SPS Feed

Computer scientists at Washington University in St. Louis’ School of Engineering & Applied Science analyzed a huge amount of data regarding an important protein and discovered its connection in human history as well as clues about its role in complex neurological diseases.

With the explosive growth of 5G wireless technology development, signal processing techniques are playing more and more important roles in many aspects. As an industry-oriented technical committee of the IEEE Signal Processing Society, Industry DSP Technology Standing Committee (IDSP-SC) is actively promoting 5G technology from a practice and industry perspective.

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.

For our March 2015 issue, we cover recent patents granted in the area of Deep Lerning techniques. The section below covers patents granted recently for natural language processing, research of neural networks, correlation analysis between data streams, document analysis and medical image enhancement.

Gopalan, Prem K (Princeton University) “Scalable inference of discrete data: User behavior, networks and genetic variation” (2015) Advisor: Blei, David M.

All Section officers and Chapter/Affinity Group chairs have been contacted with a reminder that the following annual reports are required to be eligible for a rebate:

Karl Nieman (The University of Texas at Austin) "Space-Time-Frequency Methods for Interference-Limited Communication Systems", (2014), Advisor: Brian Evans

In this series, we introduce a scientist, who uses signal processing techniques for his research findings, by means of an interview. This month, we are happy to introduce Dr. Jorge Arroyo Palacios, a postdoctoral scholar at Department of Physiological Nursing, University of California, San Francisco, whose research mainly focuses on physiological signal processing.

The PhysioNet/Computing in Cardiology Challenge 2015 is now open! This year's challenge is 'Reducing False Arrhythmia Alarms in the ICU'. False alarms in the ICU can lead to a disruption of care, impacting both the patient and the clinical staff through noise disturbances, desensitization to warnings and slowing of response times, leading to decreased quality of care. False alarm rates can be as high as 90% or more. To address this issue we have developed an expert-labelled data set of 1250 life-threatening arrhythmia alarms with five minutes of electrophysiological and pulsatile waveform data leading up to each alarm event. Competitors are challenged to devise a method of processing all the available data to reduce false alarms with minimal or no effect on true (vital) alarms.