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Section: Publications & Resources
This work explores the challenging problems of nonlinear dynamics, nonaffine structures, heterogeneous properties, and deception attack together and proposes a novel distributed model-free adaptive predictive control (DMFAPC) for multiple-input-multiple-output (MIMO) multi-agent systems (MASs). A dynamic linearization method is introduced to address the nonlinear heterogeneous dynamics which is transformed as the unknown parameters in the obtained linear data model.
Section: Publications & Resources
Publication: IEEE Transactions on Signal Processing
This paper proposes an interpretable ensembled seizure detection procedure using electroencephalography (EEG) data, which integrates data driven features and clinical knowledge while being robust against artifacts interference.
Section: Publications & Resources
Publication: IEEE Transactions on Signal Processing
Algorithmic solutions for multi-object tracking (MOT) are a key enabler for applications in autonomous navigation and applied ocean sciences. State-of-the-art MOT methods fully rely on a statistical model and typically use preprocessed sensor data as measurements. In particular, measurements are produced by a detector that extracts potential object locations from the raw sensor data collected at discrete time steps. This preparatory processing step reduces data flow and computational complexity but may result in a loss of information.
Section: Publications & Resources
Publication: IEEE Transactions on Signal Processing
Conventional beamforming methods for intelligent reflecting surfaces (IRSs) or reconfigurable intelligent surfaces (RISs) typically entail the full channel state information (CSI). However, the computational cost of channel acquisition soars exponentially with the number of IRSs. To bypass this difficulty, we propose a novel strategy called blind beamforming that coordinates multiple IRSs by means of statistics without knowing CSI.
Section: Publications & Resources
Publication: IEEE Transactions on Signal Processing
The modeling of time-varying graph signals as stationary time-vertex stochastic processes permits the inference of missing signal values by efficiently employing the correlation patterns of the process across different graph nodes and time instants. In this study, we propose an algorithm for computing graph autoregressive moving average (graph ARMA) processes based on learning the joint time-vertex power spectral density of the process from its incomplete realizations for the task of signal interpolation.
Section: Events
Date: 15-16 July 2024
Location: Niagara Falls, Canada
Section: Events
Date: 18-20 June 2024
Location: Karlshamn, Sweden
Section: Events
Date: 13 February 2024
Chapter: Switzerland Chapter
Chapter Chair: Thomas Mittelholzer
Topic: Digital Twins for Communications: How to create and use them
Section: Events
Date: 5 March 2024
Chapter: Twin Cities Chapter
Chapter Chair: Tao Zhang
Title: Signal Processing and Deep Learning for Practical Active Noise Control
