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Section: Events
Date: 16 December 2024
Time: 10:00 AM ET (New York Time)
Speaker(s): Dr. Gonzalo Arce
Tenure-Track Faculty Position in Signal Processing
Section: Events
Date: 6 December 2024
Time: 1:00 PM ET (New York Time)
Presenter(s): Dr. Alessandro Gozzi
Section: Events
Date: 4 December 2024
Time: 11:00 AM ET (New York time)
Presenter(s): Renjie Liao
Section: Publications & Resources
An occlusion-aware model for gait video processing uses SMPL-based human mesh models and machine learning to achieve superior recognition in challenging surveillance videos.
The Delft Center for Systems and Control (DCSC) is offering one PhD vacancy under the newly funded ‘Digitally modulated radars’ (DiMoRA) project. The project will be carried out in the research group of Dr. N. J. Myers, in collaboration with researchers at the faculty of EEMCS and experts from NXP Semiconductors.
Section: Publications & Resources
The Signal Processing Society (SPS) conducts webinars presented by professionals in the field of signal processing and related technologies on an ongoing basis. Webinars are also hosted periodically by SPS Technical Committees, and other network of communities. Visit the Upcoming Events page to see a list of our upcoming webinars and join us! Visit the SPS BLOG for more related content on a regular basis!
Section: Events
Date: 29 & 31 January 2025
Chapter: Germany Chapter
Chapter Chair: Gerald Enzner
Title: Machine Learning for Acoustic Scene Analysis
Do you want to dive into the exciting field of distributed machine learning with a special focus on privacy/security? This research topic is going to shake up how we understand and apply machine learning, with the aim of creating safer and more private solutions in an increasingly digitized world. Successful applicants will have the opportunity to explore and contribute to groundbreaking research questions.
Section: Publications & Resources
Publication: IEEE Transactions on Signal Processing
In this manuscript, we propose to use a variational autoencoder-based framework for parameterizing a conditional linear minimum mean squared error estimator. The variational autoencoder models the underlying unknown data distribution as conditionally Gaussian, yielding the conditional first and second moments of the estimand, given a noisy observation.
