The IEEE Signal Processing Society continues to track emerging technical areas. As a result, the Society has decided to create two new SPS Technical Interest Profile (TIP) Code categories in the fields of Data Science and Autonomous Systems to keep pace with the expanding technical disciplines.

The choice of television programs (and arguably their quality) has exponentially expanded since the first TV was introduced. But the basic concept of watching TV hasn’t changed much. We see video images, whether live or recorded, in two dimensions. 

This dissertation focuses on statistical signal processing theory and its applications to radar, complex-valued signal processing and model selection.

In 2017, four of the Society’s Major Awards were renamed after a proposal by the Awards Board. The name changes we officially approved by all parties earlier in 2018 and will be put into effect for the 2018 awards cycle.  The new award titles are noted below. 

Check back on the SPS website to keep up-to-date on upcoming Seasonal Schools! Are you looking to energize signal processing students, early stage researchers, and industry practitioners in your area? Consider hosting a Seasonal School for young engineers near you!

The IEEE Signal Processing Society (SPS) Publications group is pleased to announce the launch of the IEEE SPS Author Survey. This automated survey is being delivered to the corresponding authors when their articles are final and published on IEEE Xplore®. 

In recent years, the complexity of designing embedded signal processing systems for wireless communications has increased significantly based on the need to support increasing levels of operational flexibility and adaptivity, while also supporting increasing data rates and bandwidths.

This webinar will provide an in-depth survey look into 40 years of cross-disciplinary academic and industrial research and development work on radar tracking systems, starting from the former implementations based on the α – β, the Kalman filter, and up to the modern implementations based on random set filters.