Agenda

MSc SPS Thesis presentation

Sparse Non-uniform Optical Phased Array Design

Ankush Roy

Extreme precipitation, like floods and landslides, poses major risks to safety and the economy, underscoring the need for sophisticated weather forecasting to predict these events accurately, enhancing readiness and resilience. Nowcasting, which uses real-time atmospheric data to predict short-term weather, is key in addressing this challenge. Traditional nowcasting systems, reliant on extrapolation from rainfall radar observations and constrained by simplistic physical assumptions, often struggle to detect complex, nonlinear weather patterns. This gap has opened the door for deep learning models, which have shown significant promise in improving the accuracy and reliability of short-term weather predictions, making them a focal point of recent research and the basis of this thesis's approach.

 

This thesis introduces a deep generative model designed for the nowcasting of extreme precipitation events up to 3 hours ahead, utilizing a Vector-Quantized Variational Autoencoder (VQ-VAE) to compress radar data into a low-dimensional latent representation, and an Autoregressive Transformer for predicting future radar images. Additionally, a binary classifier works in conjunction with the Autoregressive Transformer to identify extreme versus non-extreme weather events, using these classifications to inform an Extreme Value Loss (EVL) function. This loss function aims to improve the accuracy of predicting extreme weather events by addressing the data imbalance between normal and extreme precipitation occurrences. The proposed model displays comparable performance with the state-of-the-art conventional methods and other deep learning nowcasting models in predicting extreme events.


Signal Processing Seminar

Costas Pelekanakis
TNO (Netherlands Organisation for Applied Scientific Research)

About Costas Pelekanakis

18+ years of experience in conducting theoretical and experimental research on underwater acoustic communications and networks. Co-authored 40+ papers in peer-reviewed international journals and conferences. Represented NATO in underwater acoustic communication standardization activities. Lectured courses and served the international oceanic community as an Associated Editor. IEEE Senior Member.

Additional information ...


MSc SPS Thesis presentation

Sparse Non-uniform Optical Phased Array Design

Kunlei Yu

This thesis addresses the design and optimization of sparse non-uniform optical phased arrays (OPAs) for advanced automotive LiDAR systems. As autonomous driving technologies advance, the demand for high-resolution, reliable, and compact LiDAR systems has become increasingly critical. Traditional uniform OPAs, while effective, face limitations regarding power consumption. This work introduces an innovative approach to designing sparse non-uniform OPAs that achieve desired performance metrics essential for automotive applications, including beamwidth, field of view, and sidelobe levels, while minimizing element count and, consequently, energy consumption.

Through mathematical modelling and simulation, we formulate the problem of sparse OPA design as an optimization problem, leveraging techniques from compressive sensing to identify the most efficient element arrangements. We propose using the sparse array synthesis method to formulate the sparse OPA design problem, utilizing algorithms such as LASSO, thresholding, and iterative reweighted l1-norm minimization to achieve optimal sparse configurations. Our results demonstrate substantial improvements in effectiveness, offering a practical solution to the constraints posed by current LiDAR systems. This thesis contributes to the field by providing a comprehensive framework for the design of sparse non-uniform OPAs, highlighting the trade-offs and benefits of various design strategies. The findings advance our understanding of OPA design principles.


PhD Thesis Defence

Multi-agent exploration under sparsity constraints

Christoph Manss

Additional information ...


Conferences

44th Benelux Symposium on Information Theory and Signal Processing (SITB'24, Delft)


Additional information ...


PhD Thesis Defence

Model-based feature engineering of atrial fibrillation

Hanie Moghaddasi

Additional information ...


Conferences

7th Graph Signal Processing Workshop (GSP 2024)


Following a series of successful workshops since 2016, we are pleased to announce that the 7th Edition of the Graph Signal Processing Workshop will be held June 24-26, 2024 in Delft, The Netherlands (campus TU Delft). The workshop will provide a warm welcome to experts and practitioners from academia and industry in the field of graph signal processing (GSP). The goal of GSP is to generalize classical signal processing and statistical learning tools to signals on graphs (functions defined on a graph).

Additional information ...