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The Latest News, Articles, and Events in Signal Processing
Since the establishment of the IEEE BBDITM Signal Processing Society on December 30, 2020, we have successfully organized 54 events. There was a grand inauguration of the society chapter on February 4, 2021. And several other events were organized both offline as well as online by our branch chapter.
Gloria Menegaz is a professor of bioengineering in the Department of Computer Science, University of Verona, Italy where she leads the Neuroimaging Lab. She serves as an associate editor of IEEE Signal Processing Letters and EURASIP Journal on Image and Video Processing, and she was a guest editor of Journal of Display Technology.
Date: 3-8 May 2026
Location: Barcelona, Spain
Conference Paper Submission Deadline: Coming Soon
Date: 9-12 January 2023
Location: Doha, Qatar
Conference Paper Submission Deadline: TBA
Date: November 2-4, 2022
Location: Rennes, France
Date: August 22-25, 2022
Location: Xi’an, China
Date: 14-17 September 2025
Location: Anchorage, AK, USA
Date: 27-30 October 2024
Location: Abu Dhabi, UAE
Date: 16-21 May 2027
Location: Toronto, Canada
Conference Paper Submission Deadline: Coming soon
Website: Coming soon
June 26-29, 2022
Location: Nafplio, Greece
June 20-23, 2022
Location: Trondheim, Norway
July 4-6, 2022
Location: Oulu, Finland
ASSISTANT OR ASSOCIATE PROFESSOR IN SPEECH AND LANGUAGE TECHNOLOGY (tenure track)
We design a data-driven wavelet transform, called the empirical wavelet transform, which permits to extract very accurate time-frequency information from signals, or features from images.
Manuscript Due: August 31, 2022
Publication Date: May 2023
CFP Document
Date: September 13-14, 2022
Location: London, UK
Date: September 11-14, 2022
Location: Lisbon, Portugal
Date: August 29-September 2, 2022
Location: Belgrade, Serbia
March 22-24, 2022
Location: Snowbird, UT, USA
We develop algorithms to analyzing facial expression by learning from the data. Since local characters of muscle movements play an important role in distinguishing facial expression by machines, we explore the local characters of facial expressions by introducing the attention mechanism in both supervised and self-supervised supervised manners. Our methods is experimentally shown to be effective on facial expression recognition with occlusions and facial action unit detection.
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