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In this paper, we propose spatial filters for a linear regression model, which are based on the minimum-variance pseudo-unbiased reduced-rank estimation (MV-PURE) framework. As a sample application, we consider the problem of reconstruction of brain activity from electroencephalographic (EEG) or magnetoencephalographic (MEG) measurements.
This paper addresses the problem of joint downlink channel estimation and user grouping in massive multiple-input multiple-output (MIMO) systems, where the motivation comes from the fact that the channel estimation performance can be improved if we exploit additional common sparsity among nearby users. In the literature, a commonly used group sparsity model assumes that users in each group share a uniform sparsity pattern.
Multiple-input multiple-output (MIMO) radar is known for its superiority over conventional radar due to its antenna and waveform diversity. Although higher angular resolution, improved parameter identifiability, and better target detection are achieved, the hardware costs (due to multiple transmitters and multiple receivers) and high-energy consumption (multiple pulses) limit the usage of MIMO radars in large scale networks.
The problem of quickest detection of a change in distribution is considered under the assumption that the pre-change distribution is known, and the post-change distribution is only known to belong to a family of distributions distinguishable from a discretized version of the pre-change distribution.
September 18-21, 2019
Location: Taipei, Taiwan
October 13-16, 2019
Location: Pittsburgh, PA, USA
White Paper Due: April 8, 2019
Publication Date: May 2020
CFP Document
Lecture Date: February 4, 2019
Chapter: Greece
Chapter Chair: Athanasios Rontogiannis
Topic: Graph Signal Processing
Lecture Date: February 1, 2019
Chapter: Greece
Chapter Chair: Athanasios Rontogiannis
Topic: Graph Signal Processing
Lecture Date: January 31, 2019
Chapter: Greece
Chapter Chair: Athanasios Rontogiannis
Topic: Graph Signal Processing
Lecture Date: February 19, 2019
Chapter: Princeton/Central Jersey
Chapter Chair: Donald G. 'Jerry' Bellott
Topic: Computational Imaging with Few Photons, Electrons, or Ions
Lecture Date: February 28, 2019
Chapter: Hong Kong
Chapter Chair: Yik-Chung Wu
Topic: Tackling the Cocktail Party Problem for Hearing Devices