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The Latest News, Articles, and Events in Signal Processing

Date: November 29 - December 2, 2022
Location: Hybrid - Madrid, Spain

Date: September 5-7, 2022
Location: Lippstadt, Germany

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Stellenbosch University

Postdoctoral research position:

Localisation of  the Mozilla Common Voice platform for South African languages

Stellenbosch University, South Africa

University Paris-Saclay

A full professor position is opened in Statistical Signal Processing at University Paris-Saclay and L2S, France.

Teaching

Teaching in signal processing in the Bachelor and Master programs in Electrical Engineering (E3A) of the Faculté des Sciences d’Orsay, and occasionally, in the engineering program of Polytech Paris-Saclay.

Pedagogical goals and needs for training

IEEE Transactions on Signal and Information Processing over Networks

Decentralized stochastic gradient descent (SGD) is a driving engine for decentralized federated learning (DFL). The performance of decentralized SGD is jointly influenced by inter-node communications and local updates. In this paper, we propose a general DFL framework, which implements both multiple local updates and multiple inter-node communications periodically, to strike a balance between communication efficiency and model consensus.

IEEE Transactions on Signal and Information Processing over Networks

Given a sequence of random (directed and weighted) graphs, we address the problem of online monitoring and detection of changes in the underlying data distribution. Our idea is to endow sequential change-point detection (CPD) techniques with a graph representation learning substrate based on the versatile Random Dot Product Graph (RDPG) model. We consider efficient, online updates of a judicious monitoring function, which quantifies the discrepancy between the streaming graph observations and the nominal RDPG.

IEEE Transactions on Signal and Information Processing over Networks

Due to their effectiveness in capturing similarities between different entities, graphical models are widely used to represent datasets that reside on irregular and complex manifolds. Graph signal processing offers support to handle such complex datasets. In this paper, we propose a novel graph filter design method for semi-supervised data classification.

IEEE Transactions on Signal and Information Processing over Networks

In this paper, the joint state and fault estimation problem is investigated for a class of discrete-time complex networks with measurement saturations and stochastic nonlinearities. The difference between the actual measurement and the saturated measurement is regarded as an unknown input and the system is thus re-organized as a singular system. An appropriate estimator is designed for each node which aims to estimate the system states and the loss of the actuator effectiveness simultaneously.

Telecom SudParis

Presentation of the activities and context of the position:

Télécom SudParis is recruiting an Assistant or Associate Professor in Statistical Signal Processing. The strategic objective of recruitment is to meet the demand for training for specialists in the theory and application of signal processing in the fast-growing fields of data science, signal processing, information theory, and digital communications.

University of Lausanne

Machine learning and specifically deep learning techniques are promising tools in medical image analysis and they have demonstrated very good performances in many tasks, such as image segmentation. These techniques are though data demanding and as such they need large-scale cohorts, often multi-centric datasets.

Dr. Sanjit K. Mitra, Distinguished Professor Emeritus of Electrical and Computer Engineering at University of California, Santa Barbara, has been elected recently to the Engineering Academy of Japan (EAJ) as a foreign associate. EAJ is composed of leading experts from academia, industry, and government institutions who possess a wide range of knowledge and have made outstanding contributions in engineering and technological sciences, and closely related fields...

IEEE Open Journal of Signal Processing

The objective of meta-learning is to exploit knowledge obtained from observed tasks to improve adaptation to unseen tasks. Meta-learners are able to generalize better when they are trained with a larger number of observed tasks and with a larger amount of data per task. Given the amount of resources that are needed, it is generally difficult to expect the tasks, their respective data, and the necessary computational capacity to be available at a single central location.

IEEE Open Journal of Signal Processing

The fundamental task of classification given a limited number of training data samples is considered for physicalsystems with known parametric statistical models. The standalone learning-based and statistical model-based classifiers face major challenges towards the fulfillment of the classification task using a small training set. Specifically, classifiers that solely rely on the physics-based statistical models usually suffer from their inability to properly tune the underlying unobservable parameters, which leads to a mismatched representation of the system’s behaviors.

IEEE Open Journal of Signal Processing

We develop a novel 2D functional learning framework that employs a sparsity-promoting regularization based on second-order derivatives. Motivated by the nature of the regularizer, we restrict the search space to the span of piecewise-linear box splines shifted on a 2D lattice. Our formulation of the infinite-dimensional problem on this search space allows us to recast it exactly as a finite-dimensional one that can be solved using standard methods in convex optimization.

IEEE Open Journal of Signal Processing

The paper develops novel algorithms for time-varying (TV) sparse channel estimation in Massive multiple-input, multiple-output (MMIMO) systems. This is achieved by employing a novel reduced (non-uniformly spaced tap) delay-line equalizer, which can be related to low/reduced rank filters. This low rank filter is implemented by deriving an innovative TV (Krylov-space based) Multi-Stage Kalman Filter (MSKF), employing appropriate state estimation techniques.

In this talk, we discuss a new transform technique for solving fractional programming (FP), i.e., a family of optimization problems with ratio terms. The classic FP techniques such as the Charnes-Cooper and Dinkelbach’s methods typically deal with a single ratio, and in general, do not work for multiple ratios. 

The IEEE Signal Processing Society is happy to announce the addition of twenty-three new Chapters formed in 2021. We welcome the new SPS Chapters and wish them great success in their future activities and events.

IEEE Journal of Selected Topics in Signal Processing
Dual-Functional Radar-Communication (DFRC) is a promising paradigm to achieve Integrated Sensing and Communication (ISAC) in beyond 5G. In parallel, Rate-Splitting Multiple Access (RSMA), relying on multi-antenna Rate-Splitting (RS) by splitting messages into common and private streams at the transmitter and Successive Interference Cancellation (SIC) at the receivers, has emerged as a new strategy for multi-user multi-antenna communications systems. I

The IEEE Signal Processing Society is seeking nominations for future leaders! Nominations for several SPS leadership roles are open now, and we are now soliciting for the following roles with terms beginning 1 January 2023.

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