Combining dynamic relaxation method with artificial neural networks to enhance simulation of tensegrity structures

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Animal motor control arises from the intricate interplay between neural circuits, biomechanics, and sensory feedback. Deciphering how these components interact is essential for uncovering the fundamental principles of movement generation. This dissertation ...

Deep neural networks often fail to generalize when deployed in dynamic and unpredictable real-world environments, where data distributions at test time can differ significantly from those encountered during training. Since simulating all possible distribut ...

DARTS-NETGAB is a unified framework for real-time simulation and automated design of gas-bearing supported turbocompressors, facilitating efficient transition from optimization to manufacturable designs. The framework integrates ensemble artificial neural ...

Previous methods for predicting room acoustic parameters and speech quality metrics have focused on the single-channel case, where room acoustics and Mean Opinion Score (MOS) are predicted for a single recording device. However, quality-based device select ...

Multiple drug concentrations concurrent detection and quantification based on electrochemical sensors are of great importance for therapeutic drug monitoring (TDM) and the development of personalized therapy. Cyclic voltammogram (CV) results obtained by el ...

This study introduces a Machine Learning (ML) approach for estimating the T2 spectrum and myelin water fraction (MWF) using multi-echo T2 (MET2) data from preclinical 7T Magnetic Resonance Imaging (MRI) scanners. ML methods have shown promise in MWF estima ...

The European Space Agency's Euclid mission will provide high-quality imaging for about 1.5 billion galaxies. A software pipeline to automatically process and analyse such a huge amount of data in real time is being developed by the Science Ground Segment o ...

Ultrafast ultrasound (US) revolutionized biomedical imaging with its capability of acquiring full-view frames at over 1 kHz, unlocking breakthrough modalities such as shear-wave elastography and functional US neuroimaging. Yet, it suffers from strong diffr ...

Deep convolutional neural networks (DCNNs) are currently the method of choice both for generative, as well as for discriminative learning in computer vision and machine learning. The success of DCNNs can be attributed to the careful selection of their buil ...

NKCC1 is the primary transporter mediating chloride uptake in immature principal neurons, but its role in the development of in vivo network dynamics and cognitive abilities remains unknown. Here, we address the function of NKCC1 in developing mice using e ...

In this paper we present a framework to learn a model-free feedback controller for locomotion and balance control of a compliant quadruped robot walking on rough terrain. Having designed an open-loop gait encoded in a Central Pattern Generator (CPG), we us ...

Combining dynamic relaxation method with artificial neural networks to enhance simulation of tensegrity structures

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An integrative computational modeling approach for Drosophila motor control

Adaptive Test-Time Learning Under Domain Shift: From Augmentation to Prolonged Adaptation

DARTS-NETGAB: Design Automation and Real-Time Simulation Using Neural Network Ensembles for Turbocompressors on Gas-Bearings

Multi-Channel Mosra: Mean Opinion Score and Room Acoustics Estimation Using Simulated Data and A Teacher Model

Automatic simulation of electrochemical sensors by machine learning for drugs quantification

In-Silico Trained AI for Enhanced T2Spectrum Imaging and Myelin Water Fraction Mapping in Preclinical 7T MRI

Euclid preparation XXV. The Euclid Morphology Challenge: Towards model-fitting photometry for billions of galaxies

CNN-Based Image Reconstruction Method for Ultrafast Ultrasound Imaging

Deep Polynomial Neural Networks

A limited role of NKCC1 in telencephalic glutamatergic neurons for developing hippocampal network dynamics and behavior

Learning Robot Gait Stability using Neural Networks as Sensory Feedback Function for Central Pattern Generators