Publications related to Subwavelength Acoustic Imaging in Far Field by Combining Metamaterials and Deep Learning

Related publications (22)

On the role of artificial intelligence in feature oriented multi-criteria decision analysis

Balancing economic and environmental goals in industrial applications is critical amid challenges like climate change. Multi-objective optimization (MOO) and multi-criteria decision analysis (MCDA) are key tools for addressing conflicting objectives. MOO g ...

PSE Press2025

Multimodal Nanoplasmonic and Fluorescence Imaging for Simultaneous Monitoring of Single-Cell Secretory and Intracellular Dynamics

Hatice Altug, Yen-Cheng Liu, Saeid Ansaryan, Yung-Cheng Chiang

Current imaging technologies are limited in their capability to simultaneously capture intracellular and extracellular dynamics in a spatially and temporally resolved manner. This study presents a multimodal imaging system that integrates nanoplasmonic sen ...

WILEY2025

Hardware-Software co-design Methodologies for Edge AI Optimization

Flavio Ponzina

ML-based edge devices may face memory and computational errors that affect applications' reliability and performance. These errors can be the result of particular working conditions (e.g., radiation areas in physical experiments or avionics) or could be th ...

EPFL2023

Data-driven approaches for non-invasive cuffless blood pressure monitoring

Clémentine Léa Aguet

Blood pressure (BP) is a crucial indicator of cardiovascular health. Hypertension is a common life-threatening condition and a key factor of cardiovascular diseases (CVDs). Identifying abnormal BP fluctuations can allow for early detection and management o ...

EPFL2023

Multi-agent actor-critic with time dynamical opponent model

Olga Fink

In multi-agent reinforcement learning, multiple agents learn simultaneously while interacting with a common environment and each other. Since the agents adapt their policies during learning, not only the behavior of a single agent becomes non-stationary, b ...

2023

Contrasting action and posture coding with hierarchical deep neural network models of proprioception

Alexander Mathis, Mackenzie Mathis, Matthias Bethge, Kai Jappe Sandbrink

Biological motor control is versatile, efficient, and depends on proprioceptive feedback. Muscles are flexible and undergo continuous changes, requiring distributed adaptive control mechanisms that continuously account for the body's state. The canonical r ...

eLIFE SCIENCES PUBL LTD2023

Video-driven simulation of lower limb mechanical loading during aquatic exercises

Jessy Lauer

Understanding the mechanical demands of an exercise on the musculoskeletal system is crucial to prescribe effective training or therapeutic interventions. Yet, that knowledge is currently limited in water, mostly because of the difficulty in evaluating ext ...

ELSEVIER SCI LTD2023

Streamline RimNet: Tools for Automatic Classification of Paramagnetic Rim Lesions in MRI of Multiple Sclerosis

Joe Najm, Meritxell Bach Cuadra, Cristina Granziera, Francesco La Rosa

This site provides two software tools related to "RimNet: A deep 3D multimodal MRI architecture for paramagnetic rim lesion assessment in multiple sclerosis" by Barquero et al. NeuroImage: Clinical (2020). People using in part or f ...

Zenodo2023

Label-free plasmonic microarray for multiplexed analysis of cells and tumor organoids

Yen-Cheng Liu

Investigating the dynamic activities of protein expression and signaling in living organisms is a crucial focus of intense research aimed at elucidating the processes that underlie disease progression and improving treatments and drug development. Resolvin ...

EPFL2023

Ride the Tide of Traffic Conditions: Opportunistic Driving Improves Energy Efficiency of Timely Truck Transportation

Wenjie Xu

We study the problem of minimizing fuel consumption of a heavy-duty truck traveling across the national highway network subject to a hard deadline. We focus on a real-world setting that traversing a road segment is subject to variable speed ranges due to d ...

IEEE Institute of Electrical and Electronics Engineers2023

On the symmetries in the dynamics of wide two-layer neural networks

Lénaïc Chizat

We consider the idealized setting of gradient flow on the population risk for infinitely wide two-layer ReLU neural networks (without bias), and study the effect of symmetries on the learned parameters and predictors. We first describe a general class of s ...

AMER INST MATHEMATICAL SCIENCES-AIMS2023

Twisting instabilities in elastic ribbons with inhomogeneous pre-stress: A macroscopic analog of thermodynamic phase transition

Pedro Miguel Nunes Pereira de Almeida Reis, Michael Christopher Gomez

We study elastic ribbons subject to large, tensile pre-stress confined to a central region within the cross-section. These ribbons can buckle spontaneously to form helical shapes, featuring regions of alternating chirality (phases) that are separated by so ...

PERGAMON-ELSEVIER SCIENCE LTD2023

A multimodal measurement of the impact of deepfakes on the ethical reasoning and affective reactions of students

Touradj Ebrahimi, Roland John Tormey, Patrick Jermann, Cécile Hardebolle, Vivek Ramachandran, Nihat Kotluk

Deepfakes - synthetic videos generated by machine learning models - are becoming increasingly sophisticated. While they have several positive use cases, their potential for harm is also high. Deepfake production involves input from multiple engineers, maki ...

2023

Augmented Lagrangian Methods for Provable and Scalable Machine Learning

Mehmet Fatih Sahin

Non-convex constrained optimization problems have become a powerful framework for modeling a wide range of machine learning problems, with applications in k-means clustering, large- scale semidefinite programs (SDPs), and various other tasks. As the perfor ...

EPFL2023

Formation of the cavity on a planar interface subjected to a perturbed shock wave

Ho Ling Li, Naifu Peng

We report the mechanism and modeling for the formation of cavitylike structures on a planar interface subjected to a perturbed shock wave. The cavity is distinguished from bubbles and spikes formed in the standard Richtmyer-Meshkov instability (RMI). The t ...

AMER PHYSICAL SOC2023

Robust design using multiobjective optimisation and artificial neural networks with application to a heat pump radial compressor

Jürg Alexander Schiffmann, Soheyl Massoudi, Cyril Picard

Although robustness is an important consideration to guarantee the performance of designs under deviation, systems are often engineered by evaluating their performance exclusively at nominal conditions. Robustness is sometimes evaluated a posteriori throug ...

2022

Multi-fidelity regression using artificial neural networks: Efficient approximation of parameter-dependent output quantities

Jan Sickmann Hesthaven

Highly accurate numerical or physical experiments are often very time-consuming or expensive to obtain. When time or budget restrictions prohibit the generation of additional data, the amount of available samples may be too limited to provide satisfactory ...

ELSEVIER SCIENCE SA2022

Textual Explanations and Critiques in Recommendation Systems

Diego Matteo Antognini

Artificial intelligence and machine learning algorithms have become ubiquitous. Although they offer a wide range of benefits, their adoption in decision-critical fields is limited by their lack of interpretability, particularly with textual data. Moreover, ...

EPFL2022

Perspectives in machine learning for wildlife conservation

Devis Tuia, Alexander Mathis, Mackenzie Mathis, Benjamin Alexander Kellenberger

Inexpensive and accessible sensors are accelerating data acquisition in animal ecology. These technologies hold great potential for large-scale ecological understanding, but are limited by current processing approaches which inefficiently distill data into ...

Nature Research2022

Population pharmacokinetic model selection assisted by machine learning

Jan Sickmann Hesthaven

A fit-for-purpose structural and statistical model is the first major requirement in population pharmacometric model development. In this manuscript we discuss how this complex and computationally intensive task could benefit from supervised machine learni ...

SPRINGER/PLENUM PUBLISHERS2022