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

Initiatives & Trends

How fast is online learning evolving? Are wind turbines a promising investment? And how long before a cheap hoverboard makes it to market? Attempting to answer such questions requires knowing something about the rate at which a technology is improving. Now engineers at MIT have devised a formula for estimating how fast a technology is advancing, based on information gleaned from relevant patents.

In our "What should we learn from... " series we report on a special issue from IEEE Potentials in March/April 2015. It gave some interesting comments on Biomimicry or Bioinspiration covering fields such as sensors networks, vision systems, health care and robot design which we believe are very relevant to the signal processing community.

Topological data analysis (TDA) is an emerging multi-discipline area of research spanning topology, statistics, machine learning, signal processing and computation geometry. In our video of the month, Gunnar Carlsson, Professor in the Department of Mathematics at Stanford University and co-founder of Ayasdi, offers a nice and concise introduction to TDA.

For our May 2015 issue, we cover recent patents granted in the area of visual gait recognition. The section below covers patents granted recently for gait recognition technologies in computer generated reality, visual behavior recognition, access management and motion classification.

For our April 2015 issue, we cover recent patents granted in the area of wavelet analysis. The section below covers patents granted recently for seismic data analysis, image stream compression, audio identification, continuous wavelet estimation, multidimensional data analysis, motion tracking, dispersion measurement, time domain network analysis, and video fingerprinting.

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.

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.

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.

Deep learning is becoming an important AI paradigm for pattern recognition, image/video processing and fraud detection applications in finance. The computational complexity of a deep learning network dictates need for a distributed realization. The big data lab at Impetus, led by Dr. Vijay Srinivas Agneeswaran, has built the first prototype of the distributed deep learning network over Apache Spark. They have parallelized the training phase of the network and consequently reduced training time.

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