Machine Learning

You are here

Top Reasons to Join SPS Today!

1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.

Machine Learning

Learning the MMSE Channel Estimator

By: 
David Neumann, Thomas Wiese, Wolfgang Utschick

Accurate channel estimation is a major challenge in the next generation of wireless communication networks. To fully exploit setups with many antennas, estimation errors must be kept small. This can be achieved by exploiting the structure inherent in the channel vectors. For example, line-of-sight paths result in highly correlated channel coefficients.

Full Story

Graph Neural Networks

By: 
Fernando Gama, Antonio G. Marques, Geert Leus, Alejandro Ribeiro

Filtering is the fundamental operation upon which the field of signal processing is built. Loosely speaking, filtering is a mapping between signals, typically used to extract useful information (output signal) from data (input signal). Arguably, the most popular type of filter is the linear and shift-invariant (i.e. independent of the starting point of the signal) filter, which can be computed efficiently by leveraging the convolution operation. 

Full Story

Deep Learning on Graphs: History, Successes, Challenges, and Next Steps

By: 
Michael Bronstein

Deep learning on graphs, also known as Geometric deep learning (GDL) [1], Graph representation learning (GRL), or relational inductive biases, has recently become one of the hottest topics in machine learning. While early works on graph learning go back at least a decade [2], if not two [3], it is undoubtedly the past few years’ progress that has taken these methods from a niche into the spotlight of the Machine Learning (ML) community.

Full Story

Military operations and training present a broad variety of demanding physical tasks which may impact the Warfighter physical performance and health. As it is for anyone who exercises intensely, the possibility of injury is always lurking around the corner.

Wing-Kin (Ken) Ma (The Chinese University of Hong Kong)

Lecture Date: November 7, 2018
Chapter:Tokyo/Fukuoka/Hiroshima/ Nagoya/<br />Sapporo/Shikoku/ Shin-Etsu Joint Chapter
Chapter Chair: Shoji Makino
Topic: Hyperspectral Unmixing: Insights and Beyond

Wing-Kin (Ken) Ma (The Chinese University of Hong Kong)

Lecture Date: June 1 & 7, 2018
Chapter: France 
Chapter Chair: William Puech
Topic: (1) Hyperspectral Unmixing in Remote Sensing: Learn the
Wisdom There and Go Beyond (Machine Learning Included)
(2) MIMO Transceiver Designs and Optimization: Beyond Beamforming and
Perfect Channel Information

Wing-Kin (Ken) Ma (The Chinese University of Hong Kong)

Lecture Date: June 5, 2018
Chapter: Benelux 
Chapter Chair: Francois Horlin
Topic: Hyperspectral Unmixing in Remote Sensing: Learn the
Wisdom There and Go Beyond (Machine Learning Included)

Pages

SPS on Twitter

  • The Brain Space Initiative Talk Series continues on Friday, 29 October when Dr. Selin Aviyente presents "Cross-Freq… https://t.co/Jxgu2soJCc
  • Join the Brain Space Initiative for another virtual mixing event on Wednesday, 27 October! Grab a coffee and meet w… https://t.co/KA3kuPUGw0
  • We're proud to sponsor a new journal, IEEE Transactions on Quantum Engineering, publishing regular, review, and tut… https://t.co/cZskrh9cvX
  • We are now seeking mentors and students for the launch of a new initiative, Mentoring Experiences for Underrepresen… https://t.co/i9SarNyKm9
  • This Wednesday, 13 October, join the Women in Signal Processing Committee for an IEEE Day webinar, "Promoting Diver… https://t.co/HrtVGqpwFx

SPS Videos


Signal Processing in Home Assistants

 


Multimedia Forensics


Careers in Signal Processing             

 


Under the Radar