Energy-Efficient FPGA Solutions for Large-Scale FFTs and Non-Uniform FFTs A Software-Hardware Co-Design Approach for Radio Interferometry

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At the intersection of computational imaging and biomedical optics, this thesis applies physics-based modeling and computational methods to solve inverse problems in optical imaging. We focus on three representative imaging modalities where we push the limits of imaging depth and spatial resolution: optical projection tomography (OPT); photoacoustic imaging (PAI); and single-molecule localization microscopy (SMLM). OPT and PAI overcome the diffusion limit to enable greater imaging depths, while SMLM breaks

Graph signal processing (GSP) enables studying brain structure-function coupling by examining how functional signals vary on the structural connectome (SC). While traditionally used with functional magnetic resonance imaging, GSP applied to electroencephalography (EEG) is gaining interest due EEG’s repertoire of brain activities and higher temporal resolution. To this aim, source activities are reconstructed through electrical source imaging (ESI), summarized into parcellated time-series via singular value

Building local surrogates to accelerate stationary point searches on potential energy surfaces span decades of effort. Done correctly, surrogates can reduce the number of expensive electronic structure evaluations by factors of several, and in favorable regimes by roughly an order of magnitude, while preserving the accuracy of the underlying theory; the gain depends on oracle cost, search distance, and the availability of analytical forces. We present a unified Bayesian optimization view of minimization, si

Abstract. We propose a novel algorithm based on inexact GMRES methods for linear response calculations in density functional theory. Such calculations require iteratively solving a nested linear problem [Formula: see text] to obtain the variation of the electron density [Formula: see text]. Notably each application of the dielectric operator [Formula: see text] in turn requires the iterative solution of multiple linear systems, the Sternheimer equations. We develop computable bounds to estimate the accuracy

This repository contains the data and software needed to reproduce the results from the paper "PathSteiner: Improving PathFinder with Quasi-Optimal Steiner-Tree Initialization" by Shashwat Shrivastava (EPFL), Luka Kurešević (EPFL), Alexandros Poupakis (EPFL), Chirag Ravishankar (AMD), Dinesh Gaitonde (AMD), Stefan Nikolić (University of Novi Sad), and Mirjana Stojilović (EPFL). The paper has been accepted for publication in the Proceedings of the **34th IEEE International Symposium on Field-Programmable

Computational imaging combines innovative camera designs with physics-informed algorithms to enable new sensing capabilities, modalities, and applications. Such cameras can relax optical design constraints and enable encoding of higher-dimensional data. Mask-based lensless cameras are one such design, which replaces the focusing lens with a thin mask, and image formation is shifted from the optics to digital post-processing. Classical methods recover images by solving optimization problems involving data fi

In this work, we present X-HEEP, an open-source, configurable, and extendible RISC-V platform for ultra-low-power edge applications (TinyAI). X-HEEP features the eXtendible Accelerator InterFace (XAIF), which enables seamless integration of accelerators with varying requirements along with an extensive internal configuration of cores, memory, bus, and peripherals. Moreover, it supports various development flows, including FPGA prototyping, ASIC implementation, and mixed SystemC-RTL modeling, enabling effici

The Active Subspace (AS) method is a widely used technique for identifying the most influential directions in high-dimensional input spaces that affect the output of a computational model. The standard AS algorithm requires a sufficient number of gradient evaluations (samples) of the input output map to achieve quasi-optimal reconstruction of the active subspace, which can lead to a significant computational cost if the samples include numerical discretization errors which have to be kept sufficiently small

This workshop focuses on the integration of computer vision assistance technologies in timber construction through Augmented Carpentry (AC), an open-source research project developed at IBOIS, EPFL (PhD: Settimi Andrea, thesis co-director: Dr. Julien Gamerro, thesis and lab director: Prof. Yves Weinand). Participants will develop a foundational understanding of how computer vision can revolutionize traditional woodworking techniques, leading to more efficient, and accessible digital fabrication in timber co

Distributed collaborative learning is a promising approach for building predictive models for privacy-sensitive biomedical images. Here, several data owners (clients) train a joint model without sharing their original data. However, concealed systematic biases can compromise model performance and fairness. This study presents MyThisYourThat (MyTH) approach, which adapts an interpretable prototypical part learning network to a distributed setting, enabling each client to visualize feature differences learned

The FFT algorithm is a key pillar of modern numerical computing. This document is a collection of working notes on FFT-based algorithms. Efficient implementations of the former are made available through the pyFFS package.

Volumetric ultrasound (US) is a very promising development of medical US imaging. An under-exploited advantage of volumetric US is the mitigation of the strict probe positioning constrains necessary to acquire 2D scans, potentially allowing the decoupling of US image acquisition and diagnosis. However, today’s 3D US systems are large and beset by high power and cost requirements, making them only available in well-equipped hospitals. In this work [1], [2], we propose the first telesonography-capable medical

The energy transition in Switzerland specifies reduction of the country’s primary energy consumption and greenhouse gas emissions. In the building sector, one way to achieve this is through adoption of efficient and sustainable technologies. With this aim, the building energy systems are gaining complexity. Modelling tools are becoming increasingly important in the design and evaluation of such systems. Two modelling methods are commonly used, simulation and optimization, which substantially differ in their

Sampling moiré effects are well known in signal processing. They occur when a continuous periodic signal g(x) is sampled using a sampling frequency fs that does not respect the Nyquist condition, and the signal frequency f folds-over and gives a new, false low frequency in the sampled signal. However, some visible beating artifacts may also occur in the sampled signal when g(x) is sampled using a sampling frequency fs which fully respects the Nyquist condition. We call these phenomena sub-Nyquist artifacts.

This work is unique as it provides a uniform treatment of the Fourier theories of functions (Fourier transforms and series, z-transforms), finite measures (characteristic functions, convergence in distribution), and stochastic processes (including arma series and point processes). It emphasises the links between these three themes. The chapter on the Fourier theory of point processes and signals structured by point processes is a novel addition to the literature on Fourier analysis of stochastic processes.

Areas/Paper type – 08. Integration of renewable energy in the built environment, Methodology Background – Today’s simulation technology in engineering and architecture are very impressive. As a result technology developers have a good idea of the resulting energy consumption of a building. They even have an idea of the distribution of the possible output. At CISBAT 2011, some authors presented innovative technologies with detailed impact on energy consumption (energy quantity, type, time of consumption) but

In this paper, we investigate pitch contour modelling in speech synthesis based on segmental units. A convolutional pitch target approximation model is proposed. This model allows jointly stochastic modelling of framewise pitch and pitch contour of longer units, of which the intuitive relations are revealed by a convolutional target approximation filter. The pitch contour is stylized by a linear representation called pitch target. In synthesis stage, the likelihood of the framewise model and the pitch targe

We study the use and impact of a dictionary in a tomographic reconstruction setup. First, we build two different dictionaries: one using a set of bases functions (Discrete Cosine Transform), and the other that is learned using patches extracted from training images, similar to the image that we would like to reconstruct. We use K-SVD as the learning algorithm. These dictionaries being local, we convert them to global dictionaries, ready to be applied on whole images, by generating all possible shifts of eac

Several important societal and economic world problems can be addressed by the smart use of technology. The last forty years have witnessed the realization of computational systems and networks, rooted in our ability of crafting complex integrated circuits out of billions of transistors. Nowadays, the ability of mastering materials at the molecular level and their interaction with living matter opens up unforeseeable horizons. Networking biological sensors through body-area, ad hoc and standard communicatio

Inspired by the basic processes of molecular biology, our previous studies resulted in defining a configurable molecule implementing self-replication and self-repair mechanisms made up of simple processes. The goal of our paper is to add error detection features to the molecule in order to make it able to perform also built-in self-test mechanisms. The hardware description of the molecule with all its self-organizing mechanisms leads to the simulation of an image processing array dedicated to thresholding a

Energy-Efficient FPGA Solutions for Large-Scale FFTs and Non-Uniform FFTs A Software-Hardware Co-Design Approach for Radio Interferometry

Chattez avec Graph Search

Physics-based modeling and inverse problems in optical imaging

Source-reconstructed EEG graph signal processing: pitfalls and workarounds

A Tutorial Review of Bayesian Optimization with Gaussian Processes to Accelerate Stationary Point Searches

Efficient Krylov Methods for Linear Response in Plane-Wave Electronic Structure Calculations

PathSteiner: Improving PathFinder with Quasi-Optimal Steiner-Tree Initialization

Robust, Generalizable, and Reproducible Computational Imaging: A Lensless Perspective

X-HEEP: An Open-Source, Configurable and Extendible RISC-V Platform for TinyAI Applications

A function approximation algorithm using multilevel active subspaces

Augmented Carpentry Workshop at Future of Construction 2024 (Mendrisio)

MyThisYourThat for interpretable identification of systematic bias in federated learning for biomedical images

Fourier Tools

Live Demonstration: Inexpensive 1024-Channel 3D Telesonography System on FPGA

Iterative simulation and optimization approach for energy performance evaluation of ground source heat pump systems

Tutorial: Sub-Nyquist artifacts and sampling moiré effects

Fourier Analysis and Stochastic Processes

INDEX OF ENERGY PRESENT VALUE: Assessment tool for energy savings and distributed energy production

Convolutional Pitch Target Approximation Model for Speech Synthesis

Use of Learned Dictionaries in Tomographic Reconstruction

Engineering complex systems for health, security and the environment

Bio-Inspired Self-Testing and Self-Organizing Processing Arrays