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The Latest News, Articles, and Events in Signal Processing
Manuscript Due: January 15, 2022
Publication Date: 3rd Quarter 2022
CFP Document
The IEEE Signal Processing Society invites nominations for the SPS Distinguished Industry Speaker (DIS) Program and the Distinguished Lecturer (DL) Program. Please note that the Distinguished Industry Speaker positions supplements the Distinguished Lecturer Program. The main difference and goal of the DIS Program is to educate and interact with Society members about topics that are of primary importance to industry and the signal processing community-at-large.
Join us in the second year of PROmotinG DiveRsity in Signal ProcESSing (PROGRESS), which is an initiative of the IEEE Signal Processing Society designed to motivate, provide information and support women and under-represented minorities in pursuing academic careers in signal processing.
Professor Abhay Karandikar, currently the Director of Indian Institute of Technology (IIT) Kanpur, one of the premier technical institutes of India, is an accomplished educator, innovator, and administrator. Prof Karandikar was also a Member (Part-Time) of the Telecom Regulatory Authority of India (TRAI).
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.
In an effort to continue our programs and services during this difficult time, the SPS Awards Board is now accepting nominations for all Society level awards, from paper awards to the major society awards. Nominations are due by 1 September 2021.
Dong Yu is an IEEE Fellow, ISCA Fellow, and an ACM distinguished scientist. He is currently taking the role of a distinguished scientist and vice general manager at Tencent AI Lab. Prior to joining Tencent America in 2017, he was a principal researcher at the speech and dialog research group, Microsoft Research (Redmond), Microsoft, where he joined in 1998.
The IEEE Signal Processing Society has developed guidelines related to SPS Chapter social media and Chapter Activities.
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.
Benefiting from the powerful discriminative feature learning capability of convolutional neural networks (CNNs), deep learning techniques have achieved remarkable performance improvement for the task of salient object detection (SOD) in recent years.
While current research on multimedia is essentially dealing with the information derived from our observations of the world, internal activities inside human brains, such as imaginations and memories of past events etc., could become a brand new concept of multimedia, for which we coin as “brain-media”.
Optimal rank selection is an important issue in tensor decomposition problems, especially for Tensor Train (TT) and Tensor Ring (TR) (also known as Tensor Chain) decompositions. In this paper, a new rank selection method for TR decomposition has been proposed for automatically finding near-optimal TR ranks, which result in a lower storage cost, especially for tensors with inexact TT or TR structures.
The emergence of big data and the multidimensional nature of wireless communication signals present significant opportunities for exploiting the versatility of tensor decompositions in associated data analysis and signal processing. The uniqueness of tensor decompositions, unlike matrix-based methods, can be guaranteed under very mild and natural conditions.
The papers in this special section focus on tensor decomposition for signal processing and machine learning. Tensor decomposition, also called tensor factorization, is useful for representing and analyzing multi-dimensional data. Tensor decompositions have been applied in signal processing applications (speech, acoustics, communications, radar, biomedicine), machine learning (clustering, dimensionality reduction, latent factor models, subspace learning), and well beyond.
We study distributed filtering for a class of uncertain systems over corrupted communication channels. We propose a distributed robust Kalman filter with stochastic gains, through which upper bounds of the conditional mean square estimation errors are calculated online.
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