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Section: Publications & Resources
Thanks in large part to all IEEE societies, IEEE DataPort currently has over 8.5 million global users and over 6000 datasets. IEEE DataPort is a global research data platform available to individual researchers and institutions enabling them to perform critical data management functions including storing valuable research datasets and accessing research datasets that can be used in research.
Section: Publications & Resources
With the IEEE Annual Election and several Society elections underway, a friendly reminder to review and adhere to the IEEE election campaign policies which apply to all IEEE elections.
Section: Publications & Resources
Publication: IEEE Signal Processing Magazine
White Paper Due: 1 November 2024
Publication: December 2025
Section: Publications & Resources
Representation learning considering high-order relationships in data has recently shown to be advantageous in many applications. The construction of a meaningful hypergraph plays a crucial role in the success of hypergraph-based representation learning methods, which is particularly useful in hypergraph neural networks and hypergraph signal processing.
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.
