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Call for Nominations: Chapter of the Year Award

The IEEE Signal Processing Society Chapter of the Year Award will be presented for the seventh time in 2018. The award will be presented annually to a Chapter that has provided their membership with the highest quality of programs, activities and services. Nominations should be submitted to Theresa Argiropoulos (t.argiropoulos@ieee.org), who will collect the nominations on behalf of Nikos Sidiropoulos, Vice President-Membership. Nominations must be received no later than 15 October 2017.

2017 Member-Driven Initiative

Overview
The Board of Governors of the IEEE Signal Processing Society (SPS) has allocated $20,000 for Member-Driven Initiatives for the 2017 calendar year. The funding is open to all Signal Processing Society members, Chapters and Committees. However, all proposals must include the involvement of the local Chapter to encourage geographic development. Allocation of funds will be competitive.

Access application here.

Zhu, Zhenghan. University of Rhode Island (2017)"Some Contributions to Radar Detection Theory"

Zhu, Zhenghan. University of Rhode Island (2017)"Some Contributions to Radar Detection Theory", advisor: Kay, Steven This dissertation focuses on statistical signal processing theory and its applications to radar, complex-valued signal processing and model selection. The transmit signal critically affects a radar system’s performance. Its design is an important task and is an active research area.

Kaewtip, Kantapon. University of California, Los Angeles(2017)"Robust Automatic Recognition of Birdsongs and Human Speech: a Template-Based Approach"

Kaewtip, Kantapon. University of California, Los Angeles(2017)"Robust Automatic Recognition of Birdsongs and Human Speech: a Template-Based Approach", advisor: Alwan, Abeer This dissertation focuses on robust signal processing algorithms for birdsongs and speech signals. Automatic phrase or syllable detection systems of bird sounds are useful in several applications. However, bird-phrase detection is challenging due to segmentation error, duration variability, limited training data, and background noise.

Hossein Bashashati, University of British Columbia (2017) "A User-Customized Self-Paced Brain Computer Interface"

Hossein Bashashati, University of British Columbia (2017) "A User-Customized Self-Paced Brain Computer Interface", advisor: Gary Birch

Much attention has been directed towards synchronous Brain Computer Interfaces (BCIs). For these BCIs, the user can only operate the system during specific system-defined periods. Self-paced BCIs, however, allow users to operate the system at any time he/she wishes. The classification of Electroencephalography (EEG) signals in self-paced BCIs is extremely challenging.

Tralie, Christopher J.. Duke University(2017) "Geometric Multimedia Time Series"

Tralie, Christopher J.. Duke University(2017) "Geometric Multimedia Time Series", advisor: Saprio, Guillermo Harer, John This thesis provides a new take on problems in multimedia times series analysis by using a shape-based perspective to quantify patterns in time, which is complementary to more traditional analysis-based time series techniques.

Can We Copy the Brain?

In the mid-1940s, a few brilliant people drew up the basic blueprints of the computer age. They conceived a general-purpose machine based on a processing unit made up of specialized subunits and registers, which operated on stored instructions and data. Later inventions—transistors, integrated circuits, solid-state memory—would supercharge this concept into the greatest tool ever created by humankind. So here we are, with machines that can churn through tens of quadrillions of operations per second. We have voice-recognition enabled assistants in our phones and homes.