arXiv subjects

Sara Freund

Publications and source records attributed to Sara Freund.

4 recordsLinked to original sources

arXiv2022

The secret role of undesired physical effects in accurate shape sensing with eccentric FBGs

Fiber optic shape sensors have enabled unique advances in various navigation tasks, from medical tool tracking to industrial applications. Eccentric fiber Bragg gratings (FBG) are cheap and easy-to-fabricate shape sensors that are often interrogated with simple setups. However, using low-cost interrogation systems for such intensity-based quasi-distributed sensors introduces further complications to the sensor's signal. Therefore, eccentric FBGs have not been able to accurately estimate complex multi-bend shapes. Here, we present a novel technique to overcome these limitations and provide accurate and precise shape estimation in eccentric FBG sensors. We investigate the most important bending-induced effects in curved optical fibers that are usually eliminated in intensity-based fiber sensors. These effects contain shape deformation information with a higher spatial resolution that we are now able to extract using deep learning techniques. We design a deep learning model based on a convolutional neural network that is trained to predict shapes given the sensor's spectra. We also provide a visual explanation, highlighting wavelength elements whose intensities are more relevant in making shape predictions. These findings imply that deep learning techniques benefit from the bending-induced effects that impact the desired signal in a complex manner. This is the first step toward cheap yet accurate fiber shape sensing solutions.

cs.LG↗

arXiv2022

Using Supervised Deep-Learning to Model Edge-FBG Shape Sensors

Continuum robots in robot-assisted minimally invasive surgeries provide adequate access to target anatomies that are not directly reachable through small incisions. Achieving precise and reliable motion control of such snake-like manipulators necessitates an accurate navigation system that requires no line-of-sight and is immune to electromagnetic noises. Fiber Bragg Grating (FBG) shape sensors, particularly edge-FBGs, are promising tools for this task. However, in edge-FBG sensors, the intensity ratio between Bragg wavelengths carries the strain information that can be affected by undesired bending-related phenomena, making standard characterization techniques less suitable for these sensors. We showed in our previous work that a deep learning model has the potential to extract the strain information from the full edge-FBG spectrum and accurately predict the sensor's shape. In this paper, we conduct a more thorough investigation to find a suitable architectural design with lower prediction errors. We use the Hyperband algorithm to search for optimal hyperparameters in two steps. First, we limit the search space to layer settings, where the best-performing configuration gets selected. Then, we modify the search space for tuning the training and loss calculation hyperparameters. We also analyze various data transformations on the input and output variables, as data rescaling can directly influence the model's performance. Moreover, we performed discriminative training using Siamese network architecture that employs two CNNs with identical parameters to learn similarity metrics between the spectra of similar target values. The best-performing network architecture among all evaluated configurations can predict the sensor's shape with a median tip error of 3.11 mm.

cs.RO↗

arXiv2020

Machine learning-based method for linearization and error compensation of an absolute rotary encoder

The main objective of this work is to develop a miniaturized, high accuracy, single-turn absolute, rotary encoder called ASTRAS360. Its measurement principle is based on capturing an image that uniquely identifies the rotation angle. To evaluate this angle, the image first has to be classified into its sector based on its color, and only then can the angle be regressed. In-spired by machine learning, we built a calibration setup, able to generate labeled training data automatically. We used these training data to test, characterize, and compare several machine learning algorithms for the classification and the regression. In an additional experiment, we also characterized the tolerance of our rotary encoder to eccentric mounting. Our findings demonstrate that various algorithms can perform these tasks with high accuracy and reliability; furthermore, providing extra-inputs (e.g. rotation direction) allows the machine learning algorithms to compensate for the mechanical imperfections of the rotary encoder.

eess.SP↗

arXiv2018

Transoid-to-Cisoid Conformation Changes of Single Molecules on Surfaces Triggered by Metal Coordination

Conformational isomers are stereoisomers that can interconvert over low potential barriers by rotation around a single bond. However, such bond rotation is hampered by geometrical constraints when molecules are adsorbed on surfaces. Here we show that the adsorption of 4,4'-bis(4-carboxyphenyl)-6,6'-dimethyl-2,2'-bipyridine molecules on surfaces leads to the appearance of pro-chiral single-molecules on NiO(001) and to enantiopure supramolecular domains on Au(111) surfaces containing the $transoid$ molecule conformation. Upon additional Fe adatom deposition, molecules undergo a controlled interconversion from a $transoid$ to $cisoid$ conformation as a result of coordination of the Fe atoms to the 2,2'-bipyridine moieties. As confirmed by atomic force microscopy images and X-ray photoelectron spectroscopy measurements, the resulting molecular structures become irreversibly achiral.

cond-mat.mes-hall↗