Publications associées à USSR: An UltraSound Sparse Regularization framework

Towards Trustworthy Deep Learning for Image Reconstruction

The remarkable ability of deep learning (DL) models to approximate high-dimensional functions from samples has sparked a revolution across numerous scientific and industrial domains that cannot be overemphasized. In sensitive applications, the good perform ...

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The miniaturization of integrated circuits (ICs) and their higher performance and energy efficiency, combined with new machine learning algorithms and applications, have paved the way to intelligent, interconnected edge devices. In the medical domain, they ...

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Embedded systems confront two opposite goals: low-power operation and high performance. The current trend to reach these goals is toward heterogeneous platforms, including multi-core architectures with heterogeneous cores and hardware accelerators. The lat ...

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Ultrasound Imaging: From Physical Modeling to Deep Learning

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Among the medical imaging modalities, ultrasound (US) imaging is one of the safest, most widespread, and least expensive method used in medical diagnosis. In the past decades, several technological advances enabled the advent of ultrafast US imaging, an ac ...

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CNN-Based Ultrasound Image Reconstruction for Ultrafast Displacement Tracking

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Institute of Electrical and Electronics Engineers2021

Recursive Input And State Estimation: A General Framework For Learning From Time Series With Missing Data

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Stephan Brunner, Laurent Villard, Emmanuel Lanti, Noé Thomas Elie Ohana, Claudio Gheller

Gyrokinetic codes in plasma physics need outstanding computational resources to solve increasingly complex problems, requiring the effective exploitation of cutting-edge HPC architectures. This paper focuses on the enabling of ORB5, a state-of-the-art, fir ...

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Accurate Emulation of Memristive Crossbar Arrays for In-Memory Computing

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In-memory computing is an emerging non-von Neumann computing paradigm where certain computational tasks are performed in memory by exploiting the physical attributes of the memory devices. Memristive devices such as phase-change memory (PCM), where informa ...

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Stretch: Balancing QoS and Throughput for Colocated Server Workloads on SMT Cores

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Spectre, Meltdown, and related attacks have demonstrated that kernels, hypervisors, trusted execution environments, and browsers are prone to information disclosure through micro-architectural weaknesses. However, it remains unclear as to what extent other ...

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Improving Main-memory Database System Performance through Cooperative Multitasking

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Asynchronous Simulation of Neuronal Activity

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FPGAs (Field Programmable Gate Array) are an attractive technology for high-speed data processing in space missions due to their unbeatable flexibility and best performance-to-power ratio in comparison to software. However FPGAs suffer from 3 major drawbac ...

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Computational Aesthetics and Image Enhancements using Deep Neural Networks

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Imaging devices have become ubiquitous in modern life, and many of us capture an increasing number of images every day. When we choose to share or store some of these images, our primary selection criterion is to choose the most visually pleasing ones. Yet ...

EPFL2018