we present a method which incorporates a constraint for the WNG into a least-squares beamformer design and still leads to a Convex Optimization Problem that can be solved directly, e.g. by Sequential Quadratic Programming. The effectiveness of this method is demonstrated by design examples.
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ICASSP - Design of robust superdirective beamformers as a Convex Optimization Problem
2009 IEEE International Conference on Acoustics Speech and Signal Processing, 2009Co-Authors: Edwin Mabande, Adrian Schad, Walter KellermannAbstract:Broadband data-independent beamforming designs aiming at constant beamwidth often lead to superdirective beamformers for low frequencies, if the sensor spacing is small relative to the wavelengths. Superdirective beamformers are extremely sensitive to spatially white noise and to small errors in the array characteristics. These errors are nearly uncorrelated from sensor to sensor and affect the beamformer in a manner similar to spatially white noise. Hence the White Noise Gain (WNG) is a commonly used measure for the robustness of beamformer designs. In this paper, we present a method which incorporates a constraint for the WNG into a least-squares beamformer design and still leads to a Convex Optimization Problem that can be solved directly, e.g. by Sequential Quadratic Programming. The effectiveness of this method is demonstrated by design examples.
Anatoli Juditsky - One of the best experts on this subject based on the ideXlab platform.
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CDC - Robust eigenvector of a stochastic matrix with application to PageRank
2012 IEEE 51st IEEE Conference on Decision and Control (CDC), 2012Co-Authors: Anatoli Juditsky, Boris PolyakAbstract:We discuss a definition of robust dominant eigenvector of a family of stochastic matrices. Our focus is on application to ranking Problems, where the proposed approach can be seen as a robust alternative to the standard PageRank technique. The robust eigenvector computation is reduced to a Convex Optimization Problem. We also propose a simple algorithm for robust eigenvector approximation which can be viewed as a regularized power method with a special stopping rule.
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Robust eigenvector of a stochastic matrix with application to PageRank
2012 IEEE 51st IEEE Conference on Decision and Control (CDC), 2012Co-Authors: Anatoli Juditsky, Boris PolyakAbstract:We discuss a definition of robust dominant eigenvector of a family of stochastic matrices. Our focus is on application to ranking Problems, where the proposed approach can be seen as a robust alternative to the standard PageRank technique. The robust eigenvector computation is reduced to a Convex Optimization Problem. We also propose a simple algorithm for robust eigenvector approximation which can be viewed as a regularized power method with a special stopping rule.
Shlomo Shamai Shitz - One of the best experts on this subject based on the ideXlab platform.
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on the sum rate capacity of poisson miso multiple access channels
IEEE Transactions on Information Theory, 2017Co-Authors: Yingbin Liang, Shlomo Shamai ShitzAbstract:In this paper, we analyze the sum-rate capacity of two-user Poisson multiple access channels (MAC), when the receiver is equipped with single antenna. We first characterize the sum-rate capacity of the non-symmetric Poisson MAC when each transmitter has a single antenna. While the sum-rate capacity of the symmetric Poisson MAC with single antenna at each transmitter has been characterized in the literature, the special property exploited in the existing method for the symmetric case does not hold for the non-symmetric channel anymore. We obtain the optimal input that achieves the sum-rate capacity by solving a non-Convex Optimization Problem. We show that, for certain channel parameters, it is optimal for a single user to transmit to achieve the sum-rate capacity. This is in sharp contrast to the Gaussian MAC, in which both users must transmit, either simultaneously or at different times, in order to achieve the sum-rate capacity. We then characterize the sum-rate capacity of the Poisson multiple-input single-output (MISO) MAC with multiple antennas at each transmitter and single antenna at the receiver. By converting a non-Convex Optimization Problem with a large number of variables into a non-Convex Optimization Problem with two variables, we show that the sum-rate capacity of the Poisson MISO MAC with multiple transmit antennas is equivalent to a properly constructed Poisson MAC with a single antenna at each transmitter.
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ICCS - On the sum-rate capacity of poisson MISO multiple access channels
2016 IEEE International Conference on Communication Systems (ICCS), 2016Co-Authors: Ain-ul-aisha, Yingbin Liang, Lifeng Lai, Shlomo Shamai ShitzAbstract:In this paper, we analyze the sum-rate capacity of Poisson multiple access channels (MAC) when each transmitter has multiple antennas. By converting a non-Convex Optimization Problem with a large number of variables into a non-Convex Optimization Problem with 2 variables, we show that the sum-rate capacity of the Poisson MAC with multiple transmit antennas is equivalent to a properly constructed Poisson MAC with single antenna at each transmitter. Therefore, characterizing the sum-rate capacity of the Poisson MAC with multiple transmit antennas is equivalent to characterizing the sum-rate capacity of a Poisson MAC with single antenna.
