March 2018

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10 years of news and resources for members of the IEEE Signal Processing Society

March 2018
Please refer to the following webpage for the latest updates on upcoming conferences in Signal Processing. Listing of all conferences

The Signal Processing Society (SPS) has 12 Technical Committees and 3 Special Interest Groups (SIGs) that support a broad selection of signal processing-related activities defined by the scope of the Society. Technical Committees and Special Interest Groups are actively involved in awards, conferences, publications, and educational activities, and provide technical advice to the Society's leadership.

The IEEE Signal Processing Society welcomes proposals from prospective organizers of 2018-2019 winter schools under the S3P (Seasonal Schools in Signal Processing) Program.  S3P seasonal schools are typically either summer schools or winter schools, and are geared towards providing graduate students, early stage researchers, and practitioners with background on selected topics in signal processing. 

The IEEE Workshop on Signal Processing Systems (SiPS) is a premier international forum addressing current and future challenges and new research directions on the design and implementation of signal processing systems.

Rabab Ward
Chair, 2018-2019 SPS Nominations and Appointments Committee

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 for three-year terms commencing 1 January 201​9 and ending 31 December 2021​, as well as TWO Regional Directors-at-Large...

Sensor networks deployed in high-latency environments such as underwater acoustic and satellite channels find critical applications in disaster prevention and tactical surveillance. The sensors in these networks have limited energy reserves.

Researchers from Google, Verily Life Sciences, and Stanford School of Medicine have developed an algorithm to predict cardiovascular risk factors from retinal fundus photographs. Their model was trained from 48,101 patients from the UK Biobank and 236,234 patients from EyePACS using deep learning, and validated on 12,026 patients from the UK Biobank and 999 patients from EyePACS.

Within scientific research, engineering, and consumer electronics, there is a multitude of new discrete sensor-interfaced devices. Maintaining high accuracy in signal quantization while staying within the strict power-budget of these devices is a very challenging problem. Traditional paths to solving this problem include researching more energy-efficient digital topologies as well as digital scaling.

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