Agenda

Seminar, Prof. Antonio Ríos Navarro

• Monday, 1 September 2025
• 09:30-10:30
• EEMCS, HB 17.140

Designing the Neuromorphic Auditory Sensor (NAS): From Bio-inspiration to FPGA Implementation

In this talk, we will provide a detailed look into the design and implementation of the Neuromorphic Auditory Sensor (NAS). We will explore how this bio-inspired sensor mimics the human cochlea by utilizing a cascading architecture of spiking filters to replicate the function of the basilar membrane. The session will cover the specific challenges and successful strategies for implementing this complex architecture on FPGAs.

Furthermore, we will delve into the accompanying software ecosystem developed to configure the NAS and visualize its real-time output, showcasing the complete workflow from hardware design to data analysis. Finally, we will discuss the exciting evolution of the NAS from its FPGA prototype to its more recent integration into a dedicated ASIC, highlighting the path to more power-efficient and scalable solutions.

Bio

Antonio Ríos-Navarro is an Associate Professor in the Department of Computer Architecture and Technology at the University of Seville, Spain, where he is a member of the Robotics and Technology of Computers Lab. His research focuses on AI hardware and neuromorphic computing, with an emphasis on developing real-time, efficient systems on FPGAs and SoCs for applications such as event-based vision and auditory, autonomous robot navigation, and hardware acceleration for deep learning.

Seminar, Prof. Tobi Delbruck

• Monday, 1 September 2025
• 10:30-11:30
• EEMCS, HB 17.140

Silicon Retina Event Camera Design

Event cameras are vision sensors that mimic biology’s activity-driven digital output. They offer a unique combination of low latency, high dynamic range, and sparse output that makes them attractive candidates for embedded vision systems that face the power-latency tradeoff of conventional frame cameras. After a brief historical introduction to the 50-year history of silicon retina development, this talk will mainly be about event camera pixel design at the transistor level and camera design at the board and software level.
There are many interesting circuit design aspects of event camera pixels which endow them with quick responses even under low lighting, precise threshold matching even with big transistor mismatch, and temperature-independent event threshold. These chips also require critical biasing and readout circuits that I will briefly review, along with a complex stack of logic, firmware, and software that I will also briefly review. I hope to conclude with a live demo of some of the most interesting functional characteristics of these cameras.

Bio

Tobi Delbruck received the B.Sc. degree in physics from the University of California in 1986 and PhD degree from Caltech in 1993 as the first student with the newly-established CNS program, with main PhD supervisor Carver Mead. He is an ETH Professor of Physics and Electrical Engineering, and has a position with the Institute of Neuroinformatics, University of Zurich and ETH Zurich, where he has been since 1998. The Sensors group that he co-directs together with Prof. Shih-Chii Liu works on a broad range of topics covering device physics to computer vision and control, with a theme of efficient neuromorphic sensory processing and deep neural network theory and hardware accelerators.

PhD Thesis Defence

• Thursday, 4 September 2025
• 10:00
• Aula Senaatszaal

Sintering Fundamentals of Nano-Metallic Particle Interconnects

PhD Thesis Defence

• Wednesday, 10 September 2025
• 17:30
• Aula Senaatszaal

Digital twin-based health monitoring of microelectronics

PhD Thesis Defence

• Monday, 15 -- Monday, 15 September 2025
• 10:00-11:30
• Aula Senaatszaal

Homogenization and Characterization of Additive Manufactured Dielectric Crystals for High-Frequency Electromagnetic Applications

Homogenization and Characterization of Additive Manufactured Dielectric Crystals for High-Frequency Electromagnetic Application

Signal Processing Seminar

• Wednesday, 17 September 2025
• 13:30-17:00
• Snijderszaal (LB01.010)

Personalized Auditory Scene Modification to Assist Hearing Impaired People

This symposium is on the occasion of the PhD defense of Changheng Li on 18 September. During the symposium we have external speakers (Zheng-Hua Tan, Sharon Gannot) as well as speakers from within our NWO/TTW project "Personalized Auditory Scene Modification to Assist Hearing Impaired People" (Changheng Li, Giovanni Bologni, Jordi de Vries and Timm Bäumer).

13:30 - 13:45: Walk-in
13:45 - 14:30: Sharon Gannot (Bar-Ilan) - LipVoicer: Generating Speech from Silent Videos Guided by Lip Reading
14:35 - 14:55: Changheng Li (TUDelft)
14:55 - 15:15: Giovanni Bologni (TUDelft) 
Break
15:30 - 16:15: Zheng-Hua Tan (Aalborg University) - Self-supervised learning for speech and audio applications
16:20 - 16:40: Timm Bäumer (Oldenburg University) - Evaluation of an ITD-to-ILD transformation as a method to restore the spatial benefit in speech intelligibility in hearing impaired listeners
16:40 - 17:00: Jordi de Vries (TUDelft)
17: Drinks

PhD Thesis Defence

• Thursday, 18 September 2025
• 09:03-11:30
• Aula Senaatszaal

Multi-Microphone Signal Parameter Estimation in Various Acoustic Scenarios

Many modern devices, such as mobile phones, hearing aids and (hands-free) acoustic humanmachine interfaces are equipped with microphone arrays that can be used for various applications. These applications include source separation, audio quality enhancement, speech intelligibility improvement and source localization. In an ideal anechoic chamber, the signals received by ideal microphones are just attenuated and delayed version of the original sound. However, in practice, obstacles such as the floor, the ceiling and the surrounding walls will reflect the sound to the microphones. Also, the microphone itself will generate noise, distorting the recorded signals. Lastly, it is possible that multiple point sources are active simultaneously. When we consider one point source as the target signal, the other sources could be considered interfering signals. These distortions make it difficult to get access to the target signal. Therefore, spatial filtering is often applied to the microphone signals.

To achieve satisfying performance, these spatial filters typically need to be adaptive to the (changing) scene. Specifically, the filter coefficients depend on the acoustic-scene related parameters that model the microphone signals. These parameters, such as the relative transfer functions (RTFs) of the sources, the power spectral densities (PSDs) of the sources, the late reverberation and the ambient noise, are typically unknown in practice. Therefore, estimation of these parameters is crucial and thus the main focus of the dissertation. While it is relatively straightforward to estimate these parameters in less complex acoustic scenes, these algorithms are usually not applicable and not extendable to more complex acoustic scenes. Therefore, the complexity of the estimation methods needed depends on the complexity of the acoustic scene.

In his thesis, the author considers to estimate the RTF under varying assumptions and conditions, resulting in the joint estimation of the RTF and the power spectral densities of the sources, the late reverberation, and the noise.

PhD Thesis Defence

• Wednesday, 3 December 2025
• 17:30-19:00
• Aula Senaatszaal

Model-Based Processing in Ultrasound Imaging: Sparse Reconstruction and Coded Excitation

Ultrasound is a widely used real-time imaging modality to diagnose patients. Ultrasound imaging has several modes of operation such as ultrafast Doppler which, due to the high frame-rates, is particularly suited to image blood flow inside bodily organs such as the brain. Despite its success, the ultrafast imaging technique has some downsides such as lower overall signal-to-noise ratio (SNR), especially in deeper regions due to the use of unfocussed transmissions. This thesis explores the use of advanced signal processing methods such as model-based image reconstruction to regain some of the loss in SNR.

The first part of the thesis focus on advanced model-based image reconstruction techniques, incorporating complex priors or statistical assumptions about the signal and noise instead of using a simple physical propagation model. Conventional ultrasound beamforming techniques, such as the delay-and-sum (DAS) beamformer, perform well in many clinical settings; however, they face challenges in applications requiring high structural detail or SNR, such as vascular imaging. This thesis explores deterministic and statistical model-based vascular image reconstruction techniques to improve SNR, resolution, and clarity of fine vascular details. The proposed techniques exploit the joint sparsity of the vasculature images at different time instants. These methods enhance the depiction of vascular structures while increasing SNR and suppressing background noise and artifacts.

A large part of the thesis focuses on the sparse Bayesian learning (SBL) techniques. Starting with classical SBL, this thesis introduces the application of block-sparsity-based SBL techniques, such as pattern-coupled sparse Bayesian learning with fixed-point iterations and correlated sparse Bayesian learning. Although some of the proposed techniques are not computationally efficient yet for real-time ultrasound imaging, they do provide a new contribution to signal processing and computational imaging fields.

The final chapter of the thesis focuses on improving the ultrasound transmission to enhance the SNR. An optimized coded excitation technique has been proposed as an alternative to standard coded excitation techniques. By keeping the computational complexity to a modest level, the codes are optimized to increase the SNR without a significant loss in the image resolution. The Cramér-Rao lower bound (CRB) minimization and a faster alternative Fisher information matrix (FIM) maximization have been proposed to optimize the codes. The optimized codes are tested on simulated data to demonstrate their potential for flow imaging.

To sum up, this thesis contributes to the ultrasound blood flow imaging area through solutions on image reconstruction algorithms and ultrasound transmissions to overcome current limitations and challenges. This thesis explores using advanced modelbased signal processing methods to improve image quality. Therefore, this work contributes new strategies that can inspire future research and clinical applications in vascular ultrasound imaging.