Distributed curvature sensing using long period fiber grating and machine learning numerical analysis.

In this numerical study, we propose a fiber distributed curvature sensor based on the analysis of the spectral transmission of a long period fiber grating (LPG) with a neural network. A simulation of the optical transmissions of a proposed 6-cm LPG structure for different curvature profiles is first performed using EigenMode Expansion and a coupled-mode theory algorithm. Both fiber curvature profiles and their corresponding optical transmission spectra are then injected into a four dense layer neural network which, after training, leads to a 0.40% relative median estimation error in the bending profiles. This paper demonstrates the efficiency of neural network-based optical sensors to analyze non-uniform perturbations, while also revealing long-period gratings to be promising candidates for such systems.

Scaling the performances of integrated long period grating refractometers.

In this Letter, a theoretical analysis and design methodology of integrated long period gratings (LPGs) for refractometric applications are proposed. A detailed parametric analysis is applied to a LPG model based on two strip waveguides to highlight the main design variables and their effect on the refractometric performances, with focus on the spectral sensitivity and signature response. To illustrate the proposed methodology, four variants of the same LPG design are simulated with eigenmode expansion, displaying a wide range of sensitivities up to 300,000 nm/RIU with figures of merit (FOMs) as high as 8000.

Fringe Detection and Displacement Sensing for Variable Optical Feedback-Based Self-Mixing Interferometry by Using Deep Neural Networks.

Laser feedback-based self-mixing interferometry (SMI) is a promising technique for displacement sensing. However, commercial deployment of such sensors is being held back due to reduced performance in case of variable optical feedback which invariably happens due to optical speckle encountered when sensing the motion of non-cooperative remote target surfaces. In this work, deep neural networks have been trained under variable optical feedback conditions so that interferometric fringe detection and corresponding displacement measurement can be achieved. We have also proposed a method for automatic labelling of SMI fringes under variable optical feedback to facilitate the generation of a large training dataset. Specifically, we have trained two deep neural network models, namely Yolov5 and EfficientDet, and analysed the performance of these networks on various experimental SMI signals acquired by using different laser-diode-based sensors operating under different noise and speckle conditions. The performance has been quantified in terms of fringe detection accuracy, signal to noise ratio, depth of modulation, and execution time parameters. The impact of network architecture on real-time sensing is also discussed.

Toward an Estimation of the Optical Feedback Factor C on the Fly for Displacement Sensing.

In this paper, a method based on the inherent event-based sampling capability of laser optical feedback interferometry (OFI) is proposed to assess the optical feedback factor C when the laser operates in the moderate and strong feedback regimes. Most of the phase unwrapping open-loop OFI algorithms rely on the estimation of C to retrieve the displacement with nanometric precision. Here, the proposed method operates in open-loop configuration and relies only on OFI's fringe detection, thereby improving its robustness and ease of use. The proposed method is able to estimate C with a precision of <5%. The obtained performances are compared to three different approaches previously published and the impacts of phase noise and sampling frequency are reported. We also show that this method can assess C on the fly even when C is varying due to speckle. To the best of the authors' knowledge, these are the first reported results of time-varying C estimation. In addition, through C estimation over time, it could pave the way not only to higher performance phase unwrapping algorithms but also to a better control of the optical feedback level via the use of an adaptive lens and thus to better displacement retrieval performances.

Current Developments on Optical Feedback Interferometry as an All-Optical Sensor for Biomedical Applications.

Optical feedback interferometry (OFI) sensors are experiencing a consistent increase in their applications to biosensing due to their contactless nature, low cost and compactness, features that fit very well with current biophotonics research and market trends. The present paper is a review of the work in progress at UPC-CD6 and LAAS-CNRS related to the application of OFI to different aspects of biosensing, both in vivo and ex vivo. This work is intended to present the variety of opportunities and potential applications related to OFI that are available in the field. The activities presented are divided into two main sensing strategies: The measurement of optical path changes and the monitoring of flows, which correspond to sensing strategies linked to the reconstruction of changes of amplitude from the interferometric signal, and to classical Doppler frequency measurements, respectively. For optical path change measurements, measurements of transient pulses, usual in biosensing, together with the measurement of large displacements applied to designing palliative care instrumentation for Parkinson disease are discussed. Regarding the Doppler-based approach, progress in flow-related signal processing and applications in real-time monitoring of non-steady flows, human blood flow monitoring and OFI pressure myograph sensing will be presented. In all cases, experimental setups are discussed and results presented, showing the versatility of the technique. The described applications show the wide capabilities in biosensing of the OFI sensor, showing it as an enabler of low-cost, all-optical, high accuracy biomedical applications.

Classification of laser self-mixing interferometric signal under moderate feedback.

This paper presents a different approach to classify self-mixing (SM) signals operating in the moderate feedback regime. A total of six distinct classes of SM signals can be defined based on the SM inherent shapes, which depend on both the feedback factor C and the linewidth enhancement factor α. This classification allows clear identification of SM signals for which normalization issues can arise and thus for which displacement precision is inherently reduced due to the very nature of the signal itself. Finally, it is shown that phase unwrapping approaches can theoretically retrieve displacement with subnanometer precision for usual laser diodes in the moderate feedback regime, in the absence of noise, only for α values greater than approximately 4.3.

MEMS accelerometer embedded in a self-mixing displacement sensor for parasitic vibration compensation.

A self-mixing (SM) laser displacement sensor coupled with a microelectromechanical system (MEMS) accelerometer is presented that enables reliable displacement measurements even in the case of a nonstationary laser head. The proposed technique allows the use of SM-based sensors for embedded applications. The system resolution is currently limited to approximately 300 nm due to the noise characteristics of the currently used accelerometer. It is shown that this resolution can be greatly improved by the use of a low noise accelerometer.

Comprehensive Modeling of Multimode Fiber Sensors for Refractive Index Measurement and Experimental Validation.

We propose and develop a comprehensive model for estimating the refractive index (RI) response over three potential sensing zones in a multimode fiber. The model has been developed based on a combined ray optics, Gaussian beam, and wave optics analysis coupled to the consideration of the injected interrogating lightwave characteristics and validated experimentally through the realization of three sensors with different lengths of stripped cladding sections as the sensing region. The experimental results highly corroborate and validate the simulation output from the model for the three RI sensing zones. The sensors can be employed over a very wide dynamic RI range from 1.316 to over 1.608 at a wavelength of 1550 nm, with the best resolution of 2.2447 × 10-5 RI unit (RIU) obtained in Zone II for a 1-cm sensor length.

A 64-channel readout ASIC for nanowire biosensor array with electrical calibration scheme.

A 1.8-mW, 18.5-mm(2) 64-channel current readout ASIC was implemented in 0.18-µm CMOS together with a new calibration scheme for silicon nanowire biosensor arrays. The ASIC consists of 64 channels of dedicated readout and conditioning circuits which incorporate correlated double sampling scheme to reduce the effect of 1/f noise and offset from the analog front-end. The ASIC provides a 10-bit digital output with a sampling rate of 300 S/s whilst achieving a minimum resolution of 7 pA(rms). A new electrical calibration method was introduced to mitigate the issue of large variations in the nano-scale sensor device parameters and optimize the sensor sensitivity. The experimental results show that the proposed calibration technique improved the sensitivity by 2 to 10 times and reduced the variation between dataset by 9 times.

A low-power high-performance accelerometer ASIC for high-end medical motion sensing.

A 170µW readout IC for a capacitive MEMS acceleration sensor was implemented in a 1.5V 0.13µm CMOS for high-end medical motion sensing applications. The accelerometer achieves a 45µg/vHz noise floor and a dynamic range larger than 87dB for a 400Hz bandwidth. Power reduction is achieved by introducing reset and common-mode feedback circuit techniques based on a non-unity-gain feedback configuration.