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Section: Publications & Resources
The IEEE Signal Processing Society (SPS) is happy to announce that Prof. Yonggang Wen will be the new Editor-in-Chief (EIC) of the IEEE Transactions on Multimedia (T-MM). His three-year term will be from January 2023 to December 2025.
Section: Publications & Resources
Over the past twenty years, functional connectivity of the human brain has been studied using tools from complex network theory. One such tool is community detection which is fundamental for uncovering the links between structure and function in complex networks.
Section: Publications & Resources
Thanks to their ability to monitor physical activity and health-related parameters, wearable devices are becoming nowadays more and more popular. In addition to what they already offer, an interesting capability achievable through such devices is biometric recognition.
Section: Events
Date: October 20, 2022
Time: 4:00 PM ET (New York Time)
Title: Wearable Biometrics: Novel Paradigms for Automatic People Recognition
Registration | Full webinar details
Section: Events
Date: October 4, 2022
Time: 4:00 PM (Central European Time (CET))
Title: Multi-Frequency Functional Connectivity Networks of the Brain
Registration | Full webinar details
Section: Events
Honorarium Acknowledgment
By accepting the Honorarium, the Recipient acknowledges and agrees:
IEEE SPS-DSI Webinar: 19 October 2022, by Dr. Panagiotis A. Traganitis and Dr. Georgios B. Giannakis
Section: Publications & Resources
Crowdsourcing has emerged as a powerful paradigm for tackling various machine learning, data mining, and data science tasks, by enlisting inexpensive crowds of human workers, or annotators, to accomplish learning and inference tasks.
Section: Publications & Resources
Publication: IEEE Open Journal of Signal Processing
Reviewer Instructions for OJSP Reproducibility Reviews
Author Instructions For OJSP Reproducibility Review
Reviewer Instructions for OJSP Reproducibility Reviews-->ContextOJSP has initiated an enhanced reproducibility initiative for learning-based papers, i.e., papers where the primary results are obtained using a trained computational model, such as a deep neural network or a dictionary representation.
