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Andreas Spanias
Researcher at Arizona State University
Publications - 512
Citations - 8918
Andreas Spanias is an academic researcher from Arizona State University. The author has contributed to research in topics: Speech coding & Speech processing. The author has an hindex of 36, co-authored 490 publications receiving 7895 citations. Previous affiliations of Andreas Spanias include Arizona's Public Universities & Intel.
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Max Consensus in Sensor Networks: Non-linear Bounded Transmission and Additive Noise
TL;DR: In this article, a distributed consensus algorithm for estimating the maximum value of the initial measurements in a sensor network with communication noise is proposed, where a design parameter controls the trade-off between the soft-max error and convergence speed.
Proceedings ArticleDOI
Distributed detection over fading macs with multiple antennas at the fusion center
TL;DR: A distributed detection problem over fading multiple-access channels over Ricean fading, where sensors observe a phenomenon and transmit their observations to a fusion center using the amplify-and-forward scheme.
Proceedings ArticleDOI
A cyber-physical system approach for photovoltaic array monitoring and control
Sunil Rao,Sameeksha Katoch,Pavan Turaga,Andreas Spanias,Cihan Tepedelenlioglu,Raja Ayyanar,Henry Braun,Jongmin Lee,Uday Shankar Shanthamallu,Mahesh K. Banavar,Devarajan Srinivasan +10 more
TL;DR: A machine learning and computer vision framework is proposed for improving the reliability of utility scale PV arrays by leveraging video analysis of local skyline imagery, customized machine learning methods for fault detection, and monitoring devices that sense data and actuate at each individual panel.
Journal ArticleDOI
Learning Stable Multilevel Dictionaries for Sparse Representations
TL;DR: In this paper, the authors proposed an algorithm to learn dictionaries for sparse representations from large scale data, and prove that the proposed learning algorithm is stable and generalizable asymptotically.
Journal ArticleDOI
Adaptive eigen-projection beamforming algorithms for 1D and 2D antenna arrays
TL;DR: In this paper, one-dimensional (1D) and two-dimensional adaptive algorithms that use gradient projections in selected subspaces for use in antenna beamforming were developed. And the 2D algorithm is new and is based on a stacked configuration of the enhanced 1D algorithm.