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Xu, PingMei (Princeton University), “Understanding and predicting human visual attention” (2016)

Xu, PingMei (Princeton University), “Understanding and predicting human visual attention” (2016) Advisor: Kulkarni, Sanjeev ; Xiao, Jianxiong An understanding of how the human visual system works is essential for many applications in computer vision, computer graphics, computational photography, psychology, sociology, and human-computer-interaction.

Liu, Yang, (Lehigh University), “Reliable and Efficient Transmission of Signals: Coding Design, Beamforming Optimization and Multi-Point Cooperation” (2016)

Liu, Yang, (Lehigh University), “Reliable and Efficient Transmission of Signals: Coding Design, Beamforming Optimization and Multi-Point Cooperation” (2016) Advisor: Li, Jing This dissertation focuses on reliable and efficient signal transmission strategies in classical two-point and also multi-point communication systems.

SPS Job Marketplace

Now IEEE SPS has built a streamlined mechanism for employers to add a job announcement by simply filling in a simple job opportunity submission Web form at the related TC section pages. To submit job announcements for a particular Technical Committee, the submission form can be found by visiting the page below and clicking on a particular TC. A link for the form can be found in the sidebar menu of the TC pages.

Call for Nominations: Regional Directors-at-Large

In accordance with the Bylaws of the IEEE Signal Processing Society, the membership will elect, by direct ballot, THREE Members-at-Large to the Board of Governors (BoG) for three-year terms commencing 1 January 2017 and ending 31 December 2019, as well as ONE Regional Director-at-Large each for the corresponding regions: Regions 1-6 (USA) and Region 8 (Europe, Middle East, Africa) for two-year terms commencing 1 January 2017 and ending 31 December 2018.

Special Issue on Stochastic Simulation and Optimization in Signal Processing

Many modern signal processing (SP) methods rely very strongly on probability and statistics tools to solve problems; for example, they use stochastic models to represent the data observation process and the prior knowledge available and they obtain solutions by performing statistical inference (e.g., using maximum likelihood or Bayesian strategies).