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Development of new optical transmission methods based on nonlinear Fourier transform.
Lecture Date: November 13, 2018
Chapter: Atlanta
Chapter Chair: Alessio Medda
Topic: Radar Role: From the
Underground to Outer Space
Lecture Date: November 30, 2018
Chapter: Phoenix
Chapter Chair: Suren Jayasuriya
Topic: Enhanced Beamforming via Spatially Controlled Relays
The Department of Computer Science at the National University of Singapore is seeking a postdoctoral fellow to work with Dr. Jonathan Scarlett.
Classical algorithms for the multiple measurement vector (MMV) problem assume either independent columns for the solution matrix or certain models of correlation among the columns. The correlation structure in the previous MMV formulation does not capture the signals well for some applications like photoplethysmography (PPG) signal extraction where the signals are independent and linearly mixed in a certain manner.
The focus of this paper is on detection theory for union of subspaces (UoS). To this end, generalized likelihood ratio tests (GLRTs) are presented for detection of signals conforming to the UoS model and detection of the corresponding “active” subspace. One of the main contributions of this paper is bounds on the performances of these GLRTs in terms of geometry of subspaces under various assumptions on the observation noise.
Recovery of certain piecewise continuous signals from noisy observations has been a major challenge in sciences and engineering. In this paper, in a tight-dimensional representation space, we exploit sparsity hidden in a class of possibly discontinuous signals named finite-dimensional piecewise continuous (FPC) signals. More precisely, we propose a tight-dimensional linear transformation which reveals a certain sparsity in discrete samples of the FPC signals.
Alan Bovik has a storied career. An American engineer, vision scientist and Primetime Emmy Award winner, Bovik holds the Cockrell Family Endowed Regents Chair in Engineering at The University of Texas at Austin, where he has been the Director of the Laboratory for Image and Video Engineering for more than three decades.
Speech recognition technology allows computers to take spoken audio, interpret it and generate text from it. But how do computers understand human speech? The short answer is…the wonder of signal processing.
I have an open position in my group for a fully funded PhD student. The requirements are strong background in Mathematics and Signal Processing. Familiarity with Machine Learning and Communication Theory are preferable.
May 15-17, 2019
Location: Tokyo, Japan