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Section: Publications & Resources

Publication: IEEE Signal Processing Magazine

Robots are rapidly becoming an integral part of daily life. The mechanizing of routine tasks has been underway for decades, with development making particularly remarkable progress over the past several years. Now, with the development robots that can closely interact with humans, sensing users’ needs and often relieving people of dangerous tasks, robotic technology is entering a new phase of intimacy and practicality.

Section: Events

Lecture Date: March 1, 2022 (Virtual Lecture) Chapter: Tokyo Chapter Chair: Kazuya Takeda Topic: TBA

Section: Events

Lecture Date: April 13, 2022 (Virtual Lecture) Chapter: North New Jersey Chapter Chair: Alfredo Tan Topic: TBA

Section: Publications & Resources

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.

Section: Publications & Resources

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.

Section: Publications & Resources

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. 

Section: Publications & Resources

Publication: 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.

Section: Publications & Resources

Publication: 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.

Section: Publications & Resources

Publication: 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.

Section: Publications & Resources

Publication: 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.