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Representative: Osvaldo Simeone (12/31/26)
Section: Education & Training
The following is a list of Signal Processing Society's distinguished industry speakers.
We consider a cell-free massive multiple-input multiple-output (CF-MaMIMO) communication system in the uplink, which is particularly prone to the detrimental effects of pilot contamination (PC), a phenomenon that occurs when pilot sequences assigned to user equipments (UEs) are not mutually orthogonal, eventually due to their reuse. To combat PC, the presenters propose a novel algorithm for blind or semi-blind joint channel estimation and data detection (JCD).
Deep learning solutions to detect such frauds have been presented in the literature oriented to information security and forensics. However, due to privacy concerns and the sensitive nature of personal identity documents, developing a dataset with the necessary number of examples for training deep neural networks is challenging. This talk explores different methods for synthetically generating ID card images to increase the amount of data while training fraud-detection networks.
AI keeps getting bigger—but does bigger always mean better? As Large Language Models (LLMs) take center stage, their enormous resource demands raise pressing concerns about sustainability and efficiency. In this webinar, the presenter questioned the assumption that AI must be massive to be powerful. They introduced the Expressive Neural Network (ENN), a novel architecture that reimagines activation functions through the lens of classical signal processing—specifically, the Discrete Cosine Transform (DCT).
Modern-data provide us various challenges. They are multivariate where different variables relate to each other in an irregular way. Modern-data is also non-stationary, examples including wireless signals over time-varying channels, social networks data, brain signals, etc. Graph Signal Processing methods have gained important success over the last decade for multivariate data analysis.
Self-supervised Coordinate Projection Networks (SCOPE) represent an emerging paradigm for solving highly underdetermined problems in tomographic image reconstruction. In this presentation, the presenter takes Sparse-View Computed Tomography (SVCT) as a representative application to introduce the core methodology and innovations behind this framework. SCOPE leverages implicit neural representations to model continuous imaging signals from sparse, discrete measurements, effectively acting as an implicit regularization prior for the ill-posed inverse problem.
This webinar will present the presenters work on end-to-end learned video compression, with a focus on temporal context mining. Their approach uniquely propagates both reconstructed frames and intermediate features to extract multi-scale temporal contexts, which are then effectively reused across the compression pipeline—including the contextual encoder-decoder, frame generator, and temporal context encoder. By replacing the computationally heavy auto-regressive entropy model, our codec achieves practical encoding/decoding speeds while delivering superior compression performance.
