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Section: Events
Date: 26 July 2023
Time: 11:00 AM ET (New York Time)
Speaker(s): Prof. Davide Dardari
Section: Publications & Resources
Publication: IEEE Journal of Selected Topics in Signal Processing
Last chance to submit to the Special Issue on Signal Processing for Digital Twin in 6G Multi-Tier Computing Systems. Submission Deadline: 15 August 2023.
Section: Publications & Resources
Publication: IEEE Journal of Selected Topics in Signal Processing
Last chance to submit to the Special Issue on Intelligent Robotics: Sensing, Signal Processing and Interaction. Submission Deadline: 15 September 2023.
Section: Events
Date: 7 December 2023
Time: 10:00 AM ET (New York Time)
Speaker(s): Dr. Mattia Brambilla
Section: Events
Date: 27 September 2023
Time: 10:30 AM ET (New York Time)
Speaker(s): Dr. Nhien-An Le-Khac, Dr. Aditya Kuppa
Section: Publications & Resources
Pierre Comon is currently Emeritus Research Director with CNRS. He received the Doctorate degree in 1985 from the University of Grenoble, and the Habilitation to Lead Researches in 1995, from the University of Nice, France.
A fully funded PhD position in signal processing and machine learning is available in the DASN group at the School of Electrical and Electroinic Engineering, Nanyang Technological University, Singapore.
Candidates must fulfil all admission requirements of the PhD programme:
Section: Publications & Resources
In many specific scenarios, accurateand practical cooperative learning is a commonly encountered challenge in multi-agent systems. Thus, the current investigation focuses on cooperative learning algorithms for multi-agent systems and underpins an alternate data-based neural network reinforcement learning framework. To achieve the data-based learning optimization, the proposed cooperative learning framework, which comprises two layers, introduces a virtual learning objective.
Section: Publications & Resources
We introduce graph wedgelets - a tool for data compression on graphs based on the representation of signals by piecewise constant functions on adaptively generated binary graph partitionings. The adaptivity of the partitionings, a key ingredient to obtain sparse representations of a graph signal, is realized in terms of recursive wedge splits adapted to the signal. For this, we transfer adaptive partitioning and compression techniques known for 2D images to general graph structures and develop discrete variants of continuous wedgelets and binary space partitionings.
Section: Publications & Resources
The smoothness of graph signals has found desirable real applications for processing irregular (graph-based) signals. When the latent sources of the mixtures provided to us as observations are smooth graph signals, it is more efficient to use graph signal smoothness terms along with the classic independence criteria in Blind Source Separation (BSS) approaches. In the case of underlying graphs being known, Graph Signal Processing (GSP) provides valuable tools; however, in many real applications, these graphs can not be well-defined a priori and need to be learned from data.
