SPS Webinars

Many important application domains generate distributed collections of heterogeneous local datasets. These local datasets are related via an intrinsic network structure that arises from domain-specific notions of similarity between local datasets. Networked federated learning aims at learning a tailored local model for each local dataset. 

The use of wireless frequencies above 100 GHz has attracted considerable interest for both massive bandwidth communication links and very high resolution RADAR and sensing. Systems in these frequencies have unique characteristics in terms of device nonlinearities, MIMO architectures and radio propagation that in turn present significant design challenges.

X-ray security screening is widely utilized in aviation and transportation, and its importance has sparked interest in automated screening systems. The goal of this webinar is to explore computerized X-ray security imaging methods by classifying them into traditional machine learning and modern deep learning applications.

In his seminal paper, Dr. Ronald Mahler not only developed the Probability Hypothesis Density (PHD) filter, but also detailed the Random Finite Set (RFS) framework for multi-object systems. These complex dynamical systems, in which the number of objects and their states are unknown and vary randomly with time, have a wide range of applications from surveillance, computer vision, robotics to biomedical research.

Sound field estimation using a microphone array is a fundamental problem in acoustic signal processing, which has a wide variety of applications, such as visualization/auralization of an acoustic field, spatial audio reproduction using a loudspeaker array or headphones, and active noise cancellation in a spatial region.

As a popular signal modeling technique, sparse representation (SR) has achieved great success in image fusion during the last decade. However, due to the patch-based manner adopted in standard SR models, most existing SR-based image fusion methods suffer from two drawbacks, namely, limited ability in detail preservation and high sensitivity to mis-registration, while these two issues are of great concern in image fusion. 

Many problems encountered in sensing and imaging can be formulated as estimating a low-rank object from incomplete, and possibly corrupted, linear measurements; prominent examples include matrix completion and tensor completion. 

The 2022 International Conference on Acoustics, Speech, & Signal Processing (ICASSP) invites proposals for its Signal Processing Grand Challenges (SPGC) program. ICASSP is the IEEE Signal Processing Society’s flagship conference targeting signal processing and its applications.

Adaptive (i.e., data-driven) methods have become very popular these last decades. Among the existing techniques, the empirical mode decomposition has proven to be very efficient in extracting accurate time-frequency information from non-stationary signals.