Evolutionary Active Vision Toward Three Dimensional Landmark-Navigation

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Fluorescence lifetime imaging (FLI) has been receiving increased attention in recent years as a powerful diagnostic technique in biological and medical research. However, existing FLI systems often suffer from a tradeoff between processing speed, accuracy, ...

Harmful chemical compounds are released daily in warehouses, chemical plants and during environmental emergencies. Their uncontrolled dispersion contributes to the pollution of the atmosphere and threatens human and animal lives. When gas leaks occur, the ...

Digital twins are virtual models of physical objects or systems that enable real-time monitoring and analysis. In the ﬁeld of stone masonry buildings, digital twins can be used to assess damage, predict maintenance needs, and opti- mize building performanc ...

The performance of machine learning algorithms is conditioned by the availability of training datasets, which is especially true for the field of nondestructive evaluation. Here we propose one reconfigurable specimen instead of numerous reference specimens ...

In the realm of point cloud scene understanding, particularly in indoor scenes, objects are arranged following human habits, resulting in objects of certain semantics being closely positioned and displaying notable inter-object correlations. This can creat ...

Recent advancements in deep learning have revolutionized 3D computer vision, enabling the extraction of intricate 3D information from 2D images and video sequences. This thesis explores the application of deep learning in three crucial challenges of 3D com ...

The capabilities of deep learning systems have advanced much faster than our ability to understand them. Whilst the gains from deep neural networks (DNNs) are significant, they are accompanied by a growing risk and gravity of a bad outcome. This is troubli ...

Proprioception tells the brain the state of the body based on distributed sensors in the body. However, the principles that govern proprioceptive processing from those distributed sensors are poorly understood. Here, we employ a task-driven neural network ...

We propose a method for adapting neural networks to distribution shifts at test-time. In contrast to training-time robustness mechanisms that attempt to anticipate and counter the shift, we create a closed-loop system and make use of test-time feedback sig ...

Herringbone grooved journal bearings (HGJBs) are widely used in micro-turbocompressor applications due to their high load-carrying capacity, low friction, and oil-free solution. However, the performance of these bearings is sensitive to manufacturing devia ...

Owing to the remarkable properties of the somatosensory system, human skin compactly perceives myriad forms of physical stimuli with high precision. Machines, conversely, are often equipped with sensory suites constituted of dozens of unique sensors, each ...

The transition from aquatic to terrestrial environments represents a significant event in the history of evolution. For this transition to occur, animals had to adapt their morphology, physiology and locomotory skills to handle the challenges and interacti ...

Evolutionary Active Vision Toward Three Dimensional Landmark-Navigation

Chat with Graph Search

Coupling a recurrent neural network to SPAD TCSPC systems for real-time fluorescence lifetime imaging

Gas Sensing with Rotary-Wing Nano Aerial Vehicles

Digital Twins of Stone Masonry Buildings for Damage Assessment

Reduced Training Data for Laser Ultrasound Signal Interpretation by Neural Networks

Mitigating Object Dependencies: Improving Point Cloud Self-Supervised Learning through Object Exchange

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

Safe Deep Neural Networks

Task-driven neural network models predict neural dynamics of proprioception: Neural network model weights

Rapid Network Adaptation: Learning to Adapt Neural Networks Using Test-Time Feedback

Robust Design of Herringbone Grooved Journal Bearings using Multi-Objective Optimization with Artificial Neural Networks

Multi-modal deformation and temperature sensing for context-sensitive machines

Neuromechanical Simulations of Animats with Decentralised Control