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The Latest News, Articles, and Events in Signal Processing
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.
Please visit the Conferences and Events page on the IEEE Signal Processing Society website for Upcoming Lectures by Distinguished Lecturers.
The IEEE Signal Processing Society (SPS) would like to express our concern and support for the members of our global community and all affected by the current COVID-19 pandemic.
The DSLW team is inviting you to submit regular papers to the 2021 IEEE Data Science & Learning Workshop (DSLW 2021), a new workshop organized by the IEEE Signal Processing Society. The workshop aims to bring together researchers in academia and industry to share the most recent and exciting advances in data science, learning theory and applications.
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 Signal Processing Society (SPS) has 12 Technical Committee that support a broad selection of signal processing-related activities defined by the scope of the Society.
Dr. Hema A. Murthy received the bachelor's degree from Osmania University, Hyderabad, India, in 1980, the master's degree from McMaster University, Hamilton, Canada, in 1986, and the Ph.D. degree from the Indian Institute of Technology (IIT) Madras, Chennai, India, in 1992.
This issue brings to you our interview with Dr. Hamid Palangi, a Senior Researcher at Microsoft Research Lab (AI) in Redmond, Washington. His current research interests are mainly in the areas of Natural Language Processing and Reasoning across Language and Vision.
Venkat Padmanabhan is Deputy Managing Director at Microsoft Research India in Bengaluru. He was previously with Microsoft Research Redmond, USA for nearly 9 years. Venkat’s research interests are broadly in networked and mobile systems, and his work over the years has led to highly-cited papers and paper awards, technology transfers within Microsoft, and also industry impact. He can be reached online at http://research.microsoft.com/~padmanab/.
December 14, 2020
Location: NOTE: Location changed to--Virtual Conference
May 18-21, 2021
Location: Changed to--Virtual Conference
June 5-6, 2021
Location: Toronto, Canada
Manuscript Due: January 25, 2021
Publication Date: Mid-year 2021
CFP Document
Manuscript Due: February 28, 2021
Publication Date: Early 2022
CFP Document
The DSLW team is inviting you to submit regular papers to the 2021 IEEE Data Science & Learning Workshop (DSLW 2021), a new workshop organized by the IEEE Signal Processing Society. The workshop aims to bring together researchers in academia and industry to share the most recent and exciting advances in data science, learning theory and applications.
Principal component analysis is one of the most commonly used methods for dimensionality reduction in signal processing. However, the most commonly used PCA formulation is based on the
Speech dereverberation has been an important component of effective far-field voice interfaces in many applications. Algorithms based on multichannel linear prediction (MCLP) have been shown to be especially effective for blind speech dereverberation and numerous variants have been introduced in the literature. Most of these approaches can be derived from a common framework, where the MCLP problem for speech dereverberation is formulated as a weighted least squares problem that can be solved analytically.
We study model recovery for data classification, where the training labels are generated from a one-hidden-layer neural network with sigmoid activations, also known as a single-layer feedforward network, and the goal is to recover the weights of the neural network. We consider two network models, the fully-connected network (FCN) and the non-overlapping convolutional neural network (CNN).
A new technique for locating a moving source radiating a wide-band almost-cyclostationary signal is proposed. For this purpose, the signals received on two possibly moving sensors are modeled as jointly spectrally correlated, a new nonstationarity model that allows one to describe the Doppler effect accounting for a time-scale or time-stretch factor in the complex envelopes of the received signals.
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