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A Radar Systems Test Engineer is required to provide program support to the U.S. Army Armament Research, Development and Engineering Center (ARDEC) Battlespace Applications Branch Precision Radars Team at Picatinny, NJ. Radar Systems Test Engineer is required to provide test support.
Duties:
Section: Events
June 2-5, 2019
Location: Minneapolis, MN, USA
Section: Events
Lecture Date: October 24, 2018
Chapter: Croatia
Chapter Chair: Goran Molnar
Topic: Light field image processing
Section: Events
Publication: IEEE Signal Processing Magazine
White Paper Due: December 1, 2018
Publication Date: January 2020
CFP Document
Section: Publications & Resources
The IEEE Signal Processing Society (SPS) Newsletter is pleased to announce a new twist on an existing feature: how members receive tables of contents (TOCs) alerts for their favorite SPS Publication(s).
Section: Publications & Resources
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.
Section: Publications & Resources
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.
Section: Events
May 15-17, 2019
Location: Tokyo, Japan
The Department of Computer Science at the National University of Singapore is seeking a postdoctoral fellow to work with Dr. Jonathan Scarlett.
Section: Publications & Resources
Publication: IEEE Transactions on Signal Processing
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. This transformation is designed by exploiting the fact that most of the consecutive samples are contained in special subspaces.
