Publications related to Bandlimiting

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Modern control synthesis methods rely on accurate models to derive a performant controller. Obtaining a good model is often a costly step, and has led to a renewed interest in data-driven synthesis methods. Frequency-response-based synthesis methods have been proposed, as they rely on easy-to-obtain frequency-response data and offer flexible tuning approaches. Such methods formulate the objective as a minimization over the whole spectrum, which is problematic as only a finite number of frequency points can

This paper studies the scaling behavior of state-space models (SSMs) and their structured variants, such as Mamba, that have recently arisen in popularity as alternatives to transformer-based neural network architectures. Specifically, we focus on the capability of SSMs to learn features as their network width approaches infinity. Our findings reveal that established scaling rules, such as the Maximal Update Parameterization, fail to support feature learning as these models cannot be represented in the form

Traditional sampling involves encoding a signal through (time, value)-pairs. In contrast, time encoding machines (TEMs) characterize a signal by recording time points which depend on the integral of the signal over time. We study multi-channel TEMs where c ...

Training deep neural networks with the error backpropagation algorithm is considered implausible from a biological perspective. Numerous recent publications suggest elaborate models for biologically plausible variants of deep learning, typically defining s ...

A batch process is characterized by the repetition of time-varying operations of finite duration. Due to the repetition, there are two independent “time” variables, namely, the run time during a batch and the batch index. Accordingly, the control and optimization objectives can be defined for a given batch or over several batches. This chapter describes the various control and optimization strategies available for the operation of batch processes. These include online and run-to-run control on the one hand,

We propose an analog-to-digital converter (ADC) architecture, implemented in an FPGA, that is fully reconfigurable and easy to calibrate. This approach allows to alter the design, according to the system requirements, with simple modifications in the firmw ...

We propose a sampling scheme that can perfectly reconstruct a collection of spikes on the sphere from samples of their lowpass-filtered observations. Central to our algorithm is a generalization of the annihilating filter method, a tool widely used in array signal processing and finite-rate-of-innovation (FRI) sampling. The proposed algorithm can reconstruct

K

spikes from

(K+\sqrt{K})^2

spatial samples. This sampling requirement improves over previously known FRI sampling schemes on the sphere by a fact

We study the problem of sampling

k

-bandlimited signals on graphs. We propose two sampling strategies that consist in selecting a small subset of nodes at random. The first strategy is non-adaptive, \ie, independent of the graph structure, and its performance depends on a parameter called the graph coherence. On the contrary, the second strategy is adaptive but yields optimal results. Indeed, no more than

O(\nbClass \log(\nbClass))

measurements are sufficient to ensure an accurate and stable recovery of

High-frequency, 3D magnetic sensors have been designed and manufactured in-house for installation on the Tokamak a Configuration Variable (TCV). To optimize the in-vessel volume occupation, the sensors are based on combining the Low Temperature Co-fired Ce ...

This thesis proposes a new method to design fixed-order controllers in frequency domain using convex optimization. The method is based on the shaping of open-loop transfer function in the Nyquist diagram with infinity norm constraints on weighted closed-lo ...

Bandlimiting

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Robust Data-Driven Controller Design with Finite Frequency Samples

On Feature Learning in Structured State Space Models

Multi-channel Time Encoding for Improved Reconstruction of Bandlimited Signals

Biologically plausible deep learning – but how far can we go with shallow networks?

Control and Optimization of Batch Chemical Processes

A Cryogenic 1 GSa/s, Soft-Core FPGA ADC for Quantum Computing Applications

Sampling Sparse Signals on the Sphere: Algorithms and Applications

Random sampling of bandlimited signals on graphs

3D, LTCC-type, high-frequency magnetic sensors for the TCV Tokamak

Fixed-Order Robust Controller Design by Convex Optimization Using Spectral Models