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

06 Sep

SPS Webinar: 6 September 2022 - IEEE TIFS article on Blockchain-Based Solution

Date: September 6, 2022
Time: 10:00 AM ET (New York Time)
Presenter: Dr. Nguyen Truong
Title: Decentralized Personal Data Management: A Blockchain-Based Solution
Registration | Full webinar details

Recent Advances of Deep Learning within X-ray Security Imaging

By: 
Dr. Samet Akcay

This blog explores modern deep learning applications as well as traditional machine learning techniques for automated X-ray security imaging.

Full Story

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

Pages

SPS on Twitter

  • Join Dr. Peilan Wang and Dr Jun Fang for "Channel State Information Acquisition for Intelligent Reflecting Surface-… https://t.co/jOhyA10xuG
  • The SPS Webinar Series continues on Monday, 10 October when Dr. Luisa Verdoliva presents "Media Forensics and DeepF… https://t.co/aInDvTSQZc
  • DEADLINE EXTENDED: The IEEE Transactions on Multimedia is accepting submissions for a Special Issue on Point Cloud… https://t.co/UqoOXUd8BH
  • Short courses return to ! Register for live and remote sessions, "A Hands-on Approach for Implementing Sto… https://t.co/qMoR6iqp4F
  • Join Dr. Sabyasachi Ghosh on Wednesday, 21 September for a new SPS Webinar, “Tapestry: A Compressed Sensing Approac… https://t.co/MNhu1kBmxG

SPS Videos


Signal Processing in Home Assistants

 


Multimedia Forensics


Careers in Signal Processing             

 


Under the Radar