Machine learning and related statistical signal processing are expected to endow sensor networks with adaptive machine intelligence and greatly facilitate the Internet of Things (IoT). As such, architectures embedding adaptive and learning algorithms on-chip are oft-ignored by system architects and design engineers, and present a new set of design trade-offs.

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.

In patent no. 9,477,869 a mutual-capacitance palm print identification method is provided, a mutual-capacitance palm print identification device and a mutual-capacitance palm print identification touch panel. 

The OU Analytics tool provides IEEE volunteer access to member data. It replaces SAMIEEE which was sunset in May. The OU Analytics User Group was formed to facilitate discussions among users, identify the top issues affecting user productivity, and provide a communication path between users and IEEE staff working to improve OU Analytics. 

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.

A new online portal is available for ordering Membership Development (MD) materials. Volunteers now have the ability to customize the quantities and items included to support in recruiting new members, retaining those whose membership has lapsed, and recovering former members.

5G technology, with its promises of self-driving vehicles and immersive virtual reality, will be a data-hungry generation of wireless communications.

Improving the modeling and processing of nonstationary signals remains an important yet challenging problem. In the past, the most effective approach for processing these signals has been statistical modeling.

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.