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The Latest News, Articles, and Events in Signal Processing
The Audio Engineering Society (AES), the IEEE Consumer Technology Society (CTSoc), and the IEEE Signal Processing Society (SPS) cordially invite you to a first-of-a-kind joint event discussing the state of the art perspectives in this rapidly evolving field.
Shrikanth (Shri) Narayanan is University Professor and Niki & C. L. Max Nikias Chair in Engineering at the University of Southern California, where he is Professor of Electrical & Computer Engineering, Computer Science, Linguistics, Psychology, Neuroscience, Pediatrics, and Otolaryngology-Head & Neck Surgery, Director of the Ming Hsieh Institute and Research Director of the Information Sciences Institute.
IEEE SPS has built a streamlined mechanism for employers to add a job announcement by simply filling in a simple job opportunity submission Web form related to a particular TC field. To submit job announcements for a particular Technical Committee, the submission form can be found by visiting the page below and selecting a particular TC.
The Signal Processing Society (SPS) has 12 Technical Committees that support a broad selection of signal processing-related activities defined by the scope of the Society.
The IEEE Signal Processing Society is excited to announce we are partnering with the IEEE Humanitarian Activities Committee (HAC) to conduct a special Call for Proposals to support projects in SPS chapters that utilize Signal Processing technologies to address local community challenges.
This dissertation develops Defense through AI-powered SYstem-scientific methods (DAISY) for high-confidence Cyber-Physical Systems (CPSs). We start by designating five generations of Security Paradigms (SPs) that have evolved since the birth of the Internet.
Submission Deadline: 1 February 2023
Call for Proposals Document
August 5-6, 2022
Location: Hybrid
Date: June 24, 2022
Time: 12:00 PM ET (New York Time)
Title: Fairness-aware Learning Over Networks
Registration | Full webinar details
Date: May 19, 2022
Time: 10:00 AM ET (New York Time)
Title: Scalable Algorithms for Distributed Principal Component Analysis
Registration | Full webinar details
The Principal Component Analysis (PCA) is considered to be a quintessential data preprocessing tool in many machine learning applications. But the high dimensionality and massive scale of data in several of these applications means the traditional centralized PCA solutions are fast becoming irrelevant for them.
The Principal Component Analysis (PCA) is considered to be a quintessential data preprocessing tool in many machine learning applications. But the high dimensionality and massive scale of data in several of these applications means the traditional centralized PCA solutions are fast becoming irrelevant for them.
Positions: The Gordon Center for Medical Imaging (GCMI) in the Department of Radiology at Massachusetts General Hospital (MGH) and Harvard Medical School (HMS) has multiple openings for highly qualified individuals at the postdoctoral level to work with Prof. Kuang Gong on NIH funded projects related to PET image reconstruction, medical image analysis, and machine learning methodologies.
Recent years have seen great strides being made in R&D of speech and language technologies. As these technologies continue to permeate our daily lives, they need to support diverse users and usage contexts, especially those with inputs that deviate from the mainstream.
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